Chapter 6

Problem-Solving Scientists

Introduction

For the purposes of this chapter, please assume you know nothing about problem solving and read on as if it were all sparkling new information for you to process! This may seem like a strange request, but here’s why:

Anyone who has read about lean and process improvement has almost certainly come across lofty aspirations such as “Creating a Culture of Continuous Improvement and Problem Solving!” and “We’re a Learning Organization.” That’s all well and good, but rarely do we find anything close to this when visiting companies. As an assessor for the Shingo Institute, ¹ Mike has had the opportunity to visit many organizations that are motivated to transform their culture to achieve Enterprise Excellence. Some of these organizations are quite impressive and have made great strides. That said, the area that often lags in terms of progress is the widespread adoption of problem-solving behaviors by people at all levels of the organization. We find this surprising since problem solving is such a central theme in lean, Six Sigma, and other process improvement practices. What’s more, problem solving has been the main focus of improvement initiatives for the past three decades, yet we really aren’t that good at it!

Why is this the case? Here’s our take: Most people see themselves as natural problem solvers. They have spent their lives solving problems: at school, at home, at work; “All I do is solve problems!” is a common retort. People see themselves as capable problem solvers already understand ing and possessing problem-solving skills and thus assume, “I know this stuff.” But are we really effective problem solvers? Or are we jumping to solutions based on our biases and presumptions about what we think we know? Problem solving is not about starting with a solution and then creating a justification; rather, it is the application of a methodical progression to deeply understand current state and root cause, test potential countermeasures, and adapt based on the results.

Brian Hinken describes this well as being a learner and not a knower. ² Only when we allow ourselves to acknowledge that we don’t know the answer, that we all have more to learn, do we begin to embrace a problemsolving mindset. This is much more difficult to do than it sounds. We have been conditioned as humans to recognize and reward—even envy and idolize—those who have the answers, the crazy geniuses and other naturally smart people. It takes time and courage to create a culture that rewards the opposite: knowing we don’t understand deeply enough to have the answer but being willing to struggle, learn, and discover it. ³

Think back to grade school: The teacher asks students to raise their hands if they know the answer—not if they are unsure of the answer. Most people want to raise their hands and may think they know the answer, but we’re missing an important point. Learning is much less about having a specific answer and more about the process of expanding our understanding to move to a deeper awareness of the situation we are trying to change. This significantly impacts how we should be teaching, coaching, and working with our co-workers. If you respect your people and want to build a culture of continuous improvement, move away from asking people for solutions and begin to ask open-ended questions that develop their problem-solving skills.

When you’re in a solutions frame of mind, you don’t give the topic the attention necessary to deeply understand, let alone master, the mindset and skill set needed to be a really good problem solver. But when you are in a discovery frame of mind, the role of a manager becomes that of a coach who uses questions to position the learner to expand their understanding of a condition to better understand cause and effect.

The kind of problem solving we explore in this chapter is very different from what most people think of as everyday problem solving—it’s a structured and standardized approach to the way we think about obstacles and strive to understand cause and effect. In our opinion, it is the simplicity of the lean problem-solving model that actually hinders our ability to use it to solve problems! The outward uncomplicatedness of the lean problem-solving model reinforces people’s perception that they already understand everything they need to know about it. Just because something is simple to describe does not mean that it is easy to do!

What Is a Problem?

This may seem like an obvious question and hardly worthy of discussion, but we find that most people have not really given this topic much thought and that clarity around what a problem is (and isn’t) is very useful when attempting to solve one! A problem is a situation where (a) there is dissatisfaction by customers, employees, and/or suppliers in the way things work today; or (b) a current process contains waste (non-value-added ⁴ steps), an unacceptable degree of variation, or overburden; and (c) no one has a clear and complete understanding of the cause(s) of the current condition. For our purposes, for a problem to exist, we have frustration, or a wasteful process, and lack a clear and complete understanding of the reasons why. ⁵ If that is the case, action needs to be taken in the form of an experiment to clarify and deepen our understanding of the problem and potential countermeasures. But let’s not get ahead of ourselves.

Lean can be defined in many ways, but above all, it is a system of learning and a system of respect. The ultimate purpose of lean and continuous process improvement is to develop great learning organizations with the intention of delivering value to all stakeholders (customers, employees, shareholders, suppliers, community). Great learning organizations achieve high levels of performance and sustain it even when environments change. ⁶ In fact, learning capability becomes an insurmountable competitive advantage because it takes time and hard work to develop! If you have a few years head start on your competitors in learning and applying problem solving and you make it part of your daily work, it is very difficult if not impossible for competitors to catch up; they have to go through the same sometimes-painful trials of learning new ways of thinking and behaving. There is no shortcut to learning lean problem solving—but there is a well-worn path you can follow!

There is a classic story of Toyota Loom Works where a blueprint for an automatic loom was stolen. This was technology the company had developed after years of hard work and created a significant competitive advantage at the time. When it was discovered that the plans had been stolen, the people remained composed and realized they were already further ahead in their learning than anything the blueprint could show. The realization was that it was the learning process, not the specific knowledge, which created an advantage in the marketplace. Toyota continues to share the Toyota Production System model with the world, in part because the company realizes that no one can catch up in terms of learning!

People Focus

To achieve great results, we need stable and capable processes, and for process stability we need engaged, enabled, and enthusiastic people. It is people and only people who run, tinker, adjust, and improve their work methods in order to deliver value to customers. If people are the drivers of process improvement, then lean is ultimately about developing people. This is worth repeating: lean is a learning system and we learn best and most quickly by solving problems that directly impact us. That is why this chapter is so important to you and your company!

Unfortunately, you can’t really learn problem solving by reading about it. People have to practice, and the more they do, the better they get! Again, these ideas are easy to describe and deceptively difficult to actuate at both personal and organizational levels. There are numerous books on lean problem solving ⁷ and reading them can provide insights and understanding, but they are not an effective substitute for the constructive tension that is caused when people address real challenges where the work is done and value created, at the gemba.

To learn a new habit, Mike likes to say, “It takes 40 days to make it and another 40 days to own it.” ⁸ So if you are serious about embedding problem solving into your organization’s daily work, people need to practice every day for 3 months for it to begin to take hold. Later, we’ll talk about work systems that you can leverage to reinforce daily team-based problem solving so that the practice becomes a seamless part of work (people will be learning and they may not even be aware of it!). By the way, we are talking about 80 consecutive days of applying lean problem solving. If you miss a day, the next day is day one and you start the 80-day count from the beginning. This simple technique encourages people to keep up with a daily routine until it sinks in and becomes habit.

Who Wants to Be a Scientist?

Lean problem solving is all about applying the Scientific Method ⁹ to address problems and opportunities in our daily work. One challenge is that most people don’t like to talk about, let alone deal with, problems. Problems have a negative connotation in the workplace, so when the boss asks, “Do you have any problems?” people choose to respond, “No problems here!” There are many reasons for this: our education (A = good; F = bad), the stigma of making a mistake, possible judgment by others for not solving problems on your own, and the meddling that comes from your boss if you admit to having a problem, just to name a few.

Imagine what your culture would be like if people came to work not only to do their work, but also to find better ways to do their work. Instead of simply being an employee, every staff member is a problem-solving scientist who is not afraid to make mistakes, knowing that learning from errors and surprises is the key to greater understanding.

Thomas Edison, the man often credited with inventing the electric light bulb, ¹⁰ struggled with his team of scientists to discover the filament that would successfully conduct DC current inside a light bulb. In an interview, he was asked, “Aren’t you discouraged by failing so many times in your attempt to successfully invent the electric light bulb?” Edison reportedly replied, “I have not failed 10,000 times. I have successfully found 10,000 ways that will not work.” We love this story because it captures the essence of scientific thinking. It is not about trial and error; instead it is all about trial and discovery. We learn from our setbacks as well as, and perhaps more than, from our successes. Learning happens through failure. It is persistence and the practice of a pattern of steps that lie at the essence of lean problem solving. Let’s see what those are.

Plan–Do–Check–Adjust (PDCA)

The lean problem-solving methodology is divided into four stages: Plan, Do, Check, Adjust. ¹¹ At each step of the process, there is an opportunity to reflect and learn using micro PDCA cycles, ¹² as we shall see. Figure 6.1 shows the PDCA cycle along with a brief description of each component. Although simplistic at first encounter, this model is amazingly powerful at driving methodical learning and effective problem solving. Let’s look at the main components:

Plan

The objective of this initial step is to understand and deeply perceive what is currently happening in the work process. What are the people doing? What information and material flows are taking place? What is the gap between what should be happening and what is happening? What is working and what is broken? Does the process flow smoothly? What kinds of problems are occurring and where? Where are the problems occurring and what is their nature? What else is there to know about the real story? This step is often referred to as grasp the situation. The primary method you need to use here is Go and See, which means to go where the work is happening and where the problem is occurring to see firsthand with your own eyes what is going on.

This sounds reasonable, but most people (including the authors) have to break through the baggage of past experience and internal assumptions to (1) go and see, and then (2) really see what is there! Taichi Ohno, considered the father of the Toyota Production System, ¹³ said, “Don’t look with your eyes, look with your feet. Don’t think with your head, think with your hands.” You have to break away from the comfort of your cube or office and get out to where the real work is happening. Even if that means logging onto a program and walking through a work process with staff members by stepping through an application, then do it; if it means getting out to the data center or visiting a development team, then do it—IT managers are not exempt from going to the gemba!

Planning includes understanding the process that you want to improve, explaining the business impact of the current state, identifying what may be causing the problem, identifying what needs to change and how we can measure it, performing deeper analysis to understand root cause, and identifying possible countermeasures and experiments we can do to better understand and validate their efficacy. When performed to the right level, this step of the PDCA process can take up over half of the total time you spend completing a full cycle! The better job you do in the planning phase, the more likely that you’ll understand the root cause(s) and that your countermeasures will adequately address the problem. Providing you learn from the results, the experiment is a success even if it doesn’t produce the outcome you are hoping for. The higher the quality of your work during the planning stage is, the greater is the likelihood of a successful experiment and eventual fix to your problem.

The Plan step in PDCA has nothing to do with creating a project char ter or a Gantt chart project schedule. Instead, it has everything to do with gaining a keen understanding of your current condition. The quality of your problem solving is a direct reflection of how well (or how poorly) you take the steps necessary to understand beyond the superficial level of symptoms. It all seems very straightforward: What could be easier than understanding what is going on around you?

It turns out that people are loaded down with biases, ¹⁴ prejudgments, expectations, values, and beliefs that effectively filter and distort much of what we see and hear. Reducing the effects of these preconceptions is really hard work and you need to take it very seriously. Here’s a technique that helps: When you go and see, imagine you are seeing people, systems, and work processes for the first time. Adopt a beginner’s frame of mind and assume you know nothing about the work process you are investigating. This will take awareness and humility on your part to do it effectively. The more you can get outside yourself, the easier it is to see what is really happening. Like everything else we are discussing, this takes practice, practice, and more practice!

Do

This stage is all about running a single factor experiment and comparing the results to the expectations you recorded in the Plan stage of the learning cycle. Perhaps the most common mistake we see (and made for years!) is to make multiple changes to a process at the same time. With this approach, there is no way to know which countermeasures have helped and which have hurt your improvement effort.

Before you perform your experiment, here are a few things you need to do:

• In the Planning stage, identify and document the countermeasures the team is considering. Years ago, Mike had a coach who challenged him to identify seven countermeasures before going forward. This is tough; most people can come up with two or three without even going to the gemba, but after that, you really need to stretch your thinking to come up with seven potential improvements you can seriously suggest!
• An important practice here is to write things down. Don’t rely on memory when doing this next step in the PDCA cycle. Someone once said, “If you only plan to see what happens, you will.” You need to write out your prediction, as follows: “If we do x, we expect to get y.” This deceptively powerful statement captures your prediction of what you expect to see happen and sets you up for learning regardless of the outcome of the experiment. This is the essence of the scientific method. ¹⁵
• A great question to ask yourself and the team is, “What is the smallest thing we could do to impact the problem?” This simple question often frees up people’s minds to include seemingly insignificant changes. Lean problem solving is all about making many small-step improvements versus hitting the jackpot with a single countermeasure that is the panacea to all our problems! Teams are often paralyzed by what they perceive to be the size of the task at hand. This approach supports people breaking problems into small, actionable pieces and making real progress they can build on.
• For each countermeasure, identify the problem, possible root cause(s), and a measurement that will be impacted, and guess how much it will move. It is important to answer the question, “How will we confirm the impact of the change we are making?” This step also stretches our thinking and learning by positioning our minds to be open to discovery, no matter the results of the experiment. It’s also a good idea to ask, “What do we expect to learn as a result of this experiment?”

Check

Now it’s time to begin deep learning by comparing the results of our experiment to our expectations (which we wrote down) and to see what they teach us. This step is critical to learning and it is entirely based on confirming our hunches and ideas. If you don’t like the words hunch, or guess, or intuition, then use hypothesis. As we said earlier, this practice is the scientific method and based entirely on validated learning. One of four outcomes can occur:

1. The results match our expectations. This happens less often than we like, but when it does, it suggests we have a good understanding of cause and effect and that our countermeasure has a positive impact on the problem we are attempting to solve.
2. The results do not match our expectations. This is often the outcome of our first few experiments as we begin to realize we really don’t understand the process and problems deeply enough to make a measurable improvement. At times, there is no effect or a measurable negative impact as a result of the experiment. The needle does not move at all! This can be demoralizing to the team if we don’t manage expectations, be hon est and open about where we are in the learning process, and lead with respect. The important thing to do here is to ask ourselves, “Could this be the result of our not measuring the right thing or inaccurately measuring it?” “Do we understand both the root cause and the countermeasure deeply enough to make sense of the outcome we are witnessing?”
3. The results are positive, but only partially match our expectations. In other words, we have some improvement that we can confirm, but not enough. We haven’t moved the dial far enough, so what’s our next step?

Adjust

Our final stage of the learning cycle is to react appropriately based on the discoveries made in the previous Check step. Think evidenced-based action when performing this step. Based on the results of the experiment, your next step should be

1. If the results of the experiment match our expectations and make a positive confirmable improvement, the next step is to identify how you will mobilize and spread the change so that it becomes embedded in the standard work process. What needs to change for the countermeasure to become the shared way we do the work? Who needs to know? What needs to be different in the work environment to support the change? What new behaviors need to be established? What triggers need to be put into play to begin to make this a habit? See Chapter 5 for information on standard work.
2. If the results for your experiment do not confirm what the team expected, then ask yourself, “What are the facts telling us and what do they tell us about our level of understanding of cause and effect?” When you are at this point in the learning cycle, there is naturally some disappointment and frustration because we all tend to assume we understand what is happening and are confident that our countermeasures will work.

   The next step is to take the learning from the last experiment, reflect and discuss, and then enter another cycle of PDCA with a renewed level of understanding! This takes discipline and tenacity because it is so much easier to give up and say, “It’s out of our hands, way too complicated, and beyond our circle of control!” Don’t cave in; this is a conscious choice you and your team need to make. In fact, it is the strength of the team that directly impacts the quality of your countermeasures because the best ideas emerge from high levels of collaboration and trust.

3. If the results are good, but not good enough, the team needs to consider whether they should go forward with the partially effective countermeasure and then proceed with another cycle of PDCA or place the working improvement aside, do another PDCA cycle, and then consider integrating the two. The decision you make should be based on the team’s shared thinking. This is where a good coach can play a key role in developing the learner’s judgment and critical thinking skills. The situation is also a great opportunity to build trust and respect among team members as you explore your options.
4. If the experiment has no impact or a negative influence on the problem, a new round of PDCA is clearly the next step to take. However, before you do, the team needs to ask the same questions to clearly identify the learning and discovery gained from the last trial. It is not enough to say, “Well that didn’t work, so let’s pick another countermeasure and try that.”

As we mentioned earlier, at each step of the process there is an opportunity to reflect and learn using rapid micro PDCA cycles. Think of it this way: As you move from one stage of the learning cycle to the next, ask yourself the following questions: What did I learn and what do I know now? How do I know it? What else do I need to know? Where and with whom can I go and see to deepen my understanding? Am I prepared to go to the next step in the PDCA process?

Hopefully, you are beginning to see that lean problem solving is essentially a very effective system of learning. By honoring the PDCA process daily, the key skill we are developing in our people is the acquisition of process knowledge. We like to use the term honor as it implies a code of behavior and respect for people. If we honor the PDCA process, we avoid lazy thinking that comes from taking shortcuts and avoiding work we’re not comfortable doing.

Your Brain on A3

Putting PDCA into action is the next step on your transformation journey. Unfortunately you cannot just flip a switch and have everyone in your company become an expert problem solver. A tool is needed to begin to build the daily behavior that makes problem solving a daily habit. The A3 is a simple-to-use method for building the problem-solving muscle of your organization. Done well, it will become the currency of continuous improvement and process changes in the organization.

The A3 Template

The A3 is a one-page form used to reinforce the PDCA thinking process. Very much like the PDCA process itself, the form is deceptively simple and belies the challenging brainwork that lies ahead for the person or teams filling it in. There are numerous A3 templates available on the web, ¹⁶ and they all do a pretty good job of compartmentalizing thinking into the PDCA elements. The term A3 simply refers to the size of the paper using a European standard, 11 inches × 17 inches. An example A3 template can be seen in Figure 6.2.

A great A3 tells a story about a problem or process change. How often have you wondered about the thinking behind why a process is the way it is? What if you had a way to look back at changes made to see why a certain approach was taken? A3s give you that ability. At their best, A3s provide a narrative of the thought processes and reasoning in problem solving and process change. In fact, you may have noticed that we’ve been referring to problem solutions as countermeasures for the simple fact that very few solutions are ever permanent.

Even more important than looking backward at process changes, the A3 is a way to affect PDCA thinking by providing an account of the progression of learning that takes place as the team follows a PDCA learning cycle. It gives you a straightforward process to affect behavioral change in your transformation. Let’s take a more detailed look at the A3 and then circle back to implementation in your model line. ¹⁷

Background/Problem Statement

Why should we care?

• What is the problem we are trying to solve?
• Why is it important?
• How was the problem discovered?

This section is your chance to grab the attention of the reader, to make him or her want to keep reading. Frame the problem such that the deci sion makers can understand why something needs to be done. (What is the impact to the business? How will this affect our customers?) It doesn’t matter how smart you sound if your countermeasures are never implemented.

Current Condition/Current State

How is the system currently operating? What is the impact of this problem (think safety, quality, delivery time, productivity, customer satisfaction, morale, revenue, profits, reputation, etc.)?

• What are the specific symptoms and who is feeling them?
• Can we show the problem’s size and behavior in terms of a measure using a trend line, pie chart, Pareto analysis, ¹⁸ or some other visual?
• Can we observe the problem with our own eyes?

Current state is just that: Describe the system or problem as it is today— not the ideal path or what the process documentation says, but rather as you observe it operating (e.g., “We observed that 20% of the jobs are being reworked for data accuracy and completeness”).

Analysis and Understanding

What do we understand about the current condition?

• What should be happening and what is happening?
• What is preventing us from moving to an improved target condition? ¹⁹
• What is our current understanding of specific cause and effect?
• Are dependencies clear and understood?
• Do all of the steps in the process add value?
• What is stopping the flow of information or material?

This section is your chance to put your detective skills to use.

Appropriate tools often used in Analysis are cause maps, fishbone diagrams, Pareto charts, 5 Whys, histograms, and control charts. Incidentally, this is often the section new A3 practitioners rush through as they are confident they already know the answer.

Target/Objectives/Goals

What is the desired outcome?

• What needs to be different for things to be better, right now?
• What is a baseline measurement we can capture before we make any changes?
• How can we measure success before and after the countermeasure is taken?

Be as specific as possible with your goals. Don’t just say, “Improve the performance of the database” or something similar. What specifically constitutes better performance? If the goals are too far out in the future (e.g., ideal future condition), set intermediate goals (e.g., target condition).

To avoid losing the interest of the organization, ensure that you have goals achievable within 30 to 90 days. We have found that shorter rapid cycles of learning bring about better results and higher levels of engagement. Challenge teams to perform small experiments (hours or days) versus large projects (weeks or months). The smaller the countermeasure is, the easier it is to test. The faster the experiment is to test, the quicker the learning will be.

Proposed Countermeasures

What do we want to do to make an improvement?

• What are our proposed countermeasures? (Try to identify seven!)
• How did we select the one we want to try first?
• Have we drawn a clear connection between our suggested countermeasure and a root cause?
• How will we know if a change is an improvement?

Selected countermeasures should be able to be traced directly to a root cause of the problem and must be able to be implemented. A countermeasure does no good if it is too difficult to execute or does not have the support of the people responsible for the process.

Implementation Plan

What is the plan?

• What are the tasks, responsibilities, deliverables, and due dates needed to implement the countermeasure?
• What challenges and risks do we anticipate and need to watch for?
• Have we defined some measures of success? Are these the same measures we identified earlier (target/objectives/goals)? If not, why have they changed?

The plan is all about the who, what, and when required to implement the countermeasures. The method to manage the implementation can vary from traditional Gantt charts to story cards to milestones.

Sustaining Measures and Follow-Up

If this works, how do we sustain the gains and continue to get better?

• When are we scheduled to perform a checkup to see if things are really better? How often will we check?
• How will we know if the countermeasure had the impact needed?
• How do we ensure that our learning is shared with others?
• If the countermeasure is effective, how will we sustain it?

The follow-up section often receives short shrift, but that’s a huge mistake. Don’t assume everything is fine simply because you took all the right steps in defining the problem, finding root cause, and methodically implementing countermeasures. Periodically, you need to check to ensure that things are still working well, take the time to bake the process changes into standard work, and anticipate that other countermeasures will be needed in the future—because they will be!

The A3 in Action

Once you have a solid understanding of A3s, put them into action in the model line. Commit to it; A3 is the tool you will use for problem solving. It is the primary mechanism to create world-class problem solvers in your company. If you don’t understand the problem’s cause and effect, you need an A3. Even if you think you do understand cause and effect, the process of filling out an A3 can capture the team’s reasoning as to why they took the actions they chose and be used to gain buy-in, share the learning, and document the thinking.

Use A3s with both management and associates to solve problems that are larger than just do it type issues. If someone wants to move the water cooler from an isolated corner of the building to where everyone is located, that probably doesn’t need an A3. If your primary website cannot handle the traffic on Monday mornings despite someone working on it for 6 months, that probably (most definitely!) does need an A3. If it is unclear why managers are not going on their gemba walks, create an A3. You get the point—practice A3 thinking until it becomes a part of the DNA of the organization.

Some rules to keep in mind as you get started include:

1. One owner of the A3. This individual can be chosen for a variety of reasons, but one person is ultimately responsible for its creation.
2. A qualified coach and sponsor. Someone has to be able to coach those new to the A3 process—what it is, why it is used, and how. The sponsor needs to have the authority to approve selected countermeasure experimentation and implementation.
3. Go to the gemba. It may sound redundant at this point, but get out from behind your desk and get to where the real work happens. A3s should not be created in isolation. Talk to the people involved in the process or problem—build consensus.
4. Use pencil and paper. In fairness, no one in IT ever listens to this rule. The intent is that the A3 is created and modified as you learn. We like pencil and paper, but a computer is fine as long as you realize that the A3 will change often. If you must create the A3 electronically, take a printout with you to the gemba.
5. Use pictures and drawings. Don’t worry about your artistic abilities. Simple drawings and pictures convey meaning and create a connection with the reader. The best A3s are visually appealing and can be understood even by those not familiar with the process or problem in question.
6. Do not prefill sections or work out of sequence. If someone is assigned an A3 and comes up with countermeasures before root cause, coaching on the process is needed!

At this point, start using A3s for two purposes: (a) solving problems in frontline teams and in management, and (b) creating and updating standard work and leader standard work.

Solving Problems

The A3 is your primary tool to teach and reinforce PDCA thinking. Put it into action using the already defined problem identification and escalation process on your visual boards. At the front line, you already have visuals indicating that a problem exists and who is assigned to resolve it. The A3 is a natural next step to facilitate solving the problem. Have the person assigned to the problem go through the A3 process. Name an appropriate sponsor and/or coach that can help the associate learn the process and has the authority to authorize the validated countermeasure to be implemented. Nothing is more disheartening than spending time on the A3 process only to realize a promising countermeasure will not be implemented.

Getting started with A3s is not easy, so anticipate that there will be hiccups along the way and be prepared to step in and assist. Add space on your visual boards to display A3 WIP and completed ones as well. Celebrate when one is finished and publicly praise and recognize those who worked on it. Build excitement around problem solving; making the process fun will reinforce the new philosophy that problems are not bad.

Updates to Standard Work

As you learned in Chapter 5, standard work is core to process improvement as a baseline from which we improve. Unless we stabilize the way work is done and practice a common process, there is no consistent baseline from which to deploy further improvements. But what is the process to improve the standard? A3s provide the answer.

At the end of the day, what we are trying to do is to influence how people perceive their daily work. We want them to see that work has two parts: doing the work and improving the work. When better ways of performing work are discovered through the process of solving problems, it is essential that standard work documents get updated to represent the new, improved way of doing things. Remember the Taichi Ohno ²⁰ quote from Chapter 5: “Where there is no standard, there can be no kaizen.”

When an associate has an idea to improve standard work, an A3 should be used to show that the changes have been thoughtfully considered, including any unintended effects on other processes, before the standard is changed. The A3 becomes the currency of process change within the organization. It ensures that process changes are relatively easy to make, while also ensuring that the change does not optimize a local process at the expense of the overall system. Because it drives methodical problem solving and PDCA thinking, A3s act as a governor of sorts, leveling the amount of change to what the organization can absorb at any given time. Using the A3 process for updates to standard work recognizes that process improvement must enable associates and consider the right pace of change for the organization.

Kata

For problem solving to really become embedded into people’s daily routines, lean cannot be experienced as additive—just another task piled on to their already overloaded schedule. Your people are already very busy dealing with the work tasks, projects, emergencies, and endless challenges of their jobs. To begin successfully integrating this kind of work, problem solving might be a 5to 10-minute working discussion, not in the form of a sit-down meeting or a long, drawn out analysis. It becomes a key element of our daily work as we change the way we think about doing our work and improving the way we do our work. Problem solving is the heart of improving our work. You problem solve in very short cycles so that you can check and adjust often because it is during the check/adjust phases that most of the deep learning takes place. ²¹ The trick is to embed problem solving as a normal part of the work routine rather than a separate activity.

We use the word routine in describing what needs to become a repetitive pattern of behavior as to how we address problems. Another word for a practiced routine is kata. ²² In his landmark book, Toyota Kata ⁷ , Mike Rother uses the term to capture the underlying thought patterns practiced at Toyota in its approach to problem solving and application of the lean tools and methods. As Rother points out with the familiar maxim, “All models are wrong, but some are useful.” ²³ PDCA is a useful model! It turns out that as we begin to get better at lean problem solving (what Rother calls the improvement kata or IK), we also get better at effectively applying other lean tools (like A3, visual management, and standard work). We get better because we think more clearly and more deliberately. Problem solving is not extra work; rather, it is the work!

There is a widespread misconception that kata is simply another rendering of PDCA, and on the surface it may appear that way. But if you look deeper (and begin to practice), the improvement kata provides an amazingly effective mechanism to strengthen what is often the weakest part of most people’s A3s: the analysis and proposed countermeasures sections. Although the questions appear to be simple, when used with an experienced coach, they are incredibly effective at assisting people to acknowledge what they know, how they know it, and what they need to know to get closer to truly understanding cause and effect. The more clearly we understand cause and effect, the more discerning our proposed countermeasures, and the more profound our learning is as we test them!

The improvement kata is made up of five key questions that the coach asks the learner (the person trying to understand and solve the problem). These questions may seem simple, but they are extremely powerful in supporting people to expand and deepen the way they think about dealing with problems and making improvements:

1. What is the target condition?
2. What is the actual condition right now?
3. What obstacles are now preventing you from reaching the target condition? Which one are you addressing now?
4. What is your next step/PDCA experiment?
5. How quickly can we go and see what you have learned?

The coaching kata (CK) is focused on managing the learner’s experi ence practicing problem solving. In order to be a good coach, you need to be able to perform lean problem solving skillfully following an established framework and you have to be able to teach it. Good coaching is all about guiding people to discover things on their own rather than directing them to take specific actions. Stéphane Mallarmé ²⁴ said, “To define is to kill. To suggest is to create.” A key coaching skill (worth mentioning again) is to ask questions to lead the learner to discover something new without stealing the learning experience from the learner (by telling him or her). Dave Verble ²⁵ describes this as coaching for development versus coaching for correction.

Effective coaching is a one-on-one activity because the coach and the learner need to connect at a very personal level in order to develop the trust and mutual respect required for effective learning to take place (once again, respect plays a central role in the improvement process!). Three keys to good coach ing include (a) following a set routine, (b) asking open-ended questions, and listening deeply with the intent to see the issue through the other person’s eyes. (Ask yourself, “Where is the learner in his understanding?”) This third element is particularly challenging and requires humility on the part of the coach.

People and teams using a problem-solving routine learn as they attempt to reach new levels of performance and, most importantly, adjust based on what they learn in the process of trying to make things better. The kata community has exploded since Toyota Kata was first published in 2009. We encourage you to check out the resources made available at the Toyota Kata website. ²⁶

Why Thinking Is Less Important Than Behavior!

It may seem funny that we end a chapter on problem solving with a statement like this! While it is important to understand lean problem solving intellectually, comprehension is not the key to becoming a good problem solver. The key to becoming a good problem solver is to practice being a good problem solver. In order to learn, we must experience firsthand the impact of acting differently.

The most effective practice is using the PDCA learning cycle in your work at your gemba—as a part of your daily routine! The more you integrate active learning (aka problem solving) into your daily routine, the more proficient at structured problem solving you will become. The more frequently you indulge your brain in structured thinking about problems, the sooner you’ll create reliable mental habits to think about, understand, and solve problems! When the consequences of our actions create better results, we learn by doing and shift our habits to new ways of acting and thinking.

Notes