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Real-Time Problem Solving (RTPS)

Why Real-Time Problem Solving is a Key to Habitual Excellence and How Value Capture Can Help

Solving Problems More Effectively... and More Quickly

Better problem-solving means more improvement and fewer problems. Many people focus, rightly, on "root cause problem-solving."

How do we get better at solving problems, in a way that allows us to prevent problems from occurring again? That's an important challenge.

Having a solid, disciplined, and scientific problem-solving approach gives us one of the methods required to work toward zero harm and habitual excellence .

Root-cause problem-solving is a skill that can be learned, coached, and developed.

Value Capture has learned that combining "root cause" with "real-time" is an incredibly powerful combination. "Real-time" can also be learned and practiced.

Our team has learned that solving problems one-by-one  as they occur is the key to driving results and the habits that create habitual excellence. The world's best organizations do this. We've been fortunate to learn about this directly from Toyota, Paul O'Neill , Steve Spear, and other mentors.

We can help you master real-time root-cause problem-solving, enabling your organization to establish safety and maintain flow, among other benefits. You can get better at root-cause, and you can get better at real-time...

Let's dig in.

Real-Time Problem-Solving Steps

First, psychological safety.

It cannot be emphasized enough that  none of this can be done at its full potential without a high degree of psychological safety .

Said another way, leaders and facilitators must establish and maintain professional and emotional safety throughout the process — and ideally that safety is there every day, in every setting and situation.

Then, respond immediately

Real-time means... "real-time." That means as close to "immediately" as possible. Not next month, not even the next day, but as soon as possible. STAT.

The sooner we start understanding the situation, the better. Problems are best solved when they are fresh. Think of incidents or risks as being extremely perishable. If we talk about it in next month's committee meeting, too many important details will be lost forever. That means we either won't be able to improve or our attempts will be less effective.

Any person who identifies a problem can activate their "help chain," which means a leader responds quickly -- not so the leader can solve the problem alone, but for the leader to facilitate the problem-solving discussion. Whatever problems cannot be solved to root cause at the front lines will be escalated that day to the level that  can solve the systemic root of the problem.

Investigate the problem to find the point of cause:

When the leader responds to work with the team, they work together on the following steps:

• Go and see (go to the actual place the problem was found instead of talking about it in conference rooms)
• Talk to the people who do the work (understand the real reality with them instead of guessing or assuming)
• Find the place where the problem really broke down (which might not be the same place that the problem was discovered)

Find the root cause

Once we have found  where  the problem occurred (the "point of cause"), we quite often need to dig deeper to find the "root cause." If we have relatively simple problems with simple countermeasures that can be tested quickly, root cause analysis might not be needed.

But, when we have complex problems (or sticky problems), root cause analysis is extremely helpful.

• Ask the people who were involved what circumstance caused the problem -- don't ask "who made a mistake?" but ask "how could that have occurred?"
• Ask "why did that circumstance exist?" many times until you can't ask why anymore
• Include the manager of the area (in a blame-free manner) and the person to whom they report — note that it may be necessary to move up several steps of the chain of command to get to the person responsible for all areas involved

Once we have a proper understanding of the problem and the situation, then we...

Create a target condition

Since we have avoided jumping to solutions through this process, we can now work toward designing and implementing an improved, if not ideal, target condition (or future state).

Steps in this process include:

• Ask the people who do the work to describe a solution that would prevent the last “Why?” (the root cause) from happening again
• Acknowledge any barriers to the ideal solution and incorporate practical solutions to those barriers
• Ask the people who do the work, “How can you design your work so that it can’t be done incorrectly? If you can’t prevent all errors, how can you recognize immediately that an error has occurred?” 

Take action and test your ideas

As a team, with the coaching and support of leaders, it's time to test the target condition and the improvements that get us there.

• Determine what actions will be taken by whom and by when
• Describe the hypothesis that will be tested and how you will know whether or not the experiment worked

Share your solutions

Hopefully, your organization has a "system to share" so that, in nearly real-time (same day):

• Everybody in the organization knows about the risk, problem, incident, or harm
• Everybody in the organization knows about the process changes

The knowledge that's gained by one part of the organization should be shared with others, in real time. Why should every department or every site have to repeat the same mistake to trigger their own problem solving?

One benefit of being part of a system is that we can operate like an integrated system that shares knowledge and new practices.

Learn more about problem solving:

Navigating Patient Flow Challenges: A Lean Approach to Root-Cause Problem Solving

The Power of Near-Miss Reporting: Enhancing Healthcare Quality and Safety

Hospital Command Centers: Keep What's Best and Improve the Rest

Improve Outcomes, Reduce Employee Turnover and Accelerate Innovation with a Daily Engagement System

How to Foster Problem Solving and Improvement

Improving in an Experimental Way: A3, PDSA, Kata, and More

Ideal Real-Time Problem Solving Model

Here is the way Value Capture illustrates this model. Talk to us today (a real-time conversation) if you'd like to learn more about these RTPS approaches and how we can help you.

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Results from the implementation of real-time problem solving alone  .

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Gemba in Lean: A Guide to Real-Time Problem Solving

July 29, 2023 - 10 min read

Gemba is a fundamental concept in Lean management that plays a crucial role in real-time problem solving. By understanding and implementing the principles of Gemba, organizations can identify and address issues as they arise, leading to continuous improvement and increased operational efficiency. This article serves as a comprehensive guide to Gemba in Lean, covering its origin, importance, implementation, case studies, and challenges.

Understanding the Concept of Gemba

Gemba is a Japanese term that translates to "the real place" or "the actual place." It refers to the physical location where value is created, such as the shop floor, production line, or service area. The concept of Gemba originates from the Toyota Production System (TPS), which revolutionized manufacturing practices in the 20th century. Toyota recognized the importance of direct observation and interaction with the actual production processes to identify waste and improve efficiency. In essence, Gemba embodies this philosophy of going to where the work is happening to gain a deeper understanding of the operations.

Imagine yourself transported back in time to the early days of the Toyota Production System. You find yourself in a bustling factory, surrounded by the sounds of machinery and the smell of oil. Workers move with purpose, their hands deftly maneuvering tools and components. The concept of Gemba is born out of this environment, where managers and leaders realized that true insights and improvements could only be gained by immersing themselves in the heart of production.

Gemba in the Context of Lean Management

In Lean management, Gemba serves as a cornerstone for problem-solving and continuous improvement. It emphasizes the need for leaders and managers to regularly visit the Gemba to observe, listen, and learn from the employees. By engaging with the frontline workers, managers can better understand the existing challenges and support the implementation of effective solutions.

The Importance of Gemba Walks in Lean

Gemba walks, also known as Gemba tours or Genchi Genbutsu, are an essential component of Lean management. These structured walks involve leaders and managers visiting the Gemba to observe the process, identify problems, and gather insights directly from the employees. Gemba walks not only facilitate real-time problem-solving but also foster a culture of continuous improvement and employee engagement.

Identifying Problems

During Gemba walks, leaders have the opportunity to observe the process in action and identify issues that may not be evident from reports or metrics alone. This hands-on approach to problem identification enables them to make informed decisions and develop effective solutions.

For example, imagine a manufacturing company that has been experiencing a high rate of defects in their production line. The managers could rely solely on data and reports to try and pinpoint the cause of the problem. However, by conducting Gemba walks, they can witness firsthand the specific steps in the production process where errors are occurring. They can observe the equipment being used, the techniques employed by the workers, and any other factors that may contribute to the defects. This direct observation allows them to gather valuable insights that may have been overlooked if they remained in their offices to resolve the issue.

What's more, Gemba walks provide an opportunity for managers to engage with employees and encourage open communication. By actively involving the frontline workers in the problem identification process, managers can tap into their knowledge and experience. Employees who are directly involved in the day-to-day operations are often the ones who have valuable insights into the root causes of problems. By listening to their perspectives and ideas during Gemba walks, managers can gain a holistic understanding of the issues at hand.

Facilitating Real-Time Problem Solving

Gemba walks enable real-time problem-solving by providing immediate access to information and fostering collaboration between managers and employees. When leaders visit the Gemba, they can address issues as they arise, rather than waiting for formal reports or meetings. By engaging with the employees directly, managers can gather valuable insights, encourage suggestions for improvement, and empower the frontline workers to be part of the problem-solving process.

For instance, imagine a retail store facing a sudden surge in customer complaints about long waiting times at the checkout counters. Instead of relying on customer feedback forms or relying solely on the reports generated by the point-of-sale system, the store manager decides to conduct Gemba walks. During these walks, the manager observes the checkout process, interacts with the cashiers, and talks to customers. Through these interactions, the manager discovers that the issue stems from a lack of trained staff during peak hours. With this knowledge, the manager can immediately address the problem by reallocating resources and providing additional training to the cashiers, resulting in improved customer satisfaction and the organization's continued success.

In addition to problem-solving, Gemba walks also contribute to employee engagement and empowerment. When managers actively involve employees in the problem-solving process, it creates a sense of ownership and responsibility among the workforce. Employees feel valued and recognized for their expertise, as their manager is somebody who is there to communicate and offer assistance. This leads to increased job satisfaction and motivation within a culture of continuous improvement.

Implementing Gemba in Your Organization

Implementing Gemba in your organization requires a structured approach and a commitment to continuous improvement. Here are the key steps to successfully integrate Gemba into your management practices:

Prior to conducting a Gemba walk, it is essential to establish clear objectives and define the scope of the walk. This involves identifying the specific areas or processes you want to observe, the questions you want to ask, and the metrics you want to track. By setting clear goals, you can see to it that your Gemba walk is focused and productive.

Additionally, it is crucial to prepare a checklist or a template to guide your observations and note-taking during the walk. This will help you stay organized so that you capture all the necessary information. The checklist can include items such as process steps, key performance indicators, safety protocols, and areas for improvement.

Conducting a Walk

During the Gemba walk, it is important to maintain a mindset of curiosity and openness. As you observe the process flow, pay attention to the interactions between employees and customers. Look for any signs of waste or inefficiency, such as bottlenecks, unnecessary waiting times, or excessive rework.

Engaging in conversations with the employees is another important aspect of a successful Gemba walk. Take the time to understand their challenges and concerns, gather their ideas for improvement, and provide guidance and support.

Also, it is essential to take detailed notes. These notes should capture not only your observations but also any insights or ideas that arise during the walk. Consider capturing visual evidence, such as photographs or videos, to document your observations and provide a visual reference for future discussions and analysis.

After the Gemba walk, it is important to analyze the data and information collected. Look for patterns, identify root causes of problems, and prioritize improvement opportunities. Use this analysis to develop action plans and implement changes that will lead to tangible results.

Case Studies: Gemba in Action

Real-life examples provide valuable insights into the successful implementation of Gemba in different industries. Let's explore two case studies showcasing the application of Gemba:

Gemba Success Story in Manufacturing

A manufacturing company implemented Gemba walks as part of their Lean transformation journey. The leaders regularly visited the shop floor, observing the production processes and engaging with the operators.

During the walks, the leaders noticed that the operators were spending a significant amount of time searching for tools and materials. This led to delays in production and decreased efficiency. To address this issue, the company implemented a 5S system, organizing the workstations and ensuring that all tools and materials were easily accessible. Furthermore, the Gemba walks also revealed that there was a lack of standard work instructions for certain processes. This led to variations in how operators performed their tasks, which affected product quality and consistency. The company implemented standardized work instructions, providing clear guidelines for each step of the process.

Overall, the implementation of Gemba walks in this manufacturing company brought about tangible improvements in productivity, efficiency, and employee engagement. By actively involving the operators in the improvement process, the company fostered a culture of continuous improvement and created a sense of ownership among the employees.

Gemba Application in Healthcare

In a hospital setting, Gemba walks played a crucial role in improving patient care and operational efficiency. The hospital's management team conducted regular Gemba walks to observe the patient flow, interaction between healthcare professionals and patients, and the effectiveness of processes. 

During one of the walks, the management team noticed that there were frequent delays in obtaining test results, which prolonged the time taken to diagnose and treat patients. Upon further investigation, they discovered that the laboratory was understaffed, leading to a backlog of tests. To address this issue, the hospital hired additional laboratory technicians and implemented a more efficient test result reporting system. Another area of improvement identified during the Gemba walks was the communication between healthcare professionals and patients. It was observed that there was a lack of clarity in explaining medical procedures and treatment plans to patients, leading to confusion and anxiety. To enhance communication, the hospital implemented a standardized communication protocol, so that healthcare professionals only used clear and easily understandable language when interacting with patients. 

Through the consistent application of Gemba walks, the hospital was able to identify and address various operational inefficiencies, resulting in improved patient care, reduced wait times, and enhanced overall quality of care. Ultimately, the Gemba approach empowered the hospital staff to actively participate in the continuous improvement process, fostering a culture of patient-centric care and collaboration.

Challenges and Solutions in Gemba Implementation

While Gemba walks offer numerous benefits, organizations may face challenges during the implementation phase. Awareness of these challenges and proactive solutions can ensure a successful Gemba implementation.

• Solve this by recognizing and appreciating good practices. Especially since this may be a new concept for your organization, your staff may not know what to expect. Therefore, they may act pessimistic and unsupportive of your Gemba walk implementation.
• Mitigate this by providing constructive feedback to your staff and establishing a supportive work culture. Emphasize an open-door policy, in which your door is always "open" if your staff wants to raise an issue.
• Address this by investing in training and education that clearly explains to employees how Gemba walks benefit them and the organization as a whole. Create a safe and non-judgmental environment, where your staff can feel comfortable sharing their ideas and concerns.

Leverage the power of Gemba in Lean for real-time problem-solving using Wrike's advanced features. Start a free trial today to drive continuous improvement practices in your organization.

Note: This article was created with the assistance of an AI engine. It has been reviewed and revised by our team of experts to ensure accuracy and quality.

Occasionally we write blog posts where multiple people contribute. Since our idea of having a gladiator arena where contributors would fight to the death to win total authorship wasn’t approved by HR, this was the compromise.

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PM, PPM, PgM. What is the difference between portfolio management and project management, exactly? And where does program management fit in? Here's a quick explanation of each in plain English to help you distinguish the differences. What is Portfolio Management? A portfolio is a high-level view of all the projects an organization is running in order to meet the business's main strategic objectives. It could be every project across the entire company, a division, or a department. Portfolio management involves setting priorities based on the business leadership’s agreed-on objectives, and then choosing programs and projects to undertake based on what will provide optimal business value, the level of risk involved, and available resources. According to project manager Bob Buttrick, while project management is about executing projects right, portfolio management is about executing the right projects. In Agile portfolio management, it's all about leaning into Agile principles and values to organize and plan for programs and projects within the portfolio. Project portfolio managers look at a company’s projects and evaluate whether they're are being executed well, how they could be improved, and whether the organization is experiencing the expected benefits. What is Program Management? A program is a group of related projects that all contribute to the same business objective or benefit. The program as a whole has a clear, defined goal, and each project within the program assists in meeting those goals.   Program managers look at cross-project dependencies, risks, issues, requirements, and solutions, and may coordinate with individual project managers to achieve these insights and keep the overall program healthy. They’re less concerned with the success of every single individual project, and more focused on the success of the overall initiative and achieving the larger benefit. Program managers are also concerned with making sure the right projects are chosen or prioritized in order to achieve the most business value. Successful programs work towards improvements that will have a long-term impact on the organization, and unlike projects that have a specific end date, programs may be ongoing initiatives. Organizations manage projects as a larger program because doing so gives you greater control and benefits than you may see by managing them separately. It’s also easier to coordinate and prioritize resources across projects, and oversee progress and outcomes when you look at a group of related projects. What is Project Management? While portfolios and programs focus on a higher-level view of an organization's activities, a project is a single undertaking: a series of tasks that aims to produce a specific product, service, or benefit within a defined timeline. Project managers oversee individual projects, leading teams and making sure projects are completed on time, within budget, and meet the established requirements. They determine best practices, examine processes to improve efficiency, and work with stakeholders to make sure expected benefits are realized, among other responsibilities. Good project management means teams and team members are constantly developing and improving, giving the business a competitive advantage. Learn More About Project Management If you're a new project manager and still struggling to comprehend the vocabulary as well as the processes, then we've prepared a resource that you will find useful. It's called The Project Management Guide for Beginners, and it's online and totally free to browse. Bookmark it for easy reference. Sources: PMfiles.com, Wikipedia.org, ProjectSmart.co.uk

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In Uncertain Times, Embrace Imperfectionism

• Charles Conn

A strategy that prioritizes frequent experimentation will help you gain valuable knowledge, assets, and capabilities.

Change is accelerating, with uncertainty and threat of disruption in every industry segment. The pace of change and rise of global winner-take-all competition means that conventional product-market-structure approaches to strategy, as well as core competencies thinking, are difficult to implement in practice and may yield misleading answers. Under today’s conditions, the authors argue that real-time problem solving should be the heart of strategy development rather than theoretical frameworks, and they present a framework for this approach. This approach requires companies to look at strategy through multiple lenses or vantage points, typically anchoring these outside the company in its ecosystem or beyond. Looking at your business through the eyes of your suppliers, customers, current rivals, and potential outside entrants will give you much more perspective on both threats and opportunities than you will get from staying inside your company’s prevailing mindsets and routines. It should also seek to generate new data on these perspectives through experimentation, augmenting this new data by crowdsourcing external ideas and technologies to bring collective intelligence to bear.

If you are an Amazon customer, chances are you have encountered household names such as Amazon Prime and Zappos. It’s less likely that you would have noted the baby steps Amazon took to expand beyond its core business into consumer financial services.

• CC Charles Conn is the current Board Chair of Patagonia, the global outdoor clothing and sportswear company, a partner at Monograph.bio, an investment firm focused on the healthcare sector, and a former CEO of the Rhodes Trust. He is co-author (with Rob McLean) of The Imperfectionists: Strategic Mindsets for Uncertain Times , (Wiley 2023), and Bulletproof Problem Solving (Wiley 2019).
• RM Rob McLean is a Director emeritus of McKinsey & Company and former Dean of the Australian Graduate School of Management.  He is co-author (with Charles Conn) of The Imperfectionists: Strategic Mindsets for Uncertain Times , (Wiley 2023), and Bulletproof Problem Solving (Wiley 2019).

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Ideas Made to Matter

How to use algorithms to solve everyday problems

Kara Baskin

May 8, 2017

How can I navigate the grocery store quickly? Why doesn’t anyone like my Facebook status? How can I alphabetize my bookshelves in a hurry? Apple data visualizer and MIT System Design and Management graduate Ali Almossawi solves these common dilemmas and more in his new book, “ Bad Choices: How Algorithms Can Help You Think Smarter and Live Happier ,” a quirky, illustrated guide to algorithmic thinking. 

For the uninitiated: What is an algorithm? And how can algorithms help us to think smarter?

An algorithm is a process with unambiguous steps that has a beginning and an end, and does something useful.

Algorithmic thinking is taking a step back and asking, “If it’s the case that algorithms are so useful in computing to achieve predictability, might they also be useful in everyday life, when it comes to, say, deciding between alternative ways of solving a problem or completing a task?” In all cases, we optimize for efficiency: We care about time or space.

Note the mention of “deciding between.” Computer scientists do that all the time, and I was convinced that the tools they use to evaluate competing algorithms would be of interest to a broad audience.

Why did you write this book, and who can benefit from it?

All the books I came across that tried to introduce computer science involved coding. My approach to making algorithms compelling was focusing on comparisons. I take algorithms and put them in a scene from everyday life, such as matching socks from a pile, putting books on a shelf, remembering things, driving from one point to another, or cutting an onion. These activities can be mapped to one or more fundamental algorithms, which form the basis for the field of computing and have far-reaching applications and uses.

I wrote the book with two audiences in mind. One, anyone, be it a learner or an educator, who is interested in computer science and wants an engaging and lighthearted, but not a dumbed-down, introduction to the field. Two, anyone who is already familiar with the field and wants to experience a way of explaining some of the fundamental concepts in computer science differently than how they’re taught.

I’m going to the grocery store and only have 15 minutes. What do I do?

Do you know what the grocery store looks like ahead of time? If you know what it looks like, it determines your list. How do you prioritize things on your list? Order the items in a way that allows you to avoid walking down the same aisles twice.

For me, the intriguing thing is that the grocery store is a scene from everyday life that I can use as a launch pad to talk about various related topics, like priority queues and graphs and hashing. For instance, what is the most efficient way for a machine to store a prioritized list, and what happens when the equivalent of you scratching an item from a list happens in the machine’s list? How is a store analogous to a graph (an abstraction in computer science and mathematics that defines how things are connected), and how is navigating the aisles in a store analogous to traversing a graph?

Nobody follows me on Instagram. How do I get more followers?

The concept of links and networks, which I cover in Chapter 6, is relevant here. It’s much easier to get to people whom you might be interested in and who might be interested in you if you can start within the ball of links that connects those people, rather than starting at a random spot.

You mention Instagram: There, the hashtag is one way to enter that ball of links. Tag your photos, engage with users who tag their photos with the same hashtags, and you should be on your way to stardom.

What are the secret ingredients of a successful Facebook post?

I’ve posted things on social media that have died a sad death and then posted the same thing at a later date that somehow did great. Again, if we think of it in terms that are relevant to algorithms, we’d say that the challenge with making something go viral is really getting that first spark. And to get that first spark, a person who is connected to the largest number of people who are likely to engage with that post, needs to share it.

With [my first book], “Bad Arguments,” I spent a month pouring close to $5,000 into advertising for that project with moderate results. And then one science journalist with a large audience wrote about it, and the project took off and hasn’t stopped since.

What problems do you wish you could solve via algorithm but can’t?

When we care about efficiency, thinking in terms of algorithms is useful. There are cases when that’s not the quality we want to optimize for — for instance, learning or love. I walk for several miles every day, all throughout the city, as I find it relaxing. I’ve never asked myself, “What’s the most efficient way I can traverse the streets of San Francisco?” It’s not relevant to my objective.

Algorithms are a great way of thinking about efficiency, but the question has to be, “What approach can you optimize for that objective?” That’s what worries me about self-help: Books give you a silver bullet for doing everything “right” but leave out all the nuances that make us different. What works for you might not work for me.

Which companies use algorithms well?

When you read that the overwhelming majority of the shows that users of, say, Netflix, watch are due to Netflix’s recommendation engine, you know they’re doing something right.

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School of Medicine Continuing Professional Development

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Continuing Professional Development

A3: a framework for real-time problem solving, part of the travel-free cme series, activity details.

Learning objectives:

• Explain the A3 thinking as a problem solving methodology
• Summarize the purpose of an A3 Template
• Differentiate between the  left  side and  right  side of an A3 template

This activity will be presented by Alphonse Nwerem , who is a performance improvement consultant within OHSU’s Quality Management Department. In his current role, Alphonse facilitates performance improvement initiatives in support of the clinical enterprise strategic plans and goals. He brings expertise and training in LEAN methodology, Six Sigma, project management, facilitation, classroom training and change management.

Six Sigma tools for real time problem solving

Six Sigma projects can last months, sometimes years, depending on the project’s scope.  These projects are designed to break down large, complex tasks into small components for analysis.  From that analysis, companies can work to improve their processes and eliminate excess process waste.

This timeframe works well for large organizations, like publicly traded companies, since they have the time and resources to dedicate to a long-timeframe project.  For smaller organizations however, time isn’t always on your side.  Small companies, and even sole proprietors, can benefit from tools used during a Six Sigma project, but they rarely have the luxury of time.  Small businesses need to be nimble and adapt to change quickly.

Kaizen projects often fill the role of a project for small companies.  These projects, or sprints, are often completed in 7 to 10 days and use many of the same tools that larger projects employ.  These projects help you improve a particular process or problem in a faster timeline.

You need immediate results

What if a one-week timeline is still too long?  What if you need results immediately?  Occasionally businesses will face a crisis, like a sudden drop in business or a problem with their supply line.  They may not have a week or two to run a proper project when this happens.  When that happens, companies can use the tools of Six Sigma to gain real-time insight into their problems and work to fix them immediately.  But what tools should you use?  Let’s look at three tools that will help you with real-time results:

Voice of the Customer

Voice of the customer (VOC) is one of the critical elements of a successful Six Sigma project.  Understanding your customer opinions (voice) about your product or service will help you determine where problems can arise.  If you need real-time information about a product or service, you can measure this information at the point of sale or time of service.  Don’t wait to send out a survey; ask the customer at the moment of service and record those answers for analysis later.

By monitoring customer responses in real-time, you may be able to spot issues for correction quickly.  Perhaps one of your employees is being rude to customers, or your store is dirty.  You may not be aware of the issue, but your customers will bring it to your attention.

Root Cause Analysis

The root cause of many problems can be determined using different tools.  One of the fastest is a process called 5W2H.  This stands for 5 Why and 2 How.  To use this method, you ask seven questions when you encounter a problem: Who?  What?  Why?  When?  Where?  How?  How Many?

• Who? Individuals/customers associated with problem
• What? The problem statement or definition
• When? Date and time problem was identified
• Where? Location of complaints (area, facilities, customers)
• Why? Any previously known explanations
• How? How did the problem happen (root cause), and how will the problem be corrected (corrective action)?
• How Many? Size and frequency of the problem

Much like VOC, 5W2H data can be collected in real-time by asking the associated questions at the problem point.  This is especially useful for supply chain and logistical issues since you may not have eyes on the process.

5S and Visual Controls

5S is a process to improve workplace organization.  It can be deployed quickly and often fixes problems in real-time.  It is particularly effective in industries like manufacturing, where tools can be easily misplaced.  It can do more than just fix issues however, it can also give you real-time information how effective your processes are.  A/B testing can be conducted by implementing 5S at one workstation and leaving the others as-is.  This allows you to watch processes in real-time as employees conduct normal business operations.  If the 5S setup is more effective, you can convert the remaining workstations.

These three tools are a starting point for real-time analysis, but they are far from the only practical tools for analyzing processes.  Gaining a Six Sigma Master Black Belt allows you to learn about these tools so you can deploy them in your work environment.

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Ep. 16: a deeper dive into real-time problem solving.

Sherri Stahl, RN, MHA, NEA-BC, CPXP Roxanna Gapstur, PhD, RN

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• Front Hum Neurosci

Real World Problem-Solving

Real world problem-solving (RWPS) is what we do every day. It requires flexibility, resilience, resourcefulness, and a certain degree of creativity. A crucial feature of RWPS is that it involves continuous interaction with the environment during the problem-solving process. In this process, the environment can be seen as not only a source of inspiration for new ideas but also as a tool to facilitate creative thinking. The cognitive neuroscience literature in creativity and problem-solving is extensive, but it has largely focused on neural networks that are active when subjects are not focused on the outside world, i.e., not using their environment. In this paper, I attempt to combine the relevant literature on creativity and problem-solving with the scattered and nascent work in perceptually-driven learning from the environment. I present my synthesis as a potential new theory for real world problem-solving and map out its hypothesized neural basis. I outline some testable predictions made by the model and provide some considerations and ideas for experimental paradigms that could be used to evaluate the model more thoroughly.

1. Introduction

In the Apollo 13 space mission, astronauts together with ground control had to overcome several challenges to bring the team safely back to Earth (Lovell and Kluger, 2006 ). One of these challenges was controlling carbon dioxide levels onboard the space craft: “For 2 days straight [they] had worked on how to jury-rig the Odysseys canisters to the Aquarius's life support system. Now, using materials known to be available onboard the spacecraft—a sock, a plastic bag, the cover of a flight manual, lots of duct tape, and so on—the crew assembled a strange contraption and taped it into place. Carbon dioxide levels immediately began to fall into the safe range” (Team, 1970 ; Cass, 2005 ).

The success of Apollo 13's recovery from failure is often cited as a glowing example of human resourcefulness and inventiveness alongside more well-known inventions and innovations over the course of human history. However, this sort of inventive capability is not restricted to a few creative geniuses, but an ability present in all of us, and exemplified in the following mundane example. Consider a situation when your only suit is covered in lint and you do not own a lint remover. You see a roll of duct tape, and being resourceful you reason that it might be a good substitute. You then solve the problem of lint removal by peeling a full turn's worth of tape and re-attaching it backwards onto the roll to expose the sticky side all around the roll. By rolling it over your suit, you can now pick up all the lint.

In both these examples (historic as well as everyday), we see evidence for our innate ability to problem-solve in the real world. Solving real world problems in real time given constraints posed by one's environment are crucial for survival. At the core of this skill is our mental capability to get out of “sticky situations” or impasses, i.e., difficulties that appear unexpectedly as impassable roadblocks to solving the problem at hand. But, what are the cognitive processes that enable a problem solver to overcome such impasses and arrive at a solution, or at least a set of promising next steps?

A central aspect of this type of real world problem solving, is the role played by the solver's surrounding environment during the problem-solving process. Is it possible that interaction with one's environment can facilitate creative thinking? The answer to this question seems somewhat obvious when one considers the most famous anecdotal account of creative problem solving, namely that of Archimedes of Syracuse. During a bath, he found a novel way to check if the King's crown contained non-gold impurities. The story has traditionally been associated with the so-called “Eureka moment,” the sudden affective experience when a solution to a particularly thorny problem emerges. In this paper, I want to temporarily turn our attention away from the specific “aha!” experience itself and take particular note that Archimedes made this discovery, not with his eyes closed at a desk, but in a real-world context of a bath 1 . The bath was not only a passive, relaxing environment for Archimedes, but also a specific source of inspiration. Indeed it was his noticing the displacement of water that gave him a specific methodology for measuring the purity of the crown; by comparing how much water a solid gold bar of the same weight would displace as compared with the crown. This sort of continuous environmental interaction was present when the Apollo 13 engineers discovered their life-saving solution, and when you solved the suit-lint-removal problem with duct tape.

The neural mechanisms underlying problem-solving have been extensively studied in the literature, and there is general agreement about the key functional networks and nodes involved in various stages of problem-solving. In addition, there has been a great deal of work in studying the neural basis for creativity and insight problem solving, which is associated with the sudden emergence of solutions. However, in the context of problem-solving, creativity, and insight have been researched as largely an internal process without much interaction with and influence from the external environment (Wegbreit et al., 2012 ; Abraham, 2013 ; Kounios and Beeman, 2014 ) 2 . Thus, there are open questions of what role the environment plays during real world problem-solving (RWPS) and how the brain enables the assimilation of novel items during these external interactions.

In this paper, I synthesize the literature on problem-solving, creativity and insight, and particularly focus on how the environment can inform RWPS. I explore three environmentally-informed mechanisms that could play a critical role: (1) partial-cue driven context-shifting, (2) heuristic prototyping and learning novel associations, and (3) learning novel physical inferences. I begin first with some intuitions about real world problem solving, that might help ground this discussion and providing some key distinctions from more traditional problem solving research. Then, I turn to a review of the relevant literature on problem-solving, creativity, and insight first, before discussing the three above-mentioned environmentally-driven mechanisms. I conclude with a potential new model and map out its hypothesized neural basis.

2. Problem solving, creativity, and insight

2.1. what is real world problem-solving.

Archimedes was embodied in the real world when he found his solution. In fact, the real world helped him solve the problem. Whether or not these sorts of historic accounts of creative inspiration are accurate 3 , they do correlate with some of our own key intuitions about how problem solving occurs “in the wild.” Real world problem solving (RWPS) is different from those that occur in a classroom or in a laboratory during an experiment. They are often dynamic and discontinuous, accompanied by many starts and stops. Solvers are never working on just one problem. Instead, they are simultaneously juggling several problems of varying difficulties and alternating their attention between them. Real world problems are typically ill-defined, and even when they are well-defined, often have open-ended solutions. Coupled with that is the added aspect of uncertainty associated with the solver's problem solving strategies. As introduced earlier, an important dimension of RWPS is the continuous interaction between the solver and their environment. During these interactions, the solver might be inspired or arrive at an “aha!” moment. However, more often than not, the solver experiences dozens of minor discovery events— “hmmm, interesting…” or “wait, what?…” moments. Like discovery events, there's typically never one singular impasse or distraction event. The solver must iterate through the problem solving process experiencing and managing these sorts of intervening events (including impasses and discoveries). In summary, RWPS is quite messy and involves a tight interplay between problem solving, creativity, and insight. Next, I explore each of these processes in more detail and explicate a possible role of memory, attention, conflict management and perception.

2.2. Analytical problem-solving

In psychology and neuroscience, problem-solving broadly refers to the inferential steps taken by an agent 4 that leads from a given state of affairs to a desired goal state (Barbey and Barsalou, 2009 ). The agent does not immediately know how this goal can be reached and must perform some mental operations (i.e., thinking) to determine a solution (Duncker, 1945 ).

The problem solving literature divides problems based on clarity (well-defined vs. ill-defined) or on the underlying cognitive processes (analytical, memory retrieval, and insight) (Sprugnoli et al., 2017 ). While memory retrieval is an important process, I consider it as a sub-process to problem solving more generally. I first focus on analytical problem-solving process, which typically involves problem-representation and encoding, and the process of forming and executing a solution plan (Robertson, 2016 ).

2.2.1. Problem definition and representation

An important initial phase of problem-solving involves defining the problem and forming a representation in the working memory. During this phase, components of the prefrontal cortex (PFC), default mode network (DMN), and the dorsal anterior cingulate cortex (dACC) have been found to be activated. If the problem is familiar and well-structured, top-down executive control mechanisms are engaged and the left prefrontal cortex including the frontopolar, dorso-lateral (dlPFC), and ventro-lateral (vlPFC) are activated (Barbey and Barsalou, 2009 ). The DMN along with the various structures in the medial temporal lobe (MTL) including the hippocampus (HF), parahippocampal cortex, perirhinal and entorhinal cortices are also believed to have limited involvement, especially in episodic memory retrieval activities during this phase (Beaty et al., 2016 ). The problem representation requires encoding problem information for which certain visual and parietal areas are also involved, although the extent of their involvement is less clear (Anderson and Fincham, 2014 ; Anderson et al., 2014 ).

2.2.1.1. Working memory

An important aspect of problem representation is the engagement and use of working memory (WM). The WM allows for the maintenance of relevant problem information and description in the mind (Gazzaley and Nobre, 2012 ). Research has shown that WM tasks consistently recruit the dlPFC and left inferior frontal cortex (IC) for encoding an manipulating information; dACC for error detection and performance adjustment; and vlPFC and the anterior insula (AI) for retrieving, selecting information and inhibitory control (Chung and Weyandt, 2014 ; Fang et al., 2016 ).

2.2.1.2. Representation

While we generally have a sense for the brain regions that are functionally influential in problem definition, less is known about how exactly events are represented within these regions. One theory for how events are represented in the PFC is the structured event complex theory (SEC), in which components of the event knowledge are represented by increasingly higher-order convergence zones localized within the PFC, akin to the convergence zones (from posterior to anterior) that integrate sensory information in the brain (Barbey et al., 2009 ). Under this theory, different zones in the PFC (left vs. right, anterior vs. posterior, lateral vs. medial, and dorsal vs. ventral) represent different aspects of the information contained in the events (e.g., number of events to be integrated together, the complexity of the event, whether planning, and action is needed). Other studies have also suggested the CEN's role in tasks requiring cognitive flexibility, and functions to switch thinking modes, levels of abstraction of thought and consider multiple concepts simultaneously (Miyake et al., 2000 ).

Thus, when the problem is well-structured, problem representation is largely an executive control activity coordinated by the PFC in which problem information from memory populates WM in a potentially structured representation. Once the problem is defined and encoded, planning and execution of a solution can begin.

2.2.2. Planning

The central executive network (CEN), particularly the PFC, is largely involved in plan formation and in plan execution. Planning is the process of generating a strategy to advance from the current state to a goal state. This in turn involves retrieving a suitable solution strategy from memory and then coordinating its execution.

2.2.2.1. Plan formation

The dlPFC supports sequential planning and plan formation, which includes the generation of hypothesis and construction of plan steps (Barbey and Barsalou, 2009 ). Interestingly, the vlPFC and the angular gyrus (AG), implicated in a variety of functions including memory retrieval, are also involved in plan formation (Anderson et al., 2014 ). Indeed, the AG together with the regions in the MTL (including the HF) and several other regions form a what is known as the “core” network. The core network is believed to be activated when recalling past experiences, imagining fictitious, and future events and navigating large-scale spaces (Summerfield et al., 2010 ), all key functions for generating plan hypotheses. A recent study suggests that the AG is critical to both episodic simulation, representation, and episodic memory (Thakral et al., 2017 ). One possibility for how plans are formulated could involve a dynamic process of retrieving an optimal strategy from memory. Research has shown significant interaction between striatal and frontal regions (Scimeca and Badre, 2012 ; Horner et al., 2015 ). The striatum is believed to play a key role in declarative memory retrieval, and specifically helping retrieve optimal (or previously rewarded) memories (Scimeca and Badre, 2012 ). Relevant to planning and plan formation, Scimeca & Badre have suggested that the striatum plays two important roles: (1) in mapping acquired value/utility to action selection, and thereby helping plan formation, and (2) modulation and re-encoding of actions and other plan parameters. Different types of problems require different sets of specialized knowledge. For example, the knowledge needed to solve mathematical problems might be quite different (albeit overlapping) from the knowledge needed to select appropriate tools in the environment.

Thus far, I have discussed planning and problem representation as being domain-independent, which has allowed me to outline key areas of the PFC, MTL, and other regions relevant to all problem-solving. However, some types of problems require domain-specific knowledge for which other regions might need to be recruited. For example, when planning for tool-use, the superior parietal lobe (SPL), supramarginal gyrus (SMG), anterior inferior parietal lobe (AIPL), and certain portions of the temporal and occipital lobe involved in visual and spatial integration have been found to be recruited (Brandi et al., 2014 ). It is believed that domain-specific information stored in these regions is recovered and used for planning.

2.2.2.2. Plan execution

Once a solution plan has been recruited from memory and suitably tuned for the problem on hand, the left-rostral PFC, caudate nucleus (CN), and bilateral posterior parietal cortices (PPC) are responsible for translating the plan into executable form (Stocco et al., 2012 ). The PPC stores and maintains “mental template” of the executable form. Hemispherical division of labor is particularly relevant in planning where it was shown that when planning to solve a Tower of Hanoi (block moving) problem, the right PFC is involved in plan construction whereas the left PFC is involved in controlling processes necessary to supervise the execution of the plan (Newman and Green, 2015 ). On a separate note and not the focus of this paper, plan execution and problem-solving can require the recruitment of affective and motivational processing in order to supply the agent with the resolve to solve problems, and the vmPFC has been found to be involved in coordinating this process (Barbey and Barsalou, 2009 ).

2.3. Creativity

During the gestalt movement in the 1930s, Maier noted that “most instances of “real” problem solving involves creative thinking” (Maier, 1930 ). Maier performed several experiments to study mental fixation and insight problem solving. This close tie between insight and creativity continues to be a recurring theme, one that will be central to the current discussion. If creativity and insight are linked to RWPS as noted by Maier, then it is reasonable to turn to the creativity and insight literature for understanding the role played by the environment. A large portion of the creativity literature has focused on viewing creativity as an internal process, one in which the solvers attention is directed inwards, and toward internal stimuli, to facilitate the generation of novel ideas and associations in memory (Beaty et al., 2016 ). Focusing on imagination, a number of researchers have looked at blinking, eye fixation, closing eyes, and looking nowhere behavior and suggested that there is a shift of attention from external to internal stimuli during creative problem solving (Salvi and Bowden, 2016 ). The idea is that shutting down external stimuli reduces cognitive load and focuses attention internally. Other experiments studying sleep behavior have also noted the beneficial role of internal stimuli in problem solving. The notion of ideas popping into ones consciousness, suddenly, during a shower is highly intuitive for many and researchers have attempted to study this phenomena through the lens of incubation, and unconscious thought that is internally-driven. There have been several theories and counter-theories proposed to account specifically for the cognitive processes underlying incubation (Ritter and Dijksterhuis, 2014 ; Gilhooly, 2016 ), but none of these theories specifically address the role of the external environment.

The neuroscience of creativity has also been extensively studied and I do not focus on an exhaustive literature review in this paper (a nice review can be found in Sawyer, 2011 ). From a problem-solving perspective, it has been found that unlike well-structured problems, ill-structured problems activate the right dlPFC. Most of the past work on creativity and creative problem-solving has focused on exploring memory structures and performing internally-directed searches. Creative idea generation has primarily been viewed as internally directed attention (Jauk et al., 2012 ; Benedek et al., 2016 ) and a primary mechanism involved is divergent thinking , which is the ability to produce a variety of responses in a given situation (Guilford, 1962 ). Divergent thinking is generally thought to involve interactions between the DMN, CEN, and the salience network (Yoruk and Runco, 2014 ; Heinonen et al., 2016 ). One psychological model of creative cognition is the Geneplore model that considers two major phases of generation (memory retrieval and mental synthesis) and exploration (conceptual interpretation and functional inference) (Finke et al., 1992 ; Boccia et al., 2015 ). It has been suggested that the associative mode of processing to generate new creative association is supported by the DMN, which includes the medial PFC, posterior cingulate cortex (PCC), tempororparietal juntion (TPJ), MTL, and IPC (Beaty et al., 2014 , 2016 ).

That said, the creativity literature is not completely devoid of acknowledging the role of the environment. In fact, it is quite the opposite. Researchers have looked closely at the role played by externally provided hints from the time of the early gestalt psychologists and through to present day studies (Öllinger et al., 2017 ). In addition to studying how hints can help problem solving, researchers have also looked at how directed action can influence subsequent problem solving—e.g., swinging arms prior to solving the two-string puzzle, which requires swinging the string (Thomas and Lleras, 2009 ). There have also been numerous studies looking at how certain external perceptual cues are correlated with creativity measures. Vohs et al. suggested that untidiness in the environment and the increased number of potential distractions helps with creativity (Vohs et al., 2013 ). Certain colors such as blue have been shown to help with creativity and attention to detail (Mehta and Zhu, 2009 ). Even environmental illumination, or lack thereof, have been shown to promote creativity (Steidle and Werth, 2013 ). However, it is important to note that while these and the substantial body of similar literature show the relationship of the environment to creative problem solving, they do not specifically account for the cognitive processes underlying the RWPS when external stimuli are received.

2.4. Insight problem solving

Analytical problem solving is believed to involve deliberate and conscious processing that advances step by step, allowing solvers to be able to explain exactly how they solved it. Inability to solve these problems is often associated with lack of required prior knowledge, which if provided, immediately makes the solution tractable. Insight, on the other hand, is believed to involve a sudden and unexpected emergence of an obvious solution or strategy sometimes accompanied by an affective aha! experience. Solvers find it difficult to consciously explain how they generated a solution in a sequential manner. That said, research has shown that having an aha! moment is neither necessary nor sufficient to insight and vice versa (Danek et al., 2016 ). Generally, it is believed that insight solvers acquire a full and deep understanding of the problem when they have solved it (Chu and Macgregor, 2011 ). There has been an active debate in the problem solving community about whether insight is something special. Some have argued that it is not, and that there are no special or spontaneous processes, but simply a good old-fashioned search of a large problem space (Kaplan and Simon, 1990 ; MacGregor et al., 2001 ; Ash and Wiley, 2006 ; Fleck, 2008 ). Others have argued that insight is special and suggested that it is likely a different process (Duncker, 1945 ; Metcalfe, 1986 ; Kounios and Beeman, 2014 ). This debate lead to two theories for insight problem solving. MacGregor et al. proposed the Criterion for Satisfactory Progress Theory (CSPT), which is based on Newell and Simons original notion of problem solving as being a heuristic search through the problem space (MacGregor et al., 2001 ). The key aspect of CSPT is that the solver is continually monitoring their progress with some set of criteria. Impasses arise when there is a criterion failure, at which point the solver tries non-maximal but promising states. The representational change theory (RCT) proposed by Ohlsson et al., on the other hand, suggests that impasses occur when the goal state is not reachable from an initial problem representation (which may have been generated through unconscious spreading activation) (Ohlsson, 1992 ). In order to overcome an impasse, the solver needs to restructure the problem representation, which they can do by (1) elaboration (noticing new features of a problem), (2) re-encoding fixing mistaken or incomplete representations of the problem, and by (3) changing constraints. Changing constraints is believed to involve two sub-processes of constraint relaxation and chunk-decomposition.

The current position is that these two theories do not compete with each other, but instead complement each other by addressing different stages of problem solving: pre- and post-impasse. Along these lines, Ollinger et al. proposed an extended RCT (eRCT) in which revising the search space and using heuristics was suggested as being a dynamic and iterative and recursive process that involves repeated instances of search, impasse and representational change (Öllinger et al., 2014 , 2017 ). Under this theory, a solver first forms a problem representation and begins searching for solutions, presumably using analytical problem solving processes as described earlier. When a solution cannot be found, the solver encounters an impasse, at which point the solver must restructure or change the problem representation and once again search for a solution. The model combines both analytical problem solving (through heuristic searches, hill climbing and progress monitoring), and creative mechanisms of constraint relaxation and chunk decomposition to enable restructuring.

Ollingers model appears to comprehensively account for both analytical and insight problem solving and, therefore, could be a strong candidate to model RWPS. However, while compelling, it is nevertheless an insufficient model of RWPS for many reasons, of which two are particularly significant for the current paper. First, the model does explicitly address mechanisms by which external stimuli might be assimilated. Second, the model is not sufficiently flexible to account for other events (beyond impasse) occurring during problem solving, such as distraction, mind-wandering and the like.

So, where does this leave us? I have shown the interplay between problem solving, creativity and insight. In particular, using Ollinger's proposal, I have suggested (maybe not quite explicitly up until now) that RWPS involves some degree of analytical problem solving as well as the post-impasse more creative modes of problem restructuring. I have also suggested that this model might need to be extended for RWPS along two dimensions. First, events such as impasses might just be an instance of a larger class of events that intervene during problem solving. Thus, there needs to be an accounting of the cognitive mechanisms that are potentially influenced by impasses and these other intervening events. It is possible that these sorts of events are crucial and trigger a switch in attentional focus, which in turn facilitates switching between different problem solving modes. Second, we need to consider when and how externally-triggered stimuli from the solver's environment can influence the problem solving process. I detail three different mechanisms by which external knowledge might influence problem solving. I address each of these ideas in more detail in the next two sections.

3. Event-triggered mode switching during problem-solving

3.1. impasse.

When solving certain types of problems, the agent might encounter an impasse, i.e., some block in its ability to solve the problem (Sprugnoli et al., 2017 ). The impasse may arise because the problem may have been ill-defined to begin with causing incomplete and unduly constrained representations to have been formed. Alternatively, impasses can occur when suitable solution strategies cannot be retrieved from memory or fail on execution. In certain instances, the solution strategies may not exist and may need to be generated from scratch. Regardless of the reason, an impasse is an interruption in the problem solving process; one that was running conflict-free up until the point when a seemingly unresolvable issue or an error in the predicted solution path was encountered. Seen as a conflict encountered in the problem-solving process it activates the anterior cingulate cortex (ACC). It is believed that the ACC not only helps detect the conflict, but also switch modes from one of “exploitation” (planning) to “exploration” (search) (Quilodran et al., 2008 ; Tang et al., 2012 ), and monitors progress during resolution (Chu and Macgregor, 2011 ). Some mode switching duties are also found to be shared with the AI (the ACC's partner in the salience network), however, it is unclear exactly the extent of this function-sharing.

Even though it is debatable if impasses are a necessary component of insight, they are still important as they provide a starting point for the creativity (Sprugnoli et al., 2017 ). Indeed, it is possible that around the moment of impasse, the AI and ACC together, as part of the salience network play a crucial role in switching thought modes from analytical planning mode to creative search and discovery mode. In the latter mode, various creative mechanisms might be activated allowing for a solution plan to emerge. Sowden et al. and many others have suggested that the salience network is potentially a candidate neurobiological mechanism for shifting between thinking processes, more generally (Sowden et al., 2015 ). When discussing various dual-process models as they relate to creative cognition, Sowden et al. have even noted that the ACC activation could be useful marker to identify shifting as participants work creative problems.

3.2. Defocused attention

As noted earlier, in the presence of an impasse there is a shift from an exploitative (analytical) thinking mode to an exploratory (creative) thinking mode. This shift impacts several networks including, for example, the attention network. It is believed attention can switch between a focused mode and a defocused mode. Focused attention facilitates analytic thought by constraining activation such that items are considered in a compact form that is amenable to complex mental operations. In the defocused mode, agents expand their attention allowing new associations to be considered. Sowden et al. ( 2015 ) note that the mechanism responsible for adjustments in cognitive control may be linked to the mechanisms responsible for attentional focus. The generally agreed position is that during generative thinking, unconscious cognitive processes activated through defocused attention are more prevalent, whereas during exploratory thinking, controlled cognition activated by focused attention becomes more prevalent (Kaufman, 2011 ; Sowden et al., 2015 ).

Defocused attention allows agents to not only process different aspects of a situation, but to also activate additional neural structures in long term memory and find new associations (Mendelsohn, 1976 ; Yoruk and Runco, 2014 ). It is believed that cognitive material attended to and cued by positive affective state results in defocused attention, allowing for more complex cognitive contexts and therefore a greater range of interpretation and integration of information (Isen et al., 1987 ). High attentional levels are commonly considered a typical feature of highly creative subjects (Sprugnoli et al., 2017 ).

4. Role of the environment

In much of the past work the focus has been on treating creativity as largely an internal process engaging the DMN to assist in making novel connections in memory. The suggestion has been that “individual needs to suppress external stimuli and concentrate on the inner creative process during idea generation” (Heinonen et al., 2016 ). These ideas can then function as seeds for testing and problem-solving. While true of many creative acts, this characterization does not capture how creative ideas arise in many real-world creative problems. In these types of problems, the agent is functioning and interacting with its environment before, during and after problem-solving. It is natural then to expect that stimuli from the environment might play a role in problem-solving. More specifically, it can be expected that through passive and active involvement with the environment, the agent is (1) able to trigger an unrelated, but potentially useful memory relevant for problem-solving, (2) make novel connections between two events in memory with the environmental cue serving as the missing link, and (3) incorporate a completely novel information from events occuring in the environment directly into the problem-solving process. I explore potential neural mechanisms for these three types of environmentally informed creative cognition, which I hypothesize are enabled by defocused attention.

4.1. Partial cues trigger relevant memories through context-shifting

I have previously discussed the interaction between the MTL and PFC in helping select task-relevant and critical memories for problem-solving. It is well-known that pattern completion is an important function of the MTL and one that enables memory retrieval. Complementary Learning Theory (CLS) and its recently updated version suggest that the MTL and related structures support initial storage as well as retrieval of item and context-specific information (Kumaran et al., 2016 ). According to CLS theory, the dentate gyrus (DG) and the CA3 regions of the HF are critical to selecting neural activity patterns that correspond to particular experiences (Kumaran et al., 2016 ). These patterns might be distinct even if experiences are similar and are stabilized through increases in connection strengths between the DG and CA3. Crucially, because of the connection strengths, reactivation of part of the pattern can activate the rest of it (i.e., pattern completion). Kumaran et al. have further noted that if consistent with existing knowledge, these new experiences can be quickly replayed and interleaved into structured representations that form part of the semantic memory.

Cues in the environment provided by these experiences hold partial information about past stimuli or events and this partial information converges in the MTL. CLS accounts for how these cues might serve to reactivate partial patterns, thereby triggering pattern completion. When attention is defocused I hypothesize that (1) previously unnoticed partial cues are considered, and (2) previously noticed partial cues are decomposed to produce previously unnoticed sub-cues, which in turn are considered. Zabelina et al. ( 2016 ) have shown that real-world creativity and creative achievement is associated with “leaky attention,” i.e., attention that allows for irrelevant information to be noticed. In two experiments they systematically explored the relationship between two notions of creativity— divergent thinking and real-world creative achievement—and the use of attention. They found that attentional use is associated in different ways for each of the two notions of creativity. While divergent thinking was associated with flexible attention, it does not appear to be leaky. Instead, selective focus and inhibition components of attention were likely facilitating successful performance on divergent thinking tasks. On the other hand, real-world creative achievement was linked to leaky attention. RWPS involves elements of both divergent thinking and of real-world creative achievement, thus I would expect some amount of attentional leaks to be part of the problem solving process.

Thus, it might be the case that a new set of cues or sub-cues “leak” in and activate memories that may not have been previously considered. These cues serve to reactivate a diverse set of patterns that then enable accessing a wide range of memories. Some of these memories are extra-contextual, in that they consider the newly noticed cues in several contexts. For example, when unable to find a screwdriver, we might consider using a coin. It is possible that defocused attention allows us to consider the coin's edge as being a potentially relevant cue that triggers uses for the thin edge outside of its current context in a coin. The new cues (or contexts) may allow new associations to emerge with cues stored in memory, which can occur during incubation. Objects and contexts are integrated into memory automatically into a blended representation and changing contexts disrupts this recognition (Hayes et al., 2007 ; Gabora, 2016 ). Cue-triggered context shifting allows an agent to break-apart a memory representation, which can then facilitate problem-solving in new ways.

4.2. Heuristic prototyping facilitates novel associations

It has long been the case that many scientific innovations have been inspired by events in nature and the surrounding environment. As noted earlier, Archimedes realized the relationship between the volume of an irregularly shaped object and the volume of water it displaced. This is an example of heuristic prototyping where the problem-solver notices an event in the environment, which then triggers the automatic activation of a heuristic prototype and the formation of novel associations (between the function of the prototype and the problem) which they can then use to solve the problem (Luo et al., 2013 ). Although still in its relative infancy, there has been some recent research into the neural basis for heuristic prototyping. Heuristic prototype has generally been defined as an enlightening prototype event with a similar element to the current problem and is often composed of a feature and a function (Hao et al., 2013 ). For example, in designing a faster and more efficient submarine hull, a heuristic prototype might be a shark's skin, while an unrelated prototype might be a fisheye camera (Dandan et al., 2013 ).

Research has shown that activating the feature function of the right heuristic prototype and linking it by way of semantic similarity to the required function of the problem was the key mechanism people used to solve several scienitific insight problems (Yang et al., 2016 ). A key region activated during heuristic prototyping is the dlPFC and it is believed to be generally responsible for encoding the events into memory and may play an important role in selecting and retrieving the matched unsolved technical problem from memory (Dandan et al., 2013 ). It is also believed that the precuneus plays a role in automatic retrieval of heuristic information allowing the heuristic prototype and the problem to combine (Luo et al., 2013 ). In addition to semantic processing, certain aspects of visual imagery have also been implicated in heuristic prototyping leading to the suggestion of the involvement of Broadman's area BA 19 in the occipital cortex.

There is some degree of overlap between the notions of heuristic prototyping and analogical transfer (the mapping of relations from one domain to another). Analogical transfer is believed to activate regions in the left medial fronto-parietal system (dlPFC and the PPC) (Barbey and Barsalou, 2009 ). I suggest here that analogical reasoning is largely an internally-guided process that is aided by heuristic prototyping which is an externally-guided process. One possible way this could work is if heuristic prototyping mechanisms help locate the relevant memory with which to then subsequently analogize.

4.3. Making physical inferences to acquire novel information

The agent might also be able to learn novel facts about their environment through passive observation as well as active experimentation. There has been some research into the neural basis for causal reasoning (Barbey and Barsalou, 2009 ; Operskalski and Barbey, 2016 ), but beyond its generally distributed nature, we do not know too much more. Beyond abstract causal reasoning, some studies looked into the cortical regions that are activated when people watch and predict physical events unfolding in real-time and in the real-world (Fischer et al., 2016 ). It was found that certain regions were associated with representing types of physical concepts, with the left intraparietal sulcus (IPS) and left middle frontal gyrus (MFG) shown to play a role in attributing causality when viewing colliding objects (Mason and Just, 2013 ). The parahippocampus (PHC) was associated with linking causal theory to observed data and the TPJ was involved in visualizing movement of objects and actions in space (Mason and Just, 2013 ).

5. Proposed theory

I noted earlier that Ollinger's model for insight problem solving, while serving as a good candidate for RWPS, requires extension. In this section, I propose a candidate model that includes some necessary extensions to Ollinger's framework. I begin by laying out some preliminary notions that underlie the proposed model.

5.1. Dual attentional modes

I propose that the attention-switching mechanism described earlier is at the heart of RWPS and enables two modes of operation: focused and defocused mode. In the focused mode, the problem representation is more or less fixed, and problem solving proceeds in a focused and goal directed manner through search, planning, and execution mechanisms. In the defocused mode, problem solving is not necessarily goal directed, but attempts to generate ideas, driven by both internal and external items.

At first glance, these modes might seem similar to convergent and divergent thinking modes postulated by numerous others to account for creative problem solving. Divergent thinking allows for the generation of new ideas and convergent thinking allows for verification and selection of generated ideas. So, it might seem that focused mode and convergent thinking are similar and likewise divergent and defocused mode. They are, however, quite different. The modes relate less to idea generation and verification, and more to the specific mechanisms that are operating with regard to a particular problem at a particular moment in time. Convergent and divergent processes may be occurring during both defocused and focused modes. Some degree of divergent processes may be used to search and identify specific solution strategies in focused mode. Also, there might be some degree of convergent idea verification occuring in defocused mode as candidate items are evaluated for their fit with the problem and goal. Thus, convergent and divergent thinking are one amongst many mechanisms that are utilized in focused and defocused mode. Each of these two modes has to do with degree of attention placed on a particular problem.

There have been numerous dual-process and dual-systems models of cognition proposed over the years. To address criticisms raised against these models and to unify some of the terminology, Evans & Stanovich proposed a dual-process model comprising Type 1 and Type 2 thought (Evans and Stanovich, 2013 ; Sowden et al., 2015 ). Type 1 processes are those that are believed to be autonomous and do not require working memory. Type 2 processes, on the other hand, are believed to require working memory and are cognitively decoupled to prevent real-world representations from becoming confused with mental simulations (Sowden et al., 2015 ). While acknowledging various other attributes that are often used to describe dual process models (e.g., fast/slow, associative/rule-based, automatic/controlled), Evans & Stanovich note that these attributes are merely frequent correlates and not defining characteristics of Type 1 or Type 2 processes. The proposed dual attentional modes share some similarities with the Evans & Stanovich Type 1 and 2 models. Specifically, Type 2 processes might occur in focused attentional mode in the proposed model as they typically involve the working memory and certain amount of analytical thought and planning. Similarly, Type 1 processes are likely engaged in defocused attentional mode as there are notions of associative and generative thinking that might be facilitated when attention has been defocused. The crucial difference between the proposed model and other dual-process models is that the dividing line between focused and defocused attentional modes is the degree of openness to internal and external stimuli (by various networks and functional units in the brain) when problem solving. Many dual process models were designed to classify the “type” of thinking process or a form of cognitive processing. In some sense, the “processes” in dual process theories are characterized by the type of mechanism of operation or the type of output they produced. Here, I instead characterize and differentiate the modes of thinking by the receptivity of different functional units in the brain to input during problem solving.

This, however, raises a different question of the relationship between these attentional modes and conscious vs. unconscious thinking. It is clear that both the conscious and unconscious are involved in problem solving, as well as in RWPS. Here, I claim that a problem being handled is, at any given point in time, in either a focused mode or in a defocused mode. When in the focused mode, problem solving primarily proceeds in a manner that is available for conscious deliberation. More specifically, problem space elements and representations are tightly managed and plans and strategies are available in the working memory and consciously accessible. There are, however, secondary unconscious operations in the focused modes that includes targeted memory retrieval and heuristic-based searches. In the defocused mode, the problem is primarily managed in an unconscious way. The problem space elements are broken apart and loosely managed by various mechanisms that do not allow for conscious deliberation. That said, it is possible that some problem parameters remain accessible. For example, it is possible that certain goal information is still maintained consciously. It is also possible that indexes to all the problems being considered by the solver are maintained and available to conscious awareness.

5.2. RWPS model

Returning to Ollinger's model for insight problem solving, it now becomes readily apparent how this model can be modified to incorporate environmental effects as well as generalizing the notion of intervening events beyond that of impasses. I propose a theory for RWPS that begins with standard analytical problem-solving process (See Figures ​ Figures1, 1 , ​ ,2 2 ).

Summary of neural activations during focused problem-solving (Left) and defocused problem-solving (Right) . During defocused problem-solving, the salience network (insula and ACC) coordinates the switching of several networks into a defocused attention mode that permits the reception of a more varied set of stimuli and interpretations via both the internally-guided networks (default mode network DMN) and externally guided networks (Attention). PFC, prefrontal cortex; ACC, anterior cingulate cortex; PCC, posterior cingulate cortex; IPC, inferior parietal cortex; PPC, posterior parietal cortex; IPS, intra-parietal sulcus; TPJ, temporoparietal junction; MTL, medial temporal lobe; FEF, frontal eye field.

Proposed Model for Real World Problem Solving (RWPS). The corresponding neural correlates are shown in italics. During problem-solving, an initial problem representation is formed based on prior knowledge and available perceptual information. The problem-solving then proceeds in a focused, goal-directed mode until the goal is achieved or a defocusing event (e.g., impasse or distraction) occurs. During focused mode operation, the solver interacts with the environment in directed manner, executing focused plans, and allowing for predicted items to be activated by the environment. When a defocusing event occurs, the problem-solving then switches into a defocused mode until a focusing event (e.g., discovery) occurs. In defocused mode, the solver performs actions unrelated to the problem (or is inactive) and is receptive to a set of environmental triggers that activate novel aspects using the three mechanisms discussed in this paper. When a focusing event occurs, the diffused problem elements cohere into a restructured representation and problem-solving returns into a focused mode.

5.2.1. Focused problem solving mode

Initially, both prior knowledge and perceptual entities help guide the creation of problem representations in working memory. Prior optimal or rewarding solution strategies are obtained from LTM and encoded in the working memory as well. This process is largely analytical and the solver interacts with their environment through focused plan or idea execution, targeted observation of prescribed entities, and estimating prediction error of these known entities. More specifically, when a problem is presented, the problem representations are activated and populated into working memory in the PFC, possibly in structured representations along convergence zones. The PFC along with the Striatum and the MTL together attempt at retrieving an optimal or previously rewarded solution strategy from long term memory. If successfully retrieved, the solution strategy is encoded into the PPC as a mental template, which then guides relevant motor control regions to execute the plan.

5.2.2. Defocusing event-triggered mode switching

The search and solve strategy then proceeds analytically until a “defocusing event” is encountered. The salience network (AI and ACC) monitor for conflicts and attempt to detect any such events in the problem-solving process. As long as no conflicts are detected, the salience network focuses on recruiting networks to achieve goals and suppresses the DMN (Beaty et al., 2016 ). If the plan execution or retrieval of the solution strategy fails, then a defocusing event is detected and the salience network performs mode switching. The salience network dynamically switches from the focused problem-solving mode to a defocused problem-solving mode (Menon, 2015 ). Ollinger's current model does not account for other defocusing events beyond an impasse, but it is not inconceivable that there could be other such events triggered by external stimuli (e.g., distraction or an affective event) or by internal stimuli (e.g., mind wandering).

5.2.3. Defocused problem solving mode

In defocused mode, the problem is operated on by mechanisms that allow for the generation and testing of novel ideas. Several large-scale brain networks are recruited to explore and generate new ideas. The search for novel ideas is facilitated by generally defocused attention, which in turn allows for creative idea generation from both internal as well as external sources. The salience network switches operations from defocused event detection to focused event or discovery detection, whereby for example, environmental events or ideas that are deemed interesting can be detected. During this idea exploration phase, internally, the DMN is no longer suppressed and attempts to generate new ideas for problem-solving. It is known that the IPC is involved in the generation of new ideas (Benedek et al., 2014 ) and together with the PPC in coupling different information together (Simone Sandkühler, 2008 ; Stocco et al., 2012 ). Beaty et al. ( 2016 ) have proposed that even this internal idea-generation process can be goal directed, thereby allowing for a closer working relationship between the CEN and the DMN. They point to neuroimaging evidence that support the possibility that the executive control network (comprising the lateral prefrontal and inferior parietal regions) can constrain and direct the DMN in its process of generating ideas to meet task-specific goals via top down monitoring and executive control (Beaty et al., 2016 ). The control network is believed to maintain an “internal train of thought” by keeping the task goal activated, thereby allowing for strategic and goal-congruent searches for ideas. Moreover, they suggest that the extent of CEN involvement in the DMN idea-generation may depend on the extent to which the creative task is constrained. In the RWPS setting, I would suspect that the internal search for creative solutions is not entirely unconstrained, even in the defocused mode. Instead, the solver is working on a specified problem and thus, must maintain the problem-thread while searching for solutions. Moreover, self-generated ideas must be evaluated against the problem parameters and thereby might need some top-down processing. This would suggest that in such circumstances, we would expect to see an increased involvement of the CEN in constraining the DMN.

On the external front, several mechanisms are operating in this defocused mode. Of particular note are the dorsal attention network, composed of the visual cortex (V), IPS and the frontal eye field (FEF) along with the precuneus and the caudate nucleus allow for partial cues to be considered. The MTL receives synthesized cue and contextual information and populates the WM in the PFC with a potentially expanded set of information that might be relevant for problem-solving. The precuneus, dlPFC and PPC together trigger the activation and use of a heuristic prototype based on an event in the environment. The caudate nucleus facilitates information routing between the PFC and PPC and is involved in learning and skill acquisition.

5.2.4. Focusing event-triggered mode switching

The problem's life in this defocused mode continues until a focusing event occurs, which could be triggered by either external (e.g., notification of impending deadline, discovery of a novel property in the environment) or internal items (e.g., goal completion, discovery of novel association or updated relevancy of a previously irrelevant item). As noted earlier, an internal train of thought may be maintained that facilitates top-down evaluation of ideas and tracking of these triggers (Beaty et al., 2016 ). The salience network switches various networks back to the focused problem-solving mode, but not without the potential for problem restructuring. As noted earlier, problem space elements are maintained somewhat loosely in the defocused mode. Thus, upon a focusing event, a set or subset of these elements cohere into a tight (restructured) representation suitable for focused mode problem solving. The process then repeats itself until the goal has been achieved.

5.3. Model predictions

5.3.1. single-mode operation.

The proposed RWPS model provides several interesting hypotheses, which I discuss next. First, the model assumes that any given problem being worked on is in one mode or another, but not both. Thus, the model predicts that there cannot be focused plan execution on a problem that is in defocused mode. The corollary prediction is that novel perceptual cues (as those discussed in section 4) cannot help the solver when in focused mode. The corollary prediction, presumably has some support from the inattentional blindness literature. Inattentional blindness is when perceptual cues are not noticed during a task (e.g., counting the number of basketball passes between several people, but not noticing a gorilla in the scene) (Simons and Chabris, 1999 ). It is possible that during focused problem solving, that external and internally generated novel ideas are simply not considered for problem solving. I am not claiming that these perceptual cues are always ignored, but that they are not considered within the problem. Sometimes external cues (like distracting occurrences) can serve as defocusing events, but the model predicts that the actual content of these cues are not themselves useful for solving the specific problem at hand.

When comparing dual-process models Sowden et al. ( 2015 ) discuss shifting from one type of thinking to another and explore how this shift relates to creativity. In this regard, they weigh the pros and cons of serial vs. parallel shifts. In dual-process models that suggest serial shifts, it is necessary to disengage one type of thought prior to engaging the other or to shift along a continuum. Whereas, in models that suggest parallel shifts, each of the thinking types can operate in parallel. Per this construction, the proposed RWPS model is serial, however, not quite in the same sense. As noted earlier, the RWPS model is not a dual-process model in the same sense as other dual process model. Instead, here, the thrust is on when the brain is receptive or not receptive to certain kinds of internal and external stimuli that can influence problem solving. Thus, while the modes may be serial with respect to a certain problem, it does not preclude the possibility of serial and parallel thinking processes that might be involved within these modes.

5.3.2. Event-driven transitions

The model requires an event (defocusing or focusing) to transition from one mode to another. After all why else would a problem that is successfully being resolved in the focused mode (toward completion) need to necessarily be transferred to defocused mode? These events are interpreted as conflicts in the brain and therefore the mode-switching is enabled by the saliency network and the ACC. Thus, the model predicts that there can be no transition from one mode to another without an event. This is a bit circular, as an event is really what triggers the transition in the first place. But, here I am suggesting that an external or internal cue triggered event is what drives the transition, and that transitions cannot happen organically without such an event. In some sense, the argument is that the transition is discontinuous, rather than a smooth one. Mind-wandering is good example of when we might drift into defocused mode, which I suggest is an example of an internally driven event caused by an alternative thought that takes attention away from the problem.

A model assumption underlying RWPS is that events such as impasses have a similar effect to other events such as distraction or mind wandering. Thus, it is crucial to be able to establish that there exists of class of such events and they have a shared effect on RWPS, which is to switch attentional modes.

5.3.3. Focused mode completion

The model also predicts that problems cannot be solved (i.e., completed) within the defocused mode. A problem can be considered solved when a goal is reached. However, if a goal is reached and a problem is completed in the defocused mode, then there must have not been any converging event or coherence of problem elements. While it is possible that the solver arbitrarily arrived at the goal in a diffused problem space and without conscious awareness of completing the task or even any converging event or problem recompiling, it appears somewhat unlikely. It is true that there are many tasks that we complete without actively thinking about it. We do not think about what foot to place in front of another while walking, but this is not an instance of problem solving. Instead, this is an instance of unconscious task completion.

5.3.4. Restructuring required

The model predicts that a problem cannot return to a focused mode without some amount of restructuring. That is, once defocused, the problem is essentially never the same again. The problem elements begin interacting with other internally and externally-generated items, which in turn become absorbed into the problem representation. This prediction can potentially be tested by establishing some preliminary knowledge, and then showing one group of subjects the same knowledge as before, while showing the another group of subjects different stimuli. If the model's predictions hold, the problem representation will be restructured in some way for both groups.

There are numerous other such predictions, which are beyond the scope of this paper. One of the biggest challenges then becomes evaluating the model to set up suitable experiments aimed at testing the predictions and falsifying the theory, which I address next.

6. Experimental challenges and paradigms

One of challenges in evaluating the RWPS is that real world factors cannot realistically be accounted for and sufficiently controlled within a laboratory environment. So, how can one controllably test the various predictions and model assumptions of “real world” problem solving, especially given that by definition RWPS involves the external environment and unconscious processing? At the expense of ecological validity, much of insight problem solving research has employed an experimental paradigm that involves providing participants single instances of suitably difficult problems as stimuli and observing various physiological, neurological and behavioral measures. In addition, through verbal protocols, experimenters have been able to capture subjective accounts and problem solving processes that are available to the participants' conscious. These experiments have been made more sophisticated through the use of timed-hints and/or distractions. One challenge with this paradigm has been the selection of a suitable set of appropriately difficult problems. The classic insight problems (e.g., Nine-dot, eight-coin) can be quite difficult, requiring complicated problem solving processes, and also might not generalize to other problems or real world problems. Some in the insight research community have moved in the direction of verbal tasks (e.g., riddles, anagrams, matchstick rebus, remote associates tasks, and compound remote associates tasks). Unfortunately, these puzzles, while providing a great degree of controllability and repeatability, are even less realistic. These problems are not entirely congruent with the kinds of problems that humans are solving every day.

The other challenge with insight experiments is the selection of appropriate performance and process tracking measures. Most commonly, insight researchers use measures such as time to solution, probability of finding solution, and the like for performance measures. For process tracking, verbal protocols, coded solution attempts, and eye tracking are increasingly common. In neuroscientific studies of insight various neurological measures using functional magnetic resonance imaging (fMRI), electroencephalography (EEGs), transcranial direct current stimulation (tDCS), and transcranial magnetic stimulation (tMS) are popular and allow for spatially and temporally localizing an insight event.

Thus, the challenge for RWPS is two-fold: (1) selection of stimuli (real world problems) that are generalizable, and (2) selection of measures (or a set of measures) that can capture key aspects of the problem solving process. Unfortunately, these two challenges are somewhat at odds with each other. While fMRI and various neuroscientific measures can capture the problem solving process in real time, it is practically difficult to provide participants a realistic scenario while they are laying flat on their back in an fMRI machine and allowed to move nothing more than a finger. To begin addressing this conundrum, I suggest returning to object manipulation problems (not all that different from those originally introduced by Maier and Duncker nearly a century ago), but using modern computing and user-interface technologies.

One pseudo-realistic approach is to generate challenging object manipulation problems in Virtual Reality (VR). VR has been used to describe 3-D environment displays that allows participants to interact with artificially projected, but experientially realistic scenarios. It has been suggested that virtual environments (VE) invoke the same cognitive modules as real equivalent environmental experience (Foreman, 2010 ). Crucially, since VE's can be scaled and designed as desired, they provide a unique opportunity to study pseudo-RWPS. However, a VR-based research approach has its limitations, one of which is that it is nearly impossible to track participant progress through a virtual problem using popular neuroscientific measures such as fMRI because of the limited mobility of connected participants.

Most of the studies cited in this paper utilized an fMRI-based approach in conjunction with a verbal or visual task involving problem-solving or creative thinking. Very few, if any, studies involved the use physical manipulation, and those physical manipulations were restricted to limited finger movements. Thus, another pseudo-realistic approach is allowing subjects to teleoperate robotic arms and legs from inside the fMRI machine. This paradigm has seen limited usage in psychology and robotics, in studies focused on Human-Robot interaction (Loth et al., 2015 ). It could be an invaluable tool in studying real-time dynamic problem-solving through the control of a robotic arm. In this paradigm a problem solving task involving physical manipulation is presented to the subject via the cameras of a robot. The subject (in an fMRI) can push buttons to operate the robot and interact with its environment. While the subjects are not themselves moving, they can still manipulate objects in the real world. What makes this paradigm all the more interesting is that the subject's manipulation-capabilities can be systematically controlled. Thus, for a particular problem, different robotic perceptual and manipulation capabilities can be exposed, allowing researchers to study solver-problem dynamics in a new way. For example, even simple manipulation problems (e.g., re-arranging and stacking blocks on a table) can be turned into challenging problems when the robotic movements are restricted. Here, the problem space restrictions are imposed not necessarily on the underlying problem, but on the solver's own capabilities. Problems of this nature, given their simple structure, may enable studying everyday practical creativity without the burden of devising complex creative puzzles. Crucial to note, both these pseudo-realistic paradigms proposed demonstrate a tight interplay between the solver's own capabilities and their environment.

7. Conclusion

While the neural basis for problem-solving, creativity and insight have been studied extensively in the past, there is still a lack of understanding of the role of the environment in informing the problem-solving process. Current research has primarily focused on internally-guided mental processes for idea generation and evaluation. However, the type of real world problem-solving (RWPS) that is often considered a hallmark of human intelligence has involved both a dynamic interaction with the environment and the ability to handle intervening and interrupting events. In this paper, I have attempted to synthesize the literature into a unified theory of RWPS, with a specific focus on ways in which the environment can help problem-solve and the key neural networks involved in processing and utilizing relevant and useful environmental information. Understanding the neural basis for RWPS will allow us to be better situated to solve difficult problems. Moreover, for researchers in computer science and artificial intelligence, clues into the neural underpinnings of the computations taking place during creative RWPS, can inform the design the next generation of helper and exploration robots which need these capabilities in order to be resourceful and resilient in the open-world.

Author contributions

The author confirms being the sole contributor of this work and approved it for publication.

Conflict of interest statement

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

I am indebted to Professor Matthias Scheutz, Professor Elizabeth Race, Professor Ayanna Thomas, and Professor. Shaun Patel for providing guidance with the research and the manuscript. I am also grateful for the facilities provided by Tufts University, Medford, MA, USA.

1 My intention is not to ignore the benefits of a concentrated internal thought process which likely occurred as well, but merely to acknowledge the possibility that the environment might have also helped.

2 The research in insight does extensively use “hints” which are, arguably, a form of external influence. But these hints are highly targeted and might not be available in this explicit form when solving problems in the real world.

3 The accuracy of these accounts has been placed in doubt. They often are recounted years later, with inaccuracies, and embellished for dramatic effect.

4 I use the term “agent” to refer to the problem-solver. The term agent is more general than “creature” or “person” or “you" and is intentionally selected to broadly reference humans, animals as well as artificial agents. I also selectively use the term “solver.”

Funding. The research for this Hypothesis/Theory Article was funded by the authors private means. Publication costs will be covered by my institution: Tufts University, Medford, MA, USA.

• Abraham A. (2013). The promises and perils of the neuroscience of creativity . Front. Hum. Neurosci. 7 :246. 10.3389/fnhum.2013.00246 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Anderson J. R., Fincham J. M. (2014). Discovering the sequential structure of thought . Cogn. Sci. 38 , 322–352. 10.1111/cogs.12068 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Anderson J. R., Seung H., Fincham J. M. (2014). Neuroimage discovering the structure of mathematical problem solving . Neuroimage 97 , 163–177. 10.1016/j.neuroimage.2014.04.031 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ash I. K., Wiley J. (2006). The nature of restructuring in insight: an individual-differences approach . Psychon. Bull. Rev. 13 , 66–73. 10.3758/BF03193814 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Barbey A. K., Barsalou L. W. (2009). Reasoning and problem solving : models , in Encyclopedia of Neuroscience , ed Squire L. (Oxford: Academic Press; ), 35–43. [ Google Scholar ]
• Barbey A. K., Krueger F., Grafman J. (2009). Structured event complexes in the medial prefrontal cortex support counterfactual representations for future planning . Philos. Trans. R. Soc. Lond. B Biol. Sci. 364 , 1291–1300. 10.1098/rstb.2008.0315 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Beaty R. E., Benedek M., Silvia P. J., Schacter D. L. (2016). Creative cognition and brain network dynamics . Trends Cogn. Sci. 20 , 87–95. 10.1016/j.tics.2015.10.004 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Beaty R. E., Benedek M., Wilkins R. W., Jauk E., Fink A., Silvia P. J., et al.. (2014). Creativity and the default network: a functional connectivity analysis of the creative brain at rest . Neuropsychologia 64 , 92–98. 10.1016/j.neuropsychologia.2014.09.019 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Benedek M., Jauk E., Beaty R. E., Fink A., Koschutnig K., Neubauer A. C. (2016). Brain mechanisms associated with internally directed attention and self-generated thought . Sci. Rep. 6 :22959. 10.1038/srep22959 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Benedek M., Jauk E., Fink A., Koschutnig K., Reishofer G., Ebner F., et al.. (2014). To create or to recall? Neural mechanisms underlying the generation of creative new ideas . Neuroimage 88 , 125–133. 10.1016/j.neuroimage.2013.11.021 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Boccia M., Piccardi L., Palermo L., Nori R., Palmiero M. (2015). Where do bright ideas occur in ourbrain? Meta-analytic evidence from neuroimaging studies of domain-specific creativity . Front. Psychol. 6 :1195. 10.3389/fpsyg.2015.01195 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Brandi M. l., Wohlschläger A., Sorg C., Hermsdörfer J. (2014). The neural correlates of planning and executing actual tool use . J. Neurosci. 34 , 13183–13194. 10.1523/JNEUROSCI.0597-14.2014 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Cass S. (2005). Apollo 13, we have a solution , in IEEE Spectrum On-line, 04 , 1. Available online at: https://spectrum.ieee.org/tech-history/space-age/apollo-13-we-have-a-solution
• Chu Y., Macgregor J. N. (2011). Human performance on insight problem solving : a review J. Probl. Solv. 3 , 119–150. 10.7771/1932-6246.1094 [ CrossRef ] [ Google Scholar ]
• Chung H. J., Weyandt L. L. (2014). The physiology of executive functioning , Handbook of Executive Functioning (Springer; ), 13–28. [ Google Scholar ]
• Dandan T., Haixue Z., Wenfu L., Wenjing Y., Jiang Q., Qinglin Z. (2013). Brain activity in using heuristic prototype to solve insightful problems . Behav. Brain Res. 253 , 139–144. 10.1016/j.bbr.2013.07.017 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Danek A. H., Wiley J., Öllinger M. (2016). Solving classical insight problems without aha! experience: 9 dot, 8 coin, and matchstick arithmetic problems . J. Probl. Solv. 9 :4 10.7771/1932-6246.1183 [ CrossRef ] [ Google Scholar ]
• Duncker K. (1945). On problem-solving . Psychol. Monogr. 58 , i–113. [ Google Scholar ]
• Evans J. S., Stanovich K. E. (2013). Dual-process theories of higher cognition: advancing the debate . Perspect. Psychol. Sci. 8 , 223–241. 10.1177/1745691612460685 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Fang X., Zhang Y., Zhou Y., Cheng L., Li J., Wang Y., et al.. (2016). Resting-state coupling between core regions within the central-executive and salience networks contributes to working memory performance . Front. Behav. Neurosci. 10 :27. 10.3389/fnbeh.2016.00027 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Finke R. A., Ward T. B., Smith S. M. (1992). Creative Cognition: Theory, Research, and Applications . Cambridge, MA: MIT press. [ Google Scholar ]
• Fischer J., Mikhael J. G., Tenenbaum J. B., Kanwisher N. (2016). Functional neuroanatomy of intuitive physical inference . Proc. Natl. Acad. Sci. U.S.A. 113 , E5072–E5081. 10.1073/pnas.1610344113 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Fleck J. I. (2008). Working memory demands in insight versus analytic problem solving . Eur. J. Cogn. Psychol. 20 , 139–176. 10.1080/09541440601016954 [ CrossRef ] [ Google Scholar ]
• Foreman N. (2010). Virtual reality in psychology . Themes Sci. Technol. Educ. 2 , 225–252. Available online at: http://earthlab.uoi.gr/theste/index.php/theste/article/view/33 [ Google Scholar ]
• Gabora L. (2016). The neural basis and evolution of divergent and convergent thought . arXiv preprint arXiv:1611.03609 . [ Google Scholar ]
• Gazzaley A., Nobre A. C. (2012). Top-down modulation: bridging selective attention and working memory . Trends Cogn. Sci. 60 , 830–846. 10.1016/j.tics.2011.11.014 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Gilhooly K. J. (2016). Incubation and intuition in creative problem solving . Front. Psychol. 7 :1076. 10.3389/fpsyg.2016.01076 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Guilford J. P. (1962). Creativity: its measurement and development , in A Source Book for Creative Thinking (New York, NY: Charles Scribner's Sons; ), 151–167. [ Google Scholar ]
• Hao X., Cui S., Li W., Yang W., Qiu J., Zhang Q. (2013). Enhancing insight in scientific problem solving by highlighting the functional features of prototypes: an fMRI study . Brain Res. 1534 , 46–54. 10.1016/j.brainres.2013.08.041 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hayes S. M., Nadel L., Ryan L. (2007). The effect of scene context on episodic object recognition: parahippocampal cortex mediates memory encoding and retrieval success . Hippocampus 9 , 19–22. 10.1002/hipo.20319 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Heinonen J., Numminen J., Hlushchuk Y., Antell H., Taatila V., Suomala J. (2016). Default mode and executive networks areas: association with the serial order in divergent thinking . PLoS ONE 11 :e0162234. 10.1371/journal.pone.0162234 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Horner A. J., Bisby J. A., Bush D., Lin W.-J., Burgess N. (2015). Evidence for holistic episodic recollection via hippocampal pattern completion . Nat. Commun. 6 :7462. 10.1038/ncomms8462 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Isen A. M., Daubman K. A., Nowicki G. P. (1987). Positive affect facilitates creative problem solving . J. Pers. Soc. Psychol. 52 , 1122–1131. 10.1037/0022-3514.52.6.1122 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Jauk E., Benedek M., Neubauer A. C. (2012). Tackling creativity at its roots: evidence for different patterns of EEG alpha activity related to convergent and divergent modes of task processing . Int. J. Psychophysiol. 84 , 219–225. 10.1016/j.ijpsycho.2012.02.012 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kaplan C. A., Simon H. A. (1990). In search of insight . Cogn. Psychol. 22 , 374–419. [ Google Scholar ]
• Kaufman S. B. (2011). Intelligence and the cognitive unconscious , in The Cambridge Handbook of Intelligence (New York, NY: Cambridge University Press; ), 442–467. [ Google Scholar ]
• Kounios J., Beeman M. (2014). The cognitive neuroscience of insight . Annu. Rev. Psychol. 65 , 71–93. 10.1146/annurev-psych-010213-115154 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kumaran D., Hassabis D., McClelland J. L. (2016). What learning systems do intelligent agents need? complementary learning systems theory updated . Trends Cogn. Sci. 20 , 512–534. 10.1016/j.tics.2016.05.004 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Loth S., Jettka K., Giuliani M., De Ruiter J. P. (2015). Ghost-in-the-machine reveals human social signals for human–robot interaction . Front. Psychol. 6 :1641. 10.3389/fpsyg.2015.01641 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lovell J., Kluger J. (2006). Apollo 13 . New York, NY: Houghton Mifflin Harcourt. [ Google Scholar ]
• Luo J., Li W., Qiu J., Wei D., Liu Y., Zhang Q. (2013). Neural basis of scientific innovation induced by heuristic prototype . PLoS ONE 8 :e49231. 10.1371/journal.pone.0049231 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• MacGregor J. N., Ormerod T. C., Chronicle E. P. (2001). Information processing and insight: a process model of performance on the nine-dot and related problems . J. Exp. Psychol. Learn. Mem. Cogn. 27 :176. 10.1037/0278-7393.27.1.176 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Maier N. R. (1930). Reasoning in humans. i. on direction . J. Comp. Psychol. 10 :115. [ Google Scholar ]
• Mason R. A., Just M. A. (2013). Neural representations of physics concepts . Psychol. Sci. 27 , 904–913. 10.1177/0956797616641941 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Mehta R., Zhu R. J. (2009). Blue or red? exploring the effect of color on cognitive task performances . Science 323 , 1226–1229. 10.1126/science.1169144 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Mendelsohn G. (1976). Associative and attentional processes in creative performance . J. Pers. 44 , 341–369. [ Google Scholar ]
• Menon V. (2015). Salience network , in Brain Mapping: An Encyclopedic Reference, Vol. 2 , ed Toga A. W. (London: Academic Press; Elsevier; ), 597–611. [ Google Scholar ]
• Metcalfe J. (1986). Premonitions of insight predict impending error . J. Exp. Psychol. Learn. Mem. Cogn. 12 , 623. [ Google Scholar ]
• Miyake A., Friedman N. P., Emerson M. J., Witzki A. H., Howerter A., Wager T. D. (2000). The unity and diversity of executive functions and their contributions to complex “Frontal Lobe” tasks: a latent variable analysis . Cogn. Psychol. 41 , 49–100. 10.1006/cogp.1999.0734 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Newman S. D., Green S. R. (2015). Complex problem solving . Brain Mapp. 3 , 543–549. 10.1016/B978-0-12-397025-1.00282-7 [ CrossRef ] [ Google Scholar ]
• Ohlsson S. (1992). Information-processing explanations of insight and related phenomena . Adv. Psychol. Think. 1 , 1–44. [ Google Scholar ]
• Öllinger M., Fedor A., Brodt S., Szathmáry E. (2017). Insight into the ten-penny problem: guiding search by constraints and maximization . Psychol. Res. 81 , 925–938. 10.1007/s00426-016-0800-3 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Öllinger M., Jones G., Knoblich G. (2014). The dynamics of search, impasse, and representational change provide a coherent explanation of difficulty in the nine-dot problem . Psychol. Res. 78 , 266–275. 10.1007/s00426-013-0494-8 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Operskalski J. T., Barbey A. K. (2016). Cognitive neuroscience of causal reasoning , in Oxford Handbook of Causal Reasoning , ed Waldmann M. R. (New York, NY: Oxford University Press; ), 217–242. [ Google Scholar ]
• Quilodran R., Rothé M., Procyk E. (2008). Behavioral shifts and action valuation in the anterior cingulate cortex . Neuron 57 , 314–325. 10.1016/j.neuron.2007.11.031 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ritter S. M., Dijksterhuis A. (2014). Creativity the unconscious foundations of the incubation period . Front. Hum. Neurosci. 8 :215. 10.3389/fnhum.2014.00215 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Robertson S. (2016). Problem Solving: Perspectives from Cognition and Neuroscience . New York, NY: Psychology Press. [ Google Scholar ]
• Salvi C., Bowden E. M. (2016). Looking for creativity: where do we look when we look for new ideas? Front. Psychol. 7 :161. 10.3389/fpsyg.2016.00161 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Sawyer K. (2011). The cognitive neuroscience of creativity: a critical review . Creat. Res. J. 23 , 137–154. 10.1080/10400419.2011.571191 [ CrossRef ] [ Google Scholar ]
• Scimeca J. M., Badre D. (2012). Striatal contributions to declarative memory retrieval Jason . Neuron 75 , 380–392. 10.1016/j.neuron.2012.07.014 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Simone Sandkühler J. B. (2008). Deconstructing insight: EEG correlates of insightful problem solving . PLoS ONE 3 :e1459. 10.1371/journal.pone.0001459 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Simons D. J., Chabris C. F. (1999). Gorillas in our midst: sustained inattentional blindness for dynamic events . Perception 28 , 1059–1074. [ PubMed ] [ Google Scholar ]
• Sowden P. T., Pringle A., Gabora L. (2015). The shifting sands of creative thinking: connections to dual-process theory . Think. Reason. 21 , 40–60. 10.1080/13546783.2014.885464 [ CrossRef ] [ Google Scholar ]
• Sprugnoli G., Rossi S., Emmendorfer A., Rossi A., Liew S.-L., Tatti E., et al. (2017). Neural correlates of Eureka moment . Intelligence 62 , 99–118. 10.1016/j.intell.2017.03.004 [ CrossRef ] [ Google Scholar ]
• Steidle A., Werth L. (2013). Freedom from constraints: darkness and dim illumination promote creativity . J. Environ. Psychol. 35 , 67–80. 10.1016/j.jenvp.2013.05.003 [ CrossRef ] [ Google Scholar ]
• Stocco A., Lebiere C., O'Reilly R. C., Anderson J. R. (2012). Distinct contributions of the caudate nucleus, rostral prefrontal cortex, and parietal cortex to the execution of instructed tasks . Cogn. Affect. Behav. Neurosci. 12 , 611–628. 10.3758/s13415-012-0117-7 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Summerfield J. J., Hassabis D., Maguire E. A. (2010). Differential engagement of brain regions within a corenetwork during scene construction . Neuropsychologia 48 , 1501–1509. 10.1016/j.neuropsychologia.2010.01.022 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Tang Y.-Y., Rothbart M. K., Posner M. I. (2012). Neural Correlates of stablishing, maintaining and switching brain states . Trends Cogn. Sci. 16 , 330–337. 10.1016/j.tics.2012.05.001 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Team M. E. (1970). Mission Operations Report apollo 13 . [ Google Scholar ]
• Thakral P. P., Madore K. P., Schacter D. L. (2017). A role for the left angular gyrus in episodic simulation and memory . J. Neurosci. 37 , 8142–8149. 10.1523/JNEUROSCI.1319-17.2017 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Thomas L. E., Lleras A. (2009). Swinging into thought: directed movement guides insight in problem solving . Psychon. Bull. Rev. 16 , 719–723. 10.3758/PBR.16.4.719 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Vohs K. D., Redden J. P., Rahinel R. (2013). Physical order produces healthy choices, generosity, and conventionality, whereas disorder produces creativity . Psychol. Sci. 24 , 1860–1867. 10.1177/0956797613480186 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Wegbreit E., Suzuki S., Grabowecky M., Kounios J., Beeman M. (2012). Visual attention modulates insight versus analytic solving of verbal problems . J. Probl. Solv. 144 , 724–732. 10.7771/1932-6246.1127 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Yang W., Dietrich A., Liu P., Ming D., Jin Y., Nusbaum H. C., et al. (2016). Prototypes are key heuristic information in insight problem solving . Creat. Res. J. 28 , 67–77. 10.1080/10400419.2016.1125274 [ CrossRef ] [ Google Scholar ]
• Yoruk S., Runco M. A. (2014). Neuroscience of divergent thinking . Activ. Nervosa Superior 56 , 1–16. 10.1007/BF03379602 [ CrossRef ] [ Google Scholar ]
• Zabelina D., Saporta A., Beeman M. (2016). Flexible or leaky attention in creative people? Distinct patterns of attention for different types of creative thinking . Mem Cognit . 44 , 488–498. 10.3758/s13421-015-0569-4 [ PubMed ] [ CrossRef ] [ Google Scholar ]

• Jul 20, 2023

Driving Innovation and Growth: Leveraging Collective Intelligence and Real-Time Problem-Solving

In today's fast-paced business environment, companies must adopt a real-time problem-solving approach to stay ahead of the competition. This approach requires businesses to look at strategy through multiple lenses or vantage points, typically anchoring these outside the company in its ecosystem or beyond. To do so, companies need to generate new data on these perspectives through experimentation, augmenting this new data by crowdsourcing external ideas and technologies to bring collective intelligence to bear.

In this article, I will explore how companies can leverage the real-time problem-solving approach to drive innovation and growth by generating new data and leveraging collective intelligence.

Adapting to Change and Embracing Uncertainty

Change is accelerating and disrupting every industry segment, making it challenging for companies to implement traditional product-market-structure approaches to strategy. To stay ahead of the curve, companies must adopt a real-time problem-solving approach to strategy development. This approach requires businesses to look at strategy through multiple lenses or vantage points, typically anchoring these outside the company in its ecosystem or beyond. By doing so, businesses can gain a broader perspective on threats and opportunities.

One way companies can look at their business through multiple lenses is by examining it through the eyes of suppliers, customers, current rivals, and potential outside entrants . This approach provides a much more comprehensive perspective than staying inside a company's prevailing mindsets and routines. For example, a company may find that their customers are not satisfied with a particular product or service, and that their competitors are gaining an advantage in the market. By understanding these perspectives, companies can adapt their strategies to meet changing market demands.

Another way to adopt a real-time problem-solving approach is by generating new data on these perspectives through experimentation. Companies can conduct experiments to test new products, services, or business models. By doing so, they can gain valuable insights into what works and what does not. Additionally, companies can augment the new data generated by crowdsourcing external ideas and technologies to bring collective intelligence to bear. For example, companies can leverage social media platforms to gather feedback from their customers, industry experts, and other stakeholders.

Examples of Companies that Have Successfully Used the Real-Time Problem-Solving Approach

To illustrate the effectiveness of this approach, let's take a look at how Amazon has leveraged real-time problem-solving to stay ahead of the competition. Amazon is known for its customer-centric approach, and this is evident in the way it conducts business. The company constantly experiments with new products, services, and business models to meet the changing needs of its customers. For example, Amazon Prime was launched as an experiment to test whether customers would be willing to pay an annual fee for free two-day shipping. Today, Amazon Prime has over 200 million subscribers and has become a significant revenue stream for the company. Additionally, Amazon leverages collective intelligence by crowdsourcing ideas from its customers. The company has a dedicated platform called Amazon Mechanical Turk, where it pays people to complete small tasks such as data entry, surveys, and content moderation. By doing so, Amazon has access to a massive pool of data that it can use to make informed business decisions.

Another example is Airbnb, which disrupted the hotel industry by offering a platform for people to rent out their homes to travelers. To stay ahead of the competition, Airbnb has adopted a real-time problem-solving approach by experimenting with new services and features. For example, the company launched Experiences, a service that allows travelers to book unique activities and tours hosted by locals. By doing so, Airbnb has expanded its offerings beyond accommodations, providing a more comprehensive travel experience for its customers.

Moreover, Tesla, which has revolutionized the automotive industry by offering electric vehicles. Tesla has adopted a real-time problem-solving approach by experimenting with new technologies and features, such as autonomous driving and battery technology. Additionally, Tesla leverages collective intelligence by crowdsourcing ideas from its customers. For example, the company has a dedicated platform called Tesla Ideas, where customers can submit ideas for new products, features, and improvements. By doing so, Tesla has access to a massive pool of data that it can use to make informed business decisions.

Finally, Procter & Gamble (P&G) is an example of a company that has successfully used the real-time problem-solving approach to drive innovation. P&G has a dedicated team called Connect + Develop, which is responsible for crowdsourcing new ideas and technologies from external sources. By doing so, P&G has access to a massive pool of data that it can use to develop new products and improve existing ones. For example, P&G crowdsourced the technology for its Swiffer product from an external source, which helped the company save time and resources in product development.

These examples highlight how companies can leverage the real-time problem-solving approach to drive innovation and stay ahead of the competition. By looking at strategy through multiple lenses, experimenting with new products and services, and leveraging collective intelligence, companies can gain a competitive advantage in today's fast-paced business environment.

How Companies Can Leverage Collective Intelligence

Leveraging collective intelligence is a powerful tool that companies can use to drive innovation and growth. Here are some ways that companies can leverage collective intelligence:

Crowdsourcing : One of the most common ways to leverage collective intelligence is through crowdsourcing. Companies can use social media platforms, online communities, or dedicated platforms to gather feedback and ideas from their customers, industry experts, and other stakeholders. For example, companies can launch a contest or challenge to encourage people to submit new ideas for products, services, or business models.

Open innovation : Another way to leverage collective intelligence is through open innovation. Companies can partner with other organizations, research institutions, or startups to share resources, expertise, and knowledge. By doing so, companies can access a wider pool of talent and expertise, which can help them develop new products, services, or business models.

Collaboration : Collaboration is another way to leverage collective intelligence. Companies can encourage collaboration and knowledge sharing among their employees, departments, or business units. By doing so, companies can tap into the collective intelligence of their workforce, which can help them identify new opportunities, solve complex problems, and drive innovation.

Data analytics : Finally, companies can leverage collective intelligence through data analytics. By analyzing data from various sources, such as social media, customer feedback, or sales data, companies can gain insights into market trends, customer preferences, and other factors that can influence their business. This can help companies make informed business decisions and adapt their strategies to meet changing market demands.

Leveraging collective intelligence is a powerful tool that companies can use to drive innovation and growth. By crowdsourcing ideas and feedback, partnering with other organizations, encouraging collaboration among employees, and analyzing data, companies can tap into the collective intelligence of their stakeholders and gain a competitive advantage in today's fast-paced business environment.

Potential Challenges and How Companies Can Overcome Them

Leveraging collective intelligence can be challenging for companies. Here are some potential challenges that companies may face when trying to leverage collective intelligence, and how they can overcome them:

Lack of trust : One of the biggest challenges companies may face is a lack of trust among stakeholders. For example, customers may be hesitant to share their ideas or feedback if they do not trust the company to use it appropriately. To overcome this, companies can be transparent about their intentions and how they plan to use the feedback. They can also offer incentives or rewards to encourage participation and build trust.

Information overload : Another challenge companies may face is information overload. With so much data and feedback available, it can be challenging to sift through it all and identify the most valuable insights. To overcome this, companies can use data analytics tools to analyze the data and identify patterns and trends. They can also use machine learning algorithms to automate the process of identifying the most valuable insights.

Resistance to change : Some stakeholders may be resistant to change, especially if they have been with the company for a long time and are used to doing things a certain way. To overcome this, companies can provide training and education to help stakeholders understand the benefits of leveraging collective intelligence. They can also communicate the importance of innovation and staying ahead of the competition.

Intellectual property concerns : Companies may also be concerned about intellectual property issues when sharing ideas and feedback. To overcome this, companies can use non-disclosure agreements (NDAs) to protect their intellectual property. They can also be selective about who they share information with and how they share it.

Leveraging collective intelligence can be challenging for companies, but it is also a powerful tool for driving innovation and growth. By building trust with stakeholders, using data analytics tools to analyze the data, providing training and education, and protecting intellectual property, companies can overcome these challenges and tap into the collective intelligence of their stakeholders.

Approaches to Generating New Data to Drive Collective Intelligence

Generating new data through experimentation and crowdsourcing external ideas is a powerful tool that organizations can use to stay ahead of the competition. Here are some ways that organizations can seek to generate new data on these perspectives through experimentation and crowdsourcing:

Conduct experiments : Organizations can conduct experiments to test new products, services, or business models. By doing so, they can gain valuable insights into what works and what does not. For example, a company may test a new marketing campaign in a small market to see how customers respond before launching it nationally.

Use pilot programs : Pilot programs are another way to test new products or services in a controlled environment. By doing so, organizations can gather feedback and make improvements before launching the product or service to a wider audience.

Use data analytics : Finally, organizations can use data analytics to generate new data on these perspectives. By analyzing data from various sources, such as social media, customer feedback, or sales data, organizations can gain insights into market trends, customer preferences, and other factors that can influence their business. This can help organizations make informed business decisions and adapt their strategies to meet changing market demands.

Organizations can seek to generate new data on these perspectives through experimentation and crowdsourcing. By conducting experiments, using pilot programs, and using data analytics, organizations can gain valuable insights into what works and what does not. This can help them stay ahead of the competition and adapt their strategies to meet changing market demands.

The real-time problem-solving approach is a powerful tool that companies can use to stay ahead of the competition in today's fast-paced business environment. By looking at strategy through multiple lenses, generating new data through experimentation, and leveraging collective intelligence, companies can gain a broader perspective on threats and opportunities and adapt their strategies accordingly. By adopting this approach, companies can drive innovation, improve customer satisfaction, and increase revenue. As an HR, leadership, and change management consultant, I encourage companies to adopt the real-time problem-solving approach and embrace uncertainty to drive innovation and growth.

Jonathan H. Westover, PhD is Chief Academic & Learning Officer ( HCI Academy ); Chair/Professor, Organizational Leadership (UVU); OD Consultant ( Human Capital Innovations ). Read Jonathan Westover's executive profile here .

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International Master of Public Administration

Courses and curriculum.

Penn I-MPA students do intensive and exciting coursework in three capacities: as individuals, as members of a Leadership Task Group (LTG), and as collaborators in class-wide exercises and projects.

Each LTG functions throughout the year and across the eight-course (10 c.u.*) curriculum as a small-group shared learning community.

The I-MPA program’s curricular structure

One course (I-MPA 6010) that focuses on critical issues in governance and human well-being; another course (I-MPA 6040) that immerses students in the latest and best interdisciplinary thinking about cross-sector (government, business, and nonprofit) collaboration; and a third course (I-MPA 6060) that surveys competing theories and concepts concerning leadership ethics.

Three courses, each of which imparts academically well-grounded but practical and applied lessons in leadership and problem-solving—quantitative reasoning for public decision-making (I-MPA 6030); economic reasoning for strategic decision-making (I-MPA 6055, 2 c.u.); and leadership and global public health (I-MPA 6080).

Two courses, one of which counts as 2 c.u. toward the 10 c.u. required for graduation, and each of which challenges students to independently apply the curriculum's core theories, key concepts, evidence-based techniques, and illustrative case studies: a group-organized, class-wide capstone project on global aging in which students, guided by leading experts, immerse themselves in issues pertaining to the "eldercare crisis" in Southwest and East Asia, and evaluate public-private programs that serve the most vulnerable older subpopulations in several Chinese cities (I-MPA 6095, 2 c.u.); and a highly structured but individually researched and written biographical analysis of a consequential global leader (I-MPA 6100).

The eight I-MPA courses

Four fall semester courses.

Over the last 200 years or so, despite various significant setbacks, human beings all across the world have become more likely to live longer, healthier, wealthier, and better overall. However, global progress in human well-being has been neither linear, nor universal, nor stable. What must happen if the next century-long chapter in the annals of global human well-being is to be a tale of greater health, wealth, and happiness for all or most people worldwide?

While there is no simple answer, this course argues that human well-being is best promoted and preserved only under conditions of good governance. But what is governance, and what is good governance? How has the theory and practice of governance changed over the last several decades, and how should it develop into the future? How can public managers act more strategically and effectively in the face of existent crises and emerging threats? What sort of partnerships can and should public agencies enter into with non-governmental actors in order to define and solve critical problems? Through classic texts, case studies, and group discussions, this course will explore these and other questions while giving students the practical knowledge needed to become boundary-spanning public leaders.

Knowing how to understand and use quantitative data is, increasingly, a skill critical to success in the government, nonprofit, and for-profit sectors. While numbers, statistics, and graphs abound, leaders across these sectors struggle in knowing not only how to organize, parse, and analyze data, but also how to utilize it effectively and in real time to cope with adverse conditions, solve problems, and achieve desirable outcomes. This course is uniquely designed to enhance your ability to use data effectively for real-time problem-identification, definition, decision-making, and problem-solving. In this course, students are introduced to key concepts, principles, protocols, and analytical tools and techniques relevant to quantitative reasoning, statistical analysis, and three separate but related problem-solving leadership skills: (1) describing and forecasting general social, economic, and civic trends; (2) measuring performance and results; and (3) evaluating particular social, economic, and civic interventions or programs. Students learn and apply these skills in relation to several cases.

Leaders across the world increasingly recognize the necessity of working across boundaries through various forms of collaboration. Collaboration across the government, nonprofit and business sectors has become more prevalent and important, but, at the same time, also more complicated. This course helps students understand the theory, policy, and practice of cross-sector collaboration. Students learn the purposes collaborations may serve, the forms they take, what skills and techniques are required, and the steps involved in initiating, sustaining, and evolving them. Students also learn the characteristics of the three sectors, the roles and contributions each can make to successful collaborations, and the competitive forces that are often at work in the collaborative process—as well as their possible implications.

Economic reasoning is key to strategic decision-making. This course has two parts. In Part I, students are introduced to important elements of economic reasoning, both at the microeconomic and macroeconomic levels. At the microeconomic level, topics include supply and demand, production and cost analysis, market structure and competition, market failure and the role of government. At the macroeconomic level, the course covers topics such as measuring aggregate output, economic growth, unemployment and inflation, and international trade.

In Part II, students practice applying these economic principles to the range of strategic decisions business firms face. Why business firms? Together with the government and the nonprofit sector, the business sector has a profound bearing on human well-being. For socially responsible business leaders, the challenge is to formulate successful strategies that grow firm profits, and satisfy its employees, shareholders, and customers, while also benefitting, or at least not adversely affecting, wider communities, whether local, regional, national, or transnational. Especially when facing less scrupulous business competitors, aggressive government regulators, or adversarial nonprofit advocates, civic-minded business leaders grapple with this challenge every day. How can business leaders formulate strategies to gain and sustain a competitive advantage at home or abroad? By applying economic reasoning, we will discuss firm decisions on prices, quantities, and costs, and firm decisions regarding which industries and geographic markets to enter. Moreover, we will examine how firms interact with each other through competition and collaboration. In addition, we will explore how these decisions are affected by the forces and trends in the overall macro economy.

Four spring semester courses

In a world filled with multiple and competing human well-being needs, not all of which can be addressed or acted upon fully or at once, which human well-being goals or purposes ought to matter most, which problems ought to be considered most deserving of attention and action, and which goals, purposes, or problems should be treated as top priorities with respect to their claims on attention, resources, and action? Under what, if any, conditions, should accomplishing certain human well-being ends be thought to justify policy or programmatic means that involve largely or wholly sacrificing other goals, purposes, or human well-being ideals and interests in the bargain?

Students explore how, whether, and to what extent effective boundary-spanning leadership is, ought to be, or can be made synonymous with moral or ethical boundary-spanning leadership, and by which understanding(s) of “morality” and “ethics.” Through classic and contemporary readings and case studies, students study several different philosophical and religious writings and traditions that might usefully inform the moral reasoning of present or future leaders who seek to promote human well-being by solving local, regional, national, or global problems.

This course takes an interdisciplinary and contemporary approach to global health addressing health disparities in developing countries. Through lectures, readings, case studies, and group discussions, students learn and apply such concepts or techniques as the measure of disease burden, health and human rights, women’s reproductive rights, health economics, and cost benefit and benefit analysis. The main case study concerns certain acute public health problems in developing countries such as India, Ghana, Uganda, Zambia, and others. By many public health indices, including rates of life-threatening infectious diseases, access to healthcare delivery, life expectancy, and rates of foodborne illnesses, developing countries face huge and still largely unresolved public health challenges. Millions of people in developing countries die each year from diseases that can be prevented by access to certain medicines and mitigated by participation in particular programs. Focusing mainly on malaria and other communicable diseases and non-communicable diseases in developing countries, students describe, analyze, and evaluate multiple and competing anti-malaria approaches and programmatic initiatives.

Note: This 2 c.u. course meets once a week for lecture and twice a week for recitations.  This 2 c.u. course fulfills one of two capstone requirements.

Program evaluation involves two separate but related skills: forecasting general social, economic, and civic trends; and evaluating particular social, economic, and civic programs. In this course, students learn key program evaluation concepts, principles, and techniques and then apply them in problem-solving exercises related to global aging with a focus on Asia. The elderly population of Asia is projected to exceed 900 million by the year 2050. In East and Southwest Asia, public health policies are just beginning to support "healthy aging in place," and pension systems are not yet well-developed. In this expert-led, group-organized, and class-wide capstone course, students learn about global aging and explore the humanitarian, economic and public health dilemmas posed by eldercare in East and Southwest Asia. By 2040, China alone is projected to have more than 400 million people age 60 or older. Students do individual and task group projects regarding how leading Chinese governmental bodies, ranging from national, provincial, and district-level ministries to the Chinese Communist Party, have defined the eldercare challenge; promoted "public-private partnerships" (or "PPP") programs; advanced community-based "healthy aging in place”; addressed the need for more geriatric medical practitioners and nursing professionals; and more. The last segment of the course is a multi-week research and writing project in which students describe, analyze, and assess China's subpopulation of "three needs" elderly citizens, and identify, evaluate, and prescribe reforms to existing PPP eldercare programs. The course concludes with a student-led presentation of the class's capstone report before a distinguished, multilingual, and multinational group of experts and leaders from the worlds of government, business, and the nonprofit sector.

Note: This 1 c.u. course fulfills one of two capstone requirements

Global leaders must work across three different types of boundaries: interpersonal boundaries, which involve relations with people who differ from oneself demographically, personality-wise, and otherwise; institutional boundaries, which involve working across government, nonprofit, and business organizations; and international boundaries, which involves both individual and institutional engagements that are carried on across national borders. This course introduces students to the latest and best empirical research literature on leadership. Students explore how to identify one’s own leadership-relevant traits, skills, and signature strengths, and how to learn from past and present global leaders whose careers arguably exemplify ethical and effective boundary-spanning leadership. Working quasi-independently with an assigned I-MPA advisor, each student produces a ten-point "Mini-Biographical Analysis" (M-BA) of a single significant global public leader. The M-BA is conducted as an advanced research and writing project that results in a paper that is deemed by the I-MPA faculty to be within the realm of professional if not publishable quality.

* Academic credit is defined by the University of Pennsylvania as a course unit (c.u.). A course unit (c.u.) is a general measure of academic work over a period of time, typically a term (semester or summer). A c.u. (or a fraction of a c.u.) represents different types of academic work across different types of academic programs and is the basic unit of progress toward a degree. One c.u. is usually converted to a four-semester-hour course.

3440 Market Street, Suite 100 Philadelphia, PA 19104-3335 (215) 746-6229 [email protected]

SOLVING PROBLEMS REAL TIME! | LBCC 1.10

• By Michelle Scullin Crute

LISTEN TO LEARN | SOLVING PROBLEMS REAL TIME! | LBFF 1.10

Concluding our series on the Imai Process of Continuous Improvement, where we’ve gone into a little more detail about some of the Lean tools in our toolbox, is REAL TIME PROBLEM RESOLUTION (RTPR) or the fifth stage of our Continuous Improvement Journey!

“Why do I need to have RTPR ? What problems are we trying to solve?” Real Time Problem Resolution (RTPR) is the process that utilizes the Lean tools like Visual Controls and Standard Work…Listen to find out how!

WATCH TO TRY | "Solving Problems Real Time!" with Jeff Raley | LBTT 1.10

what is REAL TIME PROBLEM RESOLUTION (RTPR) ? Is this a tool or a process? Watch our latest interview with Jeff Raley, a Retired aerospace professional and consultant. Jeff discusses with us the intent behind Real Time Problem Resolution; not only in his professional life, but also in his personal life. He explains the benefits of RTPR and how this process has made his teams successful in their commitment to continuous improvement…Watch to Learn More!

READ TO APPLY | Solving Problems Real Time! | LBCC 1.10

The Imai Process of Continuous Improvement is my favorite methodology for implementing lean. There are many philosophies around implementation of lean and many opinions surrounding this concept as well. but i have found the most benefit by following Mr. Imai’s vision…

The cover picture for the next 5 months will be the stages in the imai process with a circle around the tool we will be going into a little more detail about during the month. (As we progress each month, I will highlight the stage as I did below with the current month’s topic.) The Imai process progresses in the following stages:

• Linkage & Flow
• Standard Work
• Visual Controls (often times stages 3 & 4 are done at the same time)
• Real Time Problem Resolution

This month we discussed Real Time Problem Resolution (RTPR) and how it is the process that sustains continuous improvement. “What problem are you trying to solve?” is a common phrase I ask all the teams I have worked with or am currently working with. You may be asking yourself, “why? What’s in it for me (WIIFM)? Isn’t this just about supporting the bottom line? The problem doesn’t really effect me….right?”

The WHY : Real Time Problem Resolution is the process by which tools like Visual Controls and Standard Work are reviewed, monitored, and sustained. Yes, this does impact the bottom line and profitability…but it is still all about changing the way we think about our processes and procedures. The PROCESS, not the employee, is to be measured with the intent to understand process; rather than condemn or blame the employee. RTPR is how the team responds to the communication from the Visuals/Visual Controls and how they SEE the way that the business wants processes to interact. It’s the act of getting “life back to the good when things have gone bad”. It’s the response to the the red metrics, andon lights, or other indicators telling us something is wrong with the process and we need to address it…Not ignore or avoid it.

The WIIFM :  I believe we as humans crave order and structure. When our lives or relationships are in chaos, we do not function well. This is why people visit Therapists, to have some form of normalcy (or structure) added back to their lives. Like children need boundaries to thrive in their development, adults need it too. Real Time Problem Resolution (RTPR) sustains that structure and order by responding to, not avoiding, problems. Hopefully resulting in a more productive and efficient process yes; but also giving formal structure to the work or tasks being performed. If done well, employees can visually see how processes impact structure and what wastes are impeding Continuous Improvement. RTPR in a simple form can always be tested and implemented, as the team progresses in understanding, the definition of RTPR will change and adapt as well. If RTPR is functioning well, the Culture has achieved what it needs to for operational excellence to thrive.

Again my word of caution : If the Culture of the organization is ready to implement a Continuous Improvement Journey, it will have embraced the concept of 5S First. If the Culture is not ready, they will fight it, avoid it, and sometimes badmouth it. If you find the team is badmouthing the 5S program, it would be wise to take a step back and see how the 5S program was rolled out or is evaluated. I have found that the more bureaucratized the process of evaluating the 5Ses, the more resistant the team is. We at Leanbrella would recommend that changes should be made to return to the concepts of the WHY and the WIIFM behind doing 5S before trying a more difficult concept like Linkage & Flow, Standard Work, or Visual Controls.

Real Time Problem Resolution (RTPR) is not limited to the workplace. It can be utilized in our personal lives as well. We face a series of problems every day…Disruptions in our daily expected routine, family or friend troubles, relationship issues, etc, etc, etc. Any attempt to take back control and try to resolve these problems is RTPR! Even recognizing the issue as an actual problem that needs resolution is progress. Keep it up!

So…”What problems are yOU trying to solve?”

If you need help understanding the problems you are facing, don’t hesitate to reach out to Leanbrella for support! I have had the opportunity and privilege to identify complex issues in a variety of situations and support teams and individuals overcome those issues. If you embark on a Continuous Improvement journey and start to realize that there are more problems than you can handle, you’re not alone! Start small and keep going. Two steps forward and one step back is sTILL progress! For the bigger stuff you cannot handle, Leanbrella can help…

...Until next time, grab your Lean umbrella, "we've got you covered"

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