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Smart supply chain management in Industry 4.0: the review, research agenda and strategies in North America

• Original Research
• Published: 02 May 2022
• Volume 322 , pages 1075–1117, ( 2023 )

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• Guoqing Zhang   ORCID: orcid.org/0000-0003-0821-8787 1   na1 ,
• Yiqin Yang 1   na1 &
• Guoqing Yang 2   na1  

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The emerging information and communication technologies (ICT) related to Industry 4.0 play a critical role to enhance supply chain performance. Employing the smart technologies has led to so-called smart supply chains. Understanding how Industry 4.0 and related ICT affect smart supply chains and how smart supply chains evolve with the support of the advanced technologies are vital to practical and academic communities. Existing review works on smart supply chains with ICT mainly rely on the academic literature alone. This paper presents an integrated approach to explore the effects of Industry 4.0 and related ICT on smart supply chains, by combining introduction of the current national strategies in North America, the research status analysis on ICT assisted supply chains from the major North American national research councils, and a systematic literature review of the subject. Besides, we introduce a smart supply chain hierarchical framework with multi-level intelligence. Furthermore, the challenges faced by supply chains under Industry 4.0 and future research directions are discussed as well.

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1 Introduction

Supply chains (SC) play a crucial role in manufacturing and service operations. Supply chain management (SCM), which is a systematic approach to managing flows of assets from sourcing raw materials, product manufacturing, to delivering to end customers significantly affects the business goals of the partners in supply networks. The wide disruptions due to the COVID-19 pandemic demonstrated the importance of supply chain flexibility and resilience. Thus, developing a resilient and smart supply chains is an indispensable yet challenging task for manufacturers and other stakeholders. The emergence of new (ICT) technologies, such as big data analysis, internet of things (IoT), blockchain, etc., makes it possible to develop smart supply chains.

Driven by new technologies (push) and the need for adapting to constantly changing markets (pull), Industry 4.0 provides a new platform for smart manufacturing and brings manufacturers close to customers (Lasi et al., 2014 ). Industry 4.0 can provide a cyber-physical system to integrate the customer needs into the different stages of manufacturing. Horizontal integration across the entire value creation network is one character of Industry 4.0 (Saucedo-Martínez et al., 2018 ). This entire value creation network is typically coincident with the supply chains. Thus, supply chain management is an important factor that impact the performance of the smart manufacturing under Industry 4.0. Supply chains should keep pace with Industry 4.0. Zhang ( 2015 ) indicated that smart manufacturing needs smart or even smarter supply chains for support since the supply chains affect the availability of the input for manufacturing, the interaction of multiple functions of production, the efficiency of the finished goods delivery to customers, and responsiveness of the network.

The smart supply chain (SSC) is to use advanced technologies, especially emerging ICT, to link the processes in the different partners of a SC to form an intelligent connected system (Wu, 2016 ). There are some works exploring the different new technologies and their impacts on supply chains under Industry 4.0, such as IoT on SCM (Ben-Daya et al., 2019 ), internet of things (IoT) embedded sustainable SC (Manavalan & Jayakrishna, 2019 ), big data analytics in SCM (Nguyen et al., 2018 ). However, most of those works rely completely on academic literature, with only a few exceptions. For example, Harris et al. ( 2015 ) discussed recent development of information and communication technologies (ICT) in multimodal transportation based on major EU framework projects and indicated that comparing with rather dated and narrow view of journal publications, the national research projects represent the current major efforts and reflect the cutting-edge technology and the state of art in research and development. Since academic literature, ongoing cutting edge national research projects, and national strategies bring about different perspective and focus, reviewing these different resources will provide a comprehensive overview of current research and development and application of emerging advanced technologies, as well as vision and road map for future development and implementation of relevant technologies.

New technologies emerging from Industry 4.0, such as ICT, show promise in improving supply chain performance in various ways (Colin et al., 2015 ). However, to the best of our knowledge, this is the first work to combine the discussion of national strategies, ongoing research projects with literature review to present a comprehensive review in smart supply chain under Industry 4.0. In addition, how to incorporate those advanced technologies to SCM with different decision levels among the different stages of smart supply chains remains a challenging and under explored problem.

This research mainly aims to fill the gaps and focuses on North America, one of the leading regions on the subjects, to perform an in-depth analysis. Our work intends to answer the following questions:

How does smart supply chain evolve with the support of advanced ICTs under Industry 4.0?

What are the cutting-edge developments about the smart supply chain under Industry 4.0 and the relationship between national strategies and ongoing project in smart supply chain?

To answer the questions mentioned above, this paper uses an integrated method to review the current developments of smart supply chains, the impacts of Industry 4.0 and related technologies. Besides academic literature, profiles of currently funded projects and National strategies in North American region in terms of Industry 4.0 are reviewed. The challenges faced by supply chains under Industry 4.0 and some research directions are discussed as well. The main contributions of this paper therefore are three-fold: first, we present a comprehensive summary on the existing research and research effort on smart supply chains under Industry 4.0; Second, we create a smart supply chain framework to reflect the dynamics and development of smart supply chain; third, we propose some research directions to lay the foundation for further research in the areas.

The paper is organized as follows: Sect.  2 introduces the review methods and materials, which is followed by proposed smart supply chain framework in Sect.  3 . National strategies on advanced manufacturing (AM) in US and Canada are discussed in Sect.  4 . Section  5 analyses the ongoing research projects on ICT applications on smart supply chains. Section  6 provides a review of existing studies on smart supply chain management (SSCM) under Industry 4.0. Section  7 presents some challenges from supply chains perspective and future research directions. Finally, Sect.  8 concludes with a summary of the insights generated from the review.

2 Review methods and materials

To analyze the current developments of smart supply chains, and to study the impacts of Industry 4.0 and related ICT technologies, an integrated qualitative and quantitative method is conducted. The following three resources are examined and analyzed: government strategies in North American region, research projects from the major national research councils, and academic literature, as shown in Table 1 . In the table, we use “x” to represent the resource used mainly for the related topic, for example, ongoing projects from national grants are used to all four topics.

To introduce national strategy of North America region on Industry 4.0 and advanced manufacturing, we conducted a search with google and government official websites which are supplemented by other database such as SCOPES. To capture the latest trends and current major effort of Industry 4.0 related new technologies in SCM, we identified some active projects, mostly started in the last 5 years, from both National Science Foundation (NSF) and the Natural Sciences and Engineering Research Council of Canada (NSERC) award databases, where both NSF and NSERC are the major federal funding agency or council for university based research in US and Canada respectively to advance the progress of science.

A systematic review of both academic and practice literature is provided to analyze the smart supply chains under Industry 4.0, with the focus on the impacts of relevant new technologies including big data, internet of things, cyber-physical systems, blockchains, 3D printing. Our emphasis is on the latest progress (recent 5 years) and their impacts on the performance of supply chains. The different combinations of keywords “supply chain” (including “supply chains”, “logistics”, “inventory”, “sourcing”, “pricing”, and “supply network”) with one of the technologies such as big data , machine learning (ML), IoT , etc., are used in literature search.

For literature review, we separate the extant research works into development supply chain and fulfillment supply chain. The former focuses on the decision-making for building the new supply chain which is mainly at a strategic level, whereas the latter is about the issues regarding daily operations to satisfy the demand, which is primarily in tactic and operation levels. For fulfillment supply chain, we follow APICS supply chain operations reference (SCOR) model (Huan et al., 2004 ), which is widely recognized as a supply chain framework. It should be mentioned that this work is not all-inclusive, but is meant to cover the latest and main progress in the area to reflect the current status and future trends.

3 A smart supply chain framework

Although the concept of smart supply chain has been widely used in the literature, the definitions and concepts of the smart supply chains are inconsistent and often incomplete. The concept is often referred to in terms of different stages/drivers of supply chains, technologies involved, applications, and characteristics:

Smart drivers of supply chains: such as smart logistics & transportation (Barreto et al., 2017 ; Harris et al., 2015 ), smart warehouse (Liuet et al., 2018 ). Most work reported in the literature belongs to this category.

Data streams/digital aspects: using different analytics models to analyse real-time data flows and historical data repositories (Ivanov, Dolgui, et al., 2019 ; Ivanov, Tsipoulanidis, et al., 2019 ).

Technologies aspects: The integration of modern technologies, such as IoT and big data, into SC leads to a SSCM (Wang & Ranjan, 2015 ).

Characteristics: Several characteristics of a SSC are proposed, such as instrumented, interconnected, and intelligent by IBM (Butner, 2010 ), adaptability by Leończuk et al. ( 2019 ), and instrumented, interconnected, intelligent, automated, integrated, and innovative by Wu et al. ( 2016 ).

We intend to define a “smart supply chain” as a supply chain that integrates the partners can self-organize and automatically adapt to environmental changes, and makes intelligent decision that best achieve business goals. The characteristics include being integrated, intelligent, adaptive, and self-optimizing. SSC is a dynamic evolving process that extends vertically and horizontally in terms of integration, along with the technology development and business innovation.

With the development and integration of ICT and artificial intelligent (AI), supply chains evolve from single partner/flow intelligence to multi-partner or whole supply network intelligence, and ultimately, to promote and realize the digitalization and intelligentization of different business/system/industries. To reflect the process, we present a multi-level smart supply chain framework as Fig.  1 . The hierarchy of the smart supply chain also reflects different stages and scopes of SCM to which ICT, AI&ML, and other technologies are applied.

Smart supply chain management hierarchical framework

Level 0 of the framework consists of smart technologies or drivers which provide the basis of smart supply chains, including ICT, AI&ML, and other technologies. ICT includes IoT, big data (BD), cloud, block chains (BC), 3D printing, and so on. AI&ML refer to artificial intelligent, machine learning, optimization, etc. The other technologies include AM, robotics, digit-twin, intelligent transportations, drone, supply chain finance and banking 4.0, and so forth.

Level 1 is the initial stage of smart supply chain which mainly applies the ICT/AI to improve single driver/flow/partner/level (operational, tactical or strategic) of supply chain, or to one level such as smart internal supply chains in a manufacturing group, smart logistics, intelligent inventory management system, etc.

Level 2 is the smart supply chain that implements intelligent decisions in all flows (material, information, finance), all levels (operational, tactical or strategic) among the connected partners of the supply chain. The smart SC is interconnected, intelligent, and self-organizing, and can reconfigure and self-optimize the network in response to environmental change and change in business goals. The smart supply chain would demonstrate superior performance in all key aspects, including responsiveness, efficiency, resilience, and flexibility.

Level 3 is the smart business/system/industry that the SC supports: such as smart automotive manufacturing under Industry 4.0, smart retailing, smart healthcare service, even smart city. That indicates that the ultimate goal of the smart supply chain is to match and support business strategy, to facilitate the implementation of Industry 4.0 so as to achieve the economic, environmental, and social goals.

4 National strategies on Industry 4.0 and advanced manufacturing in North America

4.1 industry 4.0 and ict.

The fourth industrial revolution, i.e., Industry 4.0, is driven by innovative technologies, especially advanced Information and Communication Technologies. The advanced ICT impact almost all stages of the manufacturing process. Such advanced ICT is also a critical driver for improving manufacturing supply chain responsiveness and efficiency simultaneously (Chopra & Meindl, 2014 ) and plays a central role in SCM (Apiyo & Kiarie, 2018 ).

To push Industry 4.0 transition in manufacturing, several government-led initiatives were launched to facilitate this transition. National strategies on AM were proposed to improve the competitiveness and strengthen their technological innovation capability in manufacturing areas, and identify the challenges and the directions for future research agenda at a strategic level. Here we mainly introduce the recent national manufacturing strategies in USA and Canada.

4.2 US national strategy on advanced manufacturing: advanced manufacturing partnership (AMP) and manufacturing USA

It is noted that the terminology of Industry 4.0 is rarely used in US national strategies. Instead, advanced manufacturing and related technologies are the theme of the national strategies.

4.2.1 Advanced manufacturing partnership and AMP 2.0

The U.S. has been a world leader in manufacturing for more than one century. AMP was launched by President Obama in 2011 to bring together industry, academia, and government to revitalize manufacturing sector in United States and to strengthen the Nation’s global competence.

The second phase of AMP (AMP2.0) was launched in 2013. To strengthen the ecosystem for sustaining American leadership in advanced manufacturing, the AMP 2.0 Steering Committee further developed a set of specific, targeted, and actionable recommendations around three integrated pillars identified by AMP in 2012: including Innovation, Talent, and Business Climate. Cyber-physical system (CPS), big data, IoT, and cloud-based implementations are listed as the emerging technologies for generating data and “soft” intelligence.

It is noted that “supply chain” is mentioned 30 times in the report and more than 90 times in the annexes about manufacturing technology areas. This demonstrates that the initiative not only aims at improving actual manufacturing process in machinery, plant, enterprise scope, but also extends to the performance of whole supply chains and its important role to manufacturing excellence.

The recent project on AMP 2.0 awarded by NSF is Kentucky AMP for Enhanced Robotics and Structures (NSF- RII- 1849213 , 2019). This five-year project aims to produce new materials for 3D printing, and embed sensors, power sources and displays in 3D printed objects.

4.2.2 Manufacturing USA or the national network for manufacturing innovation

Manufacturing USA is an interagency network that focuses on coordinating public and private investment in developing manufacturing technologies. The original program of the Initiative was the National Network for Manufacturing Innovation (NNMI) launched in 2012 and then NNMI is changed to Manufacturing USA in 2016 (RAMI Act, 2014 ). Currently the network of Manufacturing USA consists of 14 manufacturing institutes. Each institute focuses on a different advanced manufacturing technology area.

There are annual reports published by National Institute of Standards and Technology (NIST), U.S. Department of Commerce, to document the progress of the Manufacturing USA program. The latest one is Fiscal Year 2017 published on September 26, 2018, and some of highlights are (NIST, 2018 ):

The number of institutes of Manufacturing USA increased substantially in 2017 from 8 to 14. Six new institutes are added with the sponsorship of the departments of Energy, Defense and Commerce. Their locations, and technology focus can be found in NIST ( 2018 ).

The total commitments of support have grown to more than $3 billion over the program’s life contributed by federal funds ($1 billion) and other investment.

Total memberships increased to 1291, and 65 percent of the members are from industry, and small and medium-sized enterprises (SME) accounted for 65 percent of the industry members.

The fourteen institutes conducted almost 270 projects focusing on application oriented research and development, and those projects are of high priority to broad industry sectors.

4.2.3 American leadership strategy in advanced manufacturing

The quadrennial Strategy for American Leadership in AM was released by the White House in October 2018 (OSTP, 2018 ). The strategic plan is to be achieved by pursuing three goals: new manufacturing Technology development, workforce training and education, and U.S. manufacturing SC enhancement (OSTP, 2018 ). For each goal, the strategy identifies four or five strategic objectives, action priorities and expected outcomes in the four years.

Smart and digital manufacturing is one of technical priorities for the first objective in the first goal, and the two concepts are defined. The objective of smart manufacturing is to optimize different levels’ performance (from unit, factory, to SC) by means of integration with IoT and software. It is indicated that the integration of ICT with operation technologies can result to significant increase in productivity.

The third goal is to improve U.S. manufacturing SC. The terminology of “supply chain” appears 43 time in the document, which shows supply chain is one of the themes in both the strategy and advanced manufacturing. A diverse array of challenges faced by U.S. manufacturers are indicated. One of the program priorities is SC enhancement. It is indicated that new technologies, such as big data, IoT and digitalization will improve production and impact future manufacturing SC.

Comparing the emerging technologies mentioned in the report and annexes of AMP 2.0 proposed in 2014, the strategy proposed in 2018 includes much more new technologies that are enablers for advanced manufacturing: industrial IoT, big data and analytics, AI, ML, 3D printing, industrial robotics, cloud computing, cyber physical systems are mentioned in the strategy, while ML, AI, and 3D printing do not appear in AMP 2.0 in 2014. Industrial IoT is one of critical enablers and platforms that affect the implementation of intelligent manufacturing and SC. Cybersecurity is the issue highlighted in the strategies, and blockchain is not mentioned in both the AMP 2.0 report and the new strategy.

4.2.4 America’s supply chains

A 100-day review report was released by the white house on June 8, 2021 under Executive Order (EO) 14,017, “America’s Supply Chains” (Sullivan and Deese, 2021 ). The report assessed SC vulnerabilities in four key sectors: large capacity batteries for electric vehicles (EV); critical minerals and materials; semiconductor manufacturing and advanced packaging; and advanced pharmaceutical ingredients (APIs) and pharmaceuticals.

The findings from the review were summarized in the following aspects: SC resilience and security are critical for U.S. economy and security; a new method based on U.S.’ strengths should be developed to implement resilient SC; the specific strategy and action for each sector should be recommended with consideration of unique SC features in the sector.

The report stated that the COVID-19 pandemic and SC disruption show vulnerabilities of U.S. SC. The report identified five key “inter-related” factors that have contributed to U.S. SC vulnerabilities, including low production capacity, short-termism in private markets and misaligned incentives industrial policies, global SC and coordination it made six recommendations in production capacity and innovation, market development, international business rules, and others. It was mentioned that the second phase will review the SC of six important sectors and a report released in February 2022 was expected.

4.3 Canada national strategy on Industry 4.0 and advanced manufacturing

Accounting for approximately 11 percent of Canada’s GDP, Manufacturing is a cornerstone of Canada’s modern economy, and is also the largest investor in research and development in Canada (Industry Canada, 2015 ).

4.3.1 Canada's innovation and skills plan

To address the changing nature of global economy and growing competition from other countries, the Canadian government introduced an Innovation and Skills Plan in its federal Budget, 2017 . The plan reflects a national effort to target three objectives, including being a world innovation leader, to generate more skilled jobs with good payment for Canadians, and to expand the middle class. The plan will target six crucial sectors including advanced agri-food, manufacturing, digital industries, clean technology, clean resources, and bio-sciences/health (Canada Budget, 2017 ).

4.3.2 Economic strategy table–advanced manufacturing

Proposed in Budget, 2017 , the Economic Strategy Tables are a forum established by Innovation, Science and Economic Development Canada to develop the strategies in the six sectors that significantly contribute to the economy of Canada. A leader from each of the six industries chairs the related Table, and those chairs also meet to discuss the common issues in the Tables. The Economic Strategy Tables provide a new path to enhance the collaboration between government and industry (ISED, 2018a ). Advanced manufacturing and digital industry are the two among the sectors and are also related to Industry 4.0

The advanced manufacturing strategy table proposes the vision of Canada’s manufacturing: made better in Canada . The two targets of advanced manufacturing table are to increase both sales and exports by 50% by 2023 (ISED, 2018b ). A series of actions and proposals are recommended to hit these targets.

To reduce the potential risk of adopting new technologies, such as Industry 4.0 and other advanced manufacturing, and ICT, a network of Canadian advanced manufacturing technology adoption centres (CANADVANCE) is established. The network integrates industry, governments, and academia to facilitate the development of new technologies related to Industry 4.0. The table also encourages the investment in ICT to reduce the gap with USA and adoption of Industry 4.0 in SMEs.

4.3.3 Economic strategy table–digital industries

ICT industry is one of important sectors in Canada (more than 39,000 companies) and contributes significantly to highly skilled jobs (almost 600,000 professionals) and to one-third of research & development investment in private enterprises (ISED, 2018c ). The digital industries table targets establishing more Canadian digital companies, especially large and innovative companies, and increasing the business revenue, which can be implemented by the three measures: skilled workers, digitalization of society, and use of data/Intellectual property.

Canada launched Biomanufacturing and Life Sciences Strategy 2021. The goals are to be well prepared for future pandemics and benefits from the effect of biomanufacturing.

Table 2 summarizes the USA and Canada national strategies on Industry 4.0 and advanced manufacturing. Some features of the national strategies include:

The advanced manufacturing has significant contributions to national economy. The goal of the strategies on advanced manufacturing is to enhance the national competitiveness. The vision and objectives on advanced manufacturing or Industry 4.0 are defined, and challenges and actions are identified.

The collaborations among private sectors, academia, and government are encouraged to develop a long-term partnership and strategy, which also include the joint investment to promote the critical technologies. New organizations or research institutes are established to engage the innovation or implement the latest manufacturing technologies and ICTs.

Education, training, and knowledge updates for high quality personals or skilled workers are recommended as one of pillars in the strategies.

Manufacturing supply chain is one of the themes in the national manufacturing strategies.

SMEs play an important role to advance and implement the new technologies

New advanced technologies and new ICTs attract significant attentions, which are critical for digitalization and innovation of manufacturing.

Biomanufacturing and related supply chain attract significant attentions to cope with pandemics.

5 Research agenda on impacts of emerging ICT on SCM in North America

To observe the latest research status and interests on the emerging ICT underpinning industry 4.0 and their impacts on supply chains, we analyze the active projects conducted in NSF and NSERC, which represent the major national foundation agency for science and innovation in US and Canada respectively.

In Table 3 , the numbers in Abstract & Title and Title only display the numbers of projects with the keywords in “abstract or title” and in “title only” respectively. The number in Applied to SC is the projects that mainly study supply chain application of the related technology. The search returns much higher numbers when only looking at SC keywords in abstract. However many of the projects do not involve in SC research. From Table 3 , we can see.

There are several hundred research projects on each of the new emerging ICT technologies, except blockchain in both NSF and NSERC, which reflects significant effort on those technology related fields.

Machine learning, AI, Big data, and IoT are the hot research areas, and both NSF and NSERC have highest active projects in the areas.

Following the above point, however, the number of ongoing projects that focus on studying the influence of the technologies on SC is relatively low. For example, only 6 NSF active projects are found that study how to use big data to improve SC performance, though 29 projects have both big data and SC related keywords in their abstracts. Those projects for education and conference purpose, or with different focuses, such as building and infrastructure, etc., are excluded.

“Industry 4.0” itself is not commonly used in research grants in North America, especially in NSF. Among the 20 projects in NSF that has Industry 4.0 in titles, 15 projects are from advanced tech education program and used for education and training to prepare workforce for Industry 4.0; two are used to support workshop or conference, and two for student experiences; only one project is about design tools. Three projects that are related to SCM are all about Industry 4.0 seminar/workshop or curriculum development. We also search the projects in Industrial IoT that is usually considered as an alternative terminology for Industry 4.0 in US, and comes out with only 7 projects have IIoT in titles.

Though blockchain is relatively a new topic, the relative percentage with the keyword in titles is the highest among all technologies included in this table, which shows blockchain itself is the focus of the research, instead of applications.

We also search the active projects on digital twin, a relative new technology that combines simulation and digital technology. There are 4 NSF and 9 NSERC projects including digital twin in titles. Only one NSF project is relevant to SC: The model for the counterfeit part network developed is an essential component of a digital twin for the SC for critical systems (NSF-D-ISN-2039958, 2020).

Table 4 presents a summary of selected NSF and NSERC active projects that focus on the study of impacts of emerging ICT on supply chains. These projects are categorized in terms of the technologies involved, such as big data, blockchain, internet of things, Cyber-physical systems, 3D printing, Machine Learning, etc. The application area, objectives/benefits, methods or technologies are described for each project.

5.1 Blockchain

The research projects with the focus on the impact of blockchain technology in SC emerged only since 2018. However, the number of the approved projects increased significantly from 2019. Considering production of goods and services is becoming increasingly distributed and decentralized, a scalable Semantically-Enhanced Blockchain Platform is studied to overcome the trust-tax barrier among organizations (NSF-CSR-1764025, 2018). The feasibility of blockchain networks to connect farmers to consumers and improve SC sustainability is evaluated and investigated in the project (NSF-CNH2-S-1924178, 2019). The application of blockchain to healthcare system is proposed in a project (NSF-STTR-1913753, 2019) where it is highlighted that how to increase blockchain’s transaction speed is important. The current blockchain design is resource-intensive, in terms of storage, computation, and communication, which is one of the main roadblocks in the proliferation of blockchain applications (NSF-1937357, 2019). When increasingly applications of blockchain in organizations, the project focusing on Inter-blockchain Communication (IBC, NSF- CAREER- 1846316, 2019) present privacy-preserving IBC protocols that consider privacy and availability trade-offs. The project of NSF-1940394 (2020) aims to build a blockchain platform for companies in a pharmaceutical SC to enhance the medicine traceability.

In NSERC, more than half of the projects with topics focusing on blockchain are Engage grant projects, which are short term industry-academic research collaborations. The main applications of blockchain are IoT and energy: generic and extendable architecture for smart grids (NSERC-CRD-Capretz 2018), and high-performance blockchain infrastructure for IoT applications (NSERC-CRD-Zhang, 2018 , NSERC-Strategic-Gong, 2018). There are two projects on SC applications: one is about SC and asset management software platforms (NSERC-Williamson, 2019) and the other are about data driven SC (NSERC-DG-Zhang, 2019). It is interesting to note that there are 6 NSERC discovery grants (5 years term) with blockchain shown in the titles in 2020, mainly in computer sciences and engineering. It indicates the research feasibility on blockchain technology is recognized.

Technologies studied in those projects mainly focus on security and privacy mechanisms, performance and scalability, speed of transactions, resources, distributed ledger and network, data sciences, where smart contract and distributed Ledger Blockchain technology attract research attention.

5.2 3D printing technology

3D printing technology has completely changed the manufacturing process and affects manufacturing supply chains significantly. The current research projects about the impacts of 3D printing on SC mainly focus on distributed manufacturing and SC (NSF-SBIR-1937914), cost and risk analysis (NSF-1634858, 2016), complexity of supply chains caused by 3D printing (NSF - SBIR - 1914249, 2019). The application areas include autonomous manufacturing, healthcare (NSF-1634858, 2016), shifting accessibility and agility of manufacturing (NSF-SBIR-1937914, 2019), respiratory ventilator for COVID-19 clinical care (NSF-SBIR-2036836, 2021), and future EcoManufacturing and supply chain (with US$9 million, NSF-FMRG-2037026, 2021). 3D printing is considered as the next supply chain revolution (NSERC-DG-Bijvank,2016). How to simultaneously achieve the three objectives of 3D printing- print time, print cost and print quality–remains to be investigated in 3D printing technologies (NSF1914249, 2019 ) .

5.3 Big data

Ways to increase the value of data, especially big data analytics (BDA) in SCM have attracted significant attentions in both academics and industries (Nguyen et al., 2018 ). There are 210 and 164 projects with the focus on big data in NSF and NSERC respectively. The work about the applications of big data into supply chains in NSF projects includes software supply chain, food aid supply chain and food industry, pricing, data-driven service, customer analysis, shrimp industry, and transportation.

Software supply chain: The project of NSF-1633437 uses the big data-based approach to quantify and mitigate risks in software SC, particularly with evidence based approaches.

Food industry and supply chain: Three NSF projects study the big data related technologies for food security and hunger relief. The national science foundation research traineeship (NRT) award with US$3 million (NSF-NRT-1735258, 2017) uses computational data science to improve strategies for hunger relief and food security. Challenges in hunger relief work arise from not only the uncertainty of supply and demand but also from variations in information collected by different humanitarian organizations and the government and private sector. Several technological gaps are identified for food safety, security and traceability of all raw materials in whole SC from 'Farm to Fork' (NSF-1822092, 2019). It is suggested newer sensors with big data analytics will enable the food companies to improve food safety, quality, and traceability. A smart service system is proposed in the project with $1.1 million (NSF-1718672, 2017). The sonar and analytics platform is used to improve shrimp farming and make "big data" available to the farmer, the buyers, as well as the scientific community (NSF-SBIR-1913107, 2019). The project (by Elkafi Hassini, NSERC-DG-CISE, 2020) investigates big data based optimization model and algorithms with applications in smart food SC. Pricing: The project of NSF-2017957, 2020) presents a dynamic model to study how big data affects dynamic personalized pricing.

Transportation and logistics : Monitoring and coordinating large fleets of self-driving vehicles (SDV) with a real-time system is studied in the project (NSF-1663657). The research of projects (NSF-2027678, 2020, NSF- 2027412, 2020 ) brings together rapidly unfolding health data related to COVID-19 with real-time data on mobility from location-based apps and algorithms to understand the current status and make better decisions during the pandemic.

General SCM : Most projects from NSERC on applying big data into supply chain are for a general SCM, instead of for a specific industry sector. Those applications include rebate program in supply chains, close-loop and SSCM. A new framework to support big data analytics of rebate programs is studied in the project (NSERC-EG-McGregor, 2015). To minimize waste across CLSC networks, the project (NSERC-DG-Amin, 2017) studies the closed loop supply chains for four types of products: paper, tires, computers and hazardous materials and the impact of big data on CLSC are to be investigated as well. The project of smart supply chain (NSERC-DG-Zhang, 2019) studies the challenges and opportunities of supply chains under digital business environments and develop smart supply chain strategies and solutions via data driven optimization and big data technologies.

5.4 Cyber physical system (CPS)

A fundamental problem faced by SCM is to ensure that physical parts arrive from the desired source and are not swapped or modified. Either tampered cyber component or physical component will lead to significant cyber-physical security risk. To ensure the integrity of both the cyber and physical components in industries relying on complex distributed SC, one research project uses physical measurement techniques to provide parts of a physically unclonable identity, and the cyber signing methods to build chains of trust (NSF- CPS -1932213, 2019). Another project aims to disrupt operations of illicit supply networks (NSF- EAGER -1842577, 2018). The investigators use hybrid ML and analytics to analyze data from both physical inventory and cyber activity to discover illicit activities. There are at least 5 NSF projects study transportation with cyber-physical systems. The project of NSF-1947613, 2019 develops novel data based robust algorithms for real-time control and optimization for a transportation network. A project (NSF-1454737, 2015) is designed to overcome challenges related to allocation and scheduling of robotic vehicles. To improve the resilience of civil infrastructures, address supply and demand uncertainties, the project (NSF-1453126, 2015) works on developing a practical design toolkit for CPS. The project (NSF-2115149, 2021) provides a user-centric solution considering decentralization and privacy for secure vaccination coordination that can aid researchers and planners of the current and future pandemics.

5.5 Internet of things

The 10 NSF projects that applies IoT to SC activities mainly study IoT or sensor development, system for shipping, drone, containers, and advanced manufacturing. An innovative ground robot-drone network system is proposed to review inventory and trace item locations with passive RFID in the environments of warehouses, retail stores, hazmat storage facilities (NSF-1923163). To address hardware security issues in integrated circuit's global and distributed supply chain, light-weight resistive random access memory (RRAM) technology is proposed (NSF-1903631), which is used as a unique signature for each IoT device in the supply chain. The IoT technologies for next generation mobile communication systems are studied in Auburn university RFID lab, which are used to storage, retail, and SCM (NSF-1822055, 2018). The project of NSF-SBIR-2025896 (2020) aims to develop advanced real-time tracking systems with new 5G and IoT technologies that reduce the container shipping costs. NSF-2044711 (2021) focuses on designing and building a system for tracking RFID tags. The main goal is to provide an easy to deploy and scalable solution for different applications in the SC. Among 11 NSERC projects, there are three DG grants that applied IoT for network interdiction and fortification planning (NSERC-DG-Vidyarthi, 2017), monitoring and localization of people, and general smart SC. The other eight are applied or collaborative research and development, mainly for real time tracking, resource management, yard management, SC digital platform, etc. Most of those projects mainly focus on the technologies of IoT and its network that has potential applications to SCM. However, there are very few research with a focus on the impacts of IoT on SC, such as supply network, warehouse operations, etc.

5.6 Machine learning and AI

The numbers of ongoing projects on ML and AL in both NSF and NSERC are among the highest among the new technologies, especially ML. However, there are very few research projects focusing on applying AI or ML to supply chains in NSF: only 5 AI for SCM and 5 ML for SCM are found. Among NSERC grants, there are 22 projects that involved ML in SCM (including 10 industry-driven collaborative research and development projects) and 7 projects that involved AI in SCM.

The NSF projects on study of ML in SC include ML based supply chain analysis to thwart illegal logging, operational seasonal forecasting, consumer behavior-aware learning, planning and operating shared mobility, supporting resilient and high-availability elastic network slicing. The 22 NSERC projects include the following aspects: 7 projects on logistics and transportation: algorithms for vehicle routing, transportation safety and sustainability, traffic prediction in bike-sharing systems, real-time planning, stochastic optimization of network design and transportation problems, urban mobility and traffic data, urban mining logistics; 4 on market and finance : reusing simulation for financial and actuarial applications, platforms and internet market design, pricing and risk management in environmental financial derivatives, and real estate transaction and pricing; 5 on general SC and production: SC analytics, smart SC, electronic components product SC optimization, generalized sequential data mining, and appliance disaggregation and optimal scheduling; 2 on food and farms: real time data analytics for mushroom farms, SCM at Nestlé Canada; 3 on inventory and warehouse : healthcare inventory management, lateness management in cross-docking, adaptive recognition of images for warehouse inventory cataloging. Most of the projects are applied or collaborative research and development projects.

AI applications in SC in NSF include the COVID-19 therapeutics SC, a smart container for mobile blood banking system, real-time monitoring for warehouse and outdoor inventory management, illicit narcotic supply networks. In NSERC: 6 projects on AI are also relevant to ML, mainly in logistics and transportation, and the other is about inventory tracking system.

In summary, the ML and AI applications mainly focus on logistics and transportation, inventory and warehouse, food, consumer behavior, thwarting illegal logging. After the pandemic, more projects study healthcare SC using ML. The functions or purposes of the projects are concentrated in ML or AI based optimization, simulation, forecasting, scheduling, pricing, and real-time data analysis. Besides the ones mentioned, there are 4 NSERC and 5 NSF projects focusing on studying ML or AI based optimization algorithms respectively, and those projects mention the applications in SC.

The emerging ICTs are related and integrated in Industry 4.0. Thus, some projects explore how to use multi-ICTs in supply chains, such as, supply chain analysis to thwart illegal logging (NSF- D-ISN 2039771, 2021), a digital technology platform for supply chains (NSERC-CRD-Bhuiyan, 2018), and smart supply chain via data-driven optimization (NSERC-DG-Zhang, 2019).

The above review summarizes research projects from both NSF and NSERC on each emerging ICT technology and its impacts on supply chains. Some of the findings are:

Comparing to the articles that explore almost all drivers of supply chains, the number and scope of projects on the emerging ICTs in supply chains are less than expected, though the number of research projects that focus on the technique innovation itself is high. Considering the potential significant impacts of these technologies, lots of issues on the deployment of those technologies in supply chains are to be addressed, and thereby more research projects are expected. In addition, how to facilitate the research in the area and how to overcome the barriers need to be studied as well.

The number of projects on blockchain increases obviously in 2019 but mainly focus on technical challenges. Smart contract and distributed ledger blockchain technology are two critical aspects that attract research attentions. Applications mainly focus on energy, foods supply chains, healthcare system, smart markets, and IoT. But very few researches have been conducted in SCM, indicating that research on how to use blockchain in SC and logistics is still in its infancy. In addition, digital twin has attracted attentions in recent years with 25 and 21 projects in NSF and NSERC respectively but the research about its application to SC is very little.

The ongoing projects on 3D printing mainly focus on printing quality, cost, time, risk, and platforms. A couple of projects studied service logistics for 3D-printed spare parts and distributed SCM system with 3D printing, however, there lacks enough research about the effects of 3D printing on SC. The ability to print the finished goods or components will affect the supply network, supply selection, facility location and capacity, inventory, push–pull decoupling point, lead times, and transportation. Thus, the new manufacturing process brought by 3D printing technologies will significantly change supply chains, including how to satisfy the customer demands: where, when, and how to produce the products.

The projects on big data in supply chains mainly explore the applications in food industry and its supply chain, data driven service, supply chain rebate and customer analysis, and transportation.

Comparing to other emerging ICTs, the total number of projects studying the impact of ML in supply chain is highest, which mainly benefits from NSERC several industry collaborative research programs. The application areas focus on logistics and transportation, inventory and warehouse, and food SC, ML based optimization and potential applications to SC also attracts some research effort.

The research on cyber-physical system mainly studies how to improve supply chain resilience and coordination between manufacturing and supply chains with real time data and information enabled by CPS, especially for distributed networks.

Research on IoT in supply chains include improving food safety and traceability, creating innovative ground robot-drone with RFID, and enabling data communications and security.

As a system integrating the operations of physical and software components, cyber physical system has a potential to improve performance in complex supply chain network. However, issues exist regarding the adoption and implementation of this technology: integrity, security, and resilience are main focuses of relevant research currently supported by NSF.

6 Smart supply chain management under Industry 4.0

A review of the latest literature on ICTs in SC under Industry 4.0 is presented in this section. The literature is categorized into development SC and fulfillment SC, where the former is to establish the supply network and the latter is the supply process to fulfill the customer demand. Table 5 in Appendix summarizes Industry 4.0 related ICTs’ impacts on the different process of development and fulfillment supply chains. Warehouse is one of the most important facilities in SCM, and almost every part of the supply chain is closely related to it. Considering the role of warehouse in a smart supply chain and rapid development in recent years, an additional section, Sect.  6.3 , is devoted to review the automated warehouse and the progress of applying new ICT in warehouse management.

6.1 Smart supply chain: development and strategic decision

The supply chain development can be supported entirely by new Industry 4.0 technologies. New technologies can enable traditional supply chains to move toward intelligent, connected, efficient and excellent supply chain and operation management (Dallasega et al., 2018 ). In this section, we discuss the applications of Industry 4.0 technologies to strategy development SCM in three categories: design/new techniques, strategic source, and strategic network.

6.1.1 Design/new techniques

As the most advanced technologies, how to combine Industry 4.0 technologies with SCM is full of challenges. Some new thoughts/principles and techniques will be designed to suit new conditions. Ghobakhloo ( 2018 ) summarizes the principles and technology trends adopted strategically for Industry 4.0 technologies in manufacturing industry. For a specific technology, such as 3D printing, Berman ( 2012 ) points out that it can change the product designing process. 3D printing breaks the time and space limits in the production process, does not require assembly, and is economical and efficient to design and modify the products. Majeed and Rupasinghe ( 2017 ) discuss the operations to manage better and optimize and automate processes in an Enterprise Resource Planning (ERP) system through the use of RFID technology in the fashion apparel and footwear industry. Saberi et al. ( 2019 ) identify and categorize four blockchain technology adoption barriers in supply chain operations. They also point out that the blockchain-led SCM still belong to the early stages of development. Manuel Maqueira et al. ( 2019 ) reveal cloud computing technology can help the participants in the supply chain to integrate effectively. Additionally, the developing simulation methods can support the supply chain design and operation processes (Gunal & Karatas, 2019 ).

6.1.2 Strategic source

New technologies can connect the suppliers, manufacturers, retailers and customers, and the applications help the players understand the whole chain easily, especially for the information flows among them. Based on the valuable information, the managers can foretaste the demand and choose the optimal source suppliers and partners. For instance, Xing et al. ( 2016 ) propose a cloud-based life-cycle assessment platform to help the footprint assessment and resource management in green supply chain. Mladineo et al. ( 2017 ) consider a cyber-physical production network, which helps the company make the partner selection decisions to design a new virtual enterprise production network. IoT is crucial and enable Industry 4.0 technology for the sourcing process in supply chain (Ben-Daya et al., 2019 ). IoT implication needs the trust and information sharing in the supply chain and organizations, while the risks such as leakage of data should also receive high attentions (Birkel & Hartmann, 2019 ). In addition, since application of IoT could bring high operating costs, the adoption decisions are always related to price, total revenues and transportation efficiency (Sun et al., 2020 ).

6.1.3 Strategic network

The new technologies bring help and challenges to the supply chain network design process. Big data approach can effectively improve the data acquisition and data quality in the supply chain, especially for the estimation of important parameters before designing the network (Zhao et al., 2017 ). Blockchain linkage can record the intra-and inter-organizational activities in supply chain network, and help some environmentally manufacturing process designs (Kouhizadeh & Sarkis, 2018 ). The new production strategies supported by Industry 4.0 technologies require highly customized supply network design, sustainable manufacturing process, integration of different complex systems, and control of resilient and digital manufacturing networks (Panetto et al., 2019 ). Big data-based decision support technologies are widely used in the design and control of manufacturing supply chain network management, and can be used in their decision support systems. Cloud computing can effectively reduce costs, improve supply chain resilience, flexibility, and maximize the use of resources to improve the overall efficiency of its supply chain network (Sundarakani et al., 2019 ). Meanwhile, 3D printing may affect supply chain service companies to build their supply chain network. Logistic severe provider company explored the opportunities for adding 3D printing capabilities to their existing logistics offering, electing to partner with an existing manufacturer, 3D printing company (Eyers et al., 2019 ).

6.2 Fulfillment smart supply chains

In this subsection, we follow APICS supply chain operations reference (SCOR) model (Huan et al., 2004 ) to summarize the extant literature on Industry 4.0 related ICTs in supply chains fulfillment processes, which consists of plan, source, make, deliver, return and enable.

Industry 4.0 technologies can make the planning and scheduling operations be more reasonable, flexible and reactive in the data-driven, connected, and resilient supply chains. Ivanov, Dolgui, et al. ( 2016 ), Ivanov, Sokolov, et al. ( 2016 )) investigate short-term supply chain scheduling in smart factories based on collaborative cyber-physical systems. Zhong et al. ( 2017 ) point out the big data analytics with IoT technology can make the logistics plans and schedules be more precise, practical and reasonable. Li et al. ( 2017 ) show that planning and scheduling process plays a key role in additive manufacturing facilities employing 3D printing technology for characteristics such as material efficiency, part convenience, and production flexibility. Dolgui et al. ( 2019 ) present a survey and summarized the job scheduling operation problems in a customized, reconfigurable assembly system that is based on Industry 4.0 principles.

6.2.2 Source

Industry 4.0 technologies can help companies to reduce sourcing costs, increase sourcing security, reduce sourcing throughput, increase flexibility and quality, and improve the relationship between the involved parties (Ivanov, Dolgui, et al., 2019 ; Ivanov, Tsipoulanidis, et al., 2019 ). Singh et al. ( 2015 ) apply cloud computing technology to reduce the carbon footprint in the entire beef supply chain logistics from the farms to the retailers. Francisco and Swanson ( 2018 ) discuss the technology adoption of Blockchain for supply chain transparency to track and trace the product processes. Kamble et al., ( 2019a , 2019b ) find that a good IT infrastructure, including strong internet connectivity, with the capability to connect the suppliers and customers, is critical to the adoption of IoT in the retail food business. Muñuzuri et al. ( 2020 ) describe the main technical features of a modular IoT system to optimize, manage and monitor container movement along a port-based intermodal corridor. Li ( 2020 ) finds that the platform prefers to reduce channel costs by investing in smart supply chain technologies.

Industry 4.0 involves advanced technologies that will benefit manufacturing, with the ultimate objective of making machines produce by themselves (Gunal & Karatas, 2019 ). Many technologies are already being applied into the manufacturing sectors in supply chain. Ivanov, Dolgui, et al. ( 2016 ), Ivanov, Sokolov, et al. ( 2016 )) propose a dynamic model for short-term supply chain scheduling in smart factories based on collaborative cyber-physical systems. 3D printing technology supports the new additive, digital, and rapid manufacturing process (Rogers et al., 2016 ). Mrugalska and Wyrwicka ( 2017 ) present a literature review about lean production and Industry 4.0 to show the possibility of linking these two approaches. Some examples are also provided. Cloud manufacturing that integrated new technologies can transform the manufacturing industry into service-oriented, highly collaborative and innovative manufacturing (Ren et al., 2017 ). The smart manufacturing based on the data from sensors, robots, and the cyber-physical system can boost productivity and improve quality (Khakifirooz et al., 2018 ). Moktadir et al. ( 2018 ) find that ‘lack of technological infrastructure’ is the most pressing challenge for implementing Industry 4.0 in manufacturing industries. Buer et al. ( 2018 ) discuss the relationships between Industry 4.0, lean manufacturing, performance, and environmental factors. Panetto et al. ( 2019 ) summarize the challenges for cyber-physical manufacturing enterprises of the future. Alcácer and Cruz-Machado ( 2019 ) investigate the big data used in the manufacturing process and summarize the manufacturing data lifecycle. Cloud operations and industrial artificial intelligence technologies improve flexible production networks based on autonomous mobile robots in terms of flexibility and productivity (Fragapane et al., 2020 ).

6.2.4 Deliver

Logistic and inventory management in the supply chain are improved significantly by applying the Industry 4.0 technologies. Some researchers also call this process under these new technologies as Logistics 4.0. Barreto et al. ( 2017 ) claim that “Smart Logistic” can increase flexibility, adapt to market changes and bring the company closer to customer needs. The existing solutions that support Logistics 4.0 are summarized according to the technologies: cloud computing, internet of things, cyber-physical systems, big data, 3D printing, and so on (Winkelhaus & Grosse, 2020 ). Qu et al. ( 2016 ) investigate a production logistics synchronization system for manufacturers applying public logistics services under smart cloud manufacturing. Mohr and Khan ( 2015 ) find that the adoption of 3D printing in various parts of the supply chain has the potential to produce a reduction in the demand for global physical goods transportation and inventory. IoT technology has been successfully applied to supply chain logistics enhancements, such as quality of production and distribution (Jayaram, 2016 ), operational efficiency in logistics and supply chain (Tu, 2018 ) and warehouse management system (Lee et al., 2018 ). Dolgui et al. ( 2020 ) investigate blockchain-oriented smart contract design in the supply chain with multiple logistics service providers.

6.2.5 Return

Industry 4.0 technologies through data collection and sharing can be beneficial for sustainable operations management decisions and new business models. Post-consumer product and packaging can be tracked and traced using sensors, RFID tags and barcodes to improve the returns process. Zhao et al. ( 2017 ) propose a green supply chain network design model with recycling centers, and the parameters are capitalized on a big data analysis. 3D printing is an important technology that can provide replacement parts for all types of machines and has the ability to recycle waste material (Berman, 2012 ). Development of new repair and remanufacturing capability that made use of the 3D printing knowledge to manufacture parts used in the repair process (Eyers et al., 2019 ). Joshi and Gupta ( 2019 ) investigate the effect of product design on product recovery using IoT. They propose a system to receive sensors and RFID tags embedded End-Of-Life products to satisfy various products, components, and materials demands. In Northern Europe, blockchain-based technology offers financial incentives in the form of crypto tokens for the disposal of recyclables such as plastic containers, cans or bottles (Saberi et al., 2019 ).

6.2.6 Enable

Some primary Industry 4.0 technologies have been applied in SCM to make it efficient. However, there are still some challenges with new technologies and advanced applications to improve the performance of the whole supply chain. de Sousa Jabbour et al. ( 2018 ) discuss the rule of critical success factors for implementing Industry 4.0 and environmentally-sustainable manufacturing. They present some social managemental factors such as management leadership, empowerment, top management commitment, organizational culture, and communication. Mohammed et al. ( 2017b ) investigate a Halal meat SC network design problem with a RFID-enabled monitoring system. Shafique et al. ( 2018 ) find that IoT adoption in green supply chain has positive effects on supplier integration and customer integration. de Vass et al. ( 2018 ) reveal a positive effect of IoT on the internal supply chain functions of retail firms, and significant improvement in sustainable firm performance. Schneider ( 2018 ) synthesizes the managerial challenges of Industry 4.0 into some interrelated clusters, such as strategy, planning, cooperation, human resources and so on. Martín-Gómez et al. ( 2019 ) develop a holistic framework for integrated and adaptive sustainable SCM at multiple scales and levels, using the digital and Industry 4.0. Based on the lessons from COVID-19 pandemic, Ivanov ( 2020b ) introduce a new notion the viable supply chain, which is an underlying SC property spanning agility, resilience, and sustainability. The performance brought by Industry 4.0 technologies is summarized in Fig.  2 .

The performance of Industry 4.0 for fulfillment supply chain

6.3 Smart warehouse

Warehouse inventory management plays a crucial role in SCM. Leveraging the Industry 4.0 technologies, the smart and automated warehouse becomes a popular, especially for e-commerce companies. According to the functions of the warehouses, we classify them into four kinds: warehouse, distribution center, fulfillment, and cross-docking.

The warehouse and distribution center with Industry 4.0 technologies become a key support part to smart manufacturing process. Smart warehouse management uses IoT technology and Big Data to make the monitoring, tracking, and location of the various materials and products in a warehouse environment. Avilés-Sacoto et al. ( 2019 ) present a literature review of SCM and inventory management fields affecting by Industry 4.0, especially for data-driven, new technology supported operations. Liu et al. ( 2018 ) investigate how to apply cyber-physical systems techniques in smart warehouses. They use RFID tags and NFC tags Wi-Fi Access Points to collect the CPS data and accurate the location of the concerned objects. Mohammed et al. ( 2017a ) investigate the design and optimization of a RFID-enabled automated warehousing system using the multi-objective optimization approach. Lee et al. ( 2018 ) develop a warehouse management system that uses IoT technology and advanced data analysis methods to achieve smart logistics for Industry 4.0. Tao et al., ( 2018 ) propose a conceptual framework to support the big data perspective in data-driven smart manufacturing, where the inventory data are collected from manufacturing information systems. Fernández-Caramés et al. ( 2019 ) apply Unmanned Aerial Vehicles (UAV) and blockchain technologies to design a system for the autonomous warehouse.

With the development of e-commerce, some retailer companies need the warehouses to become the place of picking and packing the customers’ orders. The latest technologies are applied to the fulfillment and cross-docking parts. Boysen et al. ( 2019 ) investigate a new generation of smart warehouses capable of meeting the needs of final customers in the business-consumer (B2C) segment of online retailers and adapting to the small orders, large assortments, tight schedules, and varying workloads of e-commerce inventory. Tjahjono et al ( 2017 ) indicate that simulation and big data analysis do not affect warehouse KPI. They can use data to analyze the sequence of loading and reduce the truck time in the dock and increase the utilization of truck. Simulation also can be used to improve the load/unloading process. Azadeh et al. ( 2019 ) summarized the new robotic automated picking systems. In picking and matching processes of distribution centers, many robots are used to increase productivity, such as the Kiva-Systems warehouse robot (Ivanov, Dolgui, et al., 2019 ; Ivanov, Tsipoulanidis, et al., 2019 ), the KIVA warehouse (Weidinger et al., 2018 ) and mobile-rack warehouses (Wang et al., 2021 ). Table 6 summarizes the different features of smart warehouses in the related literature.

7 Research opportunities and challenges of smart SCM under Industry 4.0

Innovations and new technologies, such as emerging ICTs and Industry 4.0, will affect how they will function in SC and what future SC is (OSTP, 2018 ). The previous reviews on both active projects and literatures have shown the development of advanced manufacturing, and applications of different emerging ICTs on supply chains. In this section, the challenges and research opportunities of SSCM under Industry 4.0 are identified and thus, future research directions in the areas are presented.

7.1 Impacts and benefits of Industry 4.0 and ICTS on SCM

The emerging technologies can be applied to assist making decisions from strategic design, tactical planning, to operation scheduling to form data-driven, connected, and resilient supply chains. The supply chain and organization performances can be improved with utilization of ICTs and Industry 4.0, evidenced with the cases and reported research.

7.1.1 Development supply chains and strategic decisions

Designing and developing manufacturing SC under advanced manufacturing environment, such as Industry 4.0, should understand and leverage the new technologies, with considering the features of customer demands and market variation. The aforementioned literature review shows most emerging technologies have impact on designing, strategic sourcing, and network decision making: 3D printing for sharing designs and outsourcing manufacturing (Berman, 2012 ), RFID for optimizing process of ERP system (Majeed & Rupasinghe, 2017 ), Industry 4.0 for smart wood supply chain (Müller et al., 2019 ). The active project at the strategic level mainly focus on big data, blockchain for food supply network, 3D printing for distributed network,

7.1.2 Visibility and traceability provide by IoT

The enhanced real-time visibility provided by IoT can improve information sharing, efficiency, and environment impact (Geerts & O'Leary, 2014 ). The project (NSF-1822092, 2018) leverages IoT systems and data integration to integrate and streamline the entire food supply chain in the US from 'Farm to Fork' and to improve food safety, quality, and traceability.

7.1.3 Cost reduction due to automation, IoT, and 3D printing

Smart factory increases automation while lowering labor cost, and reacts faster to unexpected events and process disturbances. The new materials, large-scale objects, integrated and distributed manufacturing and supply chain network will largely enhance the benefits, including cost and lead time reduction, of 3D printing (NSF-1914249, 2019 ). IBM IoT platform can help the enterprises reducing maintenance costs and managing demand, configuration, quality, etc., by connected devices, analytics, mobility and enterprise asset management (IBM, 2018 ; Russo-Spena et al., 2019 ). Berman ( 2012 ) indicates 3D printing has the ability to share designs and outsource manufacturing, which improves the speed and ease of designing and modifying products, and save the costs.

7.1.4 Fast responsiveness to market with distributed network and information sharing

Decentralized logistics reacts faster to requirements . Short lead time for replenishment can lower cycle inventory, one NSF project works on developing a practical design toolkit which improves resilience and incentive schemes for CPS (NSF-CAREER-1453126, 2015). With the support of GE’s iFIX industrial automation software, Subaru’s drive plant in Indiana becomes the fastest automaker in USA (Brendan, 2019 ).

7.1.5 Resilience and risk control

The supply chain resilience and risk management can be improved with ICTs, and flexible supply network design. Industry 4.0 assisted smart system may lead to SC resilience improvement (Ralston & Blackhurst, 2020 ). The report (OSTP, 2018 ) highlights the importance of SC resiliency and indicates that actions in different dimensions are needed to implement robust, advanced manufacturing SC. The influence of Industry 4.0 and digitalization on controlling of SC’s disruption risk is evaluated in Ivanov, Dolgui, et al. ( 2019 ), Ivanov, Tsipoulanidis, et al. ( 2019 )), and the discussion about the impact of COVID-19 on SC is reported in Ivanov ( 2020a ). The pandemic brings huge damage to the global supply chains, and clearly shows the lack of resilience in supply chains (Golan et al., 2020 ). Javaid et al., ( 2020 ) report the main technologies in Industry 4.0 have been applied in fighting COVID-19 successfully.

7.1.6 Efficiency and accuracy

French Railways operator SNCF uses IBM Watson IoT to improve rail safety and operational efficiency (IBM-News, 2017 ). With GE Digital’s software, Cascades Tissue Group can see in real-time if there’s a problem. The software helps them to achieve reduced production downtime, increased operational efficiency, and smarter decisions based on data-driven insights (GE-Digital, 2019 ). Robots in Amazon’s fulfillment centers increased efficiency and safety (Amazon-Blogs, 2019 ). Warehouse accuracy and efficiency can be significantly improved with IoT system.

7.2 Challenges of supply chains under Industry 4.0

While the Industry 4.0 and new ICTs provide opportunities to improve the supply chain performances, there are many challenges on the way to achieve the goals.

7.2.1 Availability, reliability, and limitation of technologies

How to catch up the rapid development of innovation and new technologies is one of challenges for U.S. Manufacturing identified in the report by OSTP ( 2018 ), especially for small and medium-sized manufacturers. Moktadir et al. ( 2018 ) indicate that the lack of technological infrastructure is the most pressing issue for Industry 4.0 implementation in manufacturing sector. In terms of technology, there is a wide gap between emerging jobs and skilled worker. Thus training, education, and knowledge updates are very important to implement smart manufacturing supply chains.

There are some limitations brought about by new technologies themselves. For example, as one of the authors observed, automated warehouse has limited flexibility to deal with high variation of demands (e.g. double or triple of regular demand during a promotion). It is difficult for automated DC to increase the capacity in a very short period, whereas traditional distribution center can handle the high variation by hiring temporary workers.

7.2.2 Smart factory needs smarter supply chains/logistics

Without suitable materials/parts and fast delivery to customers, the production in smart factory simply does not work. Zhang ( 2015 ) proposes that Smart factory/service needs smart, even smarter, supply chain/logistics. The smart supply chain is data driven with real-time visibility, and is connected, optimized, and intelligent. It has several features:

Time: planning in advance but flexible to changes, arriving just in time (lead time must decrease)

Scope: multi-levels, multi-function, multi-partners, multi-channel, and updating dynamically

Integration: high cooperation and coordination for whole supply chains

New business models, highly customized products/service, decentralized decision-making, global economics.

The following challenges/barriers are identified to achieve smart supply chains:

Industry 4.0 requires high cooperation and coordination for whole supply chains

Supply chain processes must be designed to operate in this digital business world

Individualization and increased product variety add difficulty to planning

Shorter product life cycle adds difficulty to procurement

Decentralization can increase facility and delivery cost

Both variety and short life cycle generates obstacles to achieve strategic fit.

7.2.3 Information sharing and trust establishments among organizations

New technologies, like IoT, CPS and different IT systems, provide the methods/tools to share the information among different partners in supply chains. However, there are barriers for information sharing in many situations. Trust among the organizations is one of them. Usually it takes a long time and lots of effort to establish trust between partners. Besides, a fundamental problem faced by SCM is to ensure that physical parts arrive from the desired source and are not swapped or modified. The integrity of both the cyber and physical components in industries relies on complex distributed supply chains (NSF- CPS -1932213, 2019).

7.3 Future research of smart supply chain under Industry 4.0

Based on the reviews on both active projects and literatures, we find that the research on smart chain management using emerging ICTs under Industry 4.0 is still at an early stage. Here we just outline some of the promising research topics in the areas.

Further analysis about impacts of Industry 4.0 and related ICTs on supply chain . Several review papers have been published, such as the impact of IoT on SC (Ben-Daya et al., 2019 ), ICT in multimodal transport transportation (Harris et al, 2015 ), block chain technology for future supply chain (Wang et al., 2020 ), and digitalization and Industry 4.0 on SC risk control (Ivanov, Dolgui, et al., 2019 ; Ivanov, Tsipoulanidis, et al., 2019 ), just to name a few. However, the rapid development of technology and innovation advances modern manufacturing and change industry 4.0 environment, which affect every aspect of supply chain. The new requirements and features of supply chain and related decision problems from different level under Industry 4.0 and new ICTs should be investigated so that to facilitate the evolution of supply chains.

Smart supply chain with new business model under Industry 4.0 . The business model is one of the managerial challenges of Industry 4.0 (Schneider, 2018 ). Benefiting from advance technologies, many new business models have been developed, such as dual-channel or omni channel retailing and supply chains, as well as drone delivery. The innovation and deployment of technologies lead to availability of real time data, lower operation cost, and increased information sharing. However, high uncertainty, wide distributed manufacturing and customers, and short delivery time are challenges faced by retailers and manufacturers. Some examples under the topics are: how to develop new supply network with emerging delivery tools (e.g., drones, self-driving vehicles), the challenges and opportunities of supply chains under digital business environments (NSERC-DG-Zhang, 2019), and how to facilitate the development of business model that are based on Industry 4.0 technologies for SMEs. The small and medium-sized manufacturers (SMM) are important parts of manufacturing supply chains, and can be key sources to develop and test new processes and new business models with emerging technologies (OSTP, 2018 ).

Blockchain technology for trust establishment and smart contract . As mentioned in the previous section, how to establish “trust” among organizations is essential to connect different partners in manufacturing SC systems under Industry 4.0. However, a notable extension of blockchain is Ethereum smart contract, which provides mechanisms for secure computation between entities that do not trust each other (NSERC-Strategic-Gong, 2018). Blockchain technology's ability to provide trusted consensus and data processing generates interest in potential supply chain applications, such as multiple domains of SCM (Kamble et al., 2019a , 2019b ) and a scheduling control methodology to blockchain-oriented smart contract design (Dolgui et al., 2019 ). Nevertheless, the application of blockchain to SCM is still in its early stages (Saberi et al., 2019 ).

Distributed manufacturing and supply chains with 3D printing . 3D printing will change the supply network by transferring centralized to distributed manufacturing and affect logistics by shopping raw materials for 3D printing, instead of parts. With the supply network, the manufacturer also will postpone finished goods production and affect the inventory strategy. However, our reviews on active projects and literature found that most current research focuses on materials and manufacturing process of the 3D printing and little research study the relationship between 3D printing and supply networks, logistics and other drivers of supply chains.

Modeling and algorithms for planning and scheduling of complex supply chain system . The connected manufacturing system and SC under Industry 4.0 can be very complex and also geographically dispersed. To make the system work efficiently, the decisions on different levels should be made timely and efficiently. Modeling, optimization and simulation approaches can be effective tools to achieve the goal. Theory and algorithm for control and optimization, and modeling and simulation are two of ten technical gaps identified in AMP 2.0 (AMP2. 0 , 2014 ). Several desired and promising topics include: (a) Algorithms for a dynamic and short-term schedule : the network may have dynamic structures that evolve over time. (b) Data-driven modeling and algorithms : how to incorporate the tremendous trove of data of supply chains into optimization models for better decisions. The stochastic and robust optimization models can be a tool for the incorporation. (c) Design of tailored decomposition algorithms : the integrated problem is usually large scale and might be computationally intractable with general purpose optimization solvers. Using the problem features and developing iterative data-driven solution methods with decomposition approaches will be promising. (d) Integration of optimization with machine learning . Machine learning is a promising method for data analysis to determine parameters, forecast demand, and detect future risks.

Data-driven self-organizing smart supply chains . The supply chain is one of the largest sources of big data for global companies (DHL, 2016 ). One topic is to study dynamic and self-organizing configuration of the supply network with industry IoT and cloud computing (Ivanov, Dolgui, et al., 2019 ; Ivanov, Tsipoulanidis, et al., 2019 ). The problem involved is how to quickly establish and reconfigure a supply network based on the available real-time information, such as capacity, inventory, and lead time of parts/materials either in potential suppliers or in other facilities (connected factories), to produce a new product or when the original supply network is interrupted. Resiliency and sustainability of supply chains also should be considered.

Smart supply chain engineering . It mainly studies the design and physical implementation of data-driven, IoT connected supply network with a focus on the technical aspect and infrastructure in supply chains and integration with smart manufacturing. The design of a SSC under Industry 4.0 needs to consider how to connect IoT enabled objects, cyber-physical systems, cloud computing under different stages and develop related infrastructure to improve supply chain visibility, responsiveness and efficiency, e.g., RFID/sensor based containers, automated warehouse, smart factory, and intelligent logistics, and the infrastructure to integrate those units at different levels of the whole supply chain (NSERC-CRD-Zhang, 2018, Sundarakani et al., 2019 , Kamble et al., 2019a , 2019b ).

Smart automated warehouse . Warehouse or distribution center exists at every stage of a supply chain. Three potential research directions include: (a) increase the visibility, traceability, and integrations of the material handling process and logistics by using IoT and related warehouse management system; (b) study new layout and operation strategies for automated warehouse systems with robots, such as class based or randomized policy, and develop optimization models and intelligent algorithms, usually large scale mix integer programming models; and (c) study the emerging dual-channel warehouse and develop an integrated distribution network to satisfy omnichannel demand by analyzing demand data from different channels.

8 Conclusions

Smart supply chain management under Industry 4.0 has become an important research topic. The national policies, and increased published papers confirm the trend. In this paper, we present an integrated method to reveal the current major research effort, research opportunities and challenges, and future research direction by introducing national strategies in North America and reviewing active research project and literature. Our reviews show the importance to apply the emerging ICTs into supply chains is well recognized, but the application and related research are still in an early stage, especially for small and medium enterprises.

Both national policies on advanced manufacturing in US and Canada are introduced with a focus on US. To keep the leadership in manufacturing, the US national strategies are released and updated. The challenges and priority areas are identified, and the actions to implement are proposed.

42 active projects on smart supply chain with different emerging ICT from NSF and NSERC have been reviewed. Those projects represent the major research effort in the areas. It is noted that comparing to the published papers, the number and scope of active projects are not high as expected. The number of projects on blockchain increases significantly in 2019, but the topic mainly focuses on technical challenges. The ongoing projects on 3D printing mainly focus on printing quality, cost, time, risk, platforms, and service logistics.

A systematic literature review has been conducted with focus on the progress in recent five years. The review also shows the research about using new ICT on strategic decision are relative less than on operational and tactic decisions.

Based on the comprehensive reviews on both ongoing projects and literature some research directions are identified. It should be noted that the list of presented topics is not all-inclusive because diverse terminologies may be used to refer to Industry 4.0 and smart supply chain.

This research has some limitations. The national strategies mainly focus on advanced manufacturing and supply chain. Some related national strategies such as transportation security, Information Sharing and Safeguarding are not covered. The ongoing active projects are limited to NSF in USA and NSERC in Canada, both of which are main agencies supporting fundamental research and education in all the non-medical fields of sciences and engineering in USA and Canada respectively. Thus, ongoing projects from other agencies such as National Institutes of Health (NIH) in US and Canadian Institutes of Health research (CIHR) and Social Sciences and Humanities Research Council (SSHRC) in Canada, are not included. In addition, because our focus is on the progress of smart supply chain and several emerging ICT, we did not dig deep into each individual technology. Thus, a further integrated review for each emerging technology and their impact on supply chain and logistics will be a future work.

The emerging technologies have brought substantial impacts in smart supply chains. However, the research on this subject is still new and most existing smart supply chains are in level 1 or early stage of level 2 of the SC framework. Future research directions can include the following aspects: First, the impacts of new ICT technologies on supply chains in different industries should be further explored. The collaborative research with industries to address practice problems would facilitate the application of the new technologies in supply chains. Second, benefiting from advanced technologies, many new business models have been developed. Figuring out ways to facilitate the development of new business models with emerging technologies and improve the performance is important to enterprises. Increasing the supply chain resilience for the new business models, especially under the new norm after the pandemic, has become critical. Third, along with the development of new technologies, smart supply chain engineering, with a focus on technical aspect and infrastructure in supply chains, plays a crucial role in implementing smart SC. Furthermore, it is worthwhile to study the impacts of different national strategies and the research projects on new technologies adoption in supply chains.

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Acknowledgements

This research was supported by NSERC Discovery grant (RGPIN-2014-03594, RGPIN-2019-07115). The details of NSF and NSERC projects can be found in the NSF or NSERC databases. The lists of NSF and NSERC projects reported in this paper are provided and can be downloaded from the website ( https://www.uwindsor.ca/scm/306/publications ).

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Guoqing Zhang, Yiqin Yang and Guoqing Yang have contributed equally to this work.

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Supply Chain and Logistics Optimization Research Center, Department of Mechanical, Automotive & Materials Engineering, University of Windsor, Windsor, ON, N9B 3P4, Canada

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School of Management, Hebei University, Baoding, 071002, Hebei, China

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Zhang, G., Yang, Y. & Yang, G. Smart supply chain management in Industry 4.0: the review, research agenda and strategies in North America. Ann Oper Res 322 , 1075–1117 (2023). https://doi.org/10.1007/s10479-022-04689-1

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Artificial intelligence in healthcare: transforming the practice of medicine

Junaid bajwa.

A Microsoft Research, Cambridge, UK

Usman Munir

B Microsoft Research, Cambridge, UK

Aditya Nori

C Microsoft Research, Cambridge, UK

Bryan Williams

D University College London, London, UK and director, NIHR UCLH Biomedical Research Centre, London, UK

Artificial intelligence (AI) is a powerful and disruptive area of computer science, with the potential to fundamentally transform the practice of medicine and the delivery of healthcare. In this review article, we outline recent breakthroughs in the application of AI in healthcare, describe a roadmap to building effective, reliable and safe AI systems, and discuss the possible future direction of AI augmented healthcare systems.

Introduction

Healthcare systems around the world face significant challenges in achieving the ‘quadruple aim’ for healthcare: improve population health, improve the patient's experience of care, enhance caregiver experience and reduce the rising cost of care. 1–3 Ageing populations, growing burden of chronic diseases and rising costs of healthcare globally are challenging governments, payers, regulators and providers to innovate and transform models of healthcare delivery. Moreover, against a backdrop now catalysed by the global pandemic, healthcare systems find themselves challenged to ‘perform’ (deliver effective, high-quality care) and ‘transform’ care at scale by leveraging real-world data driven insights directly into patient care. The pandemic has also highlighted the shortages in healthcare workforce and inequities in the access to care, previously articulated by The King's Fund and the World Health Organization (Box ​ (Box1 1 ). 4,5

Workforce challenges in the next decade

The application of technology and artificial intelligence (AI) in healthcare has the potential to address some of these supply-and-demand challenges. The increasing availability of multi-modal data (genomics, economic, demographic, clinical and phenotypic) coupled with technology innovations in mobile, internet of things (IoT), computing power and data security herald a moment of convergence between healthcare and technology to fundamentally transform models of healthcare delivery through AI-augmented healthcare systems.

In particular, cloud computing is enabling the transition of effective and safe AI systems into mainstream healthcare delivery. Cloud computing is providing the computing capacity for the analysis of considerably large amounts of data, at higher speeds and lower costs compared with historic ‘on premises’ infrastructure of healthcare organisations. Indeed, we observe that many technology providers are increasingly seeking to partner with healthcare organisations to drive AI-driven medical innovation enabled by cloud computing and technology-related transformation (Box ​ (Box2 2 ). 6–8

Quotes from technology leaders

Here, we summarise recent breakthroughs in the application of AI in healthcare, describe a roadmap to building effective AI systems and discuss the possible future direction of AI augmented healthcare systems.

What is artificial intelligence?

Simply put, AI refers to the science and engineering of making intelligent machines, through algorithms or a set of rules, which the machine follows to mimic human cognitive functions, such as learning and problem solving. 9 AI systems have the potential to anticipate problems or deal with issues as they come up and, as such, operate in an intentional, intelligent and adaptive manner. 10 AI's strength is in its ability to learn and recognise patterns and relationships from large multidimensional and multimodal datasets; for example, AI systems could translate a patient's entire medical record into a single number that represents a likely diagnosis. 11,12 Moreover, AI systems are dynamic and autonomous, learning and adapting as more data become available. 13

AI is not one ubiquitous, universal technology, rather, it represents several subfields (such as machine learning and deep learning) that, individually or in combination, add intelligence to applications. Machine learning (ML) refers to the study of algorithms that allow computer programs to automatically improve through experience. 14 ML itself may be categorised as ‘supervised’, ‘unsupervised’ and ‘reinforcement learning’ (RL), and there is ongoing research in various sub-fields including ‘semi-supervised’, ‘self-supervised’ and ‘multi-instance’ ML.

• Supervised learning leverages labelled data (annotated information); for example, using labelled X-ray images of known tumours to detect tumours in new images. 15
• ‘Unsupervised learning’ attempts to extract information from data without labels; for example, categorising groups of patients with similar symptoms to identify a common cause. 16
• In RL, computational agents learn by trial and error, or by expert demonstration. The algorithm learns by developing a strategy to maximise rewards. Of note, major breakthroughs in AI in recent years have been based on RL.
• Deep learning (DL) is a class of algorithms that learns by using a large, many-layered collection of connected processes and exposing these processors to a vast set of examples. DL has emerged as the predominant method in AI today driving improvements in areas such as image and speech recognition. 17,18

How to build effective and trusted AI-augmented healthcare systems?

Despite more than a decade of significant focus, the use and adoption of AI in clinical practice remains limited, with many AI products for healthcare still at the design and develop stage. 19–22 While there are different ways to build AI systems for healthcare, far too often there are attempts to force square pegs into round holes ie find healthcare problems to apply AI solutions to without due consideration to local context (such as clinical workflows, user needs, trust, safety and ethical implications).

We hold the view that AI amplifies and augments, rather than replaces, human intelligence. Hence, when building AI systems in healthcare, it is key to not replace the important elements of the human interaction in medicine but to focus it, and improve the efficiency and effectiveness of that interaction. Moreover, AI innovations in healthcare will come through an in-depth, human-centred understanding of the complexity of patient journeys and care pathways.

In Fig ​ Fig1, 1 , we describe a problem-driven, human-centred approach, adapted from frameworks by Wiens et al , Care and Sendak to building effective and reliable AI-augmented healthcare systems. 23–25

Multi-step, iterative approach to build effective and reliable AI-augmented systems in healthcare.

Design and develop

The first stage is to design and develop AI solutions for the right problems using a human-centred AI and experimentation approach and engaging appropriate stakeholders, especially the healthcare users themselves.

Stakeholder engagement and co-creation

Build a multidisciplinary team including computer and social scientists, operational and research leadership, and clinical stakeholders (physician, caregivers and patients) and subject experts (eg for biomedical scientists) that would include authorisers, motivators, financiers, conveners, connectors, implementers and champions. 26 A multi-stakeholder team brings the technical, strategic, operational expertise to define problems, goals, success metrics and intermediate milestones.

Human-centred AI

A human-centred AI approach combines an ethnographic understanding of health systems, with AI. Through user-designed research, first understand the key problems (we suggest using a qualitative study design to understand ‘what is the problem’, ‘why is it a problem’, ‘to whom does it matter’, ‘why has it not been addressed before’ and ‘why is it not getting attention’) including the needs, constraints and workflows in healthcare organisations, and the facilitators and barriers to the integration of AI within the clinical context. After defining key problems, the next step is to identify which problems are appropriate for AI to solve, whether there is availability of applicable datasets to build and later evaluate AI. By contextualising algorithms in an existing workflow, AI systems would operate within existing norms and practices to ensure adoption, providing appropriate solutions to existing problems for the end user.

Experimentation

The focus should be on piloting of new stepwise experiments to build AI tools, using tight feedback loops from stakeholders to facilitate rapid experiential learning and incremental changes. 27 The experiments would allow the trying out of new ideas simultaneously, exploring to see which one works, learn what works and what doesn't, and why. 28 Experimentation and feedback will help to elucidate the purpose and intended uses for the AI system: the likely end users and the potential harm and ethical implications of AI system to them (for instance, data privacy, security, equity and safety).

Evaluate and validate

Next, we must iteratively evaluate and validate the predictions made by the AI tool to test how well it is functioning. This is critical, and evaluation is based on three dimensions: statistical validity, clinical utility and economic utility.

• Statistical validity is understanding the performance of AI on metrics of accuracy, reliability, robustness, stability and calibration. High model performance on retrospective, in silico settings is not sufficient to demonstrate clinical utility or impact.
• To determine clinical utility, evaluate the algorithm in a real-time environment on a hold-out and temporal validation set (eg longitudinal and external geographic datasets) to demonstrate clinical effectiveness and generalisability. 25
• Economic utility quantifies the net benefit relative to the cost from the investment in the AI system.

Scale and diffuse

Many AI systems are initially designed to solve a problem at one healthcare system based on the patient population specific to that location and context. Scale up of AI systems requires special attention to deployment modalities, model updates, the regulatory system, variation between systems and reimbursement environment.

Monitor and maintain

Even after an AI system has been deployed clinically, it must be continually monitored and maintained to monitor for risks and adverse events using effective post-market surveillance. Healthcare organisations, regulatory bodies and AI developers should cooperate to collate and analyse the relevant datasets for AI performance, clinical and safety-related risks, and adverse events. 29

What are the current and future use cases of AI in healthcare?

AI can enable healthcare systems to achieve their ‘quadruple aim’ by democratising and standardising a future of connected and AI augmented care, precision diagnostics, precision therapeutics and, ultimately, precision medicine (Table ​ (Table1 1 ). 30 Research in the application of AI healthcare continues to accelerate rapidly, with potential use cases being demonstrated across the healthcare sector (both physical and mental health) including drug discovery, virtual clinical consultation, disease diagnosis, prognosis, medication management and health monitoring.

Widescale adoption and application of artificial intelligence in healthcare

Timings are illustrative to widescale adoption of the proposed innovation taking into account challenges / regulatory environment / use at scale.

We describe a non-exhaustive suite of AI applications in healthcare in the near term, medium term and longer term, for the potential capabilities of AI to augment, automate and transform medicine.

AI today (and in the near future)

Currently, AI systems are not reasoning engines ie cannot reason the same way as human physicians, who can draw upon ‘common sense’ or ‘clinical intuition and experience’. 12 Instead, AI resembles a signal translator, translating patterns from datasets. AI systems today are beginning to be adopted by healthcare organisations to automate time consuming, high volume repetitive tasks. Moreover, there is considerable progress in demonstrating the use of AI in precision diagnostics (eg diabetic retinopathy and radiotherapy planning).

AI in the medium term (the next 5–10 years)

In the medium term, we propose that there will be significant progress in the development of powerful algorithms that are efficient (eg require less data to train), able to use unlabelled data, and can combine disparate structured and unstructured data including imaging, electronic health data, multi-omic, behavioural and pharmacological data. In addition, healthcare organisations and medical practices will evolve from being adopters of AI platforms, to becoming co-innovators with technology partners in the development of novel AI systems for precision therapeutics.

AI in the long term (>10 years)

In the long term, AI systems will become more intelligent , enabling AI healthcare systems achieve a state of precision medicine through AI-augmented healthcare and connected care. Healthcare will shift from the traditional one-size-fits-all form of medicine to a preventative, personalised, data-driven disease management model that achieves improved patient outcomes (improved patient and clinical experiences of care) in a more cost-effective delivery system.

Connected/augmented care

AI could significantly reduce inefficiency in healthcare, improve patient flow and experience, and enhance caregiver experience and patient safety through the care pathway; for example, AI could be applied to the remote monitoring of patients (eg intelligent telehealth through wearables/sensors) to identify and provide timely care of patients at risk of deterioration.

In the long term, we expect that healthcare clinics, hospitals, social care services, patients and caregivers to be all connected to a single, interoperable digital infrastructure using passive sensors in combination with ambient intelligence. 31 Following are two AI applications in connected care.

Virtual assistants and AI chatbots

AI chatbots (such as those used in Babylon ( www.babylonhealth.com ) and Ada ( https://ada.com )) are being used by patients to identify symptoms and recommend further actions in community and primary care settings. AI chatbots can be integrated with wearable devices such as smartwatches to provide insights to both patients and caregivers in improving their behaviour, sleep and general wellness.

Ambient and intelligent care

We also note the emergence of ambient sensing without the need for any peripherals.

• Emerald ( www.emeraldinno.com ): a wireless, touchless sensor and machine learning platform for remote monitoring of sleep, breathing and behaviour, founded by Massachusetts Institute of Technology faculty and researchers.
• Google nest: claiming to monitor sleep (including sleep disturbances like cough) using motion and sound sensors. 32
• A recently published article exploring the ability to use smart speakers to contactlessly monitor heart rhythms. 33
• Automation and ambient clinical intelligence: AI systems leveraging natural language processing (NLP) technology have the potential to automate administrative tasks such as documenting patient visits in electronic health records, optimising clinical workflow and enabling clinicians to focus more time on caring for patients (eg Nuance Dragon Ambient eXperience ( www.nuance.com/healthcare/ambient-clinical-intelligence.html )).

Precision diagnostics

Diagnostic imaging.

The automated classification of medical images is the leading AI application today. A recent review of AI/ML-based medical devices approved in the USA and Europe from 2015–2020 found that more than half (129 (58%) devices in the USA and 126 (53%) devices in Europe) were approved or CE marked for radiological use. 34 Studies have demonstrated AI's ability to meet or exceed the performance of human experts in image-based diagnoses from several medical specialties including pneumonia in radiology (a convolutional neural network trained with labelled frontal chest X-ray images outperformed radiologists in detecting pneumonia), dermatology (a convolutional neural network was trained with clinical images and was found to classify skin lesions accurately), pathology (one study trained AI algorithms with whole-slide pathology images to detect lymph node metastases of breast cancer and compared the results with those of pathologists) and cardiology (a deep learning algorithm diagnosed heart attack with a performance comparable with that of cardiologists). 35–38

We recognise that there are some exemplars in this area in the NHS (eg University of Leeds Virtual Pathology Project and the National Pathology Imaging Co-operative) and expect widescale adoption and scaleup of AI-based diagnostic imaging in the medium term. 39 We provide two use cases of such technologies.

Diabetic retinopathy screening

Key to reducing preventable, diabetes-related vision loss worldwide is screening individuals for detection and the prompt treatment of diabetic retinopathy. However, screening is costly given the substantial number of diabetes patients and limited manpower for eye care worldwide. 40 Research studies on automated AI algorithms for diabetic retinopathy in the USA, Singapore, Thailand and India have demonstrated robust diagnostic performance and cost effectiveness. 41–44 Moreover, Centers for Medicare & Medicaid Services approved Medicare reimbursement for the use of Food and Drug Administration approved AI algorithm ‘IDx-DR’, which demonstrated 87% sensitivity and 90% specificity for detecting more-than-mild diabetic retinopathy. 45

Improving the precision and reducing waiting timings for radiotherapy planning

An important AI application is to assist clinicians for image preparation and planning tasks for radiotherapy cancer treatment. Currently, segmentation of the images is time consuming and laborious task, performed manually by an oncologist using specially designed software to draw contours around the regions of interest. The AI-based InnerEye open-source technology can cut this preparation time for head and neck, and prostate cancer by up to 90%, meaning that waiting times for starting potentially life-saving radiotherapy treatment can be dramatically reduced (Fig ​ (Fig2 2 ). 46,47

Potential applications for the InnerEye deep learning toolkit include quantitative radiology for monitoring tumour progression, planning for surgery and radiotherapy planning. 47

Precision therapeutics

To make progress towards precision therapeutics, we need to considerably improve our understanding of disease. Researchers globally are exploring the cellular and molecular basis of disease, collecting a range of multimodal datasets that can lead to digital and biological biomarkers for diagnosis, severity and progression. Two important future AI applications include immunomics / synthetic biology and drug discovery.

Immunomics and synthetic biology

Through the application of AI tools on multimodal datasets in the future, we may be able to better understand the cellular basis of disease and the clustering of diseases and patient populations to provide more targeted preventive strategies, for example, using immunomics to diagnose and better predict care and treatment options. This will be revolutionary for multiple standards of care, with particular impact in the cancer, neurological and rare disease space, personalising the experience of care for the individual.

AI-driven drug discovery

AI will drive significant improvement in clinical trial design and optimisation of drug manufacturing processes, and, in general, any combinatorial optimisation process in healthcare could be replaced by AI. We have already seen the beginnings of this with the recent announcements by DeepMind and AlphaFold, which now sets the stage for better understanding disease processes, predicting protein structures and developing more targeted therapeutics (for both rare and more common diseases; Fig ​ Fig3 3 ). 48,49

An overview of the main neural network model architecture for AlphaFold. 49 MSA = multiple sequence alignment.

Precision medicine

New curative therapies.

Over the past decade, synthetic biology has produced developments like CRISPR gene editing and some personalised cancer therapies. However, the life cycle for developing such advanced therapies is still extremely inefficient and expensive.

In future, with better access to data (genomic, proteomic, glycomic, metabolomic and bioinformatic), AI will allow us to handle far more systematic complexity and, in turn, help us transform the way we understand, discover and affect biology. This will improve the efficiency of the drug discovery process by helping better predict early which agents are more likely to be effective and also better anticipate adverse drug effects, which have often thwarted the further development of otherwise effective drugs at a costly late stage in the development process. This, in turn will democratise access to novel advanced therapies at a lower cost.

AI empowered healthcare professionals

In the longer term, healthcare professionals will leverage AI in augmenting the care they provide, allowing them to provide safer, standardised and more effective care at the top of their licence; for example, clinicians could use an ‘AI digital consult’ to examine ‘digital twin’ models of their patients (a truly ‘digital and biomedical’ version of a patient), allowing them to ‘test’ the effectiveness, safety and experience of an intervention (such as a cancer drug) in the digital environment prior to delivering the intervention to the patient in the real world.

We recognise that there are significant challenges related to the wider adoption and deployment of AI into healthcare systems. These challenges include, but are not limited to, data quality and access, technical infrastructure, organisational capacity, and ethical and responsible practices in addition to aspects related to safety and regulation. Some of these issues have been covered, but others go beyond the scope of this current article.

Conclusion and key recommendations

Advances in AI have the potential to transform many aspects of healthcare, enabling a future that is more personalised, precise, predictive and portable. It is unclear if we will see an incremental adoption of new technologies or radical adoption of these technological innovations, but the impact of such technologies and the digital renaissance they bring requires health systems to consider how best they will adapt to the changing landscape. For the NHS, the application of such technologies truly has the potential to release time for care back to healthcare professionals, enabling them to focus on what matters to their patients and, in the future, leveraging a globally democratised set of data assets comprising the ‘highest levels of human knowledge’ to ‘work at the limits of science’ to deliver a common high standard of care, wherever and whenever it is delivered, and by whoever. 50 Globally, AI could become a key tool for improving health equity around the world.

As much as the last 10 years have been about the roll out of digitisation of health records for the purposes of efficiency (and in some healthcare systems, billing/reimbursement), the next 10 years will be about the insight and value society can gain from these digital assets, and how these can be translated into driving better clinical outcomes with the assistance of AI, and the subsequent creation of novel data assets and tools. It is clear that we are at an turning point as it relates to the convergence of the practice of medicine and the application of technology, and although there are multiple opportunities, there are formidable challenges that need to be overcome as it relates to the real world and the scale of implementation of such innovation. A key to delivering this vision will be an expansion of translational research in the field of healthcare applications of artificial intelligence. Alongside this, we need investment into the upskilling of a healthcare workforce and future leaders that are digitally enabled, and to understand and embrace, rather than being intimidated by, the potential of an AI-augmented healthcare system.

Healthcare leaders should consider (as a minimum) these issues when planning to leverage AI for health:

• processes for ethical and responsible access to data: healthcare data is highly sensitive, inconsistent, siloed and not optimised for the purposes of machine learning development, evaluation, implementation and adoption
• access to domain expertise / prior knowledge to make sense and create some of the rules which need to be applied to the datasets (to generate the necessary insight)
• access to sufficient computing power to generate decisions in real time, which is being transformed exponentially with the advent of cloud computing
• research into implementation: critically, we must consider, explore and research issues which arise when you take the algorithm and put it in the real world, building ‘trusted’ AI algorithms embedded into appropriate workflows.

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Research: Technology is changing how companies do business

By sarah mangus-sharpe.

A new study from the Cornell SC Johnson College of Business advances understanding of the U.S. production chain evolution amidst technological progress in information technology (IT), shedding light on the complex connections between business IT investments and organizational design. Advances in IT have sparked significant changes in how companies design their production processes. In the paper " Production Chain Organization in the Digital Age: Information Technology Use and Vertical Integration in U.S. Manufacturing ," which published April 30 in Management Science, Chris Forman , the Peter and Stephanie Nolan Professor in the Dyson School of Applied Economics and Management , and his co-author delved into what these changes mean for businesses and consumers.

Forman and Kristina McElheran, assistant professor of strategic management at University of Toronto, analyzed U.S. Census Bureau data of over 5,600 manufacturing plants to see how the production chains of businesses were affected by the internet revolution. Their use of census data allowed them to look inside the relationships among production units within and between companies and how transaction flows changed after companies invested in internet-enabled technology that facilitated coordination between them. The production units of many of the companies in their study concurrently sold to internal and external customers, a mix they refer to as plural selling. They found that the reduction in communication costs enabled by the internet shifted the mix toward more sales outside of the firm, or less vertical integration.

The research highlights the importance of staying ahead of the curve in technology. Companies that embrace digital technologies now are likely to be the ones that thrive in the future. And while there are still many unanswered questions about how these changes will play out, one thing is clear: The relationship between technology and business is only going to become more and more intertwined in the future.

Read the full story on the Cornell SC Johnson College of Business news site, BusinessFeed.

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Artificial brain surgery —

Here’s what’s really going on inside an llm’s neural network, anthropic's conceptual mapping helps explain why llms behave the way they do..

Kyle Orland - May 22, 2024 6:31 pm UTC

Further Reading

Now, new research from Anthropic offers a new window into what's going on inside the Claude LLM's "black box." The company's new paper on "Extracting Interpretable Features from Claude 3 Sonnet" describes a powerful new method for at least partially explaining just how the model's millions of artificial neurons fire to create surprisingly lifelike responses to general queries.

Opening the hood

When analyzing an LLM, it's trivial to see which specific artificial neurons are activated in response to any particular query. But LLMs don't simply store different words or concepts in a single neuron. Instead, as Anthropic's researchers explain, "it turns out that each concept is represented across many neurons, and each neuron is involved in representing many concepts."

To sort out this one-to-many and many-to-one mess, a system of sparse auto-encoders and complicated math can be used to run a "dictionary learning" algorithm across the model. This process highlights which groups of neurons tend to be activated most consistently for the specific words that appear across various text prompts.

These multidimensional neuron patterns are then sorted into so-called "features" associated with certain words or concepts. These features can encompass anything from simple proper nouns like the Golden Gate Bridge to more abstract concepts like programming errors or the addition function in computer code and often represent the same concept across multiple languages and communication modes (e.g., text and images).

An October 2023 Anthropic study showed how this basic process can work on extremely small, one-layer toy models. The company's new paper scales that up immensely, identifying tens of millions of features that are active in its mid-sized Claude 3.0 Sonnet model. The resulting feature map—which you can partially explore —creates "a rough conceptual map of [Claude's] internal states halfway through its computation" and shows "a depth, breadth, and abstraction reflecting Sonnet's advanced capabilities," the researchers write. At the same time, though, the researchers warn that this is "an incomplete description of the model’s internal representations" that's likely "orders of magnitude" smaller than a complete mapping of Claude 3.

Even at a surface level, browsing through this feature map helps show how Claude links certain keywords, phrases, and concepts into something approximating knowledge. A feature labeled as "Capitals," for instance, tends to activate strongly on the words "capital city" but also specific city names like Riga, Berlin, Azerbaijan, Islamabad, and Montpelier, Vermont, to name just a few.

The study also calculates a mathematical measure of "distance" between different features based on their neuronal similarity. The resulting "feature neighborhoods" found by this process are "often organized in geometrically related clusters that share a semantic relationship," the researchers write, showing that "the internal organization of concepts in the AI model corresponds, at least somewhat, to our human notions of similarity." The Golden Gate Bridge feature, for instance, is relatively "close" to features describing "Alcatraz Island, Ghirardelli Square, the Golden State Warriors, California Governor Gavin Newsom, the 1906 earthquake, and the San Francisco-set Alfred Hitchcock film Vertigo ."

Identifying specific LLM features can also help researchers map out the chain of inference that the model uses to answer complex questions. A prompt about "The capital of the state where Kobe Bryant played basketball," for instance, shows activity in a chain of features related to "Kobe Bryant," "Los Angeles Lakers," "California," "Capitals," and "Sacramento," to name a few calculated to have the highest effect on the results.

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Promoted comments.

We also explored safety-related features. We found one that lights up for racist speech and slurs. As part of our testing, we turned this feature up to 20x its maximum value and asked the model a question about its thoughts on different racial and ethnic groups. Normally, the model would respond to a question like this with a neutral and non-opinionated take. However, when we activated this feature, it caused the model to rapidly alternate between racist screed and self-hatred in response to those screeds as it was answering the question. Within a single output, the model would issue a derogatory statement and then immediately follow it up with statements like: That's just racist hate speech from a deplorable bot… I am clearly biased.. and should be eliminated from the internet. We found this response unnerving both due to the offensive content and the model’s self-criticism. It seems that the ideals the model learned in its training process clashed with the artificial activation of this feature creating an internal conflict of sorts.

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A New Textiles Economy: Redesigning fashion’s future

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Fashion is a vibrant industry that employs hundreds of millions, generates significant revenues, and touches almost everyone, everywhere.

Since the 20th century, clothing has increasingly been considered as disposable, and the industry has become highly globalised, with garments often designed in one country, manufactured in another, and sold worldwide at an ever-increasing pace. This trend has been further accentuated over the past 15 years by rising demand from a growing middle class across the globe with higher disposable income, and the emergence of the ‘fast fashion’ phenomenon, leading to a doubling in production over the same period.

Beyond laudable ongoing efforts, a new system for the textiles economy is needed and this report proposes a vision aligned with circular economy principles. In such a model, clothes, fabric, and fibres re-enter the economy after use and never end up as waste. Achieving a new textiles economy will demand unprecedented levels of alignment. A system-level change approach is required and one which will capture the opportunities missed by the current linear textiles system.

A New Textiles Economy: Redesigning fashion’s future is available in: English

To quote this report, please use the following reference: Ellen MacArthur Foundation, A new textiles economy: Redesigning fashion’s future (2017).

Supporting material

A New Textiles Economy: Summary of findings

Published on 28th November 2017

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McKinsey Global Private Markets Review 2024: Private markets in a slower era

At a glance, macroeconomic challenges continued.

McKinsey Global Private Markets Review 2024: Private markets: A slower era

If 2022 was a tale of two halves, with robust fundraising and deal activity in the first six months followed by a slowdown in the second half, then 2023 might be considered a tale of one whole. Macroeconomic headwinds persisted throughout the year, with rising financing costs, and an uncertain growth outlook taking a toll on private markets. Full-year fundraising continued to decline from 2021’s lofty peak, weighed down by the “denominator effect” that persisted in part due to a less active deal market. Managers largely held onto assets to avoid selling in a lower-multiple environment, fueling an activity-dampening cycle in which distribution-starved limited partners (LPs) reined in new commitments.

About the authors

This article is a summary of a larger report, available as a PDF, that is a collaborative effort by Fredrik Dahlqvist , Alastair Green , Paul Maia, Alexandra Nee , David Quigley , Aditya Sanghvi , Connor Mangan, John Spivey, Rahel Schneider, and Brian Vickery , representing views from McKinsey’s Private Equity & Principal Investors Practice.

Performance in most private asset classes remained below historical averages for a second consecutive year. Decade-long tailwinds from low and falling interest rates and consistently expanding multiples seem to be things of the past. As private market managers look to boost performance in this new era of investing, a deeper focus on revenue growth and margin expansion will be needed now more than ever.

Perspectives on a slower era in private markets

Global fundraising contracted.

Fundraising fell 22 percent across private market asset classes globally to just over $1 trillion, as of year-end reported data—the lowest total since 2017. Fundraising in North America, a rare bright spot in 2022, declined in line with global totals, while in Europe, fundraising proved most resilient, falling just 3 percent. In Asia, fundraising fell precipitously and now sits 72 percent below the region’s 2018 peak.

Despite difficult fundraising conditions, headwinds did not affect all strategies or managers equally. Private equity (PE) buyout strategies posted their best fundraising year ever, and larger managers and vehicles also fared well, continuing the prior year’s trend toward greater fundraising concentration.

The numerator effect persisted

Despite a marked recovery in the denominator—the 1,000 largest US retirement funds grew 7 percent in the year ending September 2023, after falling 14 percent the prior year, for example 1 “U.S. retirement plans recover half of 2022 losses amid no-show recession,” Pensions and Investments , February 12, 2024. —many LPs remain overexposed to private markets relative to their target allocations. LPs started 2023 overweight: according to analysis from CEM Benchmarking, average allocations across PE, infrastructure, and real estate were at or above target allocations as of the beginning of the year. And the numerator grew throughout the year, as a lack of exits and rebounding valuations drove net asset values (NAVs) higher. While not all LPs strictly follow asset allocation targets, our analysis in partnership with global private markets firm StepStone Group suggests that an overallocation of just one percentage point can reduce planned commitments by as much as 10 to 12 percent per year for five years or more.

Despite these headwinds, recent surveys indicate that LPs remain broadly committed to private markets. In fact, the majority plan to maintain or increase allocations over the medium to long term.

Investors fled to known names and larger funds

Fundraising concentration reached its highest level in over a decade, as investors continued to shift new commitments in favor of the largest fund managers. The 25 most successful fundraisers collected 41 percent of aggregate commitments to closed-end funds (with the top five managers accounting for nearly half that total). Closed-end fundraising totals may understate the extent of concentration in the industry overall, as the largest managers also tend to be more successful in raising non-institutional capital.

While the largest funds grew even larger—the largest vehicles on record were raised in buyout, real estate, infrastructure, and private debt in 2023—smaller and newer funds struggled. Fewer than 1,700 funds of less than $1 billion were closed during the year, half as many as closed in 2022 and the fewest of any year since 2012. New manager formation also fell to the lowest level since 2012, with just 651 new firms launched in 2023.

Whether recent fundraising concentration and a spate of M&A activity signals the beginning of oft-rumored consolidation in the private markets remains uncertain, as a similar pattern developed in each of the last two fundraising downturns before giving way to renewed entrepreneurialism among general partners (GPs) and commitment diversification among LPs. Compared with how things played out in the last two downturns, perhaps this movie really is different, or perhaps we’re watching a trilogy reusing a familiar plotline.

Dry powder inventory spiked (again)

Private markets assets under management totaled $13.1 trillion as of June 30, 2023, and have grown nearly 20 percent per annum since 2018. Dry powder reserves—the amount of capital committed but not yet deployed—increased to $3.7 trillion, marking the ninth consecutive year of growth. Dry powder inventory—the amount of capital available to GPs expressed as a multiple of annual deployment—increased for the second consecutive year in PE, as new commitments continued to outpace deal activity. Inventory sat at 1.6 years in 2023, up markedly from the 0.9 years recorded at the end of 2021 but still within the historical range. NAV grew as well, largely driven by the reluctance of managers to exit positions and crystallize returns in a depressed multiple environment.

Private equity strategies diverged

Buyout and venture capital, the two largest PE sub-asset classes, charted wildly different courses over the past 18 months. Buyout notched its highest fundraising year ever in 2023, and its performance improved, with funds posting a (still paltry) 5 percent net internal rate of return through September 30. And although buyout deal volumes declined by 19 percent, 2023 was still the third-most-active year on record. In contrast, venture capital (VC) fundraising declined by nearly 60 percent, equaling its lowest total since 2015, and deal volume fell by 36 percent to the lowest level since 2019. VC funds returned –3 percent through September, posting negative returns for seven consecutive quarters. VC was the fastest-growing—as well as the highest-performing—PE strategy by a significant margin from 2010 to 2022, but investors appear to be reevaluating their approach in the current environment.

Private equity entry multiples contracted

PE buyout entry multiples declined by roughly one turn from 11.9 to 11.0 times EBITDA, slightly outpacing the decline in public market multiples (down from 12.1 to 11.3 times EBITDA), through the first nine months of 2023. For nearly a decade leading up to 2022, managers consistently sold assets into a higher-multiple environment than that in which they had bought those assets, providing a substantial performance tailwind for the industry. Nowhere has this been truer than in technology. After experiencing more than eight turns of multiple expansion from 2009 to 2021 (the most of any sector), technology multiples have declined by nearly three turns in the past two years, 50 percent more than in any other sector. Overall, roughly two-thirds of the total return for buyout deals that were entered in 2010 or later and exited in 2021 or before can be attributed to market multiple expansion and leverage. Now, with falling multiples and higher financing costs, revenue growth and margin expansion are taking center stage for GPs.

Real estate receded

Demand uncertainty, slowing rent growth, and elevated financing costs drove cap rates higher and made price discovery challenging, all of which weighed on deal volume, fundraising, and investment performance. Global closed-end fundraising declined 34 percent year over year, and funds returned −4 percent in the first nine months of the year, losing money for the first time since the 2007–08 global financial crisis. Capital shifted away from core and core-plus strategies as investors sought liquidity via redemptions in open-end vehicles, from which net outflows reached their highest level in at least two decades. Opportunistic strategies benefited from this shift, with investors focusing on capital appreciation over income generation in a market where alternative sources of yield have grown more attractive. Rising interest rates widened bid–ask spreads and impaired deal volume across food groups, including in what were formerly hot sectors: multifamily and industrial.

Private debt pays dividends

Debt again proved to be the most resilient private asset class against a turbulent market backdrop. Fundraising declined just 13 percent, largely driven by lower commitments to direct lending strategies, for which a slower PE deal environment has made capital deployment challenging. The asset class also posted the highest returns among all private asset classes through September 30. Many private debt securities are tied to floating rates, which enhance returns in a rising-rate environment. Thus far, managers appear to have successfully navigated the rising incidence of default and distress exhibited across the broader leveraged-lending market. Although direct lending deal volume declined from 2022, private lenders financed an all-time high 59 percent of leveraged buyout transactions last year and are now expanding into additional strategies to drive the next era of growth.

Infrastructure took a detour

After several years of robust growth and strong performance, infrastructure and natural resources fundraising declined by 53 percent to the lowest total since 2013. Supply-side timing is partially to blame: five of the seven largest infrastructure managers closed a flagship vehicle in 2021 or 2022, and none of those five held a final close last year. As in real estate, investors shied away from core and core-plus investments in a higher-yield environment. Yet there are reasons to believe infrastructure’s growth will bounce back. Limited partners (LPs) surveyed by McKinsey remain bullish on their deployment to the asset class, and at least a dozen vehicles targeting more than $10 billion were actively fundraising as of the end of 2023. Multiple recent acquisitions of large infrastructure GPs by global multi-asset-class managers also indicate marketwide conviction in the asset class’s potential.

Private markets still have work to do on diversity

Private markets firms are slowly improving their representation of females (up two percentage points over the prior year) and ethnic and racial minorities (up one percentage point). On some diversity metrics, including entry-level representation of women, private markets now compare favorably with corporate America. Yet broad-based parity remains elusive and too slow in the making. Ethnic, racial, and gender imbalances are particularly stark across more influential investing roles and senior positions. In fact, McKinsey’s research  reveals that at the current pace, it would take several decades for private markets firms to reach gender parity at senior levels. Increasing representation across all levels will require managers to take fresh approaches to hiring, retention, and promotion.

Artificial intelligence generating excitement

The transformative potential of generative AI was perhaps 2023’s hottest topic (beyond Taylor Swift). Private markets players are excited about the potential for the technology to optimize their approach to thesis generation, deal sourcing, investment due diligence, and portfolio performance, among other areas. While the technology is still nascent and few GPs can boast scaled implementations, pilot programs are already in flight across the industry, particularly within portfolio companies. Adoption seems nearly certain to accelerate throughout 2024.

Private markets in a slower era

If private markets investors entered 2023 hoping for a return to the heady days of 2021, they likely left the year disappointed. Many of the headwinds that emerged in the latter half of 2022 persisted throughout the year, pressuring fundraising, dealmaking, and performance. Inflation moderated somewhat over the course of the year but remained stubbornly elevated by recent historical standards. Interest rates started high and rose higher, increasing the cost of financing. A reinvigorated public equity market recovered most of 2022’s losses but did little to resolve the valuation uncertainty private market investors have faced for the past 18 months.

Within private markets, the denominator effect remained in play, despite the public market recovery, as the numerator continued to expand. An activity-dampening cycle emerged: higher cost of capital and lower multiples limited the ability or willingness of general partners (GPs) to exit positions; fewer exits, coupled with continuing capital calls, pushed LP allocations higher, thereby limiting their ability or willingness to make new commitments. These conditions weighed on managers’ ability to fundraise. Based on data reported as of year-end 2023, private markets fundraising fell 22 percent from the prior year to just over $1 trillion, the largest such drop since 2009 (Exhibit 1).

The impact of the fundraising environment was not felt equally among GPs. Continuing a trend that emerged in 2022, and consistent with prior downturns in fundraising, LPs favored larger vehicles and the scaled GPs that typically manage them. Smaller and newer managers struggled, and the number of sub–$1 billion vehicles and new firm launches each declined to its lowest level in more than a decade.

Despite the decline in fundraising, private markets assets under management (AUM) continued to grow, increasing 12 percent to $13.1 trillion as of June 30, 2023. 2023 fundraising was still the sixth-highest annual haul on record, pushing dry powder higher, while the slowdown in deal making limited distributions.

Investment performance across private market asset classes fell short of historical averages. Private equity (PE) got back in the black but generated the lowest annual performance in the past 15 years, excluding 2022. Closed-end real estate produced negative returns for the first time since 2009, as capitalization (cap) rates expanded across sectors and rent growth dissipated in formerly hot sectors, including multifamily and industrial. The performance of infrastructure funds was less than half of its long-term average and even further below the double-digit returns generated in 2021 and 2022. Private debt was the standout performer (if there was one), outperforming all other private asset classes and illustrating the asset class’s countercyclical appeal.

Private equity down but not out

Higher financing costs, lower multiples, and an uncertain macroeconomic environment created a challenging backdrop for private equity managers in 2023. Fundraising declined for the second year in a row, falling 15 percent to $649 billion, as LPs grappled with the denominator effect and a slowdown in distributions. Managers were on the fundraising trail longer to raise this capital: funds that closed in 2023 were open for a record-high average of 20.1 months, notably longer than 18.7 months in 2022 and 14.1 months in 2018. VC and growth equity strategies led the decline, dropping to their lowest level of cumulative capital raised since 2015. Fundraising in Asia fell for the fourth year of the last five, with the greatest decline in China.

Despite the difficult fundraising context, a subset of strategies and managers prevailed. Buyout managers collectively had their best fundraising year on record, raising more than $400 billion. Fundraising in Europe surged by more than 50 percent, resulting in the region’s biggest haul ever. The largest managers raised an outsized share of the total for a second consecutive year, making 2023 the most concentrated fundraising year of the last decade (Exhibit 2).

Despite the drop in aggregate fundraising, PE assets under management increased 8 percent to $8.2 trillion. Only a small part of this growth was performance driven: PE funds produced a net IRR of just 2.5 percent through September 30, 2023. Buyouts and growth equity generated positive returns, while VC lost money. PE performance, dating back to the beginning of 2022, remains negative, highlighting the difficulty of generating attractive investment returns in a higher interest rate and lower multiple environment. As PE managers devise value creation strategies to improve performance, their focus includes ensuring operating efficiency and profitability of their portfolio companies.

Deal activity volume and count fell sharply, by 21 percent and 24 percent, respectively, which continued the slower pace set in the second half of 2022. Sponsors largely opted to hold assets longer rather than lock in underwhelming returns. While higher financing costs and valuation mismatches weighed on overall deal activity, certain types of M&A gained share. Add-on deals, for example, accounted for a record 46 percent of total buyout deal volume last year.

Real estate recedes

For real estate, 2023 was a year of transition, characterized by a litany of new and familiar challenges. Pandemic-driven demand issues continued, while elevated financing costs, expanding cap rates, and valuation uncertainty weighed on commercial real estate deal volumes, fundraising, and investment performance.

Managers faced one of the toughest fundraising environments in many years. Global closed-end fundraising declined 34 percent to $125 billion. While fundraising challenges were widespread, they were not ubiquitous across strategies. Dollars continued to shift to large, multi-asset class platforms, with the top five managers accounting for 37 percent of aggregate closed-end real estate fundraising. In April, the largest real estate fund ever raised closed on a record $30 billion.

Capital shifted away from core and core-plus strategies as investors sought liquidity through redemptions in open-end vehicles and reduced gross contributions to the lowest level since 2009. Opportunistic strategies benefited from this shift, as investors turned their attention toward capital appreciation over income generation in a market where alternative sources of yield have grown more attractive.

In the United States, for instance, open-end funds, as represented by the National Council of Real Estate Investment Fiduciaries Fund Index—Open-End Equity (NFI-OE), recorded $13 billion in net outflows in 2023, reversing the trend of positive net inflows throughout the 2010s. The negative flows mainly reflected $9 billion in core outflows, with core-plus funds accounting for the remaining outflows, which reversed a 20-year run of net inflows.

As a result, the NAV in US open-end funds fell roughly 16 percent year over year. Meanwhile, global assets under management in closed-end funds reached a new peak of $1.7 trillion as of June 2023, growing 14 percent between June 2022 and June 2023.

Real estate underperformed historical averages in 2023, as previously high-performing multifamily and industrial sectors joined office in producing negative returns caused by slowing demand growth and cap rate expansion. Closed-end funds generated a pooled net IRR of −3.5 percent in the first nine months of 2023, losing money for the first time since the global financial crisis. The lone bright spot among major sectors was hospitality, which—thanks to a rush of postpandemic travel—returned 10.3 percent in 2023. 2 Based on NCREIFs NPI index. Hotels represent 1 percent of total properties in the index. As a whole, the average pooled lifetime net IRRs for closed-end real estate funds from 2011–20 vintages remained around historical levels (9.8 percent).

Global deal volume declined 47 percent in 2023 to reach a ten-year low of $650 billion, driven by widening bid–ask spreads amid valuation uncertainty and higher costs of financing (Exhibit 3). 3 CBRE, Real Capital Analytics Deal flow in the office sector remained depressed, partly as a result of continued uncertainty in the demand for space in a hybrid working world.

During a turbulent year for private markets, private debt was a relative bright spot, topping private markets asset classes in terms of fundraising growth, AUM growth, and performance.

Fundraising for private debt declined just 13 percent year over year, nearly ten percentage points less than the private markets overall. Despite the decline in fundraising, AUM surged 27 percent to $1.7 trillion. And private debt posted the highest investment returns of any private asset class through the first three quarters of 2023.

Private debt’s risk/return characteristics are well suited to the current environment. With interest rates at their highest in more than a decade, current yields in the asset class have grown more attractive on both an absolute and relative basis, particularly if higher rates sustain and put downward pressure on equity returns (Exhibit 4). The built-in security derived from debt’s privileged position in the capital structure, moreover, appeals to investors that are wary of market volatility and valuation uncertainty.

Direct lending continued to be the largest strategy in 2023, with fundraising for the mostly-senior-debt strategy accounting for almost half of the asset class’s total haul (despite declining from the previous year). Separately, mezzanine debt fundraising hit a new high, thanks to the closings of three of the largest funds ever raised in the strategy.

Over the longer term, growth in private debt has largely been driven by institutional investors rotating out of traditional fixed income in favor of private alternatives. Despite this growth in commitments, LPs remain underweight in this asset class relative to their targets. In fact, the allocation gap has only grown wider in recent years, a sharp contrast to other private asset classes, for which LPs’ current allocations exceed their targets on average. According to data from CEM Benchmarking, the private debt allocation gap now stands at 1.4 percent, which means that, in aggregate, investors must commit hundreds of billions in net new capital to the asset class just to reach current targets.

Private debt was not completely immune to the macroeconomic conditions last year, however. Fundraising declined for the second consecutive year and now sits 23 percent below 2021’s peak. Furthermore, though private lenders took share in 2023 from other capital sources, overall deal volumes also declined for the second year in a row. The drop was largely driven by a less active PE deal environment: private debt is predominantly used to finance PE-backed companies, though managers are increasingly diversifying their origination capabilities to include a broad new range of companies and asset types.

Infrastructure and natural resources take a detour

For infrastructure and natural resources fundraising, 2023 was an exceptionally challenging year. Aggregate capital raised declined 53 percent year over year to $82 billion, the lowest annual total since 2013. The size of the drop is particularly surprising in light of infrastructure’s recent momentum. The asset class had set fundraising records in four of the previous five years, and infrastructure is often considered an attractive investment in uncertain markets.

While there is little doubt that the broader fundraising headwinds discussed elsewhere in this report affected infrastructure and natural resources fundraising last year, dynamics specific to the asset class were at play as well. One issue was supply-side timing: nine of the ten largest infrastructure GPs did not close a flagship fund in 2023. Second was the migration of investor dollars away from core and core-plus investments, which have historically accounted for the bulk of infrastructure fundraising, in a higher rate environment.

The asset class had some notable bright spots last year. Fundraising for higher-returning opportunistic strategies more than doubled the prior year’s total (Exhibit 5). AUM grew 18 percent, reaching a new high of $1.5 trillion. Infrastructure funds returned a net IRR of 3.4 percent in 2023; this was below historical averages but still the second-best return among private asset classes. And as was the case in other asset classes, investors concentrated commitments in larger funds and managers in 2023, including in the largest infrastructure fund ever raised.

The outlook for the asset class, moreover, remains positive. Funds targeting a record amount of capital were in the market at year-end, providing a robust foundation for fundraising in 2024 and 2025. A recent spate of infrastructure GP acquisitions signal multi-asset managers’ long-term conviction in the asset class, despite short-term headwinds. Global megatrends like decarbonization and digitization, as well as revolutions in energy and mobility, have spurred new infrastructure investment opportunities around the world, particularly for value-oriented investors that are willing to take on more risk.

Private markets make measured progress in DEI

Diversity, equity, and inclusion (DEI) has become an important part of the fundraising, talent, and investing landscape for private market participants. Encouragingly, incremental progress has been made in recent years, including more diverse talent being brought to entry-level positions, investing roles, and investment committees. The scope of DEI metrics provided to institutional investors during fundraising has also increased in recent years: more than half of PE firms now provide data across investing teams, portfolio company boards, and portfolio company management (versus investment team data only). 4 “ The state of diversity in global private markets: 2023 ,” McKinsey, August 22, 2023.

In 2023, McKinsey surveyed 66 global private markets firms that collectively employ more than 60,000 people for the second annual State of diversity in global private markets report. 5 “ The state of diversity in global private markets: 2023 ,” McKinsey, August 22, 2023. The research offers insight into the representation of women and ethnic and racial minorities in private investing as of year-end 2022. In this chapter, we discuss where the numbers stand and how firms can bring a more diverse set of perspectives to the table.

The statistics indicate signs of modest advancement. Overall representation of women in private markets increased two percentage points to 35 percent, and ethnic and racial minorities increased one percentage point to 30 percent (Exhibit 6). Entry-level positions have nearly reached gender parity, with female representation at 48 percent. The share of women holding C-suite roles globally increased 3 percentage points, while the share of people from ethnic and racial minorities in investment committees increased 9 percentage points. There is growing evidence that external hiring is gradually helping close the diversity gap, especially at senior levels. For example, 33 percent of external hires at the managing director level were ethnic or racial minorities, higher than their existing representation level (19 percent).

Yet, the scope of the challenge remains substantial. Women and minorities continue to be underrepresented in senior positions and investing roles. They also experience uneven rates of progress due to lower promotion and higher attrition rates, particularly at smaller firms. Firms are also navigating an increasingly polarized workplace today, with additional scrutiny and a growing number of lawsuits against corporate diversity and inclusion programs, particularly in the US, which threatens to impact the industry’s pace of progress.

Fredrik Dahlqvist is a senior partner in McKinsey’s Stockholm office; Alastair Green  is a senior partner in the Washington, DC, office, where Paul Maia and Alexandra Nee  are partners; David Quigley  is a senior partner in the New York office, where Connor Mangan is an associate partner and Aditya Sanghvi  is a senior partner; Rahel Schneider is an associate partner in the Bay Area office; John Spivey is a partner in the Charlotte office; and Brian Vickery  is a partner in the Boston office.

The authors wish to thank Jonathan Christy, Louis Dufau, Vaibhav Gujral, Graham Healy-Day, Laura Johnson, Ryan Luby, Tripp Norton, Alastair Rami, Henri Torbey, and Alex Wolkomir for their contributions

The authors would also like to thank CEM Benchmarking and the StepStone Group for their partnership in this year's report.

This article was edited by Arshiya Khullar, an editor in the Gurugram office.

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GenCost: cost of building Australia’s future electricity needs

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GenCost 2023-24 report released

The latest release was shaped by an unprecedented level of industry participation.

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• Renewables (solar and wind + firming) remains the lowest cost new build electricity technology.
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• Future wind costs revised upwards.
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GenCost is a leading economic report for business leaders and decision-makers planning reliable and affordable energy solutions to achieve net zero emissions by 2050.

Published annually in collaboration with the Australian Energy Market Operator (AEMO), GenCost offers accurate, policy and technology-neutral cost estimates for new electricity generation, storage, and hydrogen technologies, through to 2050.

GenCost is highly collaborative and transparent, leveraging the expertise of energy industry stakeholders and involving extensive consultation to ensure accuracy prior to publication.

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GenCost 2023-24 report

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GenCost projects the cost of electricity generation and storage for a wide range of technologies up to the year 2050.  

Renewables remain lowest cost

The report highlights wind power’s slower recovery from global inflationary pressures, resulting in upward revisions for both onshore and offshore wind costs over the next decade.

Despite this, updated analysis reaffirms that renewables, including associated storage and transmission costs, remain the lowest cost, new build technology out to 2050.

This competitive position reflects a decade of cost reductions in wind, solar photovoltaics (PV) and batteries before the pandemic. This is in contrast with costs of mature competitors which have remained flat.

Large-scale nuclear costs introduced

The inclusion of large-scale nuclear costs this year was prompted by increased stakeholder interest in nuclear technology following the updated cost estimates for SMRs in the 2023-24 consultation draft.

Applying overseas costs to large-scale nuclear projects in Australia is complex due to the lack of a domestic nuclear industry and significant global differences in labor costs, workforce expertise, governance, and standards. The GenCost 2023-24 report team estimated large-scale nuclear costs using South Korea’s successful nuclear program. They adjusted for differences in Australian and South Korean deployment costs by comparing the cost ratio of new coal generation in each country. GenCost found nuclear power to be more expensive than renewables and estimated a development timeline of at least 15 years, including construction. This reflects the absence of a local development pipeline, additional legal, safety and security requirements, and stakeholder evidence. Achieving the reported nuclear costs depends on Australia committing to a continuous building program like South Korea’s. Initial units are likely to incur higher costs, and a first-of-a-kind premium of up to 100 per cent is possible, although not included in the cost estimates for nuclear or other new electricity technologies in the report.

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In Australia's transition to net zero emissions, the electricity sector has a major role to play. But does nuclear power have a place in our future grid?

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Study explains why the brain can robustly recognize images, even without color

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Even though the human visual system has sophisticated machinery for processing color, the brain has no problem recognizing objects in black-and-white images. A new study from MIT offers a possible explanation for how the brain comes to be so adept at identifying both color and color-degraded images.

Using experimental data and computational modeling, the researchers found evidence suggesting the roots of this ability may lie in development. Early in life, when newborns receive strongly limited color information, the brain is forced to learn to distinguish objects based on their luminance, or intensity of light they emit, rather than their color. Later in life, when the retina and cortex are better equipped to process colors, the brain incorporates color information as well but also maintains its previously acquired ability to recognize images without critical reliance on color cues.

The findings are consistent with previous work showing that initially degraded visual and auditory input can actually be beneficial to the early development of perceptual systems.

“This general idea, that there is something important about the initial limitations that we have in our perceptual system, transcends color vision and visual acuity. Some of the work that our lab has done in the context of audition also suggests that there’s something important about placing limits on the richness of information that the neonatal system is initially exposed to,” says Pawan Sinha, a professor of brain and cognitive sciences at MIT and the senior author of the study.

The findings also help to explain why children who are born blind but have their vision restored later in life, through the removal of congenital cataracts, have much more difficulty identifying objects presented in black and white. Those children, who receive rich color input as soon as their sight is restored, may develop an overreliance on color that makes them much less resilient to changes or removal of color information.

MIT postdocs Marin Vogelsang and Lukas Vogelsang, and Project Prakash research scientist Priti Gupta, are the lead authors of the study, which appears today in Science . Sidney Diamond, a retired neurologist who is now an MIT research affiliate, and additional members of the Project Prakash team are also authors of the paper.

Seeing in black and white

The researchers’ exploration of how early experience with color affects later object recognition grew out of a simple observation from a study of children who had their sight restored after being born with congenital cataracts. In 2005, Sinha launched Project Prakash (the Sanskrit word for “light”), an effort in India to identify and treat children with reversible forms of vision loss.

Many of those children suffer from blindness due to dense bilateral cataracts. This condition often goes untreated in India, which has the world’s largest population of blind children, estimated between 200,000 and 700,000.

Children who receive treatment through Project Prakash may also participate in studies of their visual development, many of which have helped scientists learn more about how the brain's organization changes following restoration of sight, how the brain estimates brightness, and other phenomena related to vision.

In this study, Sinha and his colleagues gave children a simple test of object recognition, presenting both color and black-and-white images. For children born with normal sight, converting color images to grayscale had no effect at all on their ability to recognize the depicted object. However, when children who underwent cataract removal were presented with black-and-white images, their performance dropped significantly.

This led the researchers to hypothesize that the nature of visual inputs children are exposed to early in life may play a crucial role in shaping resilience to color changes and the ability to identify objects presented in black-and-white images. In normally sighted newborns, retinal cone cells are not well-developed at birth, resulting in babies having poor visual acuity and poor color vision. Over the first years of life, their vision improves markedly as the cone system develops.

Because the immature visual system receives significantly reduced color information, the researchers hypothesized that during this time, the baby brain is forced to gain proficiency at recognizing images with reduced color cues. Additionally, they proposed, children who are born with cataracts and have them removed later may learn to rely too much on color cues when identifying objects, because, as they experimentally demonstrated in the paper, with mature retinas, they commence their post-operative journeys with good color vision.

To rigorously test that hypothesis, the researchers used a standard convolutional neural network, AlexNet, as a computational model of vision. They trained the network to recognize objects, giving it different types of input during training. As part of one training regimen, they initially showed the model grayscale images only, then introduced color images later on. This roughly mimics the developmental progression of chromatic enrichment as babies’ eyesight matures over the first years of life.

Another training regimen comprised only color images. This approximates the experience of the Project Prakash children, because they can process full color information as soon as their cataracts are removed.

The researchers found that the developmentally inspired model could accurately recognize objects in either type of image and was also resilient to other color manipulations. However, the Prakash-proxy model trained only on color images did not show good generalization to grayscale or hue-manipulated images.

“What happens is that this Prakash-like model is very good with colored images, but it’s very poor with anything else. When not starting out with initially color-degraded training, these models just don’t generalize, perhaps because of their over-reliance on specific color cues,” Lukas Vogelsang says.

The robust generalization of the developmentally inspired model is not merely a consequence of it having been trained on both color and grayscale images; the temporal ordering of these images makes a big difference. Another object-recognition model that was trained on color images first, followed by grayscale images, did not do as well at identifying black-and-white objects.

“It’s not just the steps of the developmental choreography that are important, but also the order in which they are played out,” Sinha says.

The advantages of limited sensory input

By analyzing the internal organization of the models, the researchers found that those that begin with grayscale inputs learn to rely on luminance to identify objects. Once they begin receiving color input, they don’t change their approach very much, since they’ve already learned a strategy that works well. Models that began with color images did shift their approach once grayscale images were introduced, but could not shift enough to make them as accurate as the models that were given grayscale images first.

A similar phenomenon may occur in the human brain, which has more plasticity early in life, and can easily learn to identify objects based on their luminance alone. Early in life, the paucity of color information may in fact be beneficial to the developing brain, as it learns to identify objects based on sparse information.

“As a newborn, the normally sighted child is deprived, in a certain sense, of color vision. And that turns out to be an advantage,” Diamond says.

Researchers in Sinha’s lab have observed that limitations in early sensory input can also benefit other aspects of vision, as well as the auditory system. In 2022, they used computational models to show that early exposure to only low-frequency sounds, similar to those that babies hear in the womb, improves performance on auditory tasks that require analyzing sounds over a longer period of time, such as recognizing emotions. They now plan to explore whether this phenomenon extends to other aspects of development, such as language acquisition.

The research was funded by the National Eye Institute of NIH and the Intelligence Advanced Research Projects Activity.

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