Heavy Equipment Overhaul: Flow at Takt in 1938!

This is a great contemporary film from 1938 describing the complete overhaul of a mainline 4-6-0 steam locomotive in the U.K.

What is interesting (to me) is:

  • The overhaul involves stripping the locomotive down to individual parts. Each of the parts, in turn, flows through a process of inspection / repair or replacement, with a strict timing to ensure it is delivered back to re-assembly when required.
  • There are 6 positions with a takt time of 10 hours 44 minutes. Everything is timed to this cadence.
  • I can only speculate, but with that degree of rigor in the timing, they are going to be able to see a delay or problem very quickly, and get out in front of it before it causes a delay in the main-line work.
  • The parts that come off are not necessarily the exact once that are put back on. Everything is flowing – there are multiple locomotives in overhaul.

More thoughts below the video.

(Here is a direct YouTube link for those who don’t get the embed in the email subscription: https://www.youtube.com/watch?v=ktHw1wR9XOU)

Flow in Overhaul and Repair

This is a great working example of a process flow that proves difficult for some organizations: Overhaul and repair. “We don’t know what we will find, so there is no way we can sequence and index it on a timetable.”

I’ve seen a similar operation overhauling helicopters. The intended flow was exactly the same.

  • Like the locomotive flow, they stripped everything down to the airframe. The various components had different flow paths for sheet metal, hydraulic components, power-train (engine / transmission), rotor components, electrical, avionics, and composite parts.
  • The objective was to deliver “good as new” items on time back to the re-assembly process.

Here is where they ran into problems:

  • If an item needed repair, then the repairs were done, and the item flowed back.
  • But if an item could not be repaired (needed to be scrapped and replaced) it was tagged, and returned to the “customer” – the parts bin in main assembly. It arrived just like any other part except this one was tagged as unusable. It was up to the assembly supervisor to notice, and initiate ordering a new one.

Who is your customer? What do they need?

The breakdown was that the repair line(s) saw themselves as providing a repair service. If it couldn’t be repaired, sorry.

What their customer needed was a good part to install on the helicopter. If they can create a good part by repairing the old one, great. But if it isn’t repairable, their customer still needs a good one and they need it on time.

The Importance of Timing and Sequencing

In the locomotive video, they emphasize the precise timing and sequencing to make sure each part arrives in the proper sequence, when it is needed, where it is needed.

Even if it actually worked like they describe, I can be sure it didn’t work like that when they first started.

The timing and sequencing is a hypothesis. Each time they overhaul a locomotive, in fact each individual part flow, is an experiment to test that hypothesis. Over time, it is possible to dial things in very precisely.

Why? So you can quickly identify those truly anomalous conditions that demand your intervention.

Normal vs Abnormal

Just because there are frequent issues does not negate the fact that most of the time things can probably flow pretty well. What we tend to do, however, is focus on the problem cases and give up on all of them. “What about this? What about that?” bringing up the legitimate issues and problems, causes us to lose sight of the fact that underneath it all there is a baseline pattern.

What is important is to define the point at which we need to intervene, and set up the process to detect that point. When we can clearly distinguish between routine work and true exceptions, and not try to treat everything as a special case.

Experimenting at the Threshold of Knowledge

The title of this post was a repeating theme from KataCon 3. It is also heavily emphasized in Mike Rother’s forthcoming book The Toyota Kata Practitioner’s Guide (Due for publication in October 2017).

What is the Threshold of Knowledge?

“The root cause of all problems is ignorance.”

– Steven Spear

September 1901, Dayton, Ohio: Wilbur was frustrated. The previous year, 1900, he, with his brother’s help1, had built and tested their first full-size glider. It was designed using the most up-to-date information about wing design available. His plan had been to “kite” the glider with him as a pilot. He wanted to test his roll-control mechanism, and build practice hours “flying” and maintaining control of an aircraft.

But things had not gone as he expected. The Wrights were the first ones to actually measure the lift and drag2 forces generated by their wings, and in 1900 they were seeing only about 1/3 of the lift predicted by the equations they were using.

The picture below shows the 1900 glider being “kited.”. Notice the angle of the line and the steep angle of attack required to fly, even in a stiff 20 knot breeze.  Although they could get some basic tests done, it was clear that this glider would not suit their purpose.

image

In 1901 they had returned with a new glider, essentially the same design only about 50% bigger. They predicted they would get enough lift to sustain flight with a human pilot. They did succeed in making glides and testing the principle of turning the aircraft by rolling the wing. But although it could lift more weight, the lift / drag ratio was no better.

image

What They Thought They Knew

Wilbur’s original assumptions are well summarized in a talk he gave later that month at the invitation of his mentor and coach, Octave Chanute.

Excerpted from the published transcript of Some Aeronautical Experiments presented by Wilbur Wright on Sept 18, 1901 to the Western Society of Engineers in Chicago (Please give Wilbur a pass for using the word “Men.” He is living in a different era):

The difficulties which obstruct the pathway to success in flying-machine construction are of three general classes:

  1. Those which relate to the construction of the sustaining wings;
  2. those which relate to the generation and application of the power required to drive the machine through the air;
  3. those relating to the balancing and steering of the machine after it is actually in flight.

Of these difficulties two are already to a certain extent solved. Men already know how to construct wings or aeroplanes which, when driven through the air at sufficient speed, will not only sustain the weight of the wings themselves, but also that of the engine and of the engineer as well. Men also know how to build engines and screws of sufficient lightness and power to drive these planes at sustaining speed. As long ago as 1884 a machine3 weighing 8,000 pounds demonstrated its power both to lift itself from the ground and to maintain a speed of from 30 to 40 miles per hour, but failed of success owing to the inability to balance and steer it properly. This inability to balance and steer still confronts students of the flying problem, although nearly eight years have passed. When this one feature has been worked out, the age of flying machines will have arrived, for all other difficulties are of minor importance.

What we have here is Wilbur’s high-level assessment of the current condition – what is known, and what is not known, about the problem of “powered, controlled flight.”

Summarized, he believed there were three problems to solve for powered, controlled flight:

  1. Building a wing that can lift the weight of the aircraft and a pilot.
  2. Building a propulsion system to move it through the air.
  3. Controlling the flight – going where you want to.

Based on their research, and the published experience of other experimenters, Wilbur had every reason to believe that problems (1) and (2) were solved, or easy to solve. He perceived that the gap was control and focused his attention there.

His first target condition had been to validate his concept of roll control based on “warping” (bending) the wings. In 1899 he built a kite and was able to roll, and thus turn, it at will.

At this point, he believed the current condition was that lift was understood, and that the basic concept of changing the direction by rolling the wing was valid. Thus, his next target condition was to scale his concept to full size and test it.

What Happened

Wilbur had predicted that their wing would perform with the calculated amount of lift.

When they first tested it at Kitty Hawk in 1900, it didn’t.

However, at this point, Wilbur was not willing to challenge what was “known” about flight.

Instead the 1901 glider was a larger version of the 1900 one with one major exception: It was built so they could reconfigure the airfoil easily.

Impatient, Wilbur insisted on just trying it. But, to quote from Harry Combs’ excellent history, Kill Devil Hill:

“The Wrights in their new design had also committed what to modern engineers would be an unforgivable sin. […] they made two wing design changes simultaneously and without test.4

Without going into the details (get the book if you are interested) they did manage to get some glides, but were really no closer to understanding lift than they had been the previous year.

They had run past their threshold of knowledge and had assumed (with good reason) that they understood something that, in fact, they did not (nobody did).

They almost gave up.

Deliberate Learning

Being invited to speak in September actually gave Wilbur a chance to reflect, and renewed his spirits. That fall and winter, he and his brother conducted empirical wind tunnel experiments on 200 airfoil designs to learn what made a difference and what did not. In the process, as an “oh by the way,” they invented the “Wright Balance” which was the gold standard for measuring lift and drag in wind tunnel testing until electronics took over.

They went back to what was known, and experimented from there. They made no assumptions. Everything was tested so they could see for themselves and better understand.

The result of their experiments was the 1902 Wright Glider. You can see a full size replica in the ticketing area of the Charlotte, NC airport.

I’ll skip to the results:

image

Notice that the line is now nearly vertical, and the wing pointed nearly straight forward rather than steeply tipped back.

What Do We Need to Learn?

Making process improvements is a process of research and development, just like Wilbur and Orville were going through. In 1901 they fell into the trap of “What do we need to do?” After they got back to Dayton, they recovered and asked “What do we need to learn?” “What do we not understand?”

The Coaching Kata

What I have come to understand is the main purpose of coaching is to help the learner (and the coach) find that boundary between what we know (and can confirm) and what we need to learn. Once that boundary is clear, then the next experiment is equally clear: What are we going to do in order to learn? Learning is the objective of any task, experiment, or action item, because they are all built on a prediction even if you don’t think they are.

By helping the learner make the learning task explicit, rather than implicit, the coach advances learning and understanding – not only for the learner, but for the entire organization.

Where is your threshold of knowledge? How do you know?

 

________________________________

1We refer to “the Wright Brothers” when talking about this team. It was Wilbur who, in 1899, became interested in flight. Through 1900 it was largely Wilbur with his brother helping him. After 1901, though, his letters and diary entries start referring to “we” rather than “I” as the project moved into being a full partnership with Orville.

2The Wright Brothers used the term “drift” to refer to what, today, we call “drag.”

3Wilbur is referring to a “flying machine” built by Hiram Maxim.

4I’m not so sure that this is regarded as an “unforgivable sin” in a lot of the engineering environments I have seen, though the outcomes are similar.

Coaching Kata: Walking Through an Improvement Board

Improver's Storyboard

The Coaching Kata is much more than just asking the 5 questions. The coach needs to pay attention to the answers and make sure the thinking flows.

Although I have alluded to pieces in prior posts, today I want to go over how I try to connect the dots during a coaching cycle.

Does the learner understand the challenge she is striving for?

The “5 Questions” of the Coaching Kata do not explicitly ask about the challenge the learner is striving for. This makes sense because the challenge generally doesn’t change over the course of a week or two.

But I often see challenges that are vague, defined only by a general direction like “reduce.” The question I ask at that point is “How will you know when you have achieved the challenge?

If there isn’t a measurable outcome (and sometimes there isn’t), I am probing to see if the learner really understands what he must achieve to meet the challenge.

This usually comes up when I am 2nd coaching and the learner and regular coach haven’t really reached a meeting of the minds on what the challenge is.

Is the target condition a logical step in the direction of the challenge?

And is the target condition based on a thorough grasp of the current condition?

I’m going to start with this secondary question since I run into this issue more often, especially in organizations with novice coaches. (And, by definition, that is most of the organizations where I spend time.)

It is quite common for the learner to first try to establish a target condition, and then grasp the current condition. Not surprisingly, they struggle with that approach. It sometimes helps to have the four steps of the Improvement Kata up near the board, and even go as far as to have a “You are here” arrow.

Four steps of the Improvement Kata
(c) Mike Rother

Another question I ask myself is Can I directly compare the target condition and the current condition? Can I see the gap, can I see the same indicators and measurements used for each so I can compare “apples vs apples”?

Along with this is the same question I ask for the challenge, only more so for the target condition:

How will the learner be able to tell when the target is met? Since this has a short-term deadline, I am really looking for a crisp, black-and-white line here. The target condition is either met or not met on the date.

Is there a short-term date that is in the future?

It is pretty common for a novice learner to set a target condition equal to the challenge. If they are over-reaching, I’ll impose a date, usually no more than two weeks out. “Where will you be in two weeks?” Another way to ask is “What will the current condition be in two weeks?”

Sometimes the learner has slid up to the date and past it. Watch for this! If the date comes up without hitting the target, then it is time to reflect and establish a new target condition in the future.

Is the target condition a step in the direction of the challenge?

Usually the link between the target condition and the overall challenge is pretty obvious. Sometimes, though, it isn’t clear to the coach, even if it is clear in the mind of the learner. In these cases, it is important for the coach to ask.

Key Point: The coach isn’t rigidly locked into the script of the 5 questions. The purpose of follow-up questions is to (1) actually get an answer to the Coaching Kata questions and (2) make sure the coach understands how the learner is thinking. Remember coach: It is the learner’s thinking that you are working to improve, so you have to understand it!

(And occasionally the learner will try to establish a target condition that really isn’t related to the challenge.)

Does the “obstacle being addressed” actually relate to the target condition?

(Always keep your marshmallow on top!)

The question is “What obstacles do you think are preventing you from reaching the target condition?” That question should be answered with a reading of all of the obstacles. (Again, the coach is trying to understand what the learner is thinking.) Then “Which one (obstacle) are you addressing now?”

Generally I give a pretty broad (though not infinitely broad) pass to the obstacles on the list. They are, after all, the learner’s opinion (“…do you think are…”). But when it comes to the “obstacle being addressed now” I apply a little more scrutiny.

I have addressed this with a tip in a previous post: TOYOTA KATA: IS THAT REALLY AN OBSTACLE?

It is perfectly legitimate, especially early on, for an obstacle to be something we need to learn more about. The boundary between “Grasp the current condition” and “Establish the next target condition” can be blurry. As the focus is narrowed, the learner may well have to go back and dig into some more detail about the current condition. If that is impeding getting to the target, then just write it down, and be clear what information is needed. Then establish a step that will get that information.

Sometimes the learner will write down every obstacle they perceive to reaching the challenge. The whole point of establishing a Target Condition is to narrow the scope of what needs to be worked on down to something easier to deal with. When I focus them on only the obstacles that relate directly to their Target Condition, many are understandably reluctant to simply cross other (legitimate, just not “right now”) issues off the list.

In this case it can be helpful to establish a second Obstacle Parking Lot off to the side that has these longer-term obstacles and problems on it. That does a couple of things. It can remind the coach (who is often the boss) that, yes, we know those are issues, but we aren’t working on those right now. Other team members who contribute their thinking can also know they were heard, and those issues will be addressed when they are actually impeding progress.

Does the “Next step or experiment” lead to learning about the obstacle being addressed?

Sometimes it helps to have the learner first list what they need to learn, and then fill in what they are going to do. See this previous post for the details: IMPROVEMENT KATA: NEXT STEP AND EXPECTED RESULT.

In any case, I am looking to see an “Expected result” that at least implies learning.

In “When can we go and see what we have learned from taking that step?” I am also looking for a fast turn-around. It is common for the next step to be bigger than it needs to be. “What can you do today that will help you learn?” can sometimes help clarify that the learner doesn’t always need to try a full-up fix. It may be more productive to test the idea in a limited way just to make sure it will work the way she thinks it will. That is faster than a big project that ends up not working.

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.

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*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.”