Averages, Percentages and Math

As a general rule I strongly discourage the use of averages and “percent improvement” (or reduction) type metrics for process improvement.

The Problem with Averages

Averages can be very useful when used as part of a rigorous statistical analysis. Most people don’t do that. They simply dump all of their data into a simple arithmetic mean, and determine a score of sorts for how well the process is doing.

The Average Trap

There is a target value. Let’s say it is 15. Units could be anything you want. In this example, if we exceed 15, we’re good. Under 15, not good.

“Our goal is 15, and our average performance is 20.”

Awesome, right?

Take a look at those two run charts below*. They both have an average of 20.

On the first one, 100% of the data points meet or exceed the goal of 15.

Run chart with average of 20, all points higher than 15.

On the one below, 11 points miss the goal

image

But the both have an average 5 points over the goal.

In this case, the “average” really gives you almost no information. I would have them measure hits and misses, not averages. The data here is contrived, but the example I am citing is something I have seen multiple times.

Why? Most people learned how to calculate an arithmetic mean in junior high school. It’s easy. It’s easier to put down a single number than to build a run chart and look at every data point. And once that single number is calculated, the data are often thrown away.

Be suspicious when you hear “averages” used as a performance measurement.

Using Averages Correctly

(If you understand elementary statistical testing you can skip this part… except I’ve experts who should have known better fall into the trap I am about to describe, so maybe you shouldn’t skip it after all.)

In spite of what I said above, there are occasions when using an average as a goal or as part of a target condition makes sense.

A process running over time produces a range of values that fall into a distribution of some kind.

Any sample you take is just that – a sample. Take a big enough sample, and you can become reasonably confident that the average of your sample represents (meaning is close to) the average of everything.

The move variation there is, the bigger sample you need to gain the same level of certainty (which is really expressed as the probability you are wrong).

The more certain you want to be, the bigger sample you need.

Let’s say you’ve done that. So now you have an average (usually a mean) value.

Since you are (presumably) trying to improve the performance, you are trying to shift that mean – to change the average to a higher or lower value.

BUT remember there is variation. If you take a second sample of data from an unchanged process and calculate that sample’s average, YOU WILL GET A DIFFERENT AVERAGE. It might be higher than the first sample, it might be lower, but the likelihood that it will exactly the same is very, very small.

The averages will be different even if you haven’t changed anything.

You can’t just look at the two numbers and say “It’s better.” If you try, the NEXT sample you take might look worse. Or it might not. Or it might look better, and you will congratulate yourself.

If you start turning knobs in response, you are chasing your tail and making things worse because you are introducing even more variables and increasing the variation. Deming called this “Tampering” and people do it all of the time.

Before you can say “This is better” you have to calculate, based on the amount of variation in the data, how much better the average needs to be before you can say, with some certainty, that this new sample is from a process that is different than the first one.

The more variation there is, the more difference you need to see. The more certainty you want, the more difference you need to see. This is called “statistical significance” and is why you will see reports that seem to show something is better, but seem to be dismissed as a “statistically insignificant difference” between, for example, the trial medication and the placebo.

Unless you are applying statistical tests to the samples, don’t say “the average is better, so the process is better.” The only exception would be if the difference is overwhelmingly obvious. Even then, do the math just to be sure.

I have personally seen a Six Sigma Black Belt(!!) fall into this trap – saying that a process had “improved” based on a shift in the mean of a short sample without applying any kind of statistical test.

As I said, averages have a valuable purpose – when used as part of a robust statistical analysis. But usually that analysis isn’t there, so unless it is, I always want to see the underlying numbers.

Sometimes I hear “We only have the averages.” Sorry, you can’t calculate an average without the individual data points, so maybe we should go dig them out of the spreadsheet or database. They might tell us something.

The Problem with Percentages

Once again, percentages are valuable analysis tools, so long as the underlying information isn’t lost in the process. But there are a couple of scenarios where I always ask people not to use them.

Don’t Make Me Do Math

“We predict this will give us a 23% increase in output.”

That doesn’t tell me a thing about your goal. It’s like saying “Our goal is better output.”

Here is my question:

“How will you know if you have achieved it?”

For me to answer that question for myself, I have to do math. I have to take your current performance, multiply x 1.23 to calculate what your goal is.

If that number is your goal, then just use the number. Don’t make me do math to figure out what your target is.

Same thing for “We expect 4 more units per hour.”

“How many units do you expect per hour?” “How many are you producing now?” (compared to what?)

Indicators of a W.A.G.

How often do you hear something like  “x happens 90 percent of the time”?

I am always suspicious of round numbers because they typically have no analysis behind them. When I hear “75%” or “90%” I am pretty sure it’s just speculation with no data.

These things sound very authoritative and it is easy for the uncertainty to get lost in re-statement. What was a rough estimate ends up being presented as a fact-based prediction.

At Boeing someone once defined numbers like this as “atmospheric extractions.”

If the numbers are important, get real measurements. If they aren’t important, don’t use them.

Bottom Line Advice:

Avoid averages unless they are part of a larger statistical testing process.

Don’t set goals as “percent improvement.” Do the math yourself and calculate the actual value you are shooting for. Compare your actual results against that value and define the gap.

When there is a lot of variation in the number of opportunities for success (or not) during a day, a week, think about something that conveys “x of X opportunities” in addition to a percent. When you have that much variation in your volume, fluctuations in percent of success from one day to the next likely don’t mean very much anyway.

Look at the populations – what was different about the ones that worked vs. the ones that didn’t — rather than just aggregating everything into a percentage.

Be suspicious of round numbers that sound authoritative.

_______________

*These charts are simply independent random numbers with upper and lower bounds on the range. Real data is likely to have something other than a flat distribution, but these make the point.

Toyota Kata: Is That Really an Obstacle?

“What obstacles do you think are preventing you from reaching the target condition?”

When the coach asks that question, she is curious about what the learner / improver believes are the unresolved issues, sources of variation, problems, etc. that are preventing the process from operating routinely the way it should (as defined by the target condition).

I often see things like “training” or worse, a statement that simply says we aren’t operating the way the target says.

Here is a test I have started applying.

Complete this sentence:

“We can’t (describe the target process) because ________.”

Following the word “because,” read the obstacle verbatim. Read exactly what it says on the obstacle parking lot. Word for word.

If that does not make a grammatically coherent statement that makes sense, then the obstacle probably needs to be more specific.

 

 

Another Homework Question

Another interesting homework question has shown up in the search terms. Let’s break it down:

23. if the slowest effective machine cycle time in a cell is 55 seconds and the total work content is 180 seconds, how many operator(s) should operate the cell so that labor utilization is at 100%?

I find this interesting on a couple of levels.

At a social level, the idea of cutting and pasting a homework question into Google hoping to find the answer is… interesting. Where is the thinking?

What are we teaching?

The question is asking “How many people do we need to run as fast as we can?” (as fast as the slowest machine). But how fast do they need to run? Maybe they only need a part every 95 seconds. If that is true, then I need fewer people, but I am going to run the slowest machine even slower.

In other words, “What is the takt time?” What does the customer need? How often must we provide it?

Then there is the “labor utilization” metric, with a target of 100%. Assuming the planned cycle time is actually 55 seconds (which it shouldn’t be!), we need 3.3 people in this work cell. (180 seconds of labor cycle time / 55 seconds planned cycle time: “How long does it take?” / “How long do you have?” = Minimum Required Capacity)

How about improvement? What do we need to do to get from 3.3 people to 3 people? We can solve for the labor cycle time.  55 seconds of planned cycle time * 3(people) = 165 seconds of total labor. So we need to get that 180 seconds down to a little less than 165 seconds.

Now we have a challenge. We need to save a bit over 15 seconds of cycle time. That might seem daunting. But we don’t have enough information (the current condition) to know where to begin. Then we can establish the next target condition and get started making things better.

These types of questions bother me because they imply all of these things are fixed, and they imply we run “as fast as we can” rather than “as fast as we must.”

Edit: Today I saw two more searches for:

total work content divided by slowest machine cycle time

so it looks like at least two others are working on the same assignment.  🙂

Thoughts?

Be Ready for Empowered Employees

“I want my employees to feel empowered.”

“You realize empowerment means your employees start making decisions, right?”

“Oh… I want them to feel empowered. I didn’t say wanted them to be empowered.”

(from a presentation by Mardig Sheridan)

This is a further exploration of one of my notes from the Kata Summit a few weeks ago.

Think back to your own organizational history. When people were “empowered” how often did management struggle to retain control of everything?

These same managers complain about having to make every little decision themselves, and not taking initiative.

When organizations try to take on Toyota Kata there are a couple of common patterns that frequently emerge.

One is where most of the actual coaching is done by staff practitioners, with the higher level managers pretty much staying out of the mix. Previous posts not withstanding, that actually works pretty well up to a point.

The limit is reached when the next obstacle is a limiting policy or organizational boundary that can’t be crossed.

So… while this process does build the skill of individual managers at the middle and lower levels, it doesn’t do so well building a management team. Those enlightened middle managers can be in a tough spot if their bosses are expecting them to just be a conduit for direction from above. The coaches are working to engender independent thinking in the middle level of an organization that, by the actions of its leaders, doesn’t actually want it. (Yes, that is a bit black and white, the truth is more nuanced.)

The other common approach, and the one we encourage, is one where the coach is the responsible manager – usually the learner’s boss, or at least in the chain.

Novice coaches, especially if they are actually in the chain of responsibility, often struggle with the boundary between “coaching” and “telling the learner what to do.”

He often knows the answer. Or at least he knows an answer. Or, perhaps, he knows the conclusion he has jumped to with the limited information he has.

So, creating some rationale for why, the coach gives direction rather than coaching. This can be very subtle, and is often disguised as coaching or teaching. For this, I remind coaches to “Check your intent.” If it is to “Show what you know” then step back.

The learner may well have better information. Now this puts the learner in a tough spot. He is being encouraged to explore, yet also being told what to do.

Leaders who want to create initiative, leadership, and decentralized action in their organization have to be ready to give up on the idea that they know the best answers.

Scientific Improvement Beyond The Experiment

“How do we deploy this improvement to other areas in the company?” is a very common question out there. A fair number of formal improvement structures include a final step of “standardize” and imply the improvement is laterally copied or deployed into other, similar, situations.

Yet this seems to fly in the face of the idea that the work groups are in the best position to improve their own processes.

I believe this becomes much less of a paradox if we understand a core concept of improvement: We are using the scientific method.

How I Think Science Works

In science, there is no central authority deciding which ideas are good and worth including into some kind of standard documentation. Rather, we have the concept of peer review and scientific consensus.

Someone makes what she believes is a discovery. She publishes not only the discovery itself, but also the theoretical base and the experimental method and evidence.

Other scientists attempt to replicate the results. Those attempts to replicate are often expanded or extended in order to understand more.

As pieces of the puzzle come together, others might have what seems to be an isolated piece of knowledge. But as other pieces come into place around them, perhaps they can see where their contributions and their expertise might fit in to add yet another piece or fill in a gap.

If the results cannot be replicated at all, the discovery is called into serious question.

Thus, science is a self-organized collaborative effort rather than a centrally managed process. All of this works because there is a free and open exchange among scientists.

It doesn’t work if everyone is working in isolation… even if they have the same information, because they cannot key in on the insights of others.

What we have is a continuous chatter of scientists who are “thinking out loud” others are hearing them, and ideas are kicked back and forth until there is a measure of stability.

This stability lasts until someone discovers something that doesn’t fit the model, and the cycle starts again.

How I Think Most Companies Try To Work

On the other hand, what a lot of people in the continuous improvement world seem to try to do is this:

Somebody has a good idea and “proves it out.”

That idea is published in the form of “Hey… this is better. Do it like this from now on.” image

We continue to see “standardization” as something that is static and audited into place. (That trick never works.)

What About yokoten. Doesn’t that mean “lateral deployment” or “standardize?”

According to my Japanese speaking friends (thanks Jon and Zane), well, yes, sort of.  When these Japanese jargon terms take on a meaning in our English-speaking vernacular, I like to go back to the source and really understand the intent.

In daily usage, yokoten has pretty much the same meaning [as it does in kaizen] just a bit more mundane scope…along the lines of sharing a lesson learned.

Yokogawa ni tenkai suru (literally: to transmit/develop/convey sideways) is the longer expression of which Yokoten is the abbreviation.

Yoko means “side; sideways; lateral. Ten is just the first half of “tenkai” to develop or transmit. Yokotenkai..

If you take a good look at the Toyota internal context, it is much more than just telling someone to follow the new standard. It is much more like science.

How the Scientific Approach Would Work

A work team has a great idea. They try it out experimentally. Now, rather than trying to enforce standardization, the organization publishes what has been learned: How the threshold of knowledge about the process, about a tricky quality problem, whatever, has been extended.

We used to know ‘x’, now we know x+y.

They also publish how that knowledge was gained. Here are the experiments we ran, the conditions, and what we learned at each step.

Another team can now take that baseline of knowledge and use it to (1) validate via experimentation if their conditions are similar. Rather than blindly applying a procedure, they are repeating the experiment to validate the original data and increase their own understanding.

And (2) to apply that knowledge as a higher platform from which to extend their own.

But Sometimes there is just a good idea.

I am not advocating running experiments to validate that “the wheel” is a workable concept. We know that.

Likewise, if an improvement is something like a clever mistake proofing device or jig (or something along those lines), of course you make more of them and distribute them.

On the other hand, there might be a process that the new mistake-proofing fixture won’t work for. But… if they applied the method used to create it, they might come up with something that works for them, or something that works better.

“That works but…” is a launching point to eliminate the next obstacle, and pass the information around again.

oh… and this is how rocket science is done.

Edit to add:

I believe Brian’s comment, and my response, are a valid extension of this post, so be sure to read the comments to get “the rest of the story.” (and add your own!)

Goal vs “Target Condition”

Emily sent an email asking “how would you describe the difference between GOAL and TARGET CONDITION?”

I end up on this topic enough that I thought I’d discuss it here.

I am assuming we are referring to the “Toyota / Toyota Kata” context here. I mention that because while “target condition” has a pretty clear meaning in that context, we have to rely on the everyday meaning definition for “goal.”

Thus, I can’t objectively say “goal” means this, or doesn’t mean that because it means whatever it means to you.

Still, I can give it a try.

I have drawn on the following analogy frequently because I think it demonstrates the concept pretty well.

“I believe that this nation should commit itself to achieving the goal, before this decade is out, of landing a man on the Moon and returning him safely to the Earth.”

I’d say that was a goal. It is a specific accomplishment that is clearly “done” or “not done” and it has a deadline. (Someone else once said “a goal is a dream with a deadline.”)

For those of you who don’t remember Woodstock*, let me provide some context.

President Kennedy made that speech on May 25, 1961.

imageOn May 5th, Alan Shepard had been launched into a sub-orbital space flight giving the USA a total manned space flight experience base of about 15 minutes. The previous month, the Soviet Union had launched Yuri Gagarin for a single orbit around the Earth, thus the entire planet had a total manned space flight experience of just under 2 hours (but the Russians weren’t sharing).

<— This is the best we could do.

The goal was selected for political and technical reasons. We had developed a huge rocket engine needed to do the job, and didn’t think the Russians larger rockets would scale well. So the Administration selected a goal they thought the U.S. could accomplish but believed the USSR would have a tougher time with. (They were right.)

At the time the speech was made, there were two competing approaches in play for landing a man on the moon.image

Both involved landing a large upper stage intact on the moon, then lifting off and using the whole thing to return to Earth.

The problem was thought to be how to get that huge moon landing rocket off the Earth and to the moon.

There were a couple of ideas kicking around at the time.

One was to build a huge rocket, maybe twice the size of the Saturn-V that eventually was used for Apollo 11.

imageThe design was never finalized, but the concepts were all lumped together as the “Nova” rocket. You can see the 36 story tall Saturn V (the biggest rocket ever built and launched) as the second from the right in this picture. The idea was to send the entire thing directly to the Moon with a single launch. This was called the “Direct” approach.

Given that building the Nova rocket (not to mention the launch facility) was likely to be…um…really hard, the other idea was to use multiple launches of something more like the Saturn rocket, and assemble the moon rocket in low Earth orbit, then send it on its way. This was called “Earth Orbit Rendezvous.

All through the late spring and summer of 1961 this debate was raging within NASA. Wernher von Braun, our chief “rocket guy” wanted this capability for a large lunar payload because he was interested in establishing bases and serious exploration of the moon. But that wasn’t the objective right now. It was get there fast and beat the Russians.

Another NASA engineer, John Houbolt had what was considered a bit of a high-risk (bordering on crackpot) scheme of a smaller-but-still-huge rocket, single launch, sending an expendable two-stage lander to the moon, having it land with two astronauts, then lift off and rendezvous with the return ship in lunar orbit. Not surprisingly this scheme was known as “Lunar Orbit Rendezvous.” It was risky because what was thought to be the trickiest part, the rendezvous and docking, was to be done 250,000 miles from the safety of Earth, with no way home if it didn’t work.

You can read the whole story here.

By the fall of 1962, Lunar Orbit Rendezvous had emerged as the only viable scheme to accomplish the goal by the deadline.

At the program level, they now had a target condition: How the process should operate in order to accomplish the goal. This does not mean they had worked out every detail. It only means they knew what they were trying to accomplish. This 1962 NASA film pretty much lays out the concept in as much detail as they understood at the time:

Remember, this is at the high level. But at this level they identified obstacles – things they didn’t know how to do – that had to be cleared in order to reach the target condition.

1) They had to develop the concept into a real rocket capable of pushing 90,000 pounds (and finally 100,000 pounds) into lunar orbit. They also had to develop and built the infrastructure to (according to the initial plan) launch one of these every month.

2) They had to determine if (and how) humans could spend 10+ days in space without psychological or psysiological problems. (Remember, we had no idea at the time).

3) They had to develop a space suit that would allow an astronaut to leave the spacecraft.

4) They had to develop techniques and technology for rendezvous and docking in orbit.

Each of these major obstacles could be, in turn, defined as a goal (or challenge) for the next level down. Project Gemini’s purpose was to test #2, and directly learn about #1 and #2.

imageimage

 

 

 

 

 

 

 

 

And of course, the teams working on developing space suits, developing docking technology, etc. then would set their own target conditions that progressively marched toward their goals or deliverables.

Bring this back to Earth

A goal is something you need to accomplish. It usually doesn’t assign the method, only the result and the “by when.”

A target condition is typically a major intermediate step toward the ultimate challenge. Importantly, it outlines the “how” or proposed approach, though necessarily doesn’t offer up the solutions to the problems.

The goal is “win the game.” The target condition is the game plan. To execute the game plan, we need to develop specific capabilities, or solve specific problems.

One thing the target condition does do is limit the domain of the problems that must be solved. This is critical.

There are always more problems to solve than are solvable with the resources available. By being specific about a target condition, you focus the effort on the obstacles that are actually in the way of achieving the target. As you proceed, you learn, and as you learn, the nature of those obstacles may change. Thus, the obstacles are not a static list of things to do. Rather, they are the unsolved issues that, right now, you think must be dealt with.

See this (largely redundant) post from a couple of years ago for another perspective. (oops – just realized I’d already used the Apollo analogy. Oh well. At the time all of this was going on, I was the geeky 12 year old who knew (and would talk endlessly about) every dimension, rocket engine nomenclature, fuel burn rates, etc. of the Saturn-V rocket.)

Hopefully this will spark some discussion.

———–

*If you actually remember being at Woodstock, then you likely weren’t there.  Winking smile

Does Your Solution Have A Problem? Does Your Problem Have A Customer?

Javelin.com is a site with a few good tools centered around startup product development. (“Lean Startup”). I really liked their tutorial around the “javelin board” which is a vertical PDCA record specialized for testing product ideas.

In the tutorial, the phrase that really got my attention was this:

“Not all solutions have problems, and not all problems have customers.”

If you are a regular reader, you know one of the questions I ask frequently is “What problem are you trying to solve?” This is especially important if the proposed solution is a “lean tool.” For example, “there is no standard work” is not a problem, per se. I know lots of companies that do just fine, and have more than doubled their productivity before work cycles ever emerged as something to work on. “What obstacle are you addressing now?” is a question we ask in the Coaching Kata to explore the learner’s linkages between the proposed solution, the problem (obstacle), and target condition. The obstacle itself is a hypothesis.

The javelin board process first ensures that (1) you know who the customer is and (2) that you validate that the problem you THINK they have is one they ACTUALLY have… before you go exploring solutions.

Remember as you watch this, though, that the process isn’t different from the Improvement Kata. It is just a specialized variant. The underlying thinking pattern is totally identical… and the problem Toyota Kata is trying to solve is “We have to learn this thinking pattern.” Once you understand the pattern, and apply it habitually, then these variations make perfect sense. On the other hand, if you don’t understand the underlying pattern, then these variations all look like a different approach, and you’ll end up wrestling with “which one to adopt.”

Jeff Liker: Is Lean a Waste Elimination Program or Striving for Excellence?

Jeff Liker asks (and answers) the title question in a great Industry Week blog article by the same title.

One of the biggest obstacles we in the lean community need to overcome is our own inertia around “Lean is a process for finding and eliminating waste.”

In the article, Liker brings up a point that is often lost on us: Looking for the problems and negative things kills morale.

The “waste” that you see is the result of underlying issues and culture. Stop overproduction in one place, and it either returns or pops up elsewhere because the underlying reasons for it were never addressed.

Operations that are not operating at a high level of lean typically are lacking underlying process discipline, which leads to these problems and they proliferate daily as the company is in a constant firefighting mode. Trying to eliminate waste in the current system and culture is like identifying and fighting problems—it is debilitating and a losing proposition.

[Emphasis added]

I bolded that phrase for a reason.

We aren’t talking about a technical implementation here. We are talking about a shift in the underlying culture – the habitual ways people interact with one another, with the process, respond to challenges and problems.

Today we have, thanks to Jeff Liker and a few others, an excellent picture of an ideal version of Toyota. We know what it looks like.

Getting there is an entirely different proposition, as most companies that have tried this stuff know first hand. It is hard.

What is beginning to emerge, though, is that the thinking pattern that is learned through solving these problems the right way (vs. just implementing tool sets) is the same thinking pattern required to shift the culture.

It is hard. You have to do the work. But the way to get there is emerging.

Flipping Tires

A couple of weeks ago I was talking listening to the owner of a medium-sized manufacturing company as he shared his experience of various “lean” consultants, books, etc.

One of the stories he told was about a kid at football practice. (For my European readers, this is about “American Football.”) The coach had the linemen doing drills that involved flipping over large tractor tires.

Over and over. Wax on, Wax off.

Of course, they weren’t just doing it to flip over big tires. They were learning to get leverage, use the strength of their legs, and the motions of managing momentum.

The kid, though, was complaining about flipping tires and wondering why they just didn’t play.

The danger here is we have people doing the equivalent of sitting in the bleachers watching this football practice. “Ah – they flip tires. We need to flip tires too.”

Right thing, but no context.

What this business owner was, correctly, objecting to was consultants coming in and putting people through tire flipping drills without giving them context… the why? of doing it.

Worse, they had not distinguished between flipping tires and playing the game.

Of course in our continuous improvement worlds, we have to play the game every day, and usually work on our development at the same time.

Still, we need to be clear what things we are doing to facilitate practice and learning, and what it looks like when we are “just doing it.”

Here is a test: Which of these is different from the others:

  1. Hoshin kanri
  2. Kanban
  3. Toyota kata
  4. Standard work
  5. Value Stream Mapping

This may be controversial, but I don’t think “Toyota Kata” belongs on this list.

Toyota Kata is flipping tires. Yes, we are practicing on the field, usually during the game, but it is a method for practice.

The book Toyota Kata and most of the materials out there describe that practice in the context of production systems and process improvement. That works because these are physical processes, and we can see and measure our results.

But Toyota Kata is about learning a habitual thinking pattern. It is the same thinking pattern behind Hoshin kanri. And standard work. And Value stream mapping. And kanban. And leadership development itself.

It is the same thinking pattern behind successful product development, entering new markets, and taking on personal growth and challenge. It is the same thinking pattern behind cognitive therapy.

Don’t confuse Toyota Kata with part of the system. It is how you practice the thinking behind any system (that works). (The same thinking patterns are behind Six-Sigma, Theory of Constraints, TQM, pure research, Toyota Business Practice, Practical Problem Solving, the list goes on.)

The confusion comes in because, in practice, Toyota Kata looks like a tool or part of the system itself. We teach people the theory behind it standard work; we teach people the theory behind Toyota Kata. We go to the shop floor and put it into practice.

The difference is that the standard work is intended to stay there, as a work environment where it is easier to:

  • Define the target condition.
  • See the current condition.
  • Detect obstacles as they occur.
  • Quickly implement isolated changes as experiments and see the results.

Standard work gets into place out of necessity because batching and arbitrary work cycles would be an early obstacle to seeing what is going on.

Kanban does the same thing for materials reorder and movement.

Value stream mapping is a structure for applying the thinking that TK teaches a higher operational context.

Hoshin is a structure for applying the thinking that TK teaches to a strategic context.

I could go on listing just about all of the things in the so-called “toolbox.”

The kids were flipping tires to develop the fundamental skills and strength required for blocking and tackling.

Toyota kata is a structure to develop the fundamental skills required to use any of the “lean tools” correctly.

Hopefully this generated a little thought. Comments anyone?

Toyota Kata “A3 Problem Solving”

Over the years, I’ve been exposed a number of efforts to “implement A3 problem solving” in various companies. I worked for some of those companies, I’ve observed others.

The results are nearly always the same.

Here are a couple of examples. Let me know if any of these match up with experiences you have had.

Example 1: The company had put many people through “Practical Problem Solving” training and was (ironically) trying to measure how many problem solving efforts were underway.

I was watching a presentation by one of these problem solving teams to management. Their A3 was on a computer, projected onto the screen. They were reporting their “results.” Yet there were large discontinuities in their problem solving flow. The actions they were taking simply did not link back (through any kind of identifiable cause) to the problem they were solving.

The management team listened carefully, applauded their efforts, and moved on to the next topic of their meeting.

Example 2: A different company had a form to fill out called an “MBF” or “Management by Fact.” From the labels on the boxes, it was clearly intended to be structured problem solving. By the time I worked there, however, “MBF” had become a verb. It was a solo activity, filling out the form at the desk, and reporting on it in a staff meeting.

Example 3: Well-meaning former Toyota team members, now working for a different large company wanted to “train everyone in problem solving.” They put together a “class” that presented the purpose of each block on their A3 form with the expectation that people would adopt the process.

All of these efforts had something in common.

They didn’t work.

Over the last few days, I’ve been privileged to be included in an email exchange about the relationship between A3 and Mike Rother’s Toyota Kata. My small contribution was apparently enough to get my name onto the cover, but I want to give a real nod in the direction of a Jenny Snow-Boscolo for instigating inspiring a really good exchange.

The result is here. I think this presentation does a really good job of summing up the relationship between Toyota Kata and Toyota A3. Thanks to Mike Rother for taking the initiative and putting it all together (more below)

One of the difficulties with gaining insight into Toyota’s management processes is that they really aren’t codified. This shouldn’t be a surprise. Look at your own company, and ask how much of the culture – the reflexive way things are done and interactions are structured – is written down.

(In fact, if it is written down, I would contend it is likely your actual culture has little resemblance to what is written about it. Those things tend to be more about what they wish the culture was.)

Culture, any culture, is learned through daily interaction. This is all well and good in cases where people are immersed in it from the beginning.

But the rest of us aren’t operating in that problem solving culture. Rather, we are trying to create it. And as the former Toyota Team Members from Example 3 (above) learned, it isn’t a simple matter of showing people.

Rather than two different things, we are looking at a continuum here. At one end is the culture described on Slide 9. There isn’t any formal structure to it, the process for teaching it isn’t codified. It is learned the same way you learn the way to get the job done in any company. They just learn different things than you did.

But in another organization there is no immersion. If there is anyone who is steeped in The Way, they are few and far between.

In these cases, we want to start with something more overt. And that is the purpose of having a rote drill or kata. It isn’t something you implement. It is a structure, or scaffold, to learn the basic moves. Just as mastering the musical scales is only a prelude to learning to play the instrument, the kata is the foundational structure for learning to apply the underlying thinking patterns.

So… if you are working on kata, it is critical that you are reflecting on your thinking patterns as much (or more) than you are reflecting on your improvements. It might seem rote and even busywork at first. But it is there to build a foundation.