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4 Warehouse Management System Case Studies

• July 23, 2020

Successful WMS Implementation Case Studies

Warehouse Management Systems (WMS) represent a major investment in your supply chain and operational efficiency. While it can be difficult to gauge your estimated time to value, these four cases featuring enVista clients provide a warehouse management system business case and showcase outcomes after successful WMS implementations.

In this post, we’ll cover four examples of WMS implementations and how they helped companies to optimize warehouse operations :

• Nature’s Best
• Performance Bike
• Bradshaw International
• Men’s Wearhouse

Four  WMS Implementation Examples

1. nature’s best.

*Note: Since project completion, Nature’s Best has been acquired by KeHE.*

Leading the market of health and natural foods distribution, Nature’s Best serves Certified Organic products in over twelve states. Nature’s Best was experiencing high labor costs based on time and manpower to transport temperature-controlled food products between four buildings on its DC campus. The company’s zones were organized to suit either small or large clients – a key competitive differentiator. Because of the facility layout, the overall distribution process was inefficient; each product was touched 18 times on its way to the customer. Nature’s Best technology, distribution processes and buildings also needed updating to keep pace with growth projections.

enVista was called upon to address and resolve the challenges Nature’s Best was facing and did so using its consult, implement and operate (CIO) methodology to create a customized solution.

Phase One: Consult

A supply chain strategy that met Nature’s Best’s business goals was developed. enVista conducted a material flow analysis and redesigned the internal flow processes to increase efficiencies. As a result, a global WMS was selected that addressed all of Nature’s Best’s concerns and determined construction needs for the distribution center (DC). Operations would be consolidated to one DC, and enVista designed the new facility with various temperature-controlled zones.

Phase Two: Implement

As Nature’s Best began construction, enVista supervised the integration and implemented a Manhattan Associates WMS including interface design, configuration, training, facility preparation and labor standards among other features. Nature’s Best shifted from a mechanized to non-mechanized system and from paper to radio-frequency (RF) devices with voice-based technology. enVista managed the move of $25 million of inventory and trained the company team on new system processes.

Phase Three: Operate

During go-live, enVista ensured a seamless transition into Nature’s Best new facility, systems and processes. Throughout the Consult, Implement, and Operate phases, sales were steady – even growing – and upon completion, the project was delivered on time and within budget. The chosen system required few modifications for Nature’s Best’s business model, which would lower costs and allow for easier upgrades in the future.

The new system reduced labor costs by over 30 percent and more than doubled productivity. Ninety-seven percent of full-time employees were retained, and temporary and non-value-added positions were eliminated. Nature’s Best went consultant-free only four weeks after go-live.

Download Nature’s Best Case Study

Example 2: Performance Bike

Performance Bike is a privately-held, specialty retailer focused on bicycles and accessories. The company had expanded to over 110 retail locations since its founding in 1982. Performance Bike had a DC in Chapel Hill, NC, with over 60 associates and partners with a 3PL in Long Beach, CA.

Due to compliance issues with the U.S. Postal Service barcodes, Performance Bike had to make system changes to its Manhattan PkMS WMS. The company’s experience with Manhattan Associates gave it a flexible approach when considering an upgrade. enVista’s enABLE methodology was used for the upgrade, and many of the roles typically filled by software or consulting companies were completed by Performance Bike.

Because of the prior experience and flexibility of Performance Bike and enVista, the WMS implementation was under budget and on time. Team members from both enVista and Performance Bike worked together to complete and implement the WMS. The vice president of warehouse operations for Performance Bike noted enVista’s customer-centric approach and flexibility.

The WMS upgrade was implemented in less than 6 months. There were very minor changes to the user experience, so trainings were seamless and increased productivity. Performance Bike experienced a pick per hour increase of 50 to 100 units per hour, exceeding the company’s executives’ expectations.

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Example 3: Bradshaw International

Bradshaw International’s Good Cook™ brand is sold in over 30,000 U.S. retail stores. Bradshaw is a leading marketer of kitchenware products and launched Good Cook to incorporate quality, value and service into every product. This philosophy has allowed Good Cook to achieve the best position for kitchen tools and gadgets with 43 percent of the market.

Bradshaw was asked to participate in Wal-Mart’s direct store delivery consolidation (DSDC) program as Good Cook is distributed in the stores. The program was designed to allow shippers to directly replenish Wal-Mart stores in less-than-case-pack quantities. To do so, Bradshaw had to make changes to several parts of its current distribution systems.

Changes to electric data interchange (EDI), order management, and a pick/pack operation integrated with a new WMS would be required to handle the new variable volumes. enVista was asked to help design and integrate the DSDC program. To meet Walmart’s DSDC objectives, the team had to design and build a new pick/pack module, select the technology to be used by the picking team, and integrate it with the WMS. The order management software , EDI software and billing system had to be modified to accommodate the structures required by Walmart’s DSDC program.

The team profiled SKU movements, identified pick and storage mediums to model replenishment rates, and formulated labor plans and proper work flows. Bradshaw IT and enVista worked together to make configuration changes to the RedPrairie WMS and integrate it with the Pick from Light System and EDI software. The joint team also specified data maps from order capture to order management to WMS through EDI transmissions to move the infrastructure to industry standards.

With enVista’s help, Bradshaw successfully integrated the DSDC program into operations. Shipping volumes increased beyond expectations, but the new WMS has allowed Bradshaw to keep up with demand. The program was implemented with almost no customer disruption.

Example 4: Men’s Wearhouse

Founded in 1973, Men’s Wearhouse is one of the country’s largest specialty retailers of men’s apparel with over 700 stores. The stores carry a full selection of high-end men’s clothing and accessories.

Men’s Warehouse’s main DC was a 1.1-million square foot facility in Houston, Texas. It is the core retail distribution center for Men’s Warehouse retail stores, as well as 60 percent of merchandise from K&G Retail stores, acquired in 1999. A significant percentage of its garment-on-hanger merchandise is shipped by dedicated fleet to regional hubs across the country. enVista’s main objective was to consolidate retail and e-commerce systems to run all operations on a single platform and a central material handling equipment (MHE) integration point.

enVista acted as the program management role for supply chain execution and MHE software functions for all facilities. The implementation of the new systems platform occurred in two distinct phases.

The first phase focused on using the new systems platform to more efficiently meet Men’s Wearhouse’s growing e-commerce business needs. A new WMS and distributed order management system were implemented and integrated to an e-commerce web platform. These systems allowed strategic decision making regarding e-commerce order fulfillment and room for growth, along with the accuracy and flexibility necessary for seamless execution and delivery.

Phase two focused on implementing the new WMS and a consolidated warehouse control system (WCS) in Men’s Wearhouse’s Retail Distribution Operation. The WMS replaced the retailer’s legacy system, and the WCS consolidated the management of several operations into one system. Men’s Wearhouse now has the ability to dynamically change product flow, unit allocation, and achieve more effective utilization of Case Level ASN receiving with the new WMS. The system’s ability to recognize and execute on cross-docking opportunities created improved throughput and lower overall processing costs.

With the implementation of a common WMS and standardized processes, Men’s Wearhouse was able to share recourses across operations and respond to seasonal increases in workload more effectively. The implementation of WMS and WCS gives the company the ability to consolidate its distinct Men’s Wearhouse and K&G distribution operations to reduce costs and increase overall processing efficiency.

Download Men’s Wearhouse Case Study

Conclusion: enVista’s WMS Expertise

Getting your WMS right and making sure it’s integrated properly across your supply chain technology stack is critical to realizing quick ROI and improved operational efficiencies. 

enVista has completed hundreds of WMS implementation projects in its nearly two decades of experience, and our vendor agnostic approach to consulting and implementation ensures you get objective help for your most critical projects. We’re passionate about supply chains and delivering solutions that are the right fit for our clients and would love to help you optimize your supply chain.

If you have a WMS project on the horizon or want to learn more about how we can help you, let’s have a conversation .

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Case Studies of Successful Custom WMS Implementations

Commonly, we study the success stories of others to get inspiration and find new ideas for improving our business or personal efficiency. As a custom WMS development partner, we at HQSofware, also follow best practices and trends to deliver top-notch solutions to our clients. However, based on our WMS development experience, we can already share our own examples of winning warehouse management systems.

In this article, we’ll take a look at 5 successful examples of WMS software to understand how to implement such a system to enhance warehouse productivity.

What is a Custom WMS?

Developing an E-3PL System for Warehouse Automation

Implementing Predictive Analytics for Smart Inventory Management

Providing Warehouse Navigation to Speed Up Order Picking

Improving Supply Management with a WMS

Developing a WMS Module to Boost Marketplace Operations

How to Maximize WMS Efficiency?

A warehouse management system (WMS) is a powerful tool that optimizes and streamlines warehouse operations, including inventory management, picking and packing processes, shipping management, etc. A custom WMS, designed according to your unique workflow, eliminates unnecessary features and offers a user-friendly interface. With custom WMS development , you can prioritize factors that are crucial for your organization, ensuring that the system aligns perfectly with your unique business needs.

Let’s take a look at some custom WMS examples and how they’ve helped to overcome warehouse challenges for their respective owners.

It’s no longer enough for a modern warehouse management system to merely provide inventory visibility and assist in order picking and packing. Companies are now striving to maximize operational efficiency by choosing smart systems with advanced features that can simultaneously address a wide range of issues.

In line with this trend, a US-based startup embarked on the development of comprehensive WMS software that adheres to modern requirements. A large international team of developers, including HQSoftware specialists, was involved in the project.

The main goal was to create an advanced system that can automate and optimize many logistics processes and perform some of them autonomously. For example: automatically generate replenishment orders based on customer demand forecasts, or update goods availability and price for buyers in real-time.

In addition, the system facilitates warehouse audits, provides enhanced navigation, integrates with numerous third-party systems, and enables express picking and receiving. With all its innovative features, the E-3PL platform boasts an impressive 99.9% order accuracy and 99.4% same-day order delivery.

This WMS implementation case study highlights the potential for comprehensive software to enhance virtually any warehouse operation. Achieving this requires a thorough analysis of every process within your warehouse and careful consideration of how advanced technology can drive improvements. This is especially beneficial if you decide to turn to custom WMS software development , as this allows you to build a system that meets your business needs in the best possible way.

Not wasting financial resources and being able to meet changing customer demand are deeply desired by every business and a must-have feature of modern WMS software.

Our client approached us with just such a request. They needed to upgrade a legacy WMS and implement predictive analytics capabilities to optimize warehouse inventory.

To accomplish this, our development team carefully selected the most suitable Machine Learning models capable of analyzing both historical and real-time operational data. The system provides data-driven replenishment recommendations tailored to specific parameters such as supplier delivery terms and minimum stock quantities. As a result, the client was able to reduce inventory costs by 15%.

The power of predictive analytics lies in its ability to learn and adapt to your unique business characteristics, offering personalized recommendations on how to improve it. So, you can benefit from optimal stock levels, excess-cost elimination, efficient space utilization, and a proactive response to customer needs, gaining a strong competitive edge in the market.

Since goods in a warehouse are not arranged as in a supermarket but are stored in boxes with numerical location identifiers, finding the right item can be a daunting task for pickers.

To address this WMS challenge and streamline order picking, our client — a retail company from Germany — decided to implement enhanced warehouse navigation . The feature creates optimized picking routes according to the picker and other items’ location and warehouse configuration.

The solution is delivered as a cross-platform mobile app that provides a map with a picking route and offers voice and visual guidance. With the app, pickers now spend 25% less time on order picking .

Even if you have many large warehouses with different layouts, this is no reason to overlook such a feature. Our developers can create an intuitive graphical interface that allows you to create any warehouse layout, as was implemented in this WMS case study.

Want to create a WMS solution to improve your warehouse operations? We’re ready to help! HQSoftware has a team of skilled professionals ready to tackle the project. Let’s talk! Anna Halias Business Development Manager Get a Free Quote

In an ideal world, all suppliers would have compatible systems that seamlessly integrate with your WMS. However, in reality, you may encounter scenarios where a vendor relies on Google Spreadsheets for inventory tracking or where having a large number of suppliers makes collaboration within a single WMS challenging.

Well, we know several examples of WMSs that include a separate platform for onboarding and communicating with suppliers. We’ve developed such a platform as well . It enables a US retailer to connect with all suppliers in a single interface. Here, suppliers can upload product catalogs, manage item availability, and set prices, while the customer can place purchase orders and establish collaborative agreements.

An important feature of this platform is its ability to handle various data formats, enabling suppliers to post catalogs in their preferred format, which was quite a challenge for our development team. We also ensured system scalability, enabling the customer to expand their network of suppliers.

With this platform, the customer now enjoys seamless order placement, improved supply management, and a 37% reduction in product delivery delays.

The process of publishing and updating items on a single marketplace is relatively straightforward. You fill out the product details, add images, and configure display settings. This is true for several products or catalogs hosted on a single platform. But when you’re a 3PL provider responsible for hosting hundreds of clients with thousands of items on multiple marketplaces, it’s more like a nightmare.

Therefore, for one of the 3PL providers, the HQSoftware team has developed a module for the company’s custom supply chain software system that can automatically publish and update catalogs on several marketplaces and allows for manual item updates within one interface. By synchronizing with the customer’s system, the module ensures consistent and accurate data across platforms.

Within the module, the customer can also customize the display of different products according to specific parameters, such as particular display times, different prices for different times of the day, etc. With robust data exchange mechanisms implemented by our team, the module seamlessly uploads files with all data, speeding up product management tasks by 30%.

Even if you’re not a 3PL provider, such a feature can greatly simplify marketplace management. You no longer have to manually navigate through each product listing on every platform to make changes. All this can be done with a few clicks through a user-friendly interface.

Based on the expertise we’ve gained from our WMS implementation case studies, we understand that leveraging the advantages of WMS software takes more than simply finding or developing a suitable system. It’s also important to focus on proper WMS implementation . Consider the following actions.

• Define system requirements . Conduct a thorough analysis of your warehouse operations to understand how a new system can automate and improve established processes. This analysis will help you define the specific requirements for your WMS.
• Create an implementation plan. The step-by-step plan will help you overview the whole cycle of WMS implementation and control the progress. A common WMS implementation project plan example includes several key phases, starting with planning, then analysis, then implementation, testing, training, and finally, deployment.
• Clean up data. It’s a good idea to get rid of outdated or duplicate information that can lead to errors and slow down the process of migrating data to the new WMS.
• Integrate a WMS with other systems. This greatly enriches WMS functionality and allows you to get valuable insights from both internal and external platforms.
• Test a WMS. Verify that the system can process all your data smoothly without any interruptions or delays. It is important to test the system’s interaction with third-party systems and ensure it meets the needs of end-users.
• Train employees. Comprehensive training ensures that your employees know how to use the system effectively and helps overcome potential resistance to change. Create visual and interactive manuals and step-by-step tutorials that employees can refer to when they encounter difficulties.

But one of the most important factors in successfully implementing a WMS is choosing a reliable and trusted WMS provider, especially if you opt for a custom WMS. The capabilities of your system and its successful implementation will depend directly on the expertise of your development partner.

At HQSoftware, we have extensive experience not only in creating comprehensive WMS systems but also in working with cutting-edge technologies that can enhance WMS functionality, such as Artificial Intelligence , Machine Learning , IoT , etc. Contact us to learn more about our WMS development services.

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To ensure the outstanding quality of HQSoftware’s solutions and services, I took the position of Head of Production and manager of the Quality Assurance department. Turn to me with any questions regarding our tech expertise.

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Warehouse Management Software: A Case Study 

• Post author: Maryliya M J
• Post published: January 11, 2024
• Reading time: 12 mins read

Warehouse Management Software (WMS): A Case Study 

Table of contents.

Warehouse management software (WMS) has become an integral tool for modern businesses seeking to optimize their supply chain operations. With the ever-increasing complexity of managing inventory, orders, and logistics, implementing an effective WMS solution can provide significant advantages.

Introduction to Warehouse Management Software (WMS)

Understanding warehouse management systems (wms).

Warehouse Management Software ( WMS ) is like your personal assistant for managing the chaos of a warehouse. It’s a fancy computer program that helps keep track of inventory, streamline processes, and make your life a whole lot easier. Think of it as the Hermione Granger of your warehouse – organized, efficient, and always on top of things.

Evolution and Advancements in WMS

Gone are the days of scribbling inventory numbers on a piece of paper and playing detective to find that missing box of widgets. WMS has come a long way since then. It has evolved from simple spreadsheets to sophisticated systems that can handle complex tasks like inventory tracking, order processing, and even automated picking and packing. It’s like upgrading from a flip phone to the latest iPhone – everything just works better.

Benefits of Implementing WMS

Implementing a WMS might sound like a hassle, but trust me, the benefits are totally worth it. With a WMS by your side, you can say goodbye to inventory nightmares, reduce errors, and improve overall efficiency. It’s like having a superpower that makes your warehouse run smoother than a freshly oiled machine. From enhanced inventory visibility to streamlined order fulfillment, WMS has your back.

About the Client

Our client, a prominent e-commerce company, faced challenges in efficiently managing its warehouse operations. With a growing inventory and increasing order volume, they recognized the need for a robust Warehouse Management Software (WMS) to streamline inventory tracking, order fulfillment, and overall warehouse efficiency. 

Project Overview

The project involved developing a .NET-based Warehouse Management Software that seamlessly integrated with the client’s existing systems. The primary objectives were to provide real-time visibility into stock levels, automate order processing, and optimize the warehouse layout for improved picking and packing processes. 

The Challenges

• Inefficient Inventory Tracking: The existing systems lacked real-time visibility, leading to inaccuracies in inventory tracking. 
• Manual Order Processing: Order fulfillment processes were time-consuming and prone to errors due to manual interventions. 
• Suboptimal Warehouse Layout: The warehouse layout needed optimization to enhance picking and packing efficiency. 

The Solution

Our team, comprising skilled developers and project managers, collaborated to design and implement a comprehensive Warehouse Management Software. Technologies such as .NET, SQL Server, and REST APIs were employed to ensure a robust and scalable solution. 

Are you struggling to keep up with your software development needs? Are you looking for a team of dedicated developers who can work on your project full-time and deliver high-quality results? So why wait? Contact us today to learn more about our services and to start the process of hiring your own dedicated development team. Let us help you take your project to the next level! Contact Now!

Key features of the wms.

• Real-time Stock Visibility: The WMS provided instant visibility into stock levels, reducing the risk of stockouts and overstock situations. 
• Automated Order Processing: Orders were processed seamlessly, minimizing errors and accelerating the fulfillment process. 
• Optimized Warehouse Layout: The software employed intelligent algorithms to optimize the warehouse layout, resulting in improved picking and packing efficiency. 

The Outcome

The Warehouse Management Software was successfully deployed, leading to significant improvements in warehouse operations. The client reported enhanced efficiency in inventory management, reduced order processing times, and improved overall warehouse productivity. 

By leveraging the benefits of WMS, such as enhanced inventory visibility, streamlined order fulfillment processes, and improved accuracy and efficiency, the company was able to overcome their warehouse management challenges and achieve remarkable results. The lessons learned from this case study provide valuable insights for businesses considering the adoption of WMS, emphasizing the importance of careful selection, thorough implementation planning, and seamless integration with existing systems. Ultimately, the successful implementation of WMS opens doors to increased productivity, cost savings, and improved customer satisfaction, making it a strategic investment for any organization looking to optimize their warehouse operations.

Our team’s expertise in developing a tailored Warehouse Management Software using cutting-edge technologies resulted in a solution that addressed the client’s challenges effectively. The implementation of real-time visibility, automated order processing, and warehouse layout optimization contributed to a more streamlined and efficient warehouse operation. 

Are you facing similar challenges in warehouse management? Contact us today to explore how our expertise in WMS development can transform your warehouse operations and drive efficiency. 

What is Warehouse Management Software (WMS) and why is it important?

Warehouse Management Software (WMS) is a software solution designed to efficiently manage and control various aspects of warehouse operations, including inventory management, order fulfillment, and logistics. It is important because it helps businesses optimize their warehouse processes, improve inventory accuracy, streamline operations, enhance productivity, and ultimately deliver better customer service.

How does a WMS solution benefit businesses?

A WMS solution offers numerous benefits to businesses. It provides real-time visibility into inventory levels and locations, enabling effective inventory management and reducing stockouts. It automates and streamlines order fulfillment processes, improving speed and accuracy while reducing errors. WMS also optimizes warehouse layout, resource allocation, and workflow, leading to increased operational efficiency, productivity, and cost savings.

What considerations should businesses keep in mind when selecting a WMS solution?

When selecting a WMS solution, businesses should consider factors such as scalability, ease of integration with existing systems, customization options, vendor support, and cost. It is crucial to assess specific business requirements, evaluate the software’s functionality, and determine whether it aligns with the organization’s long-term goals. Conducting thorough research, seeking recommendations, and requesting demos or trials can help make an informed decision.

Is it challenging to integrate WMS with existing systems and processes?

Integrating WMS with existing systems and processes can present challenges, mainly due to differences in data structures, formats, and compatibility. It requires careful planning, data migration, and coordination between the WMS provider and internal IT teams. However, with proper communication, collaboration, and expert guidance, businesses can overcome integration challenges and achieve seamless connectivity between WMS and other enterprise systems.

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White Paper: Tips for Choosing the Best WMS Software for Your Business

Case Study: IMG

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White Paper: Why Wave Pre-Planning Will Improve Your Warehouse Management

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Improving warehouse operations—digitally

Warehouse operations are increasing in complexity with time. The growth of e-commerce has led to a proliferation of SKUs, and there’s an ever-growing need to delight customers by offering super-fast fulfillment. Technology is a factor as well: as new automation systems come onto the market, operations leaders must face the challenge of keeping pace and understanding which technologies apply and what kind of impact they can generate.

Worldwide, companies spend an estimated $350 billion a year on warehousing, and that number grows each year as pick sizes shrink and costs balloon, raising pressure not just on margins but also on service levels.

The need to improve both sides of the equation may be obvious, but the question is how. Trial-and-error is not an option: companies cannot simply shut down a warehouse so they can tinker with new layouts and workflows to see what works best.

But the better news is that they don’t have to. A few companies are already able to design and visualize their warehouse operations virtually via “digital twin” simulations. The simulations allow companies to create virtual models of their existing facilities, and then test different scenarios—no shutdowns required.

This digital warehouse-design approach lets companies experiment with different floor plans, workflows, and other variables to assess the overall impact virtually. Operations leaders can see the impact of changes in a wide range of factors, including the SKU mix, order and shipment profiles, seasonal demand spikes, productivity initiatives, automation options, and a host of other issues that impact warehouse performance. This level of detail allows warehouse designs to be optimized in advance, before anyone starts moving physical assets.

The process typically takes six to ten weeks. The revamped warehouse designs that result can help companies improve efficiency by 20 to 25 percent, before spending the money to test or pilot the changes (exhibit).

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The power of digital simulations.

Digital warehouse design has a wide range of applications, from productivity initiatives at existing facilities to mergers, warehouse consolidations, and new warehouse construction. Across all applications, however, the benefits are reasonably consistent: sizable savings in operating expenses from productivity improvement, as well as in capital expenses from optimizing the deployment of material-handling equipment, storage assets, and targeted, right-sized automation systems.

The approach removes bottlenecks to improve efficiency and effectiveness, and enables optimal slotting and product flows to meet service requirements at the lowest possible cost. Most important, digital warehouse design identifies the full potential of a given facility, rather than forcing companies to settle for incremental improvements to the as-is layout.

Critically, the process also enables companies to gauge the potential impact of mechanization and automation options across the entire spectrum of vendors and products on the market. These technologies can be powerful tools to reduce space and increase efficiency. But in some cases, incorrect or inappropriate automation can actually create more problems than it solves. Digital modeling lets companies see what is possible from a range of technologies and applications, before they make any investment decisions.

Transcending traditional improvements

Of course, most companies know they need to improve their warehouse operations. But they struggle to identify and make the needed upgrades, due to limited data and internal expertise. Quite simply, many companies lack the full set of capabilities needed to assess various warehouse improvement options and create a business case that incorporates capex investments and running costs.

Some rely on third-party logistics providers, but even those vendors often lack the breadth of expertise to see what is truly possible with alternative warehouse designs and operations. A related problem is that most traditional methods to design or revamp warehouses use computer-aided design software (CAD)—which is resource-heavy and time-consuming, and doesn’t allow companies to calculate the impact of new changes or anticipate second-order effects. Digital warehouse design, by contrast, offers a low-risk way to visualize and optimize layouts, allowing companies to rapidly identify real, feasible solutions that deliver quick impact.

For example, a North American manufacturer decided to consolidate several regional manufacturing and warehouse locations into a single campus, with separate buildings dedicated to manufacturing and warehousing. However, it faced several challenges, including capital constraints, insufficient warehouse space, and a need to move fast.

Instead of relying on traditional tools, the company used a digital warehouse-design approach to simulate various options across warehousing, kitting (assembly of related items into a single “kit” for shipment), and value-added operations. The digital tools allowed the company to develop detailed OPEX and CAPEX estimates for both manual and automated solutions, making it far easier to evaluate various business cases. In all, the company reduced planned capex by approximately 10 percent and operating expenses by more than 30 percent.

Digital warehouse design can also help companies rethink existing facilities. For example, another company deployed digital warehouse-design and simulation tools to optimize and revamp its operations in a warehouse currently in operation. It built models to test various layouts, material-handling-flow scenarios, picking methods, and targeted automation solutions.

Starting from an optimized slotting design, the digital twin analyzed historical orders to estimate the exact labor and equipment requirements by day of the week and by hour of the day. With accurate labor-staffing models, the company was able to choose the most optimal, modular design to implement during the transition. In addition, the company could define precise, engineered standards for each operation of the warehouse, and it could monitor daily labor and equipment performance once the new system was in place. This enabled the company to reduce annual operating expenses by 18 percent.

Similarly, one business had a central regional warehouse in Eastern Europe, with annual costs of several million dollars. By constructing a digital twin, the company analyzed the potential improvements from a proposed layout change (specifically, a picking zone that enabled one-step picking). Looking at both high-season and low-season volume, the company determined that it could reduce input costs by more than 25 percent.

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Foundational capabilities.

As with most technology, digital warehouse design is not a turnkey solution. To capture its full potential, companies must understand their strategy not only for today and tomorrow, but also well into the future—in terms of product portfolio, customer-order profiles, and other factors. Leadership teams must assess the implications of design changes on the supply-chain network as well. And success requires new capabilities in warehouse design—typically engineers who understand the range of variations and permutations for a space. Most organizations do not yet have internal people ready for these roles, and thus need to source that expertise externally.

As a practical matter, the simulation process must involve operations and warehouse managers—those with the deep day-to-day knowledge and real-world insights that can only come from direct experience. Because these experts will be tasked with implementing any changes to the warehouse after the simulation ends, their buy-in will be essential. The more they contribute during the early stages of the process, the more willing they are likely to be to forego old habits and work in new ways.

Last, companies need to plan for repeated changes over time. Optimizing warehouse performance is a process without a finish line—it must happen again and again, as market forces, technology, and consumer preferences continue to evolve. In fact, digital warehouse-simulation tools could become a new way of setting performance expectations, with the tools embedded as part of the day-to-day management of each distribution center. Estimating daily performance targets based on daily volume, mix, and staffing can help warehouse managers manage the shift’s performance more precisely than any other traditional, static approach to setting goals.

Companies face a growing need to improve their warehouse operations. Digital warehouse design is one clear means to help them do so. The process may be virtual, but the results generate a real impact on performance, providing an edge in an ever-more-complex warehousing landscape.

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Felipe Bustamante is a consultant in McKinsey’s Miami office, Ashutosh Dekhne is a partner in the Dallas office, Jörn Herrmann is a senior expert in the Zürich office, and Vedang Singh is a consultant in the New Jersey office.

The authors wish to thank Aditi Brodie for her contribution to this article.

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Trends and new practical applications for warehouse allocation and layout design: a literature review

• Open access
• Published: 30 November 2023
• Volume 5 , article number  378 , ( 2023 )

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• Pierre-William Albert 1 ,
• Mikael Rönnqvist 1 &
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Companies’ productivity is critical in contemporary warehouse environment to maintain efficiency and competitiveness within their supply chain. Warehousing operations are well known for their impact on the overall supply chain and need to be wisely managed. Among those activities, allocation planning and layout design are some of the most important concepts in practice. Current research tends to emphasize homogeneous environment, which leaves heterogeneous and non-standard cases with less attention, even today. This paper aims to review the literature regarding warehouse allocation planning and layout design methods that could suit practical industry problems, with a focus on heterogeneous and non-standard spare parts. It also aims to describe the current trends in these fields. Following a literature review methodology, a total of fifty-seven articles were reviewed to identify the methods developed and used. The reviewed papers were also investigated in order to identify research gaps and future directions. The analysis concluded that more research is needed to better understand and optimize heterogeneous and non-standard spare parts environments in terms of allocation and layout design. More practical case applications also remain a gap to address.

Article highlights

This article evaluates 8 concepts/parameters related to warehouse allocation operation. This is to ensure a deep analysis of heterogeneous and non-standard parts presence in literature.

A research gap is identified regarding heterogeneous and non-standard parts in warehouse allocation and layout design methods in the scientific literature.

This article evaluates 3798 scientific papers over a 20-year timeframe. From this, we present 57 methods and 8 gaps in scientific literature.

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1 Introduction

Faced with stiff economic and international competition, companies need to become truly efficient in warehouse operation planning to fulfill their mission and remain productive [ 59 ]. Nevertheless, in real-life situations, optimizing warehousing layout design and allocation is often a challenge to achieve a feasible solution [ 88 ], as different factors may be difficult to overcome [ 85 ]. Moreover, 90% of warehouses in the world are still managed manually or with low-level automation [ 50 ]. The recent changes in global uncertainty and risk on the supply chain that Covid-19 imposed is a good example of a factor which had a real impact on warehousing systems [ 76 ]. Furthermore, customer behavior and preferences can change at any moment and logistics should be able to keep pace [ 46 ]. Thus, a robust warehouse layout design and an efficient internal product allocation process enhance the probability of success [ 24 ]. [ 31 ] highlighted three priorities for optimal management systems, involving reduction of the use of sheets to keep track of the operations performed, to manage real-time inventory needs, and to maintain high productivity levels.

In this article, we perform a literature review that focuses on warehouse layout design and allocation planning methods. We focus on heterogeneous spare parts which suits practical industry problems. [ 6 , 44 , 75 , 88 ] all deplored the lack of literature on the practical effect of real-life features on warehousing operational planning activities. Many challenges arise with heterogeneous parts and real industrial constraints. [ 13 ] suggested an increase in the complexity of warehouse handling activities. [ 75 ] highlighted the difference in complexity for heterogeneous parts characteristics and their impact on warehousing and maintenance inventory. Specific interest in evaluating spare parts in such industrial realities is clear. In fact, the aftermarket business model and operational realities are completely different than in industrial production in relation to the uncertainty factor [ 75 ]. Thus, a focus on these aftermarket spare parts becomes evident to increase the understanding on this subject and determine storage needs and capacity. [ 110 ] From an economic perspective, spare parts play a critical role in the value for the customer and enterprise. This increases the need for further research on this matter since this subject still seems to best in its infancy.

The objective of this study is therefore to highlight the most promising methodologies and approaches that could suit such environment. To achieve this goal, concepts related to warehouse operations planning from the last 20 years (2000 to 2022) were analyzed to answer the following four research questions in relation to specific keyword sets. For each keyword iteration, one or more of those elements such as non-standard/heterogeneous, trends, practical case studies, and upcoming technologies were analyzed in the initial screening:

RQ1 Which methods were developed to improve productivity in warehouse allocation planning?

RQ2 Which methods were developed to improve productivity in warehouse layout design?

RQ3 Which methods were proposed to improve productivity in warehouse regarding allocation and layout design simultaneously?

RQ4 Which methods were used to increase productivity for practical case studies, especially for heterogeneous non-standard parts environment?

To answer these questions, we used a set of specific keywords. For each keyword iteration, one or more of those elements such as non-standard/heterogeneous, trends, practical case studies, and upcoming technologies were analyzed in the initial screening.

Trends and emerging practices were also examined during the analysis. The investigations showed that smaller and more frequent transactions occur in warehouses [ 31 ]. Also, product allocation planning regarding handling constraints for workforce security management seems to be on the rise [ 86 ]. Another concept in self-storage warehousing with customer choice appears as a contemporary issue [ 113 ].

The remainder of this paper is as follows. Section  2 summarizes the main concepts of warehousing and operational planning. Section  3 describes the methodology used in this paper. Section  4 presents the answers to the research questions and some recommendations. Section  5 concludes the paper.

2 Preliminary concepts

This section presents some preliminary concepts about warehouse layout design and allocation planning.

2.1 Layout desing

Warehouse layout design typically focuses on the positioning of docks, reception, racking location, racking height, and slots dimensioning. [ 46 ] described warehouse design activities as the physical layout, the storage equipment, the handling equipment, automation solutions, information systems, labor, and activities to determine. Physical layout can be described as the aisle and lane configuration, the stacking height, while storage, and handling equipment, including racking, forklift, and carousel. [ 93 ] tries to solve a warehouse design for a block stacked warehouse. They determined the lane depth, stages, and temporary additional capacity which is of dynamic nature. Thus, a lack of flexibility and capacity which evolve dynamically can have a major impact on the system [ 21 ]. [ 36 ] proposed a review of warehouse layout design methods and identified five decisions as follows: overall structure, department layout, operation strategy, equipment selection, sizing, and dimensioning. They also introduced the importance of benchmarking and analytical operations for a better performance evaluation. [ 96 ] investigated the warehouse layout problems and concluded on the utility of the cube-per-order index (COI). [ 63 ] presented a literature review that looks at designing warehousing systems with a focus on order picking. [ 107 ] presented a review for optimal warehouse designs with case study applications. They focused on average order picking and total traveled distance for determining the design.

Sometimes layout design needs to be reviewed to be more efficient, as redesigning might be more suitable and less expensive than building a new warehouse. [ 26 ] suggested how to redesign production layout for real case scenarios with a dedicated storage method. [ 5 ] followed the redesign layout planning for a systematic layout planning usable on real case studies. Another approach for real cases is considered by [ 81 ]. They evaluated different warehouse layouts by comparing a classic layout design with other general layout concepts like fishbone, class-based, shared storage, dedicated, and random. [ 43 ] utilized the warehouse design dedicated policy for a real case study. [ 67 ] followed the redesign by proposing layout changes and handling methods for a real case raw material storing process. [ 69 ] presented a case study with limited space situation and for warehouse layout design in a specific automobile material supplier. Their main objective was to design two warehouse layouts to fulfill the company storage assignment. They followed a class-based approach. [ 40 ] presented a design methodology for converting regular warehouses into different zones. [ 84 ] resolved a warehouse layout design for a real case problem while mixing allocation techniques such as ABC class-based storage. [ 51 ] worked on the design of a multi warehouse inventory model with the rain optimization algorithm. They also presented the concept of optimal replenishment policy (ORP). Figure  1 presents a dynamic cross-docking layout design for easy picking.

An illustration of a dynamic picking zone model

2.2 Storage, product allocation, and slotting

Storage, product allocation, and slotting all refer to the capacity and ability to put items correctly in a specific place. [ 37 ] specified that one of the most important things in warehouse operations planning relates to the storage and allocation of products to the right storage positions. [ 31 ] explained that SKU and storage zones, including strong correlation together, should be the closest possible to each other. [ 48 ] described slotting as the operation to find the best location to keep an SKU inside the storage zone. Thus, many problem-solving algorithms or methods related to allocation were developed over the years. Here is a non-exhaustive list of allocation problems or methods one can find in the literature: the multi-layer allocation problem (MLAP), the storage location allocation problem (SLAP), the class-based storage location allocation problem (CBSLAP), dedicated storage, random, closest, farthest, ABC class, forward reserve, and zoning. SLAP is derived from the more general location allocation problem (LAP). [ 14 ] studied storage and stacking in the marine transportation industry. The objective was to stack sea cans in a fixed stack with limited height while the stacks had to be at a workable distance from the cranes. [ 57 ] focused on stacking goods for a real case study in container stacking in ports which they defined as the stack loading problem (SLP). [ 64 ] described the MLAP problem as a variation of the SLAP problem, including multiple floor levels for bins in the warehouse. In other words, the MLAP differs from the general SLAP by adding another dimension in the storage with floors and height. [ 104 ] followed the class-based storage location but used a constrained clustering method as well. To achieve their results, the COI index was used. [ 33 ] also followed the class-based storage method for a real case study. [ 19 ] worked on order picking in a multi-level rack warehouse with a class-based storage policy. In their paper, they pointed out important factors to take into consideration like the material handling system, product characteristics, demand trends, and the turnover rate. They also regrouped the storage assignment policies in three main groups which are random storage, dedicated storage, and class-based storage. [ 3 ] wanted to optimize storage classes via a minimum travel cost. They ranked location with frequencies, arrivals, and demand. [ 108 ] also presented a class-based storage with a finite number of items. They concluded that using more classes is not always better. [ 11 ] investigated the CBSLAP in more depth with a class-based storage location assignment overview of policies and classification. Their classification for storage policies separated the SLAP problem between dedicated and shared storage, dedicated storage being linked to parts number, turnover rates, COI, and correlated items while shared policy being separated between class-based and haphazard. [ 32 ] tried to solve the general assignment problem by including worker capabilities. [ 45 ] used a genetic algorithm to solve a multi-objective storage allocation. [ 98 ] interacted with the intelligent storage allocation with multiple objectives. They used class-based with fixed location and worked with flood management. Forward reserve is another option for storage and allocation. [ 100 ] presented a forward reserve storage strategy for customer order mixed with class-based criteria. The objective was to measure customers’ order response time and the overall system capacity in an AS/RS situation. A forward pick area can be described as a warehouse within a warehouse. It can help to stock the most popular items in small amounts to reduce the main picking zone [ 9 ]. It can then be replenished with bigger reserve storage.

3 Research methodology

To find the most promising methods that could suit practical industry problems related to allocation planning and layout design, especially for heterogeneous spare parts, a literature review approach called a conservative search and screening methodology. This approach is a reliable methodology that could be easily repeated by any reader. In fact, the methodology is used in other papers. [ 23 , 36 , 90 ] Also, to the best of our knowledge, no other papers were evaluating heterogenous environment even at a low importance concept which represent a large gap between the literature and the practical world. Thus, a conservative and screening approach was more adequate to clearly address the research question. The process together with the number of articles identified in the steps is illustrated in Fig.  2 .

Conservative search and screening methodology

3.1 General Methodology

The first step of the method involved defining the research questions. To identify promising methods that would be useful in the real world, especially when dealing with non-standard space parts, the questions to investigate were defined. The objective was to find papers related to allocation planning, layout design or both concepts simultaneously. The last question is concerned about practical case studies and heterogeneous/non-standard environment.

The second step involved determining the keywords, the research databases, the source types, the language considered, and the years of publication. We investigated different combinations of keywords related to the variability of warehouse operations concepts. Thus, we first separated those concepts as follows: “warehouse allocation planning”, “warehouse layout planning”, “non-standard shelf layout or warehouse variable layout or warehouse adaptable layout”, “warehouse operational planning”, “warehouse limited storage capacity”, “non-standard or heterogeneous or diversified spare parts”, “warehouse capacity utilization”, “warehouse total storage capacity or warehouse storage capacity”, “warehouse storage management”. Then, a mix of keywords to identify previous literature reviews was searched: “literature review and warehouse allocation”, “literature review and warehouse layout design”, “literature review and warehouse operational planning”. All keywords set iterations evaluated the best articles related to the main concepts of this research which are non-standard/heterogeneous, trends, practical case studies, and upcoming technologies.

The research database “Compendex” and “Inspec” were the ones selected as being recognized as useful tools in the field of engineering and operations management. To limit the number of papers to examine, a focus on scientific journal papers was determined and English was the only language considered. Only papers published from 2000 to 2022 were considered from an evolutionary perspective of how the current subjects evolved through time while detecting the new trends emerging from such subjects. The reason for the diminution in the number of papers between the metasearch and the search strategy is related to the keywords sets which involved a focus on allocation or layout design concepts. All the papers with a focus on other main concepts such as picking, routing, automation technology or shipping were eliminated from the search. This reduced the number of papers from 3798 to 138.

The third step involved conducting an initial screening by looking at the titles and abstract to determine whether the articles were relevant for this study or not. Once again, articles focusing on concepts other than allocation and layout design were excluded, as well as purely theoretical studies considering a perfect homogeneous environment. This decreases the number of papers to be reviewed in the next step to 84.

The following step encompassed a full-text screening. A total of 57 papers were finally kept for the literature review.

4 Results and discussion

4.1 descriptive results.

Figure  3 presents the number of reviewed papers per year. We can see that research on warehouse allocation and layout design concepts has increased in recent decades and that 85% of the literature reviewed has become available since 2010.

Statistics concerning the papers reviewed per year

Figure  4 shows the total number of papers based on the country of affiliation of the first author. All the countries with at least 3 papers reviewed are shown within this figure. Figure  5 describes the number of articles by journals in ascending order when at least 2 articles were published in them. The International Journal of Production Research, the International Journal of Advanced Manufacturing Technology, and the European Journal of Operational Research are the most frequent journals in the field. Figure  6 describes the number of papers looking at the two main concepts together (allocation and layout design) per year. Table 1 is related to the number of papers per methodology. Simulation methodology refers to research including a simulation model and/or the use of simulation software. A literature review methodology refers to a paper in which the research core is concerned with presenting the literature and papers related to the specific concepts of a scientific field. Analytical methodology refers to research papers including a mathematical model or a heuristic. Analytical represents mathematical models including algorithmic models that normally look for an exact solution and optimization model. Heuristics models are also included. A qualitative methodology refers to research papers describing conceptual frameworks, questionnaires, or factual data. Most of the reviewed papers used mathematical models to identify an optimal solution. Only a few articles exploiting heuristics were found. Table 2 presents the concepts studied in this research according to degree of popularity (number of papers and their overall percentage on the total numbers evaluated).

Papers distribution of publications based on first author’s country

Papers reviewed according to the name of journals

Papers reviewed per year based on the main concepts (allocation and layout design) considered in this study

4.2 Research questions

This part presents all relevant findings in relation to the research questions investigated.

4.2.1 Question #1

4.2.1.1 which methods were developed to improve productivity in warehouse allocation planning.

The first research question was related to warehouse allocation planning and the current approaches and methods available in the literature to solve this problem, especially regarding non-standard, heterogeneous environments. In [ 85 ] an architecture for product allocation planning with compatibility and safety constraints and with an IoT-based warehouse. A proposed multi-agent architecture for PAP with compatibility constraint was described. [ 86 ] focused on a product allocation planning (PAP) problem with handling constraints within a case study. The constraints evaluated were the weight and the shape. The model followed a zero–one quadratic assignment. They brought something new as the Covid-19 handling constraints which add additional challenges to operational planning. Their modeling led to 24% reduction in picking distance by implementing ABC clusters and odd shaped constraints. [ 98 ] described a dynamic programming approach for the SLAP problem. The main objective was to enhance order-picking with good storage location in a dynamic perspective. [ 111 ] mixed IoT, integrated production planning, and warehouse storage assignment for a practical case. A linear programming model minimizing the total production cost and warehouse operations was presented. [ 80 ] followed the same thinking with an integrated model for warehouse and inventory planning. They also evaluated the capacity restrictions in inventory planning. [ 61 ] proceeded in a separate direction with a class-based storage assignment over life cycle picking patterns. The model was based on a mixed-integer linear programming (MILP) pattern. They introduced product popularity (consumption level) as the main characteristic for an SKU storage level. The more popular it was, the closer to floor level it had to be. [ 55 ] focused on the optimization of warehouse storage capacity with a dedicated storage policy. Moreover, this paper aimed to determine the space requirements under a dedicated storage policy. Thus, they used a non-linear programming model to minimize the cost of space used in the warehouse. [ 58 ] evaluated beyond static scenarios with a dynamic storage assignment with product affinity and ABC classification. They proposed a case study to show the usefulness of such a scenario. Their method used a product affinity-based heuristic with a quadratic assignment problem. [ 41 ] created a model for storage arrangement and reallocation in storage operations. The model proposed a heuristic algorithm for storage reallocation. [ 70 ] aimed to maximize the storage space availability inside a chaotic warehouse via a heuristic for the SLAP problem in a multi-level case. Their method was also usable for determining the right timing for relocation. [ 3 ] checked the allocation problem for the unit-load warehouse and tried to optimize it via storage classes. The focus of the paper was on describing a potential solution for practical cases. The method was solved with linear programming. [ 38 ] evaluated the impact of storage space on storage policy performance in a unit-load warehouse. The study also found that class-based storage performs better than turnover-based policy. [ 99 ] pointed out that literature normally evaluates single-period instead of multiple-period for orders in warehouse. Thus, warehouses should rather be seen as a dynamic place which needs to evolve over time. The proposed innovative way for warehouse storage allocation evaluated the entry-item-quantity and then sorted all SKU. [ 104 ] followed a constrained clustering method for class-based SLAP in a warehouse. The originality of such method lies on the constrained cluster and in the practical storage constraints evaluated. They used the COI index for grouping and sub-grouping and then found results with a heuristic assignment model. Multiproduct slot allocation heuristic was the main object in [ 10 ]. The objective was to minimize storage space with such a heuristic. The model linked to a real industry case and showed good results with a dedicated storage policy. [ 108 ] discovered an interesting correlation that more classes in a class-based allocation strategy are not always better. Their research evaluated the class-based storage with a finite number of items in a SLAP problem. They found that 3 to 8 classes were normally sufficient to obtain near optimal solutions. [ 102 ] also evaluated class-based storage policies for the allocation problem. This time, the focus was on optimal dimensions for multi-deep storage systems instead of standard warehouse. This is related to AS/RS automated storage systems. Once again, the utilization of class-based strategies was relevant. [ 47 ] looked at large distribution centers for their storage location assignment heuristic based on slot selection and frequent itemset grouping. They evaluated a new dimension of SKU by integrating itemset grouping, which led to better agglomeration of parts in a smaller slot. [ 29 ] worked on the SLAP problem with order picking optimization in the automobile industry. They solved it with a class-based policy and batching and routing problems via an integer programming model. In [ 59 ] a linear programming and decision-making model was presented to improve warehouse utilization. The objectives were to find the best decision for storage racks with box placement.

4.2.2 Question #2

4.2.2.1 which methods were developed to improve productivity in warehouse layout design.

The second question focused on the warehouse layout design concept, with a focus on non-standard, heterogeneous environments. Layout design modeling for a real world just-in-time warehouse was presented in [ 42 ]. They proposed another perspective for layout design related to just-in-time warehouse instead of a traditional one. [ 66 ] used a particle swarm optimization algorithm for the multiple-level warehouse layout design problem. The paper proposed solutions such as orientation of storage racks and the overall configuration. For non-conventional warehouse design, [ 30 ] discussed a heuristic comparative assessment for unit-load warehouse. They used the FlexSim software to present a simulation of different designs. [ 16 ] proposed solutions to determine slot sizes in a unit-load warehouse. The purpose of this paper was to go beyond the equal height slot generally found in the literature and to present different slot size possibilities in one solution. [ 78 ] tried to enhance manual order picking areas via the design of the layout structure. Their model minimized the travel distances in the picking area with parallel structure. Another possibility with warehouse models is self-storage warehouses. Thus, [ 113 ] described a design for self-storage warehouse with customer choice. They used a customer choice model to evaluate the probability of purchase. This model was a mixed-integer model solved with the branch-and-price algorithm. A guideline was proposed by [ 83 ] for developing a design in a case-picking warehouse. The main objective was to determine guidelines for good design in manual case-picking warehouses. [ 79 ] explained how to estimate optimal ABC zone sizes in manual warehouses. They presented the benefits of ABC class-based storage policy for SLAP problems. The zoning experiments were compared between one to three zones (20/30/50) patterns. [ 103 ] investigated a fuzzy multi-level warehouse layout problem. A tabu search was utilized and presented a good steppingstone for the layout problem. [ 73 ] mixed the determination of warehouse layout and control policies in their paper. [ 39 ] studied the uncertainty theory method in relation to the multi-level warehouse layout problem. Uncertainty factors were included, and two different models’ scenarios were evaluated. [ 94 ] followed a different path with the imperfect staggering in product inflow with queuing theory. The paper explored the block stacking layout problem with a model of the imperfect staggering effects. [ 6 ] discussed a structured approach for warehouse design. The paper of [ 15 ] related to economic and ergonomic performance measures helped to understand the rack design for the warehouse layout design problem. [ 17 ] focused on the fishbone warehouse design with vertical travel. The paper presented a three-dimensional design with a fishbone layout. Another point in layout design is to manage the deep lane storage system layout. [ 1 ] proposed an iterative decision-support model for this problem with block stacking storage environment. A simulation and the analytic hierarchy process for decision support systems were presented in [ 76 ]. The focus was on air cargo warehouse capacity design. Thus, they tried to integrate a practical case study with a complex simulation. The question between automated or manual storage systems was the subject of [ 110 ]. The authors proposed insights for managers to select the most appropriate storage systems. [ 91 ] wanted to optimize layout for a three-dimensional order picking warehouse. Their work helped to determine layout warehouse while determining the depth and number of levels of such a warehouse. On a more global note, [ 23 ] presented the major changes in the last years in terms of warehousing. This paper encompassed the new trends and technologies and gave insights concerning what is happening nowadays. [ 92 ] evaluated the design of an order-picking warehouse with vertical travel and space sharing. The warehouse was a single block rack-based and the model integrated practical case data.

4.2.3 Question #3

4.2.3.1 which methods were proposed to improve productivity in warehouse regarding allocation and layout design simultaneously.

Regarding the study of both concepts simultaneously (allocation and layout design), only a few publications were found. Those articles mainly focused on reviews or general models. In [ 13 ], assembly kits were an important variable to determine warehouse layout design and assignment procedure. The main difference with previous research was based on the physical attributes of the parts evaluated and their occurrence in assembly kits. In [ 24 ], an overview of the problems found in layout design, storage assignment, and order concepts of warehouse operational planning such as zoning was proposed. They described many advantages of good practices in warehouse operational planning, such as transportation/production economies, reduced lead time uncertainty, increased customer service, and satisfied changing market conditions. Another review was found in [ 35 ]. The review based on warehouse operations enhanced the reader’s knowledge on various operations planning problems, including the layout design and allocation problem. [ 105 ] tried to minimize operating costs in a picker-to-part warehousing system. From this, an evaluation on multiple concepts from warehouse operational planning (WOP) was developed, which included allocation and layout design. The paper proposed an algorithm determining the number of zones, bins, and racks. [ 97 ] integrated an optimized fishbone warehouse layout with a SLAP problem and a picker routing. With those 3 concepts from WOP, they proposed a 3D layout design with a turnover-based assignment for allocation. In another spectrum, [ 34 ] incorporated human factors in order picking planning. [ 13 ] designed diagonal cross-aisle warehouses including a class-based storage assignment strategy. They worked on a non-traditional model such as diagonal cross-aisle with unit load and compared their results with a traditional and random one. In [ 90 ], the design of efficient order picking systems with a planning combination was studied. The main objective was to show which combination policies could be used for such problem.

4.2.4 Question #4

4.2.4.1 which methods were used to increase productivity for practical case studies, especially for heterogeneous non-standard parts environment.

For the last question, the literature related to practical case studies and heterogenous non-standard spare parts (SKU) was evaluated. However, a few numbers of articles on non-standard spare parts were found while practical case studies appeared to be a little more frequent. [ 30 ] worked on a heuristic for non-conventional warehouse designs. The fishbone design seemed to be the most effective method in terms of warehouse designs [ 13 ]. worked on assembly kits with variable parts and physical attributes. They looked for solutions to agglomerate the assembly kits parts in corridors and to create such a kit in a single warehouse corridor. Part categorization was the main procedure for such a solution. Then, one could determine the warehouse corridor and structure afterwards. [ 78 ] focused on the limited storage capacity problem found in some warehouses. They proposed an optimal policy for multi-item systems with stochastic demand and backlogged shortages. The solution was an inventory model for multiple products. [ 75 ] worked on maintenance inventory allocation with heterogeneous part characteristics. The paper helped to better understand dynamic demand in non-standard parts systems for specific maintenance with aeroengine. They used an ABC-VED matrix for the classification of distinct categories. [ 12 ] considered a non-traditional warehouse with a diagonal cross-aisle design.

In [ 86 ], the case study analytical model was related to a PAP problem with handling constraints in the retail sector and with manual order-picking operations. The model developed a shape parameter linked with data to distinguish non-conventional parts against standard ones. [ 42 ] evaluated the just-in-time warehouse real-world practical case with the layout design model problem. The case study was in the retail industry. [ 16 ] compared data from different distribution centers in the industry. A key part of their study was the evaluation of slot sizes in warehouses. From this, one could evaluate different slot sizes in a non-standard environment. For the automotive industry cases, [ 29 ] proposed a solution for the SLAP problem and order picking optimization. The solution showed that proximity for storage location is the main factor to minimize the total travel time in assembly warehouses. [ 113 ] evaluated a self-storage in Rotterdam for the design of self-storage warehouses with customer choices. The paper included layout design and a real case study for a specific non-standard warehousing aspect in self-storage. [ 111 ] proposed an IoT assisted case from a food company for warehouse storage assignment and integrated production planning. With the beverage industry, [ 1 ] from a block stacking warehouse presented a way to design and manage deep lane storage system layout. This helped in minimizing the storage space and time inefficiencies.

In their simulation, [ 76 ] worked in the air cargo warehouse capacity design. This case study gave insights concerning how to minimize the cost while enhancing the performance in a highly dynamic environment. [ 58 ] looked at a general case study on dynamic storage assignment including product affinity and ABC classification. [ 3 ] evaluated their method with a third-party logistics provider and a unit-load warehouse. They proposed an approach to optimize storage classes for this kind of environment. The method used the layout information and the product flow. They found that random allocation is the best choice in highly dynamic environment with few allocation bins available at any time. The busier the more random the allocation should be since emergency allocation tends to increase drastically traveling time inside the warehouse. [ 101 ] worked with the steel supply chain in a highly dynamic and look-like SKU environment. Their method was based on a managerial optimization with RFID warehouse. Thus, the allocation and retrieval process could easily enhance its productivity with proper RFID system implementation [ 10 ]. validated their multiproduct slot allocation heuristic with a real industrial case. Even if the main purpose of the paper did not directly involve a practical case implementation, their heuristic aiming at minimizing storage space using a dedicated storage policy meant it seemed to be possible to implement in real life. The constrained clustering method for class-based SLAP by [ 104 ] followed the same reality as in the previous paper. The main purpose of the paper was not directly related to a case study. However, this article tended to obtain satisfactory results for practical issues.

Figure  7 presents a resume of the analyzed paper related to the main criteria of this study.

Summary categorization of the analyzed papers

4.3 Discussion

In this section, we discuss the approaches and methods found when conducting the review and their application to solve practical cases. A discussion focusing on the gaps identified as well as the opportunities and trends observed is also proposed.

4.3.1 Warehouse allocation methods

Based on the allocation methods reviewed in this paper, the most common elements considered by the authors were the following ones:

Determining an SKU classification

Considering ABC class within the SKU classification

Allocating block-unit warehouse (standard location)

Minimizing traveling time for SKU picking/slotting

Using the shortest path algorithm for allocation

One of the interesting methodologies proposed in the literature is the forward area where the warehouse storage location is split between a large warehouse containing all the SKUs and a smaller more convenient zone in the forward area. A main challenge identified when reviewing allocation solutions resides with the homogeneous standard evaluation. Most companies are not always standard in their operations and SKU dimensions. Thus, the literature should focus more on flexible warehouse storage solutions with different widths and sizes. More research should be done with methodologies suitable for unconventional items (heterogeneous) size problems.

4.3.2 Layout design discussion

The literature reviewed highlighted that designing storage zones and rack dimensions were the main elements studied by the authors. Nevertheless, the methods typically involved static solutions. There seems to be a lack of research on flexible layout design. Most of the layout design-oriented research also focused on classic rack layout problems with general, same-size SKUs. However, in practical case situations, SKUs of various sizes and dimensions can be handled. More research could certainly be conducted for evaluating the impact of SKUs characteristics with non-standard or variation in dimensions, evaluating the impact of dynamic factors over time, the impact of e-commerce/mass customization, as well as the impact and productivity of non-conventional layouts (e.g., cross-docking, JIT, heterogeneous parts).

Also, workforce behavior and management cannot be neglected to fully implement new designs, e.g., the impact of heavyweight SKUs on potential injury for a worker. Some possibilities for future research could include evaluating managerial and workforce variables in WOP research, including union or work restriction realities in algorithms and simulation models, and evaluating the impact of unconventional temperature environment on well-being and performance.

The Covid-19 epidemic showed how dramatically such events could impact the overall practice and realities in warehousing. The crisis has shaken the foundation of many concepts such as lead times and security stocks. [ 31 ] reported important changes over the years in warehousing activities. Among them, smaller and more frequent transactions [ 23 , 89 ]. This reality drastically increases the operations planning because of the volume increase of purchase orders and leads to more come and go for the pickers inside the warehouse. Another reality is an increase in item returns. This can be seen with e-commerce [ 23 ]. [ 89 ] dug into the emerging practice due to the recent change in order number and the increasing “just-in-time” demand from customers. In terms of time, [ 82 ] explained that companies should be able to complete customer orders in less than 24 h for more than 98% of overall order. [ 46 ] talked about the increasing complexity for inventory management where workforce seems to need to develop new capacities and understanding of warehousing activities. [ 51 ] made a three-decade literature review about warehouse research. Their results showed that the emerging keywords in terms of warehousing are related to automated & intelligent warehousing such as integrated WMS, application of industry 4.0, and AI sensors. [ 55 ] presented a case study to design and describe an application of Internet of Things (IOT) in the industry 4.0 era. [ 21 ] made a literature review presenting flexible automated warehouses. In terms of emerging technologies, dynamic/flexible automation is an important part of it. In this matter, flexibility can be interpreted as giving more opportunities to adjust to market changes such as e-commerce, customization, and just-in-time. Automation can be defined as the technology that processes tasks without human workforce or assistance. Emerging technologies presented in [ 21 ] are Internet of things (IOT), cyber-physical systems (CPS), Big data, artificial intelligence (AI), and cloud computing. The future of warehousing is slowly converging to fully automated systems and unmanned procedures. One should determine which level of automation is required to be effective and depending on the resources. There is an opportunity for future research to look at automation levels and the impact on productivity and cost, the minimum automation level to adopt in a specific warehouse, cross-automation on multilayer warehouses, including the impact of human workforce on floor levels and automatic picking on floor levels, dynamic and flexibility related to automation, as well as methodologies for automated implantation in practical case studies.

4.3.3 Mixed concepts

As stated in RQ3, some of the papers in this review integrated both concepts of warehouse allocation and layout design. However, most of the papers were literature reviews, explaining the concepts and presenting what has been done so far. Only one paper included an analytical model for a potential solution with a practical case and only one with a non-standard environment. There seems to be a necessity to conduct more research on both concepts supported by practical case studies. The reasons for such low interest in this matter are still unknown.

4.3.4 Practical cases and non-standard

During the analysis, practical case studies were found in the literature. However, most of the time, practical cases meant that some data from a real warehouse were integrated to a mathematical model to generate results. Those results do not necessarily confirm whether the implementation of such a solution is suitable for real-life practical cases. Moreover, some papers proposed high-level algorithms that probably only a few companies could use, and which would necessitate skilled workforce and/or a significant implementation cost. More research could thus look at practical case implementations involving allocation and layout design methods.

4.3.5 Future research directions and gaps

Based on the papers investigated, future research directions and gaps to fill in the literature could be summarized as follows.

Workforce satisfaction and well-being increase overall productivity in warehouses. New or adapted methods could therefore be proposed to consider this key element in heterogeneous environment and make the comparison with the standard one. This could increase the understanding of productivity related to specific environment [ 34 , 86 ].

Non-conventional environment such as cold storage and heated warehouses affects workers’ behavior inside warehouses. New management/human resources research mixed with allocation planning or layout design in those kinds of environments could increase productivity. To the best of our understanding, no research evaluates those impacts while mixing them with new innovative productivity solution [ 85 ].

Implementing solutions to practical real-life case studies increases the value for industry and enhances the research opportunity in the warehouse operations field. New adapted methods should be proposed to limit the gap between theoretical models and real-life applications that, most of the time, are stated in the literature [ 16 , 30 , 39 ].

Combined concepts solutions give a faster and more flexible way of solving problems for managerial teams. Most of the papers propose one concept without evaluating the overall warehouse activities for practical case scenarios. This could lead to improvement in one concept but cause problems for other ones. Thus, evaluating the impact of one warehouse operational concept on another could give a better understanding on operations correlation in real-life cases. As shown in the results, few papers really evaluate more than one concept and even fewer integrate them to practical situations, which presents an important gap in the literature.

Non-conventional or heterogeneous warehouse types such as cross-docking, heterogeneous, and JIT environment are not uncommon in the industry. However, as shown in the results, few studies are related to those. New methods and approaches could be proposed with those elements in further research.

The Covid-19 pandemic created drastic changes in terms of warehousing health and safety concerns regarding workforce protection in recent years. Research should investigate those impacts on real-life industry with new approaches regarding this topic [ 76 ].

Data-driven operations and automated solutions, enabled by Industry 4.0 technology, are an upcoming reality for warehousing systems [ 21 , 27 , 55 ]. They are expected to play a critical role in increasing warehousing efficiency. Thus, future research could study the impact of this evolution in modern warehousing methods using real case studies to facilitate their implementation. Future research could also include the effect of combining such technologies with sustainable practices on resources consumption and long-term warehouses productivity.

As warehouses may play a role in ensuring more resilient supply chains, some research could be conducted to identify the new warehouse allocation and layout design techniques to adopt so as to build resilience and increase the overall effectiveness in warehouse systems.

5 Conclusion

This paper presented a methodological literature review for warehouse allocation and layout design methods in general and for heterogeneous and non-standard spare parts in particular. The main objectives were to find relevant information regarding methodologies and concepts in that field of study and one of which could be implemented in practical cases.

The main findings were related to the lack of non-heterogeneous and non-standard environments concerning warehouse allocation and layout design methods. We find that very few papers integrated those concepts and even fewer with practical applications. At present, the literature is mainly composed of mathematical models looking at one specific concept such as layout design or allocation. A few papers try to integrate both concepts together and most of the time they do it from a literature review perspective.

The limitations of this paper reside in the specific keywords and sets involved. This could lead to different results if any other keywords were to be added or removed in subsequent research. The time frame can also limit the results. Another specification is the possibility that some research papers are not included in the final version due to initial screening keywords elimination.

We believe that future research might and will focus on emerging technologies and automatization in WOP related to allocation and layout design for productivity enhancement. It might be an interesting opportunity to mix emerging technologies and automation with management policies and workforce. More research on the impact of Covid-19 health issues with WOP is another opportunity for future research. As previously stated, the lack of practical case studies remains a challenge for future research. Also, it would be of great interest to include a bibliometric-based scientific approach for a better understanding of the evolution of this research field with spare parts and heterogeneous components. Finally, we also believe that more complex algorithms and mathematical models will evolve with emerging technologies and hopefully evaluate non-standard environments from a practical implementation perspective.

6 Conflict of interest

All of the authors have no relevant financial or non-financial interests to disclose. Also, none of the authors received support from any organization for the submitted work. Thus, no funding, no employment, no financial or non-financial interests have been received by any authors of this paper. The authors declare that they have no competing interests.

Data availability

All data generated or analyzed in this study are included in this published article [and its supplementary information files].

Code availability

All material and/or code generated or analyzed in this study are included in this published article [and its supplementary information files].

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Albert, PW., Rönnqvist, M. & Lehoux, N. Trends and new practical applications for warehouse allocation and layout design: a literature review. SN Appl. Sci. 5 , 378 (2023). https://doi.org/10.1007/s42452-023-05608-0

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This is not me

The transformation from manual to smart warehousing: an exploratory study with Swedish retailers

The International Journal of Logistics Management

ISSN : 0957-4093

Article publication date: 22 June 2022

Issue publication date: 19 December 2022

To meet customers' expectations on shorter lead times, high product availability, flexibility, and variation in delivery and return options, retailers have turned their attention to warehousing and are making big investments in technology. Currently, technology providers are pushing for smart warehousing, a new and under-researched phenomenon. This study aims to conceptualize the term and examine pathways toward implementing smart warehousing.

Design/methodology/approach

An exploratory survey was administered to 50 leading Swedish retailers in varying segments. A two-tailed t -test for equality of means was used to detect significant differences between current and future states.

The study found that future smart warehouses will be automated, autonomous, digital, and connected, but that retailers will follow different paths along this journey, driven by contextual trends, e.g. sales growth, wider product assortment, shorter lead-time offerings, and integration of brick-and-mortar and online stores. Interestingly, the study revealed that many of the retailers that aim to create smart warehouses in five years are not the retailers with the most developed technology today.

Research limitations/implications

The paper operationalizes smart warehousing in two dimensions: degree of automation and degree of digitalization and connectivity of information platforms. Based on the findings, 16 theoretical propositions are put forth that, based on contextual factors, explain different pathways for retailers to implement smart warehousing.

Practical implications

The empirical insights and theoretical discussions provide practically useful guidance, including outlined trends, for selecting and benchmarking automation and complementary technologies in warehouse operations.

Originality/value

This paper conceptualizes and operationalizes smart warehousing – an original approach. It is also one of the first to investigate the technological transformation in retail warehousing empirically, explaining how and why retailers choose different pathways toward smart warehousing.

• Transformation

Kembro, J. and Norrman, A. (2022), "The transformation from manual to smart warehousing: an exploratory study with Swedish retailers", The International Journal of Logistics Management , Vol. 33 No. 5, pp. 107-135. https://doi.org/10.1108/IJLM-11-2021-0525

Emerald Publishing Limited

Copyright © 2022, Joakim Kembro and Andreas Norrman

Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

1. Introduction

The retail industry has been undergoing a digital transformation coupled with customers' expectations of shorter lead times (i.e. demanding same-day delivery), high product availability, flexibility when and where to shop, and varying delivery (e.g. click-and-collect, pick-up points, home delivery) and return options ( Galipoglu et al. , 2018 ; Tokar et al. , 2020 ). To meet these demands, the logistics network, particularly the warehouse, has been highlighted as a critical component ( Kembro et al. , 2018 ). The warehouse, previously viewed as a “necessary evil” in the supply chain, now plays a key role in fulfilling customer orders and significantly influences both logistics costs and service levels ( Faber et al. , 2018 ). As Rouwenhorst et al. (2000 , p. 515) put it: “[T]he efficiency and effectiveness in any distribution network … is largely determined by the operation of the nodes in such a network, i.e. the warehouses.”

To improve warehouse operations, online global giants, e.g. Alibaba and Amazon have made large investments in automated material-handling technology ( Alibaba Cloud, 2019 ; Amazon, 2020 ). With increased competition and more mature and varied technologies, the automation trend also has spread among retailers and logistics service providers worldwide ( Reiser, 2020 ; MH&L, 2020 ). An important driver is that the latest generation of automation technologies offers flexibility to handle different types of products and adjust to demand variations, enabling effective and efficient storage, handling, and sorting of large product flows ( Azadeh et al. , 2019 ; Kembro et al. , 2022 ). To improve warehouse operations further, retailers couple automated material handling with digitalization and connectivity of information platforms. Examples of new technologies that are relevant for warehouse operations include artificial intelligence (AI), Internet of things (IoT), cyber-physical system (CPS), big data, 5G, and intelligent video analysis (IVA) ( Kembro et al. , 2017 ; Kamali, 2019 ; Taboada and Shee, 2021 ; Winkelhaus and Grosse, 2020 ; Chung, 2021 ).

This combination of technologies sometimes is termed smart warehousing ( Mahroof, 2019 ; Kamali, 2019 ), a term that is gaining increased attention, often in connection with Industry 4.0, Logistics 4.0, and IoT ( Lee et al. , 2018 ; Winkelhaus and Grosse, 2020 ; van Geest et al. , 2021 ; Issaoiu et al. , 2021 ). It is used in tech blogs, industry reports, conference proceedings, and scientific journal papers. However, extant smart-warehousing research is fragmented when it comes to its substance (see, e.g. Bolu and Korcak, 2019 ; Chung, 2021 ; Zhang et al. , 2021 ). Previous research has instead listed or developed sub-applications of smart technologies that could be used. This gap is important to address to bring research together, facilitate joint discussions, and enable analysis of patterns on a more holistic level, instead of focusing only on specific applications of certain technologies. Another important research gap is the lack of clarity on how to implement smart warehousing ( Azadeh et al. , 2019 ; Winkelhaus and Grosse, 2020 ; van Geest et al. , 2021 ). During the journey toward smart warehousing, companies operate in different contexts and, therefore, are likely to follow different paths ( Kembro and Norrman, 2021 ). However, extant research does not provide any guidance or explanation for different pathways on how and why retailers calibrate their timing, technology, and focus to suit certain operations during their transformation from manual to smart warehousing.

To address these gaps, this study aims to conceptualize smart warehousing and explain pathways on how to implement it. By administering an empirical exploratory survey to 50 Swedish retailers, we make several contributions. Theoretically, we contribute by operationalizing smart warehousing in two dimensions – degree of automation and degree of digitalization and connectivity of information platforms – and by conceptualizing future smart warehouses as automated, autonomous, digital, and connected. We also contribute by identifying different pathways to smart warehousing and explaining how retailers calibrate their timing, technology, and focus to suit certain operations using 16 theoretical propositions. For managers, our study outlines pioneering practices that can help other retailers understand critical issues earlier, as well as how to address them. Our findings provide insights into technologies that are expected to grow in use and criticality to support material handling in single warehouses and in increasingly complex and decentralized networks.

The paper is structured as follows. In Section 2 , we present a theoretical background on warehouse operations in retailing and technology's role in warehouse management. In Section 3 , we describe our empirical study's design. In Section 4 , the findings are presented and analyzed, describing how companies intend to transform from manual to smart warehousing. In Section 5 , based on our findings, we submit theoretical propositions, then conceptualize smart warehousing and explain pathways. Finally, in Section 6 , we outline conclusions and suggestions for future research.

2. Theoretical background

2.1 warehouse operations in retailing.

Warehouses represent “the points in the supply chain where [the] product pauses, however briefly, and is touched” ( Bartholdi and Hackman, 2016 , p. 3). In the retail context, warehouses primarily are used to consolidate and store a range of products to reduce transportation costs and lead times. Different types of retail warehouses include the distribution center (DC), online fulfilment center (OFC), and micro-fulfilment center (MFC). Warehouse operations include receiving, put-away, storage, picking, sorting, packing, and shipping. Along with managing increases in online orders, warehouses also might handle returns and cross-docking, in which goods move directly from receiving to shipping ( Kembro et al. , 2018 ).

In a warehouse, arriving products are checked for quality, registered, and potentially re-packed before being put away in assigned storage locations ( Frazelle, 2016 ). Storage, which includes a reserve and picking area, typically is divided into zones, depending on stock-keeping unit (SKU) characteristics (e.g. size and temperature requirements) and/or order characteristics (e.g. online orders vs store replenishment) ( Eriksson et al. , 2019 ). SKUs can be dedicated or randomly assigned to a location. A common approach is to combine the two (also termed class-based storage ), i.e. SKUs are placed randomly within a dedicated area, thereby reducing travel while avoiding congestion ( Gu et al. , 2007 ). Picking comprises most of the operational cost and has been by far the most-researched warehousing topic. Picking efficiency can be improved by putting the fastest-moving products in the most convenient locations ( Gu et al. , 2007 ) and by selecting appropriate picking methods. The four most common picking methods are single, batch, zone, and wave ( Bartholdi and Hackman, 2016 ). Kembro and Norrman (2019) described the SKU extraction method as one “which implies that a large number of customer orders are accumulated, and the picker only goes to one or a few storage locations and (manually) takes out hundreds of the same SKU to a trolley or pallet. These products are then moved to a sorting system” (p. 521). Eventually, orders are packed and shipped. If an order involves multiple flows (e.g. wave picking or cross-docking), the first step is to sort and merge the various order lines per customer and destination. SKUs are registered thereafter for departure and positioned based on the designated gate and time window ( Bartholdi and Hackman, 2016 ).

To improve warehouse operations' efficacy and efficiency, several configuration aspects are considered, including physical layout (e.g. placement of docks, aisle configuration, and lane depth) and storage and handling equipment (e.g. racks and forklifts for put-away and picking) ( Kembro et al. , 2018 ). Important resources also include labor management (e.g. scheduling, rotation, and shifts) ( De Leeuw and Wiers, 2015 ), information systems (e.g. warehouse management systems [WMS] and warehouse-control systems [WCS]) ( Kembro and Norrman, 2019 ), and automation technologies, e.g. conveyors and robots ( Baker and Halim, 2007 ; Azadeh et al. , 2019 ). These configurations' goal is to improve utilization of resources and capacities (labor, space, and equipment), increase throughput, reduce material-handling time, and increase operations and design flexibility ( Kembro and Norrman, 2019 ). However, which warehouse configuration to use depends on a range of contextual factors ( Hassan et al. , 2015 ; Faber et al. , 2018 ; Eriksson et al. , 2019 ; Kembro and Norrman, 2021 ), e.g. order-fulfillment time requirements, assortment range, number of transactions, and sizes of goods.

2.2 Technology's role in warehouse management

Just a decade ago, warehousing worldwide predominantly were manual operations, but increasing competition and expectations on shorter lead times (demand for same-day delivery), high product availability, and a variation of delivery and return options have necessitated improvements to transform from manual to smart warehousing (e.g. Chung, 2021 ; Kembro and Norrman, 2021 ; Zhang et al. , 2021 ). As described below, this transformation mainly involves technology, e.g. automated material handling and information systems.

2.2.1 Automated material handling

Warehouse automation has been around for many years. Defined as “the direct control of handling equipment producing movement and storage of loads without the need for operators or drivers” ( Rowley, 2000 , p. 38), it includes equipment, e.g. automated storage and retrieval systems (AS/RS), automated guided vehicles (AGVs), and conveyorized sorting systems. Positive aspects of warehouse automation include improved space utilization and service, as well as lower operating costs and reduced picking errors. However, it requires significant investments, and more static automation technologies may involve flexibility risks, e.g. during extreme demand peaks ( Baker and Halim, 2007 ; Kembro and Norrman, 2020 ). Therefore, these investments require careful analysis, considerable scale, and a long-term vision ( De Koster, 2018 ).

In recent years, retailers have increased the pace of implementing automation in warehouse operations, and various new technologies are being tested to make material handling more effective and efficient ( Kembro and Norrman, 2020 ). Azadeh et al. (2019) conducted a comprehensive review of recent developments in various automation technologies in warehousing, identifying the following categories: Crane/Automated Forklift (e.g. AS/RS, push-back rack); Carousels and Dispensers (e.g. horizontal carousel, A-frame); Shuttle (aisle-based, e.g. horizontal AVS/R system; grid-based, e.g. robotic compact storage and retrieval systems); and AGVs (e.g. movable racks in robotic mobile fulfillment systems). One interesting development is the increased flexibility in automation technologies, with different solutions available for different SKU characteristics, while it is also less difficult to adjust to demand variations. It is also relevant to consider whether humans will work in warehouses in the future and what their role will be. Azadeh et al. (2019 , p. 940) noted that: “Human picking in collaboration with AGVs is one of the most recent technologies that is becoming popular in practice because of its simplicity and flexibility.”

2.2.2 Digitalization and connectivity of information platforms

Simultaneously, retailers develop information systems to manage and control their warehouse operations. As Kembro and Norrman (2019) asserted, the three most common systems include enterprise resource planning (ERP) systems; WMS; and WCS. ERP connects information across a range of organizational functions, e.g. sourcing, inventory management, production planning, and financial matters. WMS is used for shorter-term planning and control of resources and order fulfilment for various warehouse operations. WCS is used to control automated systems, e.g. robots and advanced sorting algorithms. A related system is the warehouse execution system (WES), which synchronizes various automation technologies' operation with workers and could be viewed as an integration or combination involving WMS and WCS. Several other systems besides these also exist, e.g. for labelling, administering, and transport administration system (TAS). With the increasing importance of sharing real-time inventory and order information, both internally and externally, it becomes vital for all systems within each warehouse and across the logistics network to be integrated. For this purpose, Kembro and Norrman (2019) noted that retailers increasingly use a distributed order management (DOM) system.

2.3 The transformation toward smart warehousing

Smart warehousing is a term that is gaining increased attention, often in connection with Industry 4.0, Logistics 4.0, and IoT (see literature reviews in Winkelhaus and Grosse, 2020 ; Chung, 2021 ; Issaoui et al. , 2021 ). The term has not been defined precisely, but generally has been described as something more than using automated material handling and traditional information and communication technology (ICT), e.g. ERP, WMS, and WCS. Chung (2021 , p. 1) defined smart technologies as “applications of artificial intelligence and data science technologies, e.g. machine learning, big data, to create cognitive awareness (autonomous) of an object with the support of information and communication technologies, e.g. IoT and blockchain.” Previous research often either has listed different smart technology applications ( Winkelhaus and Grosse, 2020 ; Chung, 2021 ; Issaoui et al. , 2021 ), developed focused algorithms (e.g. Jiang et al. , 2021 ), proposed IoT-based WMS ( Lee et al. , 2018 ; Aamer and Sahara, 2021 ), or referred to architectures for information systems related to data collection and administration ( Jabbar et al. , 2018 ; van Geest et al. , 2021 ).

Several studies have used smart warehousing to describe a warehouse that includes a combination of automated material handling and AI. For example, Bolu and Korcak (2019) discussed smart warehouses in terms of automation technologies that make warehouse robots and systems smarter, including autonomous robots that perform most activities in a warehouse, as well as information systems that keep track of every object's movement in the warehouse. Mahroof (2019) noted that these robots carry out most warehouse work, have Wi-Fi capabilities, and use self-charging batteries and laser-detection technology. Mahroof (pp. 178–179) concluded that “the new automation age is here, whereby industrial robots and computers are being used beyond their traditional scope of performing highly accurate repetitive tasks, routine physical work tasks, through to more complex tasks that require cognitive capabilities, e.g. making tacit judgments, sensing emotion, and driving processes which previously seemed impossible.” Along the same lines, Zhang et al. (2021) investigated Alibaba's smart warehouses, which use a set of AI applications, along with collectively working robots and other related human and organizational resources, to improve key business processes' efficiency and efficacy. The researchers noted that while humans focus on higher-value tasks that require creativity, robots and AI are used to execute repetitive, time-consuming, and/or hazardous tasks. With WMS, WCS, and AGVs as foundational systems, AI uses algorithms as building blocks to automate, augment, or transform processes. Examples of algorithms include sales forecasting, location recommendation, 3D packing, order wave combination, route planning, robot scheduling, and robotic motion control.

Extending this perspective, Kamali (2019) described the smart warehouse as using a mix of technologies, including, e.g. robotics systems, IoT, radio-frequency identification (RFID), enterprise asset management (EAM), and digital twins with 3-D representations of objects and their components, e.g. sensors. Other relevant technologies include wide area network (WAN), cloud computing, automatic identification technology, CPS, and blockchain. Kamali argued that AI allows for gaining unprecedented insight into products, components, and even materials' life cycles, noting that smart warehouses make activities more efficient, save on labor costs, reduce errors, and generate higher productivity. Thus, the automation and AI focus is complemented (e.g. Lee et al. , 2018 ; Kamali, 2019 ; Issaoui et al. , 2021 ) by adding (inter)connectivity in terms of IoT and wide area networks/cloud for information management. Some studies have developed frameworks for WMS in the IoT context ( Lee et al. , 2018 ; Aamer and Sahara, 2021 ) or reference architectures ( Jabbar et al. , 2018 ; van Geest et al. , 2021 ). Another relevant technology is IVA, which enables advanced analytics and decision making in real time in the warehouse ( Kembro et al. , 2017 ). Using a mix of technologies is supported by Attaran (2020) , who pointed out that AI and robotics, cloud computing, 3D printing, advanced analytics, blockchain, AR, RFID, IoT, and cloud technology drive digital trends and elicit change in supply chain management. Similar reasoning can be found across tech blogs and suppliers. For example, Flytware (2021) stated: “Smart warehousing is essentially a set of interconnected and/or automated technologies for streamlining warehousing operations in an efficient manner.” The connectivity dimension links the warehouse as part of a digital network, pointing to the growing importance of Industry 4.0, digitalization, and a unified platform for multiple device connectivity in real time, which can be implemented through 5G networks ( Taboada and Shee, 2021 ). Another aspect sometimes stressed is robot autonomy ( Winkelhaus and Grosse, 2020 ; Chung, 2021 ; Jiang et al. , 2021 ).

To sum up, smart warehousing is receiving increased attention in both practice and academia. but extant research is fragmented, with no consensus reached on a definition of smart warehousing . Previous research instead has listed or developed sub-applications of smart technologies that could be used. This literature gap is important to address to facilitate discourse and create a common understanding of what smart warehousing is. As a starting point, our literature review revealed two smart warehousing dimensions: the development of automated material handling and development of digitalization and connectivity of information platforms. The review also revealed that pathways toward implementing smart warehousing are missing. Previous studies emphasized contextual factors' importance, suggesting that companies may follow different paths. However, extant research has not provided any guidance or explanation as to how and why retailers calibrate their timing, technology, and focus to suit certain operations during their transformation from manual to smart warehouse management.

3. Research design and method

This study aimed to grasp and generate practically relevant insights and theoretical propositions about a new phenomenon – smart warehousing – on which current knowledge is scarce concerning both the current situation and future developments. By illuminating this area for consideration, we seek to influence the definition of its problem domain, leading to contributions of the “theoretical pre-science” type ( Corley and Gioia, 2011 ).

For this, we used an exploratory survey (distinguished from explanatory and descriptive research). It is useful for becoming more familiar with a topic and identifying new possibilities and dimensions of interest ( Malhotra and Grover, 1998 ), as well as identifying interesting patterns during the early stages of the research-maturity cycle ( Malhotra and Grover, 1998 ; Edmondson and McManus, 2007 ), rather than testing a theory-driven hypothesis. The study is theory-elaborative and tries to adapt theory to contemporary practices and challenges ( Corley and Gioia, 2011 ; Ketokivi and Choi, 2014 ). Therefore, conducting an exploratory survey is appropriate for revealing trends, interesting patterns, and facets of the phenomenon with the purpose of developing propositions for future research studies. In this way, exploratory surveys (conducted with experts in the field) are similar to case studies and other qualitative methods, which also are useful for discovering new facets of phenomena under study ( Forza, 2002 ). Exploratory surveys have been used in supply chain and operations management research before, e.g. related to information systems: Themistocleous et al. (2001) examined problems related to ERP systems, and Kembro and Norrman (2019) investigated omnichannel logistics issues. These studies applied more open-ended research questions in combination with closed-ended survey questions using ranking or Likert scales.

3.1 Data collection

This study is the first part of a larger study, the Swedish Retail Logistics panel, in which Swedish retail companies (pure-play online retailers, henceforth referred to as e-tailers , as well as retailers with both online and brick-and-mortar stores) have been invited to participate in a series of exploratory surveys. The retailers were identified through addresses from different databases and listings of leading Swedish retailers (e.g. related to growth or turnover), e.g. the report “Who is who in Swedish retail 2020” ( Lindecrantz, 2020 ). Altogether, 50 retail companies provided input for this study (out of 300-plus companies who were sent invitations). The retailers represent a wide range of retail sectors and product types (see Table 1 ; respondents could provide multiple answers). The major retail sectors, currently at the forefront of online sales, are well-represented by their leading companies in the survey. Most of the retailers that are part of the panel are top-five (Sweden) in their segments based on turnover.

Our empirical data collection is based on an online survey. The respondents were senior supply chain/logistics managers divided between the following positions: 37 directors (or heads) of supply chain management/logistics/operations; one CEO; four warehouse managers; five managers responsible for development/design of logistics and/or warehouse solutions; one innovation lead; one head of e-business; and one head of an online store. The survey was pre-tested on five company representatives to test the questions' general appropriateness, functionality, and structure. The feedback was used to modify the survey instrument, mainly by increasing clarity in survey questions and explaining terminology. The respondents were asked to use a perceptual Likert scale ranging from 1 (“agree to a very low degree”) to 7 (“agree to a very high degree”) to assess their current focus and development in different areas. To address the limitations of scales in an exploratory study, most questions that included statements with fixed-scale alternatives were complemented with open-ended questions to explore alternative answers on the specific topic.

The main survey questions focused on the following issues: (1) company data and contextual factors, e.g. size, turnover, type of products, type of channel, assortment range, and lead-time offerings; (2) to what degree the companies invest in and use automation in warehouse operations – both in general and for different material-handling processes; (3) to what degree companies use technologies (other than automation) in the warehouse, e.g. regarding information platforms and connectivity. The respondents were asked to list and explain their three main focus areas related to the implementation of warehouse automation. Some questions included pre-determined answering alternatives, e.g. different types of technologies (with clarifying examples to reduce the risk of misunderstanding). We used many different sources to develop the survey questions, including scientific literature (discussing warehouse operations in retailing, warehouse automation and technology, and smart warehousing and technologies) and gray literature, e.g. business journals and tech blogs. We also scanned the market to understand more about automation technology and smart warehousing that either already exist or are being developed. As we initially looked for responses from both very small e-tailers (with large growth) and larger traditional and omnichannel (i.e. integrated store and online) retailers, we defined scales using a logarithmic approach to illustrate the large difference between potential answers regarding company data and contingency factors.

3.2 Data analysis

With an exploratory aim and too few respondents to conduct advanced statistical analysis, we described patterns in the participants' perceptions of current and future practice. We were inspired by multiple case study analyses (see, e.g. Miles and Huberman, 1994 ), with the aim of developing propositions for future research by pattern-matching data from open-ended and closed-ended questions. The same set of questions was used for both current and future states, and to detect significant differences in any context, we used a two-tailed t -test for equality of means.

To support the formulation of theoretical propositions about different pathways toward smart warehousing, we analyzed the data based on different contextual factors. First, we clustered the data based on channel strategy (with today's share of retailers stated): e-tailing (may include showrooms) (26%); partly integrated multichannel (both physical stores and online sales, but separated or only partly integrated channels) (50%); omnichannel (high degree of integration of store and online channels) (16%); and store focus, without (or locally store-driven) online sales (8%). Interestingly, our data indicate a strong trend (in five years) toward implementation of an omnichannel strategy among the panel retailers. Retailers selling only offline decreased (which is not a surprise), while store-driven online sales increased. Another observation is that e-tailers continued to focus on online sales and, with one exception, did not plan to open physical stores. The other contextual factors that we focused on in our analysis included turnover, assortment range, and sizes of goods.

4. Findings and analysis

To understand how Swedish retailers intend to transform from manual to smart warehouse management, we examined current trends and future intentions on several aspects. We present these findings in the following sub-sections and use them to develop 16 theoretical propositions.

4.1 Overall degree of automated warehouse operations

The study indicated an increased willingness to invest in automation, with retailers making large investments (>SEK 100 million annually), more than doubling such expenditures (8–22%), while those investing SEK 31–100 million annually increased such expenditures from 12 to 22%. Retailers not investing at all in automation decreased from 32 to 8%, indicating a statistically significant (see t -tests in Appendix ) and a strong increase in the overall degree of automation (in the warehouse with the most recent investment). On a scale from 1 (very low degree) to 7 (very high degree), the mean values increased from 2.07 (five years ago) via 3.15 (at present) to 5.13 (in five years). The retailers represented four clusters. The first group of retailers made large investments in recent years, but currently focuses on fine-tuning automation technology (e.g. by supplementing it with other technology). The second cluster has not yet automated to a large extent but intend to invest significantly in automated systems in the coming years. A third group represents retailers who already have made significant investments and continue to invest heavily in warehouse automation in their logistics networks. The fourth cluster, which is decreasing in number, represents retailers that have not and do not intend to automate their warehouses.

The degree of automation was analyzed on a deeper level, related to different warehouse operations ( Figure 1 ). The operations that have been automated to a greater extent represented outbound flows. Above all, “sorting outgoing goods” (average 3.30) stood out, while picking (2.89) and packing (2.80) also were higher, indicating that the most labor-intensive processes were automated first to justify return on investment. The focus on sorting also reflected the increased extent, variety, and complexity of sorting at the operational material-handling node level ( Kembro et al. , 2022 ). However, on the scale from 1 to 7, values below 4 still indicated a low degree of automation. The operations that currently are automated to a very low degree (thereby still requiring a greater share of manual work) are goods receipt (1.61), sorting of incoming goods (1.63), and handling returns (1.50). These flows included a wide variety of goods and requirements for quality control before storage. It is also important to determine the storage zone and balance incoming flows to avoid bottlenecks. Previous research (e.g. Kembro and Norrman, 2019 ) has indicated that retailers often position their most experienced staff in goods receipt.

Five years from now, a significant increase (see t -tests in Appendix ) in automation is expected, with several operations expected to average above 4.00 (storage 4.24, picking 4.98, packing 4.89, and outgoing sorting 5.32), indicating a clear trend toward a relatively high degree of automation (i.e. not only an increase, but also an increase to a relatively high level). Despite current low levels, the trend toward a higher degree of automation for incoming goods is interesting. Insights from a previous study ( Kembro and Norrman, 2019 ) indicate that automation of inbound processes requires greater work and coordination with suppliers, e.g. standardizing boxes/labels and balancing flows handled in warehouses (to avoid bottlenecks). It also seems that retailers want to try to make return handling more efficient, with some retailers stating that “automation of return flows is one of our top-three focus areas for technology in warehousing.”

Analyses of individual retailers and the number of warehouse operations that one has stated as highly automated (response alternatives 6–7 on a scale of 1–7) in five years provide additional insights ( Table 2 ). Twenty percent of the retailers answered that they will have automated five or more operations to a very high degree. Of these, many will have automated most of the warehouse operations to a high degree, and some even plan to use a fully automated warehouse. Altogether, 44% indicated that they will automate one to four operations significantly. Simultaneously, many retailers (38%) have not specified any of their operations as being highly automated, indicating that a high demand for manual work will remain in warehouses, but this differs between different retailers.

The more labor-intensive the processes, the higher the priority for retailers to automate. This implies that retailers begin by automating outbound picking, packing, and sorting (and related storage), followed by shipping and cross-docking to focus ultimately on inbound receipt, sorting, and handling returns.

4.2 Contextual factors' influence

To understand differences between the retailers, we first analyzed the data based on channel strategy. This analysis revealed that today's omnichannel retailers automated early (most indicated values 4–7 already five years ago), while e-tailers and retailers with partially integrated multichannels caught up by increasing the degree of automation sharply for the past five years. Retailers with a store focus remain at a low level. On a more detailed level, future omnichannel retailers, to some extent, will lead automation of incoming goods (receipt of goods, sorting of incoming goods, storage). E-tailers, to some extent, have less “cross-docking” compared with other retailers (which is logical considering that incoming pallets and cartons must be broken down for picking for e-customers). Instead, to a greater extent, they have invested in “sorting outgoing goods.” The greatest uncertainty (answer alternative “do not know”) applies to cross-docking and return handling, which, in itself, is an interesting observation.

Second, we analyzed the retailers' turnover. Most of the retailers in the panel (94%) currently have a turnover larger than 100 MSEK (∼10 MEuro), of which 36 (72%) sell more than one billion SEK (100 MEuro). Two of the retailers are global giants and sell over 100 billion SEK (∼10 billion Euro). Thus, relatively large retailers (rather than medium-size and small companies) dominate the panel. In five years, both the smaller and larger retailers plan quick growth. The analysis indicates that retailers with low turnover tend not to automate, which is in line with the large capital/investments required for automation. We noted a correlation between high turnover and high degree of automation ( Figure 2 ). An interesting observation is that medium-size companies presently have a relatively low degree of automation, but the tendency is that they are taking a big leap in five years.

Third, we analyzed assortment range, which generally continues to increase. Partially integrated multichannel retailers have driven this development (of which most aim to be highly integrated omnichannel retailers within five years). Altogether, 68% of these retailers will have an assortment range larger than 30,000 articles. The analysis indicates that retailers with a relatively small assortment tend to maintain a low degree of automation throughout their warehouses, while those with a larger assortment range (which are dominant in the study) increase their degree of automation the most ( Figure 3 ).

Fourth, we analyzed sizes of goods ( Figure 4 ). Presently, this mainly entails handling very small (0–1 liter) and small (1.1–50 liter) goods with automation. In five years, the trend is toward a significant increase in all sizes of goods (with the smallest for bulky, i.e. goods that do not fit on EUR pallets), with high averages for very small/small goods (5.60/5.39). Automated handling of medium-size goods (51–200 liters) also has been increasing (from 1.94 to 3.38). For large goods (>200 liters, which can fit on EUR pallets), a significant increase was found, but still at a generally low level. Among the answers were several “Do not knows,” which, in this case, also represent “not applicable,” a question that is not relevant to the retailer if it does not handle goods of this size in its warehouses.

Omnichannel retailers increased automated handling of large and complex order and goods flows early, but as other retailers catch up on their investments, channel strategy will have less explanatory value on the overall degree of automation.

The larger the turnover and assortment range, and the smaller the sizes of the goods, the more retailers invest in automation technology to improve material handling throughout the warehouse.

4.3 Choice of automation technology for material handling

Another interesting aspect is the choice of automation technology for material handling ( Figure 5 ). We observed generally rare use (Answer Option 1) of most examined automation technologies, but some stood out and are significantly more common than the others: stationary automated sorting systems (3.50); stationary automated storage and retrieval (AS/RS) (2.93); compact/grid-based storage (3.12); and automated packaging systems (2.88). Interestingly, for these technologies, the situation is dipolar , i.e. retailers did not use them at all or rarely. This is explained through our analysis: Some automation technologies tend to be chosen for a certain context (e.g. product characteristics), while others are used more generally among retailers. For storage and picking, companies mainly choose an automation technology depending on goods/flow characteristics: Omnichannel retailers with a mix of goods and order characteristics (e.g. both store replenishment and online) typically choose stationary AS/RS storage, while e-tailers typically choose compact/grid-based storage (e.g. AutoStore) to handle large assortments of relatively small goods (e.g. clothes) that individual online customers order. Retailers that handle piece-pick-intense online orders (i.e. large volumes of small goods) opt for A-frame automatic dispensers. Simultaneously, there tends to be a more general approach in terms of automation of packaging, weighing, dimensioning, sorting, and palletizing of outgoing goods (though mainly retailers with store networks use automatic palletizing).

In five years, use of most of these automation technologies will increase, a pattern in line with the current situation observed. We detected significant increases ( t -test; see Appendix ) in stationary automated sorting (5.06), automated packaging (5.06), compact/grid-based storage (4.56), cubing (4.91), robotic palletizing (4.41), stationary automated storage and retrieval (3.72), A-frame systems (2.58), robotized piece-picking (2.25), automated guided vehicles (2.34), and self-driving flexible forklifts (2.00). Several technologies reached over 4.00 (average), and most are dipolar , i.e. while some retailers are strong advocates (blue), others will not implement such technology at all (black/red). This implies that retailers will opt for different technologies as they automate material handling in their warehouses. However, the future is not as dipolar as the current situation (the color scale is more gradual). One possible explanation is that when retailers' warehouses (e.g. turnover and assortment ranges) grow, they must handle a larger mix of orders, flows, and goods, with more varied characteristics. Thus, retailers introduce multiple zones and may benefit from using a mix of different technologies (e.g. one technology designed to handle smaller goods in one zone and another technology tailored for larger goods in another zone).

The less a warehouse activity is influenced by contextual factors – which mainly applies to inbound and outbound due to more standardized processes and handling units – the more standardized automation technologies are used to improve material handling.

The more a warehouse's activity is influenced by contextual factors – which mainly applies to storage and picking adjusted to, for example, SKU and order characteristics – the more tailored the automation technologies used to improve material handling.

The larger the assortment of relatively small goods, the more retailers automate by using compact/grid-based storage and goods-to-person technology to improve space utilization and increase efficiency of storage and retrieval activities.

The higher the volume of small goods for individual customers, the more retailers automate by using A-frame technology to reduce costs and lead time for piece-picking activities.

The higher the volume of mixed goods and integrated channels, the more retailers automate by using stationary automated storage and retrieval technology to increase space utilization, and the greater the efficacy and efficiency in handling large throughputs of more varied order and product flows.

An interesting observation is that many retailers choose static, rather than flexible, automation technologies mainly for storage, picking, and sorting. This may mean less leeway to change and adapt automation technologies in the future – particularly considering that retailers are making very large investments now and that the future budget for major changes may be limited. Thus, this trend could mean that automation/warehousing, to a greater extent, will dictate conditions in the future (e.g. setting boundaries or creating opportunities) for changes in product assortment, logistics networks, and overall logistics strategy. If retailers invest in scalable and flexible solutions, it may be easier to grow with acquisitions, add more varied store formats, or create more flexible offerings and a wider variety of products. From a historical perspective, large investments in (static) automation technologies were made in the early 2000s, influencing later strategic decisions related to, for example, the designing of networks and warehouses to handle increasing numbers of online orders.

Investments in static automation technology for storage, picking, and sorting reduce a retailer's ability to adapt warehouse operations to future contextual changes, e.g. reduced customer order lead times and wider variety of product offerings.

Today's large investments in static automation technology for storage, picking, and sorting imply that warehouse operations to a greater extent may dictate conditions for changes in product assortment, logistics networks, and overall logistics strategy decisions.

4.4 Complementary technologies in warehouses

The survey also examined other technologies that complement automated material handling ( Figure 6 ), e.g. those related to digitalization and connectivity. Today, WMS is used frequently (average 5.98) and is viewed, more or less, as standard for controlling and managing daily operations in warehouses. Corresponding with WMS, WCS (3.71) and WES (3.07) are used to control and coordinate a variety of processes and automation. In addition to these, pick-by-voice (3.13) and pick-by-light/put-to-light (2.37) are used to some extent.

Digitalization and connectivity technologies, e.g. private networks (4G, 5G) and AI, are used to a small extent (2.74 and 2.25, respectively). However, some individual retailers have invested to a large extent and may be viewed as pioneers. Relatively speaking, more e-tailers than omnichannel retailers submitted high values (6, 7) for WMS, WCS, WCE, private networks, and AI. Today, other technologies are not used at all in principle, including RFID technology (1.08), pick-by-vision (1.29), and IVA (1.12), nor are other hyped technologies used, e.g. the IoT, digital twins, drone technology, or blockchain.

The trend in five years indicates that WMS (average 6.62), WCS (5.95), and WES (5.34) will become the backbone of warehouse management, with large significant increases in WCS and WES. Strong future trends also indicate significant increases for several technologies that humans use to strengthen workers' abilities, e.g. to pick faster or reduce picking errors (so-called “human augmentation”), including put-to-light/pick-by-light (4.45), pick-by-voice (3.16), and pick-by-vision (2.05). The increasing use of these technologies indicates that there still will be human workers in the future who conduct material-handling activities (particularly picking) in warehouses. The sharp rise in use of put-to-light/pick-by-light partly points to the importance of faster labor with lower error rates.

WMS, WCS, and WES represent the backbone of warehouse management, whereas hyped technologies – e.g. RFID, IoT, drone technology, and blockchain – will play a limited role in warehousing in the years to come.

The bigger the focus on online customers, the more retailers invest in technologies that support data management, connectivity, and real-time analysis.

5. The pathway toward smart warehousing

Building on our findings and analysis, we sought to understand to what degree smart warehousing was a tendency or trend among the panel's retailers. With support from the literature, we used survey data to operationalize two dimensions: (1) degree of automation and (2) degree of digitalization and connectivity of information platforms. Degree of automation focuses on automation of material handling, i.e. the handling of physical goods. Examples include stationary automated storage, compact/grid-based storage, A-frame systems, automated sorting systems, and automated weighing and dimensioning. Degree of digitalization and connectivity of information platforms focuses on technologies for handling, analysis, and coordination of information and includes, for example, WMS, WCS, WES, AI, IoT, and private networks.

For each dimension, we summarized the number of technologies (per dimension) for each retailer, in which a high degree of implementation was indicated (Values 5, 6, and 7). We then plotted this value for each retailer in Figure 7 , indicating each company's current position (yellow square) and its intentions in five years (red circle). Automation of material handling is illustrated on the Y -axis, with degree of digitalization and connectivity of information platforms on the X -axis. As previously described, several retailers increased automation of material handling (moving upward along the Y -axis). Simultaneously, many retailers are investing in information platforms and increased digitalization (moving to the right). As the gray arrows illustrate in Figure 7 , the overall trend is that the technology frontier moves diagonally upward to the right, implying that retailers to varying degrees are investing in both automation technologies and information platforms, representing a general tendency toward an intentional technological shift in retailers' warehouses over the next five years.

We conducted additional analyses of the individual retailers' movements. Interestingly, the retailers that perceive themselves as having smart warehouses in five years are not the retailers that are most automated or digitalized today. Instead, several retailers are planning major technological upgrades over the next five years, i.e. moving from having limited technology in warehousing to being at the forefront of development toward smart warehousing. We illustrated this technology shift with four retailers' movements (the blue dotted arrows in Figure 7 ).

Our analysis further indicates that retailers follow different paths toward smart warehousing. On one hand, some retailers follow an automation-focused path toward smart warehousing, i.e. while investing in a wide range of technologies to implement smart warehousing, these retailers emphasize technologies that automate material handling of their physical goods flows. This may arise from the need to manage more varied warehouse operations ( Kembro and Norrman, 2020 ). Examples include handling online customers vs store replenishment; a variation in flows, including handling returns and cross-docking; and large variations in SKU sizes (e.g. pieces, cartons, and pallets). The more varied the operations, the wider the range of automation technology needed for material handling. Another driver is increased sorting complexity (due to, e.g. multiple destinations, delivery modes, and transporters), creating a need for additional automation technology ( Kembro et al. , 2022 ). The historical footprint is also relevant, in which retailers that automated certain material flows early (e.g. store replenishment) add automation technologies dedicated to meeting continuously changing online customers' requirements ( Eriksson et al. , 2022 ).

On the other hand, our data indicate that some retailers, particularly e-tailers, will follow a more digitalization-focused path toward smart warehousing. While these retailers invest in automated material handling, they emphasize information platforms and technology that enable real-time data analysis (e.g. AI). These retailers have less variation in operations and, therefore, require a narrower range of automation technologies for material handling. For example, an e-tailer may have a high automation degree, but can handle warehouse operations with only one main form of automation technology (e.g. AutoStore). This enables a greater focus on other complementary technologies for digitalization and connectivity of information platforms. An important driver is online sales, in which e-tailers generally are more focused on virtual contact with customers, requiring a range of integrated IT systems. Another driver is the need to connect multiple material-handling nodes (e.g. DC, OFC, retail stores) in the logistics network ( Kembro and Norrman, 2019 ).

The advancement of the smart warehousing frontline is driven not by retailers with a current high degree of automated material handling, but rather by pioneering retailers that make a major technological shift from limited use of technology.

Retailers with different channel strategies take different implementation routes toward smart warehousing, in which more-integrated omnichannel retailers follow an automation-focused path, whereas less-integrated retailers and e-tailers follow a more digitalization-focused path.

5.1 Conceptualizing smart warehousing

At this stage, it is also relevant to define smart warehouse (which is currently missing in the literature). Based on extant literature ( Section 2 ) and on an analysis of retailers' current and intended technological implementation, we conceptualize future smart warehouses as: Automated , i.e. robots will handle a large part of physical material handling; Autonomous , in which robots make their own decisions regarding task distribution (e.g. order management) and movements in the warehouse – a combination of autonomous automation also can be called autonomization; Digital , i.e. integrated information platforms handle warehouse management (e.g. inventory levels, sequencing of order picks), including functionality for analysis of large amounts of data (AI/machine learning), e.g. for improved forecasting; and Connected , in which moving resources and products are monitored, controlled, and coordinated in real time. It also enables real-time analysis (e.g. via IVA) of in-store activity to allow for fast decision-making and further development of processes.

We summarize these insights in Figure 8 , which outlines both the two dimensions of smart warehousing, as well as the different stages and pathways toward this goal.

5.2 Connecting multiple smart warehouses in extended logistics networks

Moving beyond the single smart warehouse, retailers will use multiple material-handling nodes in their future logistics networks ( Hübner et al. , 2022 ; Kembro et al. , 2022 ). Our findings indicate that retailers will expand from zero or one to between two and five large distribution warehouses. Our study also found that retailers are adding more and varied material-handling nodes (e.g. DC, OFC, MFC) in their decentralized logistics networks. These are complemented by transformed physical stores, which are becoming the center of retail operations ( Hübner et al. , 2022 ).

Apart from market expansion, the main reason for expanding the number of material-handling nodes is the extremely short lead times from customer order to final delivery. Global giants, e.g. Alibaba and Amazon largely have driven this development, with their increasingly competitive promises to customers ( Kembro et al. , 2022 ). In our study, 52% of multichannel retailers (of which most aim to be highly integrated omnichannel retailers within five years) intend to offer standard lead times under 24 h. A similar pattern is visible for e-tailers. No matter how fast a central warehouse fulfills an order, the transportation times to final destination (e.g. home delivery, C&C) may result in lead times that exceed customer expectations. This development will require well-coordinated logistics networks (e.g. use of drop-shipment and small-scale warehouses, e.g. OFCs or MFCs in and around cities, i.e. closer to end customers). It also will drive the need for effective and efficient material handling across network nodes, which can be implemented, e.g. by investing in new automation technology, as well as advanced and integrated information systems. As Kembro and Norrman discussed ( 2019 ), future warehouses and stores, to a greater extent, will be interconnected, among other ways, through the use of a so-called DOM system. For example, inventory levels are coordinated between different material-handling nodes, and an online order can be routed to/managed in different nodes depending on several defined parameters/goals (e.g. reducing lead times or lowering handling costs).

With increasingly competitive lead-time promises to customers, retailers use more and varied (smaller, localized) material-handling nodes that need to be interconnected in smart warehouse networks.

With requirements on effective and efficient warehousing across logistics networks, retailers increasingly use automated material-handling technology in different types of decentralized material-handling nodes, e.g. micro-fulfillment centers.

6. Conclusions and future research

This study aimed to conceptualize the term smart warehousing and explain pathways on how to implement it. By empirically studying this novel phenomenon, our research influences the definition of its problem domain and offers multiple contributions of the “theoretical pre-science” type ( Corley and Gioia, 2011 ).

Contributing to recent and limited literature on smart warehousing ( Azadeh et al. , 2019 ; Mahroof, 2019 ; Chung, 2021 ; Zhang et al. , 2021 ), we put forth 16 propositions related to automation and complementary technology, as well as pathways toward smart warehousing. Our analysis indicates that the future smart warehouse will be automated, autonomous, digital, and connected, but that retailers will follow different paths along this journey. To support our analysis, we operationalized smart warehousing into two dimensions: degree of automation and degree of digitalization and connectivity of information platforms. This is an important contribution to the literature in different fields (e.g. logistics, operations research, and information systems) that mention smart warehousing without defining it. Our operationalization also could influence future conversion of smart warehousing, enabling analysis of patterns on a more holistic level and focusing not just on specific applications of certain technologies that characterize much of current research.

Interestingly, our study revealed that many of the retailers that aim to create smart warehouses in five years are not the retailers with the most developed technology today. In this transition, retailers followed different technological pathways driven by contextual trends, e.g. the growth of sales, wider product assortment, shorter lead-time offerings, and channel strategy. By demonstrating how the continuously evolving retail landscape influences back-end logistics, we contribute to the literature on retail logistics and warehousing (e.g. Galipoglu et al. , 2018 ; Kembro et al. , 2018 ), as well as related automation technology and information platforms ( Kembro and Norrman, 2019 ). Specifically, we explain why retailers calibrate their timing, technology, and focus to suit certain operations. The study found that retailers first automate labor-intensive, outbound warehousing operations, with an emphasis on small or very small goods. Next, many automate larger-size goods and expand their focus to include inbound operations. An important observation is that automation of outgoing flows is more non-contextual (i.e. similar across retailers), while storage and picking technologies seem more tailored to contextual factors (e.g. characteristics of goods). We also conclude that although new automation technology is available for a wider range of retail segments and sizes, it still requires a large investment. This may explain why retailers with larger turnovers and assortment ranges invest more in automation.

Our study proposes this and provides explanations as to why some retailers focus on advanced automation technology while others tend to pioneer digitalization and connectivity. Retailers generally have a solid understanding of information systems for automated material handling, but have limited knowledge about emerging smart warehousing technologies related to digitalization and connectivity. In five years, WMS, WCS, and WES will be the backbone of warehouse operations, complemented with technologies that support data management and real-time analysis, including AI, IoT, RFID, and IVA. This technological development also is important for connecting logistics networks with multiple, different, and decentralized material-handling nodes (e.g. automated MFCs) to meet growing demand for very short lead times from placed order to delivery.

This study provides practically useful guidance for managers by outlining what is trending now and five years down the road. In many companies and countries, the transformation toward smart warehousing has only just begun. Empirical insights from pioneering practice can help other retailers understand critical issues earlier, as well as how to address them. Our findings provide insights into technologies expected to grow in use and criticality to support both material handling in single warehouses and increasingly complex and decentralized networks. Managers also can use our 16 propositions to reflect on what the near future holds and use them as input for scenario analysis.

Pre-science studies' observations naturally elicit speculation that needs more research. We noted that, related to automation, retailers' current tendency to invest in static automation solutions could limit their future strategic options. Future research could investigate whether these kinds of technological investments follow existing strategy – or whether they instead are driving or delimiting future strategies (e.g. to be able to grow, we need to automate vs our current automation technology, which restricts/supports our scaling up). Explanations as to why some retailers seem to lead the digitalization and connectivity journey should be studied: Are fewer capital investments (compared with automation) required? Do they have fewer nodes and simpler flows on which to focus? Have they reached a higher maturity level regarding information technology?

The conceptualization and operationalization of smart warehousing can be developed further through additional empirical evidence collected in other markets. To build theory, in-depth case study research could be employed to better understand different contingency factors' influence. Of special interest would be research on implementation and transformation (using theoretical lenses, e.g. dynamic capabilities or technology adoption models), economic assessment of investment and performance, and an examination of barriers and opportunities related to the interaction between human and smart technologies in future warehouses. The literature presented mixed perspectives on humans' role in future warehousing. Some warehouse operations remain difficult to automate and may need to be carried out manually ( Azadeh et al. , 2019 ). While some researchers trumpet their unmanned warehouses as a defining characteristic or goal for smart warehouses ( Aamer and Sahara, 2021 ; Jiang et al. , 2021 ), others see robots and AI eventually replacing humans ( Jabber et al. , 2018 ). Some have argued that humans will not be replaced, but rather supported ( Winkelhaus and Grosse, 2020 ), with the intent to better connect people, objects, and physical systems ( Lee et al. , 2018 ). Thus, future research could study which factors explain to what extent future warehouses will be manual, automated, or smart.

Like most research designs, this study has limitations. The sample of retailers (50) that answered the survey was relatively small, not random, and only included retailers from one country. However, we argue that the sample is sufficient for developing propositions regarding the researched phenomenon ( Forza, 2002 ) because Sweden is among the leaders in online sales, and among the top-10 industrial digital transformation countries in 2020 ( Top 10 industrial digital transformation countries in 2020 | InfotechLead ). Furthermore, the most important product segments are covered, including leading retailers within each segment. To pinpoint theoretical and managerial implications further, our research needs to be complemented by and tested through more research. Specifically, the 16 propositions can be tested as hypotheses in future research with more empirical evidence by expanding testing to other markets, both in larger countries at similar stages of transformation toward omnichannels (e.g. the US, UK, and Germany) and in countries that are developed in terms of online sales. To understand smart warehousing technologies, logistics service providers and industrial companies also should be examined. Due to their deeper backgrounds with Industry 4.0 and their connection to smart production, industrial companies might make investments and implement smart warehousing differently than retailers.

In conclusion, the pace of development toward smart warehousing will increase in the coming years. Various systems and technologies will be developed and integrated within and across various material-handling nodes, providing many opportunities for researchers to examine and analyze new challenges and solutions, creating new knowledge in warehousing and retail logistics. Only the future can tell us how smart warehouses evolve and why.

Degree of automation per process, today and in five years ( n  = 50)

Overall degree of warehouse automation over time, related to turnover

Overall degree of warehouse automation over time, related to assortment range

Automation of different sizes of goods, today and in five years

Choice of automation technology for material handling, today and in five years

Other complementary technology used in warehouses, today and in five years

Technology shifts in retailers' warehouses over the next five years; several retailers may hide behind the same point in the chart

Conceptualization of smart warehousing

Multiple, connected smart warehouses in expanding, decentralized logistics networks

Retail sectors and product categories in the panel

Number of highly automated processes per company five years ahead

T-tests to detect significant differences between current and future states

Funding : The Swedish Retail and Wholesale Council funded this research study.

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Tokar , T. , Williams , B.D. and Fugate , B.S. ( 2020 ), “ I heart logistics – just don't ask me to pay for it: online shopper behavior in response to a delivery carrier upgrade and subsequent shipping charge increase ”, Journal of Business Logistics , Vol.  41 No.  3 , pp.  182 - 205 .

van Geest , M. , Tekinerdogan , B. and Catal , C. ( 2021 ), “ Design of a reference architecture for developing smart warehouses in Industry 4.0 ”, Computers in Industry , Vol.  124 , 103343 , pp.  1 - 21 .

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Efficiency Through Warehouse Consolidation: A Case Study

September 22, 2023

Cottonwood Distribution was good at what it does. But they were growing and wanted to get even better. With several dozen small retail delivery locations every day to service, Cottonwood wanted to become more efficient - making better use of time, space and other resources in order to better serve, and attract, clients. Hoj Innovations, a Salt Lake City-based warehousing and logistics efficiency expert, had the perfect plan.

  The Client: Cottonwood Distribution

Cottonwood Distribution, based in Salt Lake City, Utah, is a leading logistics and inventory distribution division specializing in serving a network of gas stations, convenience stores, and independent grocery retailers. Operating as an integral division of a large, locally-operated gas station and convenience store chain, Cottonwood Distribution extends its supply chain solutions to a variety of similar clients.

With years of industry experience, Cottonwood has developed a robust system to handle a wide array of goods—from perishables and packaged foods to cleaning supplies and other essentials. The company is known for its high service levels, guaranteeing on-time delivery and efficient inventory management. Its expertise lies in simplifying complex logistical operations, making it easier for retailers to focus on sales and customer service, rather than being bogged down by supply chain issues.

Leveraging cutting-edge technology and logistical insights, Cottonwood Distribution is committed to delivering excellence in all facets of its operations. With a solid track record for reliability and a focus on sustainable business practices, it has become the go-to distribution partner for businesses seeking efficiency, accuracy, and a streamlined supply chain.

The Problem

Like many growing companies, Cottonwood Distribution was facing several logistical and operational challenges that were hindering its ability to effectively meet client needs and operate at peak efficiency. One of the most pressing problems was the dispersion of inventory across two separate warehouse facilities. This setup led to a range of issues, each contributing to overall inefficiencies:

Inefficient Space Utilization: Operating from two warehouses meant that Cottonwood Distribution was not using its storage space as effectively as it could. This not only increased costs but also made inventory management more complex.

Redundant Delivery Operations: Due to the split inventory, often two delivery trucks were dispatched to fulfill a single order for a store. This was both economically and environmentally inefficient, increasing fuel costs and carbon footprint, and complicating scheduling and route planning.

Inventory Tracking Issues: Managing inventory levels and accurately tracking items became an unpleasant task with a dual-warehouse system. The separation made it harder to have real-time visibility into stock levels, leading to problems such as overstocking, understocking, or stockouts.

Increased Real Estate and Labor Costs: Operating out of two locations required more manpower for managing, picking, packing, and shipping inventory. This increased operational costs in ways ranging from multiple lease and utility payments to site management, reducing overall profit margins.

Coordination Complexities: Having two warehouses added unnecessary layers of complexity in coordination among warehouse staff, delivery teams, and management, making the operations susceptible to errors and delays.

Recognizing these challenges, Cottonwood Distribution sought to consolidate its operations under one roof to streamline processes, reduce redundancies, and improve overall operational efficiency.

Why Hoj Innovations?

When it came to selecting a partner to tackle their logistical challenges, Cottonwood Distribution had several compelling reasons to choose Hoj Innovations:

Local and Regional Market Leadership: Hoj Innovations is not just a service provider but a local and regional authority in warehouse design and logistics efficiency. This close proximity meant better understanding of local market dynamics, quicker response times, and easier coordination throughout the project.

Decades of Experience: With more than 50 years of experience in the industry, Hoj Innovations has a proven track record of providing efficient and effective solutions for warehouse design and logistical challenges. This extensive experience was a strong indicator of reliability and expertise.

Technology-Driven Solutions: Hoj Innovations is known for leveraging cutting-edge technology to address logistical problems. Given that Cottonwood Distribution was seeking to modernize its operations, Hoj's emphasis on technology-driven solutions was a major draw.

Customized Service: Hoj Innovations offered the ability to create customized solutions tailored to Cottonwood Distribution's specific challenges. Instead of a one-size-fits-all approach, Hoj's flexibility in design and implementation ensured that the final system would effectively meet Cottonwood's needs.

Reputation and Testimonials: Hoj Innovations had demonstrable success stories and glowing client testimonials, which added to Cottonwood's confidence in their ability to deliver meaningful results.

Alignment in Values and Goals: Both Cottonwood Distribution and Hoj Innovations prioritize sustainability, efficiency, and excellence in service delivery. This alignment in corporate values and objectives made for a seamless partnership and was a contributing factor in Cottonwood's decision.

Given these considerations, choosing Hoj Innovations was not just a strategic decision but also a natural fit for Cottonwood Distribution’s ambitions to consolidate and modernize their warehousing operations.

The Solution

Hoj Innovations rose to the occasion by designing and implementing a state-of-the-art warehouse facility tailored to Cottonwood Distribution's specific operational needs. The solution tackled all the problems the distribution company had been facing, from inventory management to delivery inefficiencies.

System Designed and Installed: The core of the solution was a robust new warehouse, intelligently designed to incorporate three decks for inventory storage. This multilevel storage system optimized the available vertical space, substantially increasing the warehouse's storage capacity. To complement this, an automated conveyor system was integrated to facilitate seamless order picking, packing, and distribution processes.

Key Features of the System

Three-Deck Storage System: Maximizing the use of vertical space, this design substantially increased storage capacity, addressing the problem of inefficient space utilization.

First In, First Out Pallet Racks: With large, heavy and time-sensitive products rotating through the warehouse, Hoj installed massive pallet racking systems capable of storing 21 pallets at any time. As one pallet was depleted, the next would slide into place and become the next pallet from which order items were picked. 

Automated Conveyor System: Designed to move inventory items between the different levels and zones of the warehouse, this system accelerated the picking and packing process, making it highly efficient and less labor-intensive.

Centralized Control: The new design consolidated all operations under one roof, eliminating the challenges of managing multiple warehouses.

Sustainable Design: Energy-efficient systems were implemented, aligning with both companies' focus on sustainability.

Addressing Specific Problems

Inefficient Space Utilization: The three-deck storage system optimized the use of available space, allowing for more inventory to be stored in the same footprint.

Redundant Delivery Operations: With all inventory consolidated in one location, only one delivery truck was needed for each order, cutting down on fuel costs, emissions and driver costs.

Increased Labor Costs: The automated conveyor system reduced the manpower needed for picking, packing, and distributing, thereby lowering labor costs.

Coordination Complexities: Centralizing operations reduced the layers of complexity in coordination, making the entire operation more streamlined and efficient.

The partnership between Hoj Innovations and Cottonwood Distribution marked a transformative journey in optimizing warehouse and logistical operations. What started as a two-warehouse system fraught with inefficiencies and redundancies was streamlined into a single, technologically advanced facility. The intelligently designed three-deck storage system maximized space, while the automated conveyor system revolutionized the picking, packing, and distribution processes.

The result was a significant increase in operational efficiency, a reduction in labor and delivery costs, and a vastly improved inventory tracking system. This transformation not only addressed Cottonwood Distribution's immediate issues but also set them on a path for scalable, future growth. It's a testament to how custom solutions, deep industry expertise, and a focus on technological innovation can lead to game-changing results.

Contact Hoj Innovations

Are you facing similar challenges in your warehouse operations? Do inefficiencies and complexities hold back your business potential? Let us help you transform your logistics from a bottleneck into a competitive advantage.

Want to see how we can revolutionize your warehouse operations? Contact us today.

By taking action now, you take the first step toward a more efficient, more profitable future. Don’t let yesterday’s logistics hold back tomorrow’s success. Reach out to us for a consultation and begin your journey toward operational excellence.

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AI in Warehouse Management: Revolutionizing Inventory and Demand Forecasting

Introduction to AI in Warehouse Management

Imagine a world where warehouses operate with precision, efficiency, and intelligence. Gone are the days of manual inventory counts, guesswork on demand forecasting, and tedious tasks that consume valuable time. Thanks to advancements in artificial intelligence (AI), this vision is becoming a reality for warehouse management .

In today’s fast-paced business environment, optimizing inventory management and accurately predicting customer demand are crucial for success. AI offers unprecedented solutions to these challenges by harnessing the power of data analytics and machine learning algorithms. By leveraging AI technologies, warehouses can streamline operations, reduce costs , improve accuracy, and ultimately revolutionize their inventory and demand forecasting processes.

Join us as we dive into the exciting world of AI in warehouse management – exploring its advantages, real-life case studies showcasing its implementation success stories, potential limitations faced along the way, predictions for future developments in this field – all while providing practical tips on how you can implement AI within your own warehouse operations.

Get ready to unlock new levels of efficiency as we embark on this transformative journey together!

Advantages of Implementing AI in Inventory and Demand Forecasting

AI has revolutionized the world of inventory and demand forecasting, bringing a multitude of advantages to warehouse management systems . With AI-powered tools, businesses can now accurately predict customer demands, optimize their inventory levels, and streamline their supply chain processes .

One major advantage of implementing AI in inventory and demand forecasting is enhanced accuracy. Traditional methods heavily relied on manual data entry and human intuition, which often led to errors or inaccuracies. However, with AI algorithms analyzing large volumes of historical data, businesses can make more precise predictions about future demand patterns.

Another advantage is improved efficiency. By automating inventory management tasks using AI technology, warehouses can significantly reduce the time spent on manual processes such as stock counting or replenishment. This allows employees to focus on higher-value activities while ensuring that the right products are available at the right time.

Additionally, implementing AI in warehouse management enables real-time insights into demand fluctuations. By monitoring market trends and customer behavior patterns in real-time, businesses can quickly adapt their inventory levels and production schedules accordingly. This agility helps prevent stockouts or overstock situations that could lead to lost sales or excess carrying costs.

Furthermore, AI-driven analytics provide valuable insights for better decision-making. Through advanced machine learning algorithms, businesses can identify hidden correlations between various factors influencing demand patterns – such as seasonality or promotional activities – allowing them to make informed decisions about pricing strategies or product launches.

Lastly but not leastly important is cost reduction achieved through optimized inventory levels with the help of AI technologies. By accurately predicting customer demands and adjusting stocking quantities accordingly, businesses avoid excess carrying costs associated with surplus inventories while minimizing stockouts that result in missed opportunities.

In conclusion! The advantages brought by implementing AI in inventory and demand forecasting are undeniable: increased accuracy, improved efficiency, cost reduction opportunities cost reduction opportunities drive significant improvements in warehouse management systems .

How AI is Used in Warehouse Management: Case Studies

AI is rapidly transforming the way warehouse management operates, revolutionizing inventory and demand forecasting. Let’s explore some case studies that highlight how AI is being used in this field.

One example is Amazon, a company known for its innovative use of technology. They have implemented AI-powered robots in their warehouses to automate various tasks such as picking and packing items. These robots are equipped with computer vision capabilities, allowing them to navigate through the warehouse and locate products efficiently.

Another case study involves Walmart, one of the world’s largest retailers. They have successfully integrated AI into their supply chain operations by using predictive analytics algorithms. By analyzing historical sales data and external factors like weather patterns, they can forecast demand more accurately. This enables them to optimize inventory levels and reduce stockouts or overstock situations.

A third case study involves JD.com, a major e-commerce company based in China. They utilize AI-powered drones for delivery purposes within their vast network of warehouses. The drones can autonomously pick up packages from designated areas and deliver them to customers’ doorsteps faster than traditional methods.

These examples demonstrate how AI is being utilized across different aspects of warehouse management – from automation to demand forecasting and even last-mile delivery optimization.

By harnessing the power of machine learning algorithms and real-time data analysis, companies can achieve higher efficiency levels while reducing costs associated with human error or inefficiencies in manual processes.

The potential applications of AI in warehouse management are vast, offering opportunities for businesses to enhance operational efficiency , improve customer satisfaction, and ultimately gain a competitive edge in today’s fast-paced market environment

Challenges and Limitations of AI in Warehouse Management

Implementing AI in warehouse management is not without its challenges and limitations. While the benefits are clear, there are several factors that need to be considered.

One of the main challenges is the initial investment required for implementing AI systems. The cost can be significant, especially for small businesses with limited resources. Additionally, there may be a learning curve involved in training employees to use and understand these new technologies.

Another challenge is data integration. In order for AI systems to work effectively, they require access to accurate and comprehensive data. This can be a challenge if companies have multiple systems or databases that do not communicate well with each other.

Furthermore, while AI has advanced greatly in recent years, it still has limitations when it comes to complex decision-making processes. For example, unexpected events or changes in customer demand may require human intervention and judgment.

There are concerns about job displacement due to increased automation through AI. Some worry that implementing AI in warehouse management could lead to job losses or reduced employment opportunities for humans.

Despite these challenges and limitations, many companies are still willing to invest in AI technologies because of the potential long-term benefits they offer. By addressing these challenges head-on and finding ways around them, businesses can harness the power of AI in their warehouse management operations

The Future of AI in Warehouse Management

As technology continues to advance at an exponential rate, the future of AI in warehouse management looks promising. With its ability to optimize inventory and demand forecasting processes, AI is set to revolutionize the way warehouses operate.

One exciting development on the horizon is the use of autonomous robots in warehouse management. These robots can navigate through aisles, pick up items, and even deliver them to packaging areas. By utilizing AI algorithms, these robots can work efficiently and autonomously, reducing human error and increasing productivity.

Another area where AI is expected to make a significant impact is predictive analytics. By analyzing historical data and external factors such as weather patterns or market trends, AI algorithms can provide accurate demand forecasts. This will enable warehouse managers to optimize their inventory levels accordingly and avoid stockouts or excess inventory.

Machine learning algorithms are also playing a vital role in optimizing warehouse operations. These algorithms can analyze vast amounts of data from various sources such as sales records, customer behavior patterns, or production schedules. By identifying patterns and trends within this data, machines can make intelligent decisions about order fulfillment strategies or optimal storage locations for different products.

Furthermore, advancements in computer vision technology are making it possible for AI systems to identify objects accurately within a warehouse environment. This has immense potential for improving inventory management by enabling real-time tracking of products throughout the supply chain .

However, while the future seems bright for AI in warehouse management, there are still challenges that need addressing. One major concern is ensuring data security and privacy when implementing AI systems that rely heavily on collecting sensitive information about customers or suppliers.

Additionally,long-term maintenance costs associated with implementing advanced technologies like autonomous robots may pose financial challenges for some warehouses.

Despite these challenges,it’s evident that integrating artificial intelligence into warehouse management will lead to increased efficiency ,lower operational costs,and improved customer satisfaction.

AI has already proven its worth with successful case studies,and as technology continues advancing,the possibilities seem endless.

As we move forward,warehouses that embrace AI will gain a competitive edge in the

Tips for Implementing AI in Your Warehouse

1. Evaluate your needs: Before implementing AI in your warehouse management system, it’s important to assess your specific requirements and goals. Consider factors such as inventory size, order volume, and demand forecasting accuracy.

2. Choose the right technology: There are various AI solutions available for warehouse management, ranging from robotic automation systems to machine learning algorithms for demand forecasting. Research different options and select the technology that aligns best with your business objectives .

3. Start small and scale up: It can be overwhelming to completely overhaul your warehouse operations with AI all at once. Instead, start by implementing AI in one area of your warehouse and gradually expand its use across different processes.

4. Ensure data quality: Accurate data is crucial for effective implementation of AI in inventory management and demand forecasting. Invest in robust data collection methods, ensure regular updates, and implement measures to maintain data integrity.

5. Collaborate with experts: Seek guidance from professionals who specialize in integrating AI into warehouse management systems. They can provide valuable insights based on their experience working with similar businesses.

6. Train employees: Introducing new technologies like AI may require training employees to understand how these systems work and utilize them effectively within their roles.

Stay updated on advancements: The field of AI is constantly evolving, so it’s essential to stay informed about new developments that could benefit your warehouse operations further.

Remember that implementing AI requires careful planning, proper execution,and ongoing monitoring to maximize its benefits.

Conclusion:

In this fast-paced and ever-evolving world of warehousing, AI has emerged as a game-changer. By revolutionizing inventory and demand forecasting, AI technology is helping businesses streamline their operations, optimize efficiency, and boost profitability.

The advantages of implementing AI in warehouse management are clear. From improved accuracy in demand forecasting to enhanced inventory management capabilities, businesses can make data-driven decisions that lead to better customer satisfaction and reduced costs.

Through real-life case studies, we have seen how companies like Amazon and Walmart have successfully harnessed the power of AI to transform their warehouse operations. The use of machine learning algorithms and predictive analytics has empowered these companies to stay ahead in an increasingly competitive market .

However, it’s important to acknowledge that there are challenges and limitations associated with implementing AI in warehouse management. These include the initial investment cost, integration complexities, training requirements for staff members, as well as potential data privacy concerns. Overcoming these obstacles requires careful planning and collaboration between IT teams, warehouse managers, and key stakeholders.

Looking into the future, it is evident that AI will continue to play a pivotal role in transforming warehouse management practices . With advancements in technology such as robotics automation systems (RAS) combined with artificial intelligence algorithms becoming more affordable and accessible for businesses of all sizes,

Implementing AI into your warehouse doesn’t have to be an overwhelming task. By following some best practices such as clearly defining goals before implementation or starting small with pilot projects before scaling up across your entire operation – you can ensure a successful integration process.

In conclusion

AI is reshaping the way warehouses operate by providing accurate demand forecasting tools while optimizing inventory levels efficiently at every stage from receiving goods through shipping orders out-the-door! As we move forward into this new era where machines work alongside humans seamlessly – expect even greater innovations on horizon!

So whether you’re running a small e-commerce business or managing a large-scale logistics operation , embracingAI technologies will prove to be a wise investment in the future success of your warehouse management strategy .

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• July 15, 2022

Full Guide to Successful Data Warehouse Implementation in 2023

• With <3 by Acuto

Data warehouse implementation is the process of launching a data warehouse in your organisation to consolidate data from multiple sources.

Having a single repository of all your data allows you to get a top-down view of all your information and enables cross-channel reporting.

However, the process of implementing a data warehouse can seem difficult for agencies when they are first introduced to it.

Keep reading to learn how you can easily implement a data warehouse in your agency, what its benefits are, and what problems it can solve for marketing agencies.

What is a Data Warehouse?

A data warehouse combines data from several sources into a single, coherent repository . It serves as a single source of truth, allowing agencies to perform comprehensive data analysis and reporting. A data warehouse is also the foundation of a business intelligence system.

Data warehouses are designed to support decision-making by providing a consistent view of the organisation’s data . 

They are typically populated with data from multiple sources, including transactional systems, operational databases, and external data sources. The data in a data warehouse is typically organised by subject area, such as customers, products, sales, etc.

What is Data Warehouse Implementation?

Data warehouse implementation is the process of designing, building, and launching a new data warehouse . This process generally includes four main phases:

• Planning and designing
• Component selection
• Implementation
• Testing and launching

Each of these phases has its own set of activities and deliverables that need to be completed before moving on to the next phase.

In this guide, we’ll take a closer look at each phase of data warehouse implementation. Most importantly, you’ll learn exactly what you need to do to ensure success while strictly following the best data warehouse implementation practices.

4 Steps to Successfully Implementing a Data Warehouse

Let’s delve into each step of implementing a data warehouse to see what exactly the process involves:

#1. Planning and Designing

The first step in any data warehouse implementation is planning and design. The phase of data warehouse planning is critical to the success of the project because it sets the foundation for everything that comes after. 

During the planning and design phase, you’ll need to answer important questions such as:

• What is the purpose of the data warehouse?
• What data needs to be included?
• How will the data warehouse be structured?
• What tools and technologies will be used?

Answering these questions will help you create a detailed plan for the project that can be used to guide the rest of the implementation.

#2. Component Selection

Once the planning and design phase is complete, it’s time to start selecting the components of the data warehouse. This includes choosing the hardware, software, and other tools that will be used to build and operate the data warehouse. 

Some of the most important decisions you’ll need to make during this phase include:

• Data sources : These are the systems where data is stored and accessed. Data warehouses typically incorporate data from multiple sources, including transactional databases, OLAP databases, and flat files.
• Metadata : In simple words, this is data about data. It includes information such as titles of tables and columns, data types, length of data, etc. It’s used to describe the structure of the data in a data warehouse and how it is related to other data in the warehouse. Metadata also gives users information on what’s included in a data warehouse.
• Data staging area : This is a temporary location where data from the various data sources is combined and transformed.
• ETL (Extract, Transform, and Load) tools : These tools are used for extracting data from source systems, transforming it into a format that can be loaded into the data warehouse, and then loading it into the data warehouse. This process happens in the staging area.
• Database : This is the central repository where data from the various data sources is combined and made available for reporting and analysis. Data warehouses are typically OLAP databases.
• Data marts : These are subsets of the data warehouse that are designed for specific use cases or groups of users. Data marts contain only the data that is relevant to those use cases, and they are typically much smaller in size than the entire data warehouse. Data marts can be built using ETL tools, or they can be created manually.
• Reporting and analysis tools : These are the tools that the agency would use to access and analyse data in the data warehouse. Reporting and analysis tools can include SQL query tools, OLAP tools, and data visualisation tools.

#3. Implementation

After the components have been selected, it’s time to start putting everything together. This is where the data warehouse will be built and configured according to the plans created in the earlier phases. 

The implementation phase generally includes the following activities:

• Installing and configuring the data warehouse platform
• Creating the data storage environment
• Building the ETL process
• Configuring the reporting and analysis tools

#4. Testing and Launching

Once the data warehouse has been built and configured, it’s time to put it through its paces . This is important to ensure that everything is working as expected and that there are no major issues that need to be addressed before launch. 

The testing phase generally includes the following activities:

• Determining data accuracy
• Measuring performance
• Conducting user acceptance testing

Once the data warehouse has been tested and validated, it’s time to launch it . This generally includes making the data warehouse available to employees and training them on how to use it.

There are a few key things to keep in mind as you work through each phase of data warehouse implementation. First, be sure to involve all stakeholders in the project from the beginning. This will help ensure that everyone is on the same page and that there are no surprises later on. 

Second, make sure to document everything as you go. Documentation will come in handy if there are any issues that need to be debugged or if changes need to be made down the road. And finally, don’t forget to test, test, test!

Advantages of Implementing a Data Warehouse in Your Agency

There are many advantages to implementing a data warehouse in your agency. A data warehouse can give you the ability to:

• Combine data from multiple sources : Data warehouses allow you to consolidate all your data, making it easier to get a complete picture of your agency’s operations.
• Access data in real time : Data warehouses allow you to access data in real time, giving you the most up-to-date information possible.
• Generate reports and analytics : Data warehouses give you the ability to generate cross-channel reports and draw analytics that can help you improve your agency’s operations.
• Improve decision making : Data warehouses can help you make well-informed decisions by providing accurate and up-to-date information.
• Save time and money : Data warehouses can help you save time and money by reducing the need for manual data entry and data cleansing.

Data Warehouse Implementation Trends in 2023

The trends of data warehousing in 2023 are transforming the industry. Here are some of them you should be aware of:

#1. Cloud-Based Data Warehouses

Cloud-based data warehouses are becoming increasingly popular, as they offer a number of advantages over traditional on-premises data warehouses. Cloud-based data warehouses are more scalable, more flexible, and easier to manage.

As of 2022, over 60% of all corporate data is stored in the cloud. This is up from 30% in 2015.

#2. Real-Time Data Access

There is an increasing demand for real-time data access in data warehousing. Real-time data can help organisations make better decisions and respond more quickly to changes in the marketplace. 

The global data warehousing market is projected to reach $51.18b by 2028.

#3. Self-Service Data

Having self-service data is another growing trend in data warehouse implementation. Data warehouses equip agencies with self-service data, which allows them to access and analyse data without having to rely on IT staff.

#4. Big Data

Big data is another trend that is impacting data warehouse implementation. Big data refers to data sets that are too large and complex to be processed by traditional database management systems.

As such, organisations are turning to custom data warehouse solutions to store and analyse big data.

Implementing a Data Warehouse to Solve Common Problems in Marketing Agencies

There’s no question that easy access to data is becoming increasingly important in the world of marketing.

Agencies are under constant pressure to deliver targeted, personalised campaigns and show ROI for their efforts. But in many cases, agencies are struggling to keep up with the sheer volume and complexity of data they need to deal with on a daily basis.

There are a number of common problems marketing agencies face that can be solved by implementing a data warehouse. Let’s see what they are and how a data warehouse can solve them:

#1. Inaccurate or Outdated Data

Data warehouses help solve the problem of working with inaccurate or outdated data by providing a single source of truth for an organisation.

The data warehouse is updated and cleaned on a regular basis with the latest data from operational databases. This ensures that the data in the warehouse is accurate and up to date.

#2. Siloed Data

One of the benefits of using a data warehouse is that it can help break down silos of information. When data is stored in separate databases, it can be difficult to get a complete picture of what is happening.

By consolidating data into a single database, analysts can more easily discover relationships and patterns to unlock new insights.

#3. Lack of Visibility Into the Business

Data warehouses help organisations get a 360-degree view of their business by providing a consolidated view of their data.

Data warehouses also provide a historical view of the data, which is important for understanding how a business has changed over time.

This allows for enhanced business intelligence and allows agencies to make data-driven decisions.

Implement a Data Warehouse With the Help of Acuto

Data warehouse implementation can be a complex and costly process, but it can also be extremely beneficial for organisations.

By following the latest trends in data warehouse implementation, organisations can ensure that their data warehouses are up-to-date and ready to meet the demands of the future.

Acuto can help you implement a custom data warehouse quickly and easily, and at a lower cost than pre-built data warehouse solutions. Contact us today to learn more about our services.

Need a Custom Data Warehousing Solution?​

Key Takeaways

By now, you should be familiar with the entire process of data warehouse implementation. Let’s go over the main points that we covered:

• A data warehouse is a centralised repository of all your agency’s data.
• Data warehouse implementation is the process of designing, building, testing, and launching a data warehouse in your agency.
• Using a data warehouse in your agency helps you combine data from multiple sources, analyse data in real time, generate cross-channel reports, make informed decisions, and save time and money.
• By following the latest trends in data warehousing, you can ensure that you are ready to meet the demands of the future.
• Data warehouses are especially useful for marketing agencies because they clean inaccurate data, break down data silos, and help you get a 360-degree view of your business.
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Practical Data Warehousing: Successful Cases

Table of contents:.

No matter how smooth the plan may be in theory, practice will certainly make adjustments. Because each real case has its own characteristics, which in the general case cannot be taken into account. Let's see how the world's leading brands have adapted to their needs a well-known way of storing information — data warehousing.

The Reason for Making Decisions

The need to make business decisions based on data analysis has long been beyond doubt. But to get this data, it needs to be collected, sorted and prepared for analytics .

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This is what data warehousing specialists do. To focus on the best performance, it makes sense to consider how high-quality custom assemblies came out of this constructor.

Data warehousing interacts with a huge amount of data

A data warehousing is a digital storage system that integrates and reconciles large amounts of data from different sources. It helps companies turn data into valuable information and make informed decisions based on it. Data warehousing combines current and historical data and acts as a single source of reliable information for business.

After raw data mining (extract, transform, load) info enters the warehouse from operating systems, such as an enterprise data resource planning system or a customer relationship management system. Sources also include databases, partner operational systems, IoT devices, weather apps, and social media. Infrastructure can be on-premises or cloud-based, with the latter option predominating in recent times.

Data warehousing is necessary not only for storing information, but also for processing structured and unstructured data: video, photos, sensor indicators. Some data warehousing options use built-in analytics and in-memory database data technology (info is stored in RAM rather than on a hard drive). This is necessary to access reliable data in real time.

After data is sorted, it is sent to data marts for further analysis by BI or data science .

Why consider data warehousing cases

Consideration of known options for data warehousing is necessary, first of all, in order not to keep making the same mistakes. Based on a working solution, you can improve your own performance.

• When using data warehouses, executives access data from different sources, they do not have to decide blindly.
• Data warehousing is needed for quick retrieval and analysis. When using warehouses, you can quickly request large amounts of data without involving personnel for this.
• Before uploading to the warehouse, the system creates data cleansing tasks and puts them for further processing, ensuring converting the data into a consistent format for subsequent analyst reports.
• The warehouse contains large amounts of historical data and allows you to study past trends and issues to predict events and improve the business structure.

Blindly repeating other people's decisions is also impossible. Your case is unique and probably requires a custom approach. At best, well-known storage solutions can be taken as a basis. You can do it yourself, or you can contact DATAFOREST specialists for professional services. We have a positive experience and positive customer stories of data warehousing creating and operating.

Case 1: How the Amazon Service Does Data Warehousing

Amazon is one of the world's largest and most successful companies with a diversified business: cloud computing, digital content, and more. As a company that generates vast amounts of data (including data warehousing services), Amazon needs to manage and analyze its data effectively.

Two main businesses

Amazon's data warehousing needs are driven by the company's vast and diverse data sources, which require sophisticated tools and technologies to manage and analyze effectively.

1. One of the main drivers of Amazon's business is its e-commerce platform , which allows customers to purchase a wide range of products through its website and mobile apps. Amazon's data warehousing needs in this area are focused on collecting, storing, and analyzing data related to customer behavior, purchase history, and other metrics. This data is used to optimize Amazon's product recommendations engine, personalize the shopping experience for individual customers, and identify growth strategies.

2. Amazon's other primary business unit is Amazon Web Services (AWS), which offers cloud computing managed services to businesses and individuals. AWS generates significant amounts of data from its cloud data infrastructure, including customer usage and performance data. To manage and analyze this modern data effectively, Amazon relies on data warehousing technologies like Amazon Redshift, which enables AWS to provide real-time analytics and insights to its customers.

3. Beyond these core businesses, Amazon also has significant data warehousing needs in digital content (e.g., video, music, and books). Amazon's advertising business relies on data analysis to identify key demographics and target ads more effectively to specific audiences.

By investing in data warehousing and analytics capabilities, Amazon through digital transformation can maintain its competitive edge and continue to grow and innovate in the years to come.

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Obstacles on the way to the goal.

Amazon faced several specific implementation details and challenges in its data warehousing efforts.

• The brand needed to integrate data from various sources into a centralized data warehouse. It required the development of custom data pipelines to collect and transform data into a standard format.

• Amazon's data warehousing needs are vast and constantly growing, requiring a scalable solution. The company distributed data warehouse architecture center using technologies like Amazon Redshift, allowing petabyte-scale data storage and analysis.

• As a company that generates big data, Amazon would like to ensure that its data warehousing solution could provide real-time data analytics and insights. Achieving high performance requires optimizing data storage, indexing, and querying processes.

• Amazon stores sensitive customer data in its warehouse, prioritizing data security. To protect against security threats, the brand implements various security measures, including encryption, access controls, and threat detection.

• Building and maintaining a data warehousing solution can be expensive. Amazon leverages cloud-based data warehousing solutions (Redshift) to minimize costs, which provide a cost-effective, pay-as-you-go pricing model.

Amazon's data warehousing implementation required careful planning, significant investment in technology and infrastructure, and ongoing optimization and maintenance to ensure high performance and reliability.

Change for the better

When Amazon considered all the needs, found the right tools, and implemented a successful data warehouse, the company got the following main business outcomes:

• Improved data driven decision

• Better customer enablement

• Cost effective decision

• Improved performance

• Competitive advantage

• Scalability

Amazon's data warehousing implementation has driven the company's growth and success. Not surprisingly, a data storage service provider must understand data storage. The cobbler's children don't need to have no shoes.

Case 2: Data Warehousing Adventure with UPS

United Parcel Services (UPS) is an American parcel delivery and supply chain management company founded in 1907 with an annual revenue of 71 billion dollars and logistics services in more than 175 countries. In addition, the brand distributes goods, customs brokerage, postal and consulting services. UPS processes approximately 300 million tracking requests daily. This effect was achieved, among others, thanks to intelligent data warehousing.

One mile for $50 million

In 2013, UPS stated that it hosted the world's largest DB2 relational database in two United States data centers for global operations. Over time, global operations began to increase, as did the amount of semi structured data. The goal was to use different forms of storage data to make better users business decisions.

One of the fundamental problems was route optimization. According to an interview with the UPS CTO, saving 1 mile a day per driver could save 1.5 million gallons of fuel per year or $50 million in total savings.

However, the data was distributed in DB2; some included repositories, some local, and some spreadsheets. UPS needed to solve the data infrastructure problem first and then optimize the route.

Four letters "V."

The big data ecosystem efficiently handles the four "Vs": volume, validity, velocity, and variety. UPS has experimented with Hadoop clusters and integrated its storage details and computing system into this ecosystem. They upgraded data warehousing and computing power to handle petabytes of data, one of UPS's most significant technological achievements.

The following Hadoop components were used:

• HDFS for storage

• Map Reduce for fast processing

• Kafka streaming

• Sqoop (SQL-to-Hadoop) for ingestion

• Hive & Pig for structured queries on unstructured data

• monitoring system for data nodes and names

But that's just speculation because, due to confidentiality, UPS didn't declassify the tools and technologies they used in their big data ecosystem.

Constellation of Orion

The result was a four-year ORION (On-Road Integrated Optimization and Navigation) route optimization project. Costs — about one billion dollars a year. ORION used the results to data stores and calculate big data and got analytics from more than 300 million data points to optimize thousands of routes per minute based on real-time information. In addition to the economic benefits, the Orion project shortened approximately 100 million shipping miles and a 100,000-ton reduction in carbon emissions.

Case 3: 42 ERP Into One Data Warehouse

In general, the topic of specific cases of data warehousing implementation is sufficiently secret. There may be cases of consent and legitimate interests in the contracts. There are open-source examples of work, but the vast majority are on paid libraries. The subject is so relevant that you can earn money from it. Therefore, sometimes there are "open" cases, but the brand name is not disclosed.

Brand X needs help

World leader in industrial pumps, valves, actuators, controls, etc., needed help extracting data from disparate ERP systems. They wanted it from 42 ERP instances, standardized flat files, and collected all the information in one data warehouse. The ERP systems were from different vendors (Oracle, SAP, BAAN, Microsoft, PRMS) to complicate future matters.

The client also wanted a core set of metrics and a central dashboard to combine all the information from different locations worldwide. The project resulted from a surge in demand for corporate data from database management. The company knew its data warehousing needed a central repository for all data from its locations worldwide. Requests often came from top to bottom, and when an administrator required access to the correct data, there were logistical extracting problems. And the project gets started.

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The foundation stone.

The hired third-party developer center has made a roadmap, according to which ERP data was taken from 8 major databases and placed in a corporate data warehouse. It entailed integrating 5 Oracle ERP instances with 3 SAP ERP. Rapid Marts have also been integrated into Oracle ERP systems to improve the project's progress.

One of the main challenges was the need for more standardization of fields or operational data definitions in ERP systems. To solve this problem, the contractor has developed a data service tool that allows access to the back end of the database and displays info suitably. Since then, the customer has known which fields to use and how to set them each time a new ERP instance is encountered. These data definition patterns were the project's foundation stone and completely changed how customer data is handled. It was a point to launch consent.

All roads lead to data warehousing

The company has one common and consistent way to obtain critical indicators. The long-term effect of the project is the ease of obtaining information. What was once a long and inconsistent process of getting relevant information at an aggregate level is now streamlined to store data in one central repository with one team controlling it.

Data Warehousing: Different Cases — General Conclusions

Each data warehouse organization has unique methods and tools because business needs differ. In this case, data warehousing can be compared with a mosaic and a children's constructor. You can make different figures from the same parts, arranging the elements especially. And if one part is lost or broken, you need to make a new one or find another one and "process it with a rasp."

Generalities between different cases of data warehousing

There are several common themes and practices among successful data warehousing implementations, including:

• Successful data warehousing implementations start with clearly understanding the business objectives and how the warehouse (or data lake) can support those objectives.

• The data modeling process is critical to the success of data warehousing.

• The data warehouse is only as good as the data it contains.

• Successful data warehousing requires efficient data integration processes that can operate large volumes of data and ensure consistency and accuracy.

• Data warehousing needs ongoing performance tuning to optimize query performance.

• A critical factor in data warehousing is a user-friendly interface that makes it easy for end users to access the data and perform complex queries and analyses.

• Continuous improvement is essential to ensure the data warehouse remains relevant and valuable to the business.

Competent data warehousing implementations combine technical expertise and a deep understanding of business details and user needs.

Your case is not mentioned anywhere

When solving the problem of organizing data warehousing , one would like to find a description of the same case and do everything according to plan. But the probability of this event is negligible — you will have to adapt to the specifics of the customer's business and consider your knowledge and capabilities, as well as the technical and financial conditions of the project. Then it would help if you took a piece of the puzzle or parts of the constructor and built your data warehouse. Minus — you have to work. Plus — it will be your decision on data storage and only your implementation.

Data Warehousing Is Like a Trampoline

Changes in data warehousing , like any technological and methodological changes, are carried out to improve the data collection, storage, and analysis level. It takes the customer to a new level in his activity and the contractor — to his own. Like a jumper and a trampoline: separately, it is just a gymnast and just equipment, and in combination, they give a certain third quality — the possibility of a sharp rise.

If you are faced with the problem of organizing a new data warehousing system, or you are simply interested in what you read, let's exchange views with DATAFOREST.

What is the benefit of data warehousing for business?

A data warehouse is a centralized repository that contains integrated data from various sources and systems. Data warehousing provides several benefits for businesses: improved decision-making, increased efficiency, better customer insights, operational efficiency, and competitive advantage.

What is the definition of a successful data warehousing implementation?

The specific definition of a successful data warehouse implementation will vary depending on the goals of the organization and the particular use case for data warehousing. Some common characteristics are: meeting business requirements, high data quality, scalability, user adoption, and positive ROI.

What are the general considerations for implementing data warehousing?

Implementing data warehousing involves some general considerations: business objectives, data sources, quality and modeling, technology selection, performance tuning, user adoption, ongoing maintenance, and support.

What are the most famous examples of the implementation of data warehousing?

There are many famous examples of the implementation of data warehousing across industries:

• Walmart has one of the largest data warehousing implementations in the world

• Amazon's data warehousing solution is known as Amazon Redshift

• Netflix uses a data warehouse to store and analyze data from its streaming platform

• Coca-Cola has a warehouse to consolidate data from business units and analyze it

• Bank of America analyzes customer data by data warehousing to improve customer experience

What are the challenges while implementing data warehousing, and how to overcome them?

Based on the experiences of organizations that have implemented data warehousing, some common challenges and solutions are:

• Ensuring the quality of the data that is being stored and analyzed. You must establish data quality standards and implement data validation and cleansing by data types.

• Integrating from disparate data sources. Establishing a clear data integration strategy that considers the different data sources, formats, and protocols involved is vital.

• As the amount of data stored in a data warehouse grows, performance issues may arise. A brand should regularly monitor query performance and optimize the data warehouse to ensure that it remains efficient and effective.

• To ensure that sensitive data stored in the data warehouse is secure. It involves implementing appropriate measures such as access controls, encryption, and regular security audits. They are details of privacy security.

• Significant changes to existing processes and workflows. Solved by establishing a transparent change management process that involves decision-makers and users at all levels.

What is an example of how successful data warehousing has affected a business?

An example of how successful data warehousing has affected Amazon is its recommendation engine. It suggests products to customers based on their browsing and purchasing history. By using artificial intelligence and machine learning algorithms to analyze customer data, Amazon has improved the fully managed accuracy of its recommendations, resulting in increased sales and customer satisfaction.

What role does data integration play in data warehousing?

Data integration is critical to data warehousing, enabling businesses to consolidate and standardize data from multiple sources, ensure data quality, and establish effective data governance practices.

How are data quality and governance tracked in data warehousing?

Data quality and governance are tracked in data warehousing through a combination of data profiling, monitoring, and management processes and establishing data governance frameworks that define policies and procedures for managing data quality and governance. So, businesses can ensure that their data is accurate, consistent, and compliant with regulations, enabling effective decision-making and driving business applications' success.

Are there any measures to the benefits of data warehousing?

The benefits of business data warehousing can be measured through improvements in data quality, efficiency, decision-making, revenue and profitability, and customer satisfaction. By tracking these metrics, businesses can assess the effectiveness of their data warehousing initiatives and make informed decisions about future investments in data management and analytics with cloud services.

How to avoid blunders when warehousing data?

By following the best practices, businesses can avoid common mistakes, minimize the risk of blunders when warehousing data, and ensure their data warehousing initiatives are successful and practical to be analyzed with business intelligence.

Aleksandr Sheremeta

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Unified Data Warehouse Implementation & Oracle and Hadoop Migration to GCP

Challenges:.

One of the security services providers in the US faced challenges with their on-premise Oracle and Cloudera Hadoop environment, which led to excessive execution time for a query/code and higher costs of operation.

The client wanted to build a Unified Data Warehouse on GCP that leverages cloud-native technologies, lowers operational costs and offers better risk mitigation than current on-prem based solutions. To facilitate this client also wanted to migrate the existing Data Warehouse functionality from On-premise Oracle and Cloudera Hadoop to GCP.

Also, another objective was to enable end-users to run SQL Queries on the Google BigQuery platform even prior to the actual application migration.

• Datametica helped the client migrate their historical and Incremental load of On-prem  Oracle and Cloudera Hadoop databases to the Google Cloud Platform.
• One time history load was done by Datametica in which the data was loaded to the GCS bucket from the source system and then the data was moved within the Google BigQuery.
• Converted Oracle SQL code to Google BigQuery SQL code using Datametica’s Raven – Automated Workload Conversion Tool that automated the SQL conversion.
• Finally, the Google BigQuery data were compared using Datametica’s Pelican – The Automated Validation tool with source oracle data to ensure data integrity and quality assurance.
• Pelican Tool was also used to validate the data loaded into Google BigQuery by doing a field-level comparison with the data present in the on-prem Hadoop Platform.
• Implemented the existing Data Base model with optimal configuration of Partition & Cluster keys for cost optimization,
• Implemented best practices suited to the Google BigQuery platform for efficient and performance execution,
• Performed comparative analysis of execution times in Google BigQuery with on-prem Oracle execution times.
• Datametica established IAM roles, firewalls, VPC for GCP for the projects.
• Leveraged various Google Native tools like transfer appliance, Google Storage, Google BigQuery, Cloud DataProc, Cloud Composer, Stackdriver, Cloud Dataflow and etc to deliver successful complete Google Cloud implementation.
• Reduce scan cost by implementing partitioning and clustering
• Reduced the execution time by 85% that is the execution time was reduced to 8 minutes from 70 minutes earlier.
• Cloud Migration cost was reduced by 65%
• Ensured 100% data accuracy via Pelican

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Open Access

Good practices for clinical data warehouse implementation: A case study in France

* E-mail: [email protected]

Affiliations Mission Data, Haute Autorité de Santé, Saint-Denis, France, Inria, Soda team, Palaiseau, France

Affiliation Mission Data, Haute Autorité de Santé, Saint-Denis, France

Affiliations Univ. Lille, CHU Lille, ULR 2694—METRICS: Évaluation des Technologies de santé et des Pratiques médicales, Lille, France, Fédération régionale de recherche en psychiatrie et santé mentale (F2RSM Psy), Hauts-de-France, Saint-André-Lez-Lille, France

Affiliation Sorbonne Université, Inserm, Université Sorbonne Paris-Nord, Laboratoire d’informatique médicale et d’ingénierie des connaissances en e-Santé, LIMICS, France

• Matthieu Doutreligne, 
• Adeline Degremont, 
• Pierre-Alain Jachiet, 
• Antoine Lamer, 
• Xavier Tannier

Published: July 6, 2023

• https://doi.org/10.1371/journal.pdig.0000298
• Reader Comments

29 Sep 2023: Doutreligne M, Degremont A, Jachiet PA, Lamer A, Tannier X (2023) Correction: Good practices for clinical data warehouse implementation: A case study in France. PLOS Digital Health 2(9): e0000369. https://doi.org/10.1371/journal.pdig.0000369 View correction

Real-world data (RWD) bears great promises to improve the quality of care. However, specific infrastructures and methodologies are required to derive robust knowledge and brings innovations to the patient. Drawing upon the national case study of the 32 French regional and university hospitals governance, we highlight key aspects of modern clinical data warehouses (CDWs): governance, transparency, types of data, data reuse, technical tools, documentation, and data quality control processes. Semi-structured interviews as well as a review of reported studies on French CDWs were conducted in a semi-structured manner from March to November 2022. Out of 32 regional and university hospitals in France, 14 have a CDW in production, 5 are experimenting, 5 have a prospective CDW project, 8 did not have any CDW project at the time of writing. The implementation of CDW in France dates from 2011 and accelerated in the late 2020. From this case study, we draw some general guidelines for CDWs. The actual orientation of CDWs towards research requires efforts in governance stabilization, standardization of data schema, and development in data quality and data documentation. Particular attention must be paid to the sustainability of the warehouse teams and to the multilevel governance. The transparency of the studies and the tools of transformation of the data must improve to allow successful multicentric data reuses as well as innovations in routine care.

Author summary

Reusing routine care data does not come free of charges. Attention must be paid to the entire life cycle of the data to create robust knowledge and develop innovation. Building upon the first overview of CDWs in France, we document key aspects of the collection and organization of routine care data into homogeneous databases: governance, transparency, types of data, data reuse main objectives, technical tools, documentation, and data quality control processes. The landscape of CDWs in France dates from 2011 and accelerated in the late 2020, showing a progressive but still incomplete homogenization. National and European projects are emerging, supporting local initiatives in standardization, methodological work, and tooling. From this sample of CDWs, we draw general recommendations aimed at consolidating the potential of routine care data to improve healthcare. Particular attention must be paid to the sustainability of the warehouse teams and to the multilevel governance. The transparency of the data transformation tools and studies must improve to allow successful multicentric data reuses as well as innovations for the patient.

Citation: Doutreligne M, Degremont A, Jachiet P-A, Lamer A, Tannier X (2023) Good practices for clinical data warehouse implementation: A case study in France. PLOS Digit Health 2(7): e0000298. https://doi.org/10.1371/journal.pdig.0000298

Editor: Dukyong Yoon, Yonsei University College of Medicine, REPUBLIC OF KOREA

Copyright: © 2023 Doutreligne et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: MD, AD, PAJ salaries were funded by the French Haute Autorité de Santé (HAS). XT received fundings to participate in interviews and participate to the article redaction. AL received no fundings for this study. The funders validated the study original idea and the study conclusions. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: The first author did a (non-paid) visiting in Leo Anthony Celi’s lab during the first semester of 2023.

Introduction

Real-world data.

Health information systems (HIS) are increasingly collecting routine care data [ 1 – 7 ]. This source of real-world data (RWD) [ 8 ] bears great promises to improve the quality of care. On the one hand, the use of this data translates into direct benefits—primary uses—for the patient by serving as the cornerstone of the developing personalized medicine [ 9 , 10 ]. They also bring indirect benefits—secondary uses—by accelerating and improving knowledge production: on pathologies [ 11 ], on the conditions of use of health products and technologies [ 12 , 13 ], on the measures of their safety [ 14 ], efficacy or usefulness in everyday practice [ 15 ]. They can also be used to assess the organizational impact of health products and technologies [ 16 , 17 ].

In recent years, health agencies in many countries have conducted extensive work to better support the generation and use of real-life data [ 8 , 17 – 19 ]. Study programs have been launched by regulatory agencies: the DARWIN EU program by the European Medicines Agency and the Real World Evidence Program by the Food and Drug Administration [ 20 ].

Clinical data warehouse

In practice, the possibility of mobilizing these routinely collected data depends very much on their degree of concentration, in a gradient that goes from centralization in a single, homogenous HIS to fragmentation in a multitude of HIS with heterogeneous formats. The structure of the HIS reflects the governance structure. Thus, the ease of working with these data depends heavily on the organization of the healthcare actors. The 2 main sources of RWD are insurance claims—more centralized—and clinical data—more fragmented.

Claims data is often collected by national agencies into centralized repositories. In South Korea, the government agency responsible for healthcare system performance and quality (HIRA) is connected to the HIS of all healthcare stakeholders. HIRA data consists of national insurance claims [ 21 ]. England has a centralized healthcare system under the National Health Service (NHS). Despite not having detailed clinical data, this allowed the NHS to merge claims data with detailed data from 2 large urban medicine databases, corresponding to the 2 major software publishers [ 22 ]. This data is currently accessed through Opensafely, a first platform focused on Coronavirus Disease 2019 (COVID-19) research [ 23 ]. In the United States, even if scattered between different insurance providers, claims are pooled into large databases such as Medicare, Medicaid, or IBM MarketScan. Lastly, in Germany, the distinct federal claims have been centralized only very recently [ 24 ].

Clinical data on the other hand, tends to be distributed among many entities, that made different choices, without common management or interoperability. But large institutional data-sharing networks begin to emerge. South Korea very recently launched an initiative to build a national wide data network focused on intensive care. United States is building Chorus4ai, an analysis platform pooling data from 14 university hospitals [ 25 ]. To unlock the potential of clinical data, the German Medical Informatics Initiative [ 26 ] created 4 consortia in 2018. They aim at developing technical and organizational solutions to improve the consistency of clinical data.

Israel stands out as one of the rare countries that pooled together both claims and clinical data at a large scale: half of the population depends on 1 single healthcare provider and insurer [ 27 ].

An infrastructure is needed to pool data data from 1 or more medical information systems—whatever the organizational framework—to homogeneous formats, for management, research, or care reuses [ 28 , 29 ]. Fig 1 illustrates for a CDW, the 4 phases of data flow from the various sources that make up the HIS:

• Collection and copying of original sources.
• Integration of sources into a unique database.
• Deduplication of identifiers.
• Standardization: A unique data model, independent of the software models harmonizes the different sources in a common schema, possibly with common nomenclatures.
• Pseudonymization: Removal of directly identifying elements.
• Provision of subpopulation data sets and transformed datamarts for primary and secondary reuse.
• Usages thanks to dedicated applications and tools accessing the datamarts and data sets.

In France, the national insurer collects all hospital activity and city care claims into a unique reimbursement database [ 13 ]. However, clinical data is historically scattered at each care site in numerous HISs. Several hospitals deployed efforts for about 10 years to create CDWs from electronic medical records [ 30 – 39 ]. This work has accelerated recently, with the beginning of CDWs structuring at the regional and national levels. Regional cooperation networks are being set up—such as the Ouest Data Hub [ 40 ]. In July 2022, the Ministry of Health opened a 50 million euros call for projects to set up and strengthen a network of hospital CDWs coordinated with the national platform, the Health Data Hub by 2025.

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

CDW: Four steps of data flow from the Hospital Information System: (1) collection, (2) transformations, and (3) provisioning. CDW, clinical data warehouse.

https://doi.org/10.1371/journal.pdig.0000298.g001

Based on an overview of university hospital CDWs in France, this study makes general recommendations for properly leveraging the potential of CDWs to improve healthcare. It focuses on: governance, transparency, types of data, data reuse, technical tools, documentation, and data quality control processes.

Material and methods

Interviews were conducted from March to November 2022 with 32 French regional and university hospitals, both with existing and prospective CDWs.

Ethics statement

This work has been authorized by the board of the French High Authority of Health (HAS). Every interviewed participant was asked by email for their participation and informed on the possible forms of publication: a French official report and an international publication. Furthermore, at each interview, every participant has been asked for their agreement before recording the interview. Only 1 participant refused the video to be recorded.

Semi-structured interviews were conducted on the following themes: the initiation and construction of the CDWs, the current status of the project and the studies carried out, opportunities and obstacles, and quality criteria for observational research. S1 Table lists all interviewed people with their team title. The complete form, with the precised questions, is available in S2 Table .

The interview form was sent to participants in advance and then used as a support to conduct the interviews. The interviews lasted 90 min and were recorded for reference.

Quantitative methods

Three tables detailed the structured answers in S1 Text . The first 2 tables deal with the characteristics of the actors and those of the data warehouses. We completed them based on the notes taken during the interviews, the recordings, and by asking the participants for additional information. The third table focuses on ongoing studies in the CDWs. We collected the list of these studies from the dedicated reporting portals, which we found for 8 out of 14 operational CDWs. We developed a classification of studies, based on the typology of retrospective studies described by the OHDSI research network [ 41 ]. We enriched this typology by comparing it with the collected studies resulting in the 6 following categories:

• Outcome frequency : Incidence or prevalence estimation for a medically well-defined target population.
• Population characterization : Characterization of a specific set of covariates. Feasibility and prescreening studies belong to this category [ 42 ].
• Risk factors : Identification of covariates most associated with a well-defined clinical target (disease course, care event). These studies look at association study without quantifying the causal effect of the factors on the outcome of interest.
• Treatment effect : Evaluation of the effect of a well-defined intervention on a specific outcome target. These studies intend to show a causal link between these 2 variables [ 43 ].
• Development of diagnostic and prognostic algorithms : Improve or automate a diagnostic or prognostic process, based on clinical data from a given patient. This can take the form of a risk, a preventive score, or the implementation of a diagnostic assistance system. These studies are part of the individualized medicine approach, with the goal of inferring relevant information at the level of individual patient’s files.
• Medical informatics : Methodological or tool oriented. These studies aim to improve the understanding and capacity for action of researchers and clinicians. They include the evaluation of a decision support tool, the extraction of information from unstructured data, or automatic phenotyping methods.

Studies were classified according to this nomenclature based on their title and description.

Fig 2 summarizes the development state of progress of CDWs in France. Out of 32 regional and university hospitals in France, 14 have a CDW in production, 5 are experimenting, 5 have a prospective CDW project, 8 did not have any CDW project at the time of writing. The results are described for all projects that are at least in the prospective stage minus the 3 that we were unable to interview after multiple reminders (Orléans, Metz, and Caen), resulting in a denominator of 21 university hospitals.

Base map and data from OpenStreetMap and OpenStreetMap Foundation. Link to the base layer of the map: https://github.com/mapnik/mapnik . CDW, clinical data warehouse.

https://doi.org/10.1371/journal.pdig.0000298.g002

Fig 3 shows the history of the implementation of CDWs. A distinction must be made between the first works—in blue—, which systematically precede the regulatory authorization—in green—from the French Commission on Information Technology and Liberties (CNIL).

CDW, clinical data warehouse.

https://doi.org/10.1371/journal.pdig.0000298.g003

The CDWs have so far been initiated by 1 or 2 people from the hospital world with an academic background in bioinformatics, medical informatics, or statistics. The sustainability of the CDW is accompanied by the construction of a cooperative environment between different actors: Medical Information Department (MID), Information Systems Department (IT), Clinical Research Department (CRD), clinical users, and the support of the management or the Institutional Medical Committee. It is also accompanied by the creation of a team, or entity, dedicated to the maintenance and implementation of the CDW. More recent initiatives, such as those of the HCL (Hospitals of the city of Lyon) or the Grand-Est region, are distinguished by an initial, institutional, and high-level support.

The CDW has a federating potential for the different business departments of the hospital with the active participation of the CRD, the IT Department, and the MID. Although there is always an operational CDW team, the human resources allocated to it vary greatly: from half a full-time equivalent to 80 people for the AP-HP, with a median of 6.0 people. The team systematically includes a coordinating physician. It is multidisciplinary with skills in public health, medical informatics, informatics (web service, database, network, infrastructure), data engineering, and statistics.

Historically, the first CDWs were based on in-house solution development. More recently, private actors are offering their services for the implementation and implementation of CDWs (15/21). These services range from technical expertise in order to build up the data flows and data cleaning up to the delivery of a platform integrating the different stages of data processing.

Management of studies

Before starting, projects are systematically analyzed by a scientific and ethical committee. A local submission and follow-up platform is often mentioned (12/21), but its functional scope is not well defined. It ranges from simple authorization of the project to the automatic provision of data into a Trusted Research Environment (TRE) [ 44 ]. The processes for starting a new project on the CDW are always communicated internally but rarely documented publicly (8/21).

Transparency

Ongoing studies in CDWs are unevenly referenced publicly on hospital websites. Some institutions have comprehensive study portals, while others list only a dozen studies on their public site while mentioning several hundreds ongoing projects during interviews. In total, we found 8 of these portals out of 14 CDWs in production. Uses other than ongoing scientific studies are very rarely documented. The publication of the list of ongoing studies is very heterogeneous and fragmented between several sources: clinicaltrials.gov, the mandatory project portal of the Health Data Hub [ 45 ] or the website of the hospital data warehouse.

Strong dependance to the HIS.

CDW data reflect the HIS used on a daily basis by hospital staff. Stakeholders point out that the quality of CDW data and the amount of work required for rapid and efficient reuse are highly dependent on the source HIS. The possibility of accessing data from an HIS in a structured and standardized format greatly simplifies its integration into the CDW and then its reuse.

Categories of data.

Although the software landscape is varied across the country, the main functionalities of HIS are the same. We can therefore conduct an analysis of the content of the CDWs, according to the main categories of common data present in the HIS.

The common base for all CDWs is constituted by data from the Patient Administrative Management software (patient identification, hospital movements) and the billing codes. Then, data flows are progressively developed from the various softwares that make up the HIS. The goal is to build a homogeneous data schema, linking the sources together, controlled by the CDW team. The prioritization of sources is done through thematic projects, which feed the CDW construction process. These projects improve the understanding of the sources involved, by confronting the CDW team with the quality issues present in the data.

Table 1 presents the different ratio of data categories integrated in French CDWs. Structured biology and texts are almost always integrated (20/21 and 20/21). The texts contain a large amount of information. They constitute unstructured data and are therefore more difficult to use than structured tables. Other integrated sources are the hospital drug circuit (prescriptions and administration, 16/21), Intense Care Unit (ICU, 2/21), or nurse forms (4/21). Imaging is rarely integrated (4/21), notably for reasons of volume. Genomic data are well identified, but never integrated, even though they are sometimes considered important and included in the CDW work program.

https://doi.org/10.1371/journal.pdig.0000298.t001

Data reuse.

Today, the main use put forward for the constitution of CDWs is that of scientific research.

The studies are mainly observational (non-interventional). Fig 4 presents the distribution of the 6 categories defined in Quantitative methods for 231 studies collected on the study portals of 9 hospitals. The studies focus first on population characterization (25%), followed by the development of decision support processes (24%), the study of risk factors (18%), and the treatment effect evaluations (16%).

https://doi.org/10.1371/journal.pdig.0000298.g004

The CDWs are used extensively for internal projects such as student theses (at least in 9/21) and serve as an infrastructure for single-service research: their great interest being the de-siloing of different information systems. For most of the institutions interviewed, there is still a lack of resources and maturity of methods and tools for conducting inter-institutional research (such as in the Grand-Ouest region of France) or via European calls for projects (EHDEN). These 2 research networks are made possible by supra-local governance and a common data schema, respectively, eHop [ 46 ] and OMOP [ 47 ]. The Paris hospitals, thanks to its regional coverage and the choice of OMOP, is also well advanced in multicentric research. At the same time, the Grand-Est region is building a network of CDW based on the model of the Grand-Ouest region, also using eHop.

CDW are used for monitoring and management (16/21).

The CDW have sometimes been initiated to improve and optimize billing coding (4/21). The clinical texts gathered in the same database are queried using keywords to facilitate the structuring of information. The data are then aggregated into indicators, some of which are reported at the national level. The construction of indicators from clinical data can also be used for the administrative management of the institution. Finally, closer to the clinic, some actors state that the CDW could also be used to provide regular and appropriate feedback to healthcare professionals on their practices. This feedback would help to increase the involvement and interest of healthcare professionals in CDW projects. The CDW is sometimes of interest for health monitoring (e.g., during COVID-19) or pharmacovigilance (13/21).

Strong interest for CDW in the context of care (13/21).

Some CDWs develop specific applications that provide new functionalities compared to care software. Search engines can be used to query all the hospital’s data gathered in the CDW, without data compartmentalization between different softwares. Dedicated interfaces can then offer a unified view of the history of a patient’s data, with inter-specialty transversality, which is particularly valuable in internal medicine. These cross-disciplinary search tools also enable healthcare professionals to conduct rapid searches in all the texts, for example, to find similar patients [ 32 ]. Uses for prevention, automation of repetitive tasks, and care coordination are also highlighted. Concrete examples are the automatic sorting of hospital prescriptions by order of complexity or the setting up of specialized channels for primary or secondary prevention.

Technical architecture

The technical architecture of modern CDWs has several layers:

• Data processing: connection and export of source data, diverse transformation (cleaning, aggregation, filtering, standardization).
• Data storage: database engines, file storage (on file servers or object storage), indexing engines to optimize certain queries.
• Data exposure: raw data, APIs, dashboards, development and analysis environments, specific web applications.

Supplementary cross-functional components ensure the efficient and secure operation of the platform: identity and authorization management, activity logging, automated administration of servers and applications.

The analysis environment (Jupyterhub or RStudio datalabs) is a key component of the platform, as it allows data to be processed within the CDW infrastructure. A few CDWs had such operational datalab at the time of our study (6/21) and almost all of them have decided to provide it to researchers. Currently, clinical research teams are still often working on data extractions in less secure environments.

Data quality, standard formats

Quality tools..

Systematic data quality monitoring processes are being built in some CDWs. Often (8/21), scripts are run at regular intervals to detect technical anomalies in data flows. Rare data quality investigation tools, in the form of dashboards, are beginning to be developed internally (3/21). Theoretical reflections are underway on the possibility of automating data consistency checks, for example, demographic or temporal. Some facilities randomly pull records from the EHR to compare them with the information in the CDW.

Standard format.

No single standard data model stands out as being used by all CDWs. All are aware of the existence of the OMOP (research standard) [ 47 ] and HL7 FHIR (communication standard) models [ 48 ]. Several CDWs consider the OMOP model to be a central part of the warehouse, particularly for research purposes (9/21). This tendency has been encouraged by the European call for projects EHDEN, launched by the OHDSI research consortium, the originator of this data model. In the Grand-Ouest region of France, the CDWs use the eHop warehouse software. The latter uses a common data model also named eHop. This model will be extended with the future warehouse network of the Grand Est region also choosing this solution. Including this grouping and the other establishments that have chosen eHop, this model includes 12 establishments out of the 32 university hospitals. This allows eHop adopters to launch ambitious interregional projects. However, eHop does not define a standard nomenclature to be used in its model and is not aligned with emerging international standards.

Documentation.

Half of the CDWs have put in place documentation accessible within the organization on data flows, the meaning and proper use of qualified data (10/21 mentioned). This documentation is used by the team that develops and maintains the warehouse. It is also used by users to understand the transformations performed on the data. However, it is never publicly available. No schema of the data once it has been transformed and prepared for analysis is published.

Principal findings

We give the first overview of the CDWs in university hospitals of France with 32 hospitals reviewed. The implementation of CDW dates from 2011 and accelerated in the late 2020. Today, 24 of the university hospitals have an ongoing CDW project. From this case study, some general considerations can be drawn that should be valuable to all healthcare system implementing CDWs on a national scale.

As the CDW becomes an essential component of data management in the hospital, the creation of an autonomous internal team dedicated to data architecture, process automation, and data documentation should be encouraged [ 44 ]. This multidisciplinary team should develop an excellent knowledge of the data collection process and potential reuses in order to qualify the different flows coming from the source IS, standardize them towards a homogenous schema and harmonize the semantics. It should have a sound knowledge of public health, as well as the technical and statistical skills to develop high-quality software that facilitates data reuse.

The resources specific to the warehouse are rare and often taken from other budgets or from project-based credits. While this is natural for an initial prototyping phase, it does not seem adapted to the perennial and transversal nature of the tool. As a research infrastructure of growing importance, it must have the financial and organizational means to plan for the long term.

The governance of the CDW has multiple layers: local within the university hospital, interregional, and national/international. The first level allow to ensure the quality of data integration as well as the pertinence of data reuse by clinicians themselves. The interregional level is well adapted for resources mutualization and collaboration. Finally, the national and international levels assure coordination, encourage consensus for committing choices such as metadata or interoperability, and provide financial, technical, and regulatory support.

Health technology assessment agencies advocate for public registration of comparative observational study protocols before conducting the analysis [ 8 , 17 , 49 ]. They often refer to clinicaltrials.gov as potential but not ideal registration portal for observational studies. The research community advocates for public registrations of all observational studies [ 50 , 51 ]. More recently, it emphasizes the need for more easy data access and the publication of study code [ 29 , 52 , 53 ]. We embrace these recommendations and we point to the unfortunate duplication of these study reporting systems in France. One source could be favored at the national level and the second one automatically fed from the reference source, by agreeing on common metadata.

From a patient’s perspective, there is currently no way to know if their personal data is included for a specific project. Better patient information about the reuse of their data is needed to build trust over the long term. A strict minimum is the establishment and update of the declarative portals of ongoing studies at each institution.

Data and data usage

When using CDW, the analyst has not defined the data collection process and is generally unaware of the context in which the information is logged. This new dimension of medical research requires a much greater development of data science skills to change the focus from the implementation of the statistical design to the data engineering process. Data reuse requires more effort to prepare the data and document the transformations performed.

The more heterogeneous a HIS system is, the less qualitative would be the CDW built on top of it. There is a need for increasing interoperability, to help EHR vendors interfacing the different hospital softwares, thus facilitating CDW development. One step in this direction would be the open source publication of HIS data schema and vocabularies. At the analysis level, international recommendations insist on the need for common data formats [ 52 , 54 ]. However, there is still a lack of adoption of research standards from hospital CDWs to conduct robust studies across multiple sites. Building open-source tools on top of these standards such as those of OHDSI [ 41 ] could foster their adoption. Finally, in many clinical domains, sufficient sample size is hard to obtain without international data-sharing collaborations. Thus, more incitation is needed to maintain and update the terminology mappings between local nomenclatures and international standards.

Many ongoing studies concern the development of decision support processes whose goal is to save time for healthcare professionals. These are often research projects, not yet integrated into routine care. The analysis of study portals and the interviews revealed that data reuse oriented towards primary care is still rare and rarely supported by appropriate funding. The translation from research to clinical practice takes time and need to be supported on the long run to yield substantial results.

Tools, methods, and data formats of CDW lack harmonization due to the strong technical innovation and the presence of many actors. As suggested by the recent report on the use of data for research in the UK [ 44 ], it would be wise to focus on a small number of model technical platforms.

These platforms should favor open-source solutions to assure transparency by default, foster collaboration and consensus, and avoid technological lock-in of the hospitals.

Data quality and documentation

Quality is not sufficiently considered as a relevant scientific topic itself. However, it is the backbone of all research done within a CDW. In order to improve the quality of the data with respect to research uses, it is necessary to conduct continuous studies dedicated to this topic [ 52 , 54 – 56 ]. These studies should contribute to a reflection on methodologies and standard tools for data quality, such as those developed by the OHDSI research network [ 41 ].

Finally, there is a need for open-source publication of research code to ensure quality retrospective research [ 55 , 57 ]. Recent research in data analysis has shown that innumerable biases can lurk in training data sets [ 58 , 59 ]. Open publication of data schemas is considered an indispensable prerequisite for all data science and artificial intelligence uses [ 58 ]. Inspired by data set cards [ 58 ] and data set publication guides, it would be interesting to define a standard CDW card documenting the main data flows.

Limitations

The interviews were conducted in a semi-structured manner within a limited time frame. As a result, some topics were covered more quickly and only those explicitly mentioned by the participants could be recorded. The uneven existence of study portals introduces a bias in the recording of the types of studies conducted on CDW. Those with a transparency portal already have more maturity in use cases.

For clarity, our results are focused on the perimeter of university hospitals. We have not covered the exhaustive healthcare landscape in France. CDW initiatives also exist in primary care, in smaller hospital groups and in private companies.

Conclusions

The French CDW ecosystem is beginning to take shape, benefiting from an acceleration thanks to national funding, the multiplication of industrial players specializing in health data and the beginning of a supra-national reflection on the European Health Data Space [ 60 ]. However, some points require special attention to ensure that the potential of the CDW translates into patient benefits.

The priority is the creation and perpetuation of multidisciplinary warehouse teams capable of operating the CDW and supporting the various projects. A combination of public health, data engineering, data stewardship, statistics, and IT competences is a prerequisite for the success of the CDW. The team should be the privileged point of contact for data exploitation issues and should collaborate closely with the existing hospital departments.

The constitution of a multilevel collaboration network is another priority. The local level is essential to structure the data and understand its possible uses. Interregional, national, and international coordination would make it possible to create thematic working groups in order to stimulate a dynamic of cooperation and mutualization.

A common data model should be encouraged, with precise metadata allowing to map the integrated data, in order to qualify the uses to be developed today from the CDWs. More broadly, open-source documentation of data flows and transformations performed for quality enhancement would require more incentives to unleash the potential for innovation for all health data reusers.

Finally, the question of expanding the scope of the data beyond the purely hospital domain must be asked. Many risk factors and patient follow-up data are missing from the CDWs, but are crucial for understanding pathologies. Combining city data and hospital data would provide a complete view of patient care.

Supporting information

S1 table. list of interviewed stakeholders with their teams..

https://doi.org/10.1371/journal.pdig.0000298.s001

S2 Table. Interview form.

https://doi.org/10.1371/journal.pdig.0000298.s002

S1 Text. Study data tables.

https://doi.org/10.1371/journal.pdig.0000298.s003

Acknowledgments

We want to thanks all participants and experts interviewed for this study. We also want to thanks other people that proof read the manuscript for external review: Judith Fernandez (HAS), Pierre Liot (HAS), Bastien Guerry (Etalab), Aude-Marie Lalanne Berdouticq (Institut Santé numérique en Société), Albane Miron de L’Espinay (ministère de la Santé et de la Prévention), and Caroline Aguado (ministère de la Santé et de la Prévention). We also thank Gaël Varoquaux for his support and advice.

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