Author: Mark

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Make Sure Failure = Learning

Take a look at this cool video from Space-X that highlights all of the failures that preceded their successful (and now more or less routine) landing of a recoverable orbital booster rocket. Then let’s discuss it a bit.

(Here is the direct link if you don’t get the embed in your feed: https://www.youtube.com/watch?v=bvim4rsNHkQ)

When we see failure, or even failure after failure, it is easy to forget that learning is rarely linear.

A Culture of Learning

Organizations like Space-X (and their counterparts such as Blue Origin) are in the business of learning. They are pushing the edges of what is known and moving into new territory. For organizations that understand that setbacks, mistakes, failures and the like are an inevitable part of learning, these things – while costly and unpleasant – are regarded as part of the process.

We have seen the same mechanisms in play – a process of experimentation toward progressive target conditions toward a visionary challenge – behind pretty much every breakthrough achievement throughout history.

No Mistakes = No Learning

At the opposite end of the spectrum are organizations with no tolerance for mistakes. They expect everything (and every one) to get everything right every time. They dismiss as incompetent any notion of failure, and attack as weakness any admission of “I don’t know” or “I don’t know how.”

A few years ago, as I was teaching Toyota Kata coaching with a client, a middle manager approached me during a break and said – point blank – that it was not his responsibility to develop his people. “Our policy is to hire competent people, and we expect them to be able to do the job.” He wasn’t the only one to say that, so I built the impression that this belief was, indeed, part of their culture. Needless to say they struggle a bit with getting innovation to happen because they try to mechanize the process.

Mistakes = Tuition

Here’s how I look at it. When a mistake happens – especially one that is expensive – you have paid considerable tuition. Your choice now is to either extract as much learning as you can from the event, or to try to ignore it and move on. The later choice is like paying your tuition up-front, then skipping all of your classes and wonder why you aren’t getting it.

Learning = Adapting to Change

Organizations that manage in ways that regard learning as part of their everyday experience are much more adaptive to changes and surprises than those who just execute their routines every day. The paradox here is that organizations who value learning are generally the most disciplined at following their routines. This discipline makes execution a hypothesis test, and they can quickly see when their process isn’t appropriate and adapt and learn quickly as an organization. They strengthen their routines, and through those routines, embed what they have learned in the organization’s DNA for future generations.

Organizations that figure it out as they go, on the other hand, tend to rely on individuals to adapt, but there is no mechanism to capture that learning beyond the individual or small group. Sometimes there is a “lessons learned” document, but that’s it. Those reports rarely result in the changes in organizational behavior that reflect learning. I suppose the most egregious case would be the loss of the space shuttle Columbia upon re-entry for exactly the same organizational failures that resulted in the loss of Challenger.

Technical vs. Cultural Learning

Space-X is solving a technical problem with science and engineering. I hope (and expect) that as they become more successful they will always be striving for something really hard that will drive them to the next level. Based on what I see publicly, I think that is embedded into their culture by Elon Musk. (But I don’t really know. If anyone from Space-X is reading this, how about getting in touch? I’d love to learn more.)

I expect this works for Space-X because they have a culture of learning.

What doesn’t work, though, is to try to apply technical solutions to transition a rote-execution culture into a learning culture. Changing the culture – the default behaviors and responses of people as they interact – isn’t about improving the mechanics of the work process. You certainly can work on the work processes, but the starting condition is what evolved in the context of the organization’s culture. The mechanics of the “improved” process that we try to duplicate evolved in the context of a learning culture. The ecosystems are different. It is difficult for a lean process to survive in a culture that expects everything to run perfectly and doesn’t have robust mechanisms to turn problems into improvements.

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Creative Safety Supply: Kaizen Training and Research Page

Normally when I get an email from a company pointing me to the great lean resource on their web page, I find very little worth discussing. But Creative Safety Supply in Beaverton, Oregon has some interesting material that I think is worth taking a look at.

First, to be absolutely clear, I have not done business with them, nor do I have any business relationship. I can’t speak, one way or the other, about their products, customer service, etc

With that out of the way, I found their Kaizen Training and Research Page interesting enough to go through it here and comment on what I see.

What, exactly, is “PDCA?”

The section titled Kaizen History goes through one of the most thorough discussions of the evolution of what we call “PDCA” I have ever read, tracing back to Walter Shewhart. This is the only summary I have ever seen that addresses the parallel but divergent histories of PDCA through W. Edwards Deming on the one hand and Japanese management on the other. There has been a lot of confusion over the years about what “PDCA” actually is. It may well be that that confusion originates from the same term having similar but different definitions depending on the context. This section is summed up well here:

The Deming Circle VS. PDCA

In August of 1980, Deming was involved in a Roundtable Discussion on Product Quality–Japan vs. the United States. During the roundtable discussion, Deming said the following about his Deming Circle/PDSA and the Japanese PDCA Cycle, “They bear no relation to each other. The Deming circle is a quality control program. It is a plan for management. Four steps: Design it, make it, sell it, then test it in service. Repeat the four steps, over and over, redesign it, make it, etc. Maybe you could say that the Deming circle is for management, and the QC circle is for a group of people that work on faults encountered at the local level.”

So… I learned something! Way cool.

Rapid Change vs. Incremental Improvement

A little further down the page is a section titled Kaizen Philosophy. This section leans heavily on the thoughts / opinions of Masaaki Imai through his books and interviews. Today there is an ongoing debate within the lean community about the relative merits of making rapid, radical change, vs. the traditional Japanese approach of steady incremental improvement over the long-haul.

In my opinion, there is nothing inherently wrong with making quick, rapid changes IF they are treated as an experiment in the weeks following. You are running to an untested target condition. You will likely surface many problems and issues that were previously hidden. If you leave abandon the operators and supervisors to deal with those issues on their own, it is likely they simply don’t have the time, skill or clarity of purpose required to work through those obstacles and stabilize the new process.

You will quickly learn what the knowledge and skill gaps are, and need to be prepared to coach and mentor people through closing those gaps. This brings us to the section that I think should be at the very top of the web page:

Respect for People

Almost every discussion about kaizen and continuous improvement mentions that it is about people, and this page is no different. However in truth, the improvement culture we usually describe is process focused rather than people focused, and other than emphasizing the importance of getting ideas from the team, “employee engagement is often lip-service. There is, I think, a big difference between “employee engagement” and “engaging employees.” One is passive, waiting for people to say something. The other is active development of leaders.

Management and Standards

When we get into the role of management, the discussion turns somewhat traditional. Part of this, I think, is a common western interpretation of the word “standards” as things that are created and enforced by management.

According to Steve Spear (and other researchers), Toyota’s definition of “standard” is quite different. It is a process specification designed as a prediction. It is intended to provide a point of reference for the team so they can quickly see when circumstances force them to diverge from that baseline, revealing a previously unknown problem in the process.

Standards in this world are not something static that “management should make everyone aware of” when they change. Rather, standards are established by the team, for the team, so the team can use them as a target condition to drive their own work toward the next level.

This doesn’t mean that the work team is free to set any standard they like in a vacuum. This is the whole point of the daily interaction between leaders at all levels. The status-quo is always subjected to a challenge to move to a higher level. The process itself is predicted, and tested, to produce the intended quality at the predicted cost, in the predicted time, with the predicted resources. Because actual process and outcomes are continuously compared to the predicted process and outcomes, the whole system is designed to surface “unknowns” very quickly.

This, in turn, provides opportunities to develop people’s skills at dealing with these issues in near-real time. The whole point is to continuously develop the improvement skills at the work team level so we can see who the next generation of leaders are. (Ref: Liker and Convis, “The Toyota Way to Lean Leadership”)

Staging improvement as a special event, “limited time only” during which we ask people for input does not demonstrate respect, nor does it teach them to see and solve those small issues on a daily basis.

There’s more, but I’m going to stop here for now.

Summary

Creative Safety Supply clearly “gets it.” I think this page is well worth your time to read, but (and this is important), read it critically. There are actually elements of conflicting information on the page, which is awesome because it gives you (the reader) an opportunity to pause and think.

From that, I think this one-page summary really reflects the state of “lean” today: There IS NO CANONICAL DEFINITION. Anyone who asserts there is has, by definition, closed their mind to the alternatives.

We can look at “What Would Toyota Do?” as somewhat of a baseline, but ultimately we are talking about an organizational culture. Toyota does what they do because of the ways they structure how people interact with one another. Other companies may well achieve the same outcomes with different cultural mechanisms. But the interactions between people will override process mechanics every time.

Hopefully I created a lot of controversy here.  🙂

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There Are No Silver Bullets

There are no quick, simple solutions

Occasionally I get an email from someone who asks a question like “How can I improve cycle time in the [fill in the blank here] industry?” Generally my reply is along the lines of “I don’t know, but I can help you figure it out.” I’ll give them some homework, often pointing them at Mike Rother’s Toyota Kata Practice Guide online, and asking them to do the Process Analysis step and get back to me with what they have seen and learned.

Lone Ranger with Silver Bullet
Who was that masked man?

This is usually followed by silence (cue the crickets here). Perhaps they think there is an easy answer and a single email can just tell them what to do to get that 20% performance improvement.

Unfortunately it doesn’t work like that. Process improvement involves work. There aren’t easy fixes (that last). There isn’t any solution anyone can give you that can just be implemented, nor can anyone learn it for you.

The real work is adjusting your culture

Digging a little deeper, if you want that productivity improvement to reach even a fraction of your full potential or sustain for any length of time, you have to go beyond technical solutions. When I said process improvement involves work, the technical mechanics are the easy part. The real work is understanding what social and cultural norms in your organization are holding you back and dealing with those.

Fortunately we have learned a lot more about the influence of the organization’s culture and how to influence the culture. But influencing the culture doesn’t happen by accident. And you can’t outsource your own thinking, reflection and learning.

 

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Learning Starts With “I Don’t Know”

If an organization wants to encourage learning, they have to get comfortable with not having all of the answers. Learning only happens when we discover something we don’t know, and then actively pursue understanding it. Many organizations, though, equate “having the answers” or “already knowing” with “competence.” Thus, if I say “I don’t know” then I am setting myself up for being regarded as incompetent.

What I see in these organizations is people will take great pains to hide problems. They will try very hard to figure things out, but do so in the background always reporting that everything is going fine. They live in the hope that someone else’s problem will emerge as the show-stopper before theirs does, and give them the extra time to sort out their issue.

Meanwhile, the bosses are frustrated because people aren’t being truthful with them. But what should they expect if “truth” attracts accusations of being incompetent?

But… there is hope.

I was talking to a friend last week who works in a huge company that seems to be making an earnest effort to shift their culture. There is nearly unanimous agreement that the existing culture isn’t working for them. On the other hand, actually changing culture is really, really hard because it involves changing people’s immediate, habitual responses to things.

Nevertheless, I was encouraged when my friend recounted a recent meeting where someone admitted two things:

  1. There was an unexpected problem that came out in a recent test.
  2. They, right now, don’t know how to fix it.

Just to be clear, these two things coming out in this meeting is a big deal. This has been a culture where unexpected problems have not been warmly received. Bringing them up without a confident assessment about a prospective solution was inviting the kind of intervention that is rarely helpful.

This time, though, was a little different.

The leaders started going down the expected responses such as “What do you mean we don’t know what to do?” then… stopped short. They paused, and realized this was not in line with their newly stated values of creating trust and accepting failure as an inherent part of learning.

And they changed their tone. They shifted the conversation from trying to assign responsibility blame for the test failure toward asking what we, the organization, needed to learn to better understand what happened.

My thoughts are:

Kudos to the person who was brave enough to test the waters and admit “I don’t know.”

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Obstacles: Right Now vs. Longer Term

A couple of weeks ago Gemba Academy filmed my Toyota Kata class and some shop floor work with a live audience at one of their customer’s sites. One of the participants asked a really good question. Upon reflection, I think I can answer it better here than I did “live,” so I’m going to take a do-over.

image

Background

The team had analyzed their current condition, had established a pretty good target condition, and was working through obstacles.

One of the obstacles was around the fact that the written procedures had not kept up with the way the work was really being performed. This is actually pretty common in industry. The people doing the work know how to do it, and get it done in ways that are better than what is in the documents.

Nevertheless, they needed to update those procedures. If they did not, then new people, or workers that might be rotated into the area temporarily for some reason, would struggle to perform the work in the best way.

The Question

This obstacle was not in the way of reaching their target condition process. However they knew the target process would not be stable in the face of people rotation or turnover. The question was along the lines of:

Is it an obstacle if we can’t sustain the target condition unless we address it?

Answer: It Depends

At the risk of bringing up some really old U.S. political humor, “It depends on what your definition of ‘target condition’ is.”

Here is what I am thinking now.

The first step is to get the target process, as they defined it, to work at all. To do this, I would work to control variables, including trying hard to avoid rotating people through there while I am getting it dialed in.

Once we have established that the target process can work with experienced people, then the next target condition might well be to get this process anchored well enough that it will sustain over time without tons of intervention.

Maybe my next target condition is to be able to sustain the target process no matter who is doing it (assuming they have the basic qualification to do that kind of work).

One of the obstacles in the way of that target condition could well be “Our documentation is obsolete.”

Most documentation I have encountered in any industry is actually pretty poor. So this represents an opportunity to experiment your way into developing process documentation that (1) can actually be followed as written and (2) might even be useful for training someone. I’ve never seen that work without a process of iterative trials.

So in this case, I would say “Get it to work the way you intend it to first.” Make that your target condition. THEN start looking at what erodes it if new people step in. In this case, especially, that is going to involve much more than simply updating documentation. How can you set up the work area so that anyone knows what must be done next? What do you need to teach? What do you need to communicate?

I’m Still Thinking About This

Finally, I think this is one of those real-world cases where there isn’t a hard right or wrong answer. There wouldn’t be any harm in updating the process documentation early – except that I expect they will have to do it over once they learn more.

And – not all “obstacles” are actually problems to solve. Sometimes (though less often than we think), there is just something that has to be done that we already know how to do – we just haven’t done it. In those cases, just do it and move on – EXCEPT: Make sure you predict the result of your “just do it,” and CHECK to make sure it worked the way you thought it would. I’ll lay even money it doesn’t, but you won’t know unless you construct it as an experiment. “Just do it-s” usually turn in to “Oh… that didn’t work quite like we thought it would.”

Just make sure you are deliberately learning rather than doing things by rote.

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A Machine Productivity Search Question

Here is another test or quiz question that showed up in my search logs:

a machine tool is producing 90 pcs per day .using improved cutting tools,the output is raised to 120 pcs per day. what is the increase in productivity of the machine?

I guess the answer seems pretty obvious…

90 x 1.33, rounded a bit, is 120, which gives us a 33% productivity improvement, right?

Not so fast…

(pun intended)

How fast does the machine need to run?

Is there demand for the additional 30 pieces per day, or are they just being put into inventory with the hope they will sell at some point in the future?

This is actually pretty common where cost accounting systems allocate overhead against production output rather than actual sales.

But what if you are only selling 90 pieces per day?

After three days you will have a day’s worth in inventory. You are running the machine more than you have to, adding wear and tear. You are consuming material to make parts you aren’t selling. At some point you are going to have to shut down the machine – idle it. What is your productivity then?

What Problem Are You Trying to Solve?

It always comes down to this question. Is there a real-world, customer-impacting reason you need the additional output? If so, then yes, this is a valid countermeasure, similar to one I have overseen myself. If the machine is too slow, what do we have to do to run it faster (while maintaining quality and not breaking anything)?

But if the machine is fast enough, then why are you trying to make it run faster?

And what will happen if we do? Use real numbers. You don’t have revenue until a customer with real money (not transfer pricing) actually pays for your product. Pretending otherwise looks great on the balance sheet for a while, but the paper profits aren’t tangible, you can’t use that “money” to buy anything else, or distribute to share holders. In fact, it is just money you have spent not money you have earned.

Machine Utilization at Home

At least here in the USA, a typical home washing machine will run a cycle in about 25 minutes. The dryer takes about 40 minutes to complete a cycle. If you wanted “maximum efficiency” from the washing machine, all you will get is a big pile of wet laundry. There is no point in running the washer any more often than once every 40 minutes. The dryer is pacing the system.

If I could modify the washing machine to run in 15 minutes instead of 25, how much more productivity do I have? The question is nonsense.

This example makes perfect sense to people. Then I often get arguments about how the factory floor is somehow different?

It’s The System, not the Machine

Key Point: You can’t look at one machine in isolation and calculate how “efficient” or “productive” it is unless it is pacing your system. In this case, we don’t have enough information.

Now, I know this example was just a made up case. But I have seen well-meaning production people fall into this trap all of the time. You have to look at the system, not individual machines.

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Think Big, Change Small

Anton, my Dutch friend, had a study mission group of Healthcare MBA students from the University of Amsterdam visiting Seattle last week.

Friday morning I spent about four hours with them going through the background and basics of the Improvement Kata and Coaching Kata, and worked to tie that in to what they observed in their visits to local companies. They were a great, engaging group that was fun to work with.

One thing I do to close out every session I do with a group is ask “What did we learn?” and write down their replies on a flip chart. I find that helps foster some additional discussion and consolidate learning. It also gives me feedback on what “stuck” with them.

Sometimes I get a gem that would make a good title for a blog post. The title of this post is one of those.

Think Big

Alice and the Cat

Alice went on, “Would you tell me, please, which way I ought to go from here?”

“That depends a good deal on where you want to get to,” said the Cat.

“I don’t much care where…“ said Alice, “…so long as I get somewhere,” Alice added in explanation.

“Oh, you’re sure to do that,” said the Cat, “if only you walk long enough.”

The question, of course, is whether or not where Alice ends up is where she intends to go. A lot of continuous improvement activity takes this approach –  Look for waste. Brainstorm ideas. Implement them. Just take steps. And, like Alice, you will surely end up somewhere if you do this enough.

I had a former boss (back in the late 90’s) advocate this approach. “We are painting the wall with tennis balls dipped in paint.” The idea, I think, was that sooner or later all of the splotches would start to connect into a coherent color. Maybe. But, at the same time, he was also very impatient for tangible results. Actually that isn’t true. He was impatient for tangible activity which is not the same thing at all.

Direction and Challenge Establish Meaning

In her journeys through Wonderland, Alice learns that objective truth has no meaning in a world of random nonsense. The story, of course, is a parody of the culture and times of Victorian England. It does, however, reflect the frustrations many practitioners can feel when they are just trying to “make improvements.”

As one thing is “fixed,” another pops up for any number of reasons:

  • The “new” problem may well have been hidden by the “fixed” one.
  • Leadership may be chasing short-term symptoms and constantly redirecting the effort.

Day to day it just seems like random stuff, and can get pretty demoralizing.

The point of “Think Big” is that being clear about where WE (not just you) are trying to go helps everyone understand the meaning of what they are doing. That is the whole point of “Understand the Direction and Challenge” as the first step of the Improvement Kata. “What is the meaning behind what you are working on?” It is really a verification check by the coach that the coach has adequately communicated meaning to the learning.

Establish “Why” not “What”

At the same time, it is important for the organization to be clear on why improvement is necessary. I have discussed this a number of times, but keep referring back to Learning to See in 2013 where I ask “Why are you doing this at all?” as the question everyone skips past.

“Where we are going” should not simply be your model of your [Fill Company Name In Here] Production System.

No matter how well explained or understood, a model does not directly address the “Why are we doing this at all?” question that provides meaning to the effort.

It may well establish a good representation of what you would like your process structure to look like, but it does not give people any skill in actually putting these systems into practice, nor a reason to put in the effort required to learn something completely new.

Change Small

Small changes = fast progress as long as there is a coherent direction.

The classic 5 day kaizen event is often an attempt to make a radical improvement in a short period of time. Things usually look really impressive at the end of the week, and even into the next few weeks. What happens, though, is that the follow-up is usually more about finishing up implementation action items than it is working to stabilize the new process.

The problem comes from the baseline assumption that we already understand all of the problems, and our changes will solve them. We line things up, get 1:1 flow running, and yes, there is a dramatic reduction in the nominal throughput time simply because we have eliminated all of the inventory queues.

There is tons of research that backs up the assertion we can’t expect people to be creative when they are under pressure to perform. They are going to revert to their existing habits. During the event itself,  the short time period and high expectations put pressure on people to just implement stuff. People are likely to defer to the suggestions and lead of the workshop leader and install the standard “lean tools” without full understanding of how they work or what effect they will have on the process and people dynamics.

Come Monday morning, we put all of those changes to the test… at once. The people are working in a different way. The problems that will be surfaced are different. The tighter the flow, the more sensitive the system will be to small problems. It is pretty easy to overwhelm people, especially the supervisors who have to decide right now what to do when things don’t seem to be working.

That same pressure to perform exists, only now it is pressure to produce, and possibly even catch up production from what was lost during the previous week. Once again, we can’t expect people to think creatively when these new issues come up, they are going to revert to what they know.

When we do see successful “big change” it is usually the result of many small changes that have each been tested and anchored.

So why is the “blitz” approach so appealing? I think I got some insight into the reason in a conversation with a continuous improvement director in a large corporation. He had so little opportunity to actually engage and break things loose that, when he did, he felt the need to push in everything he could.

My interpretation of this goes back to the first line above: Small changes = fast progress as long as there is a coherent direction. In his case, there wasn’t coherent direction. He had a week, maybe two, to push as hard as he could in the direction he felt things should go. The rest of the time, things were business as usual.

This is why “think big” is important. It provides organizational alignment, and reduces the pressure to seize a limited opportunity and, frankly, inject chaos.

Small, Quick Changes

Because we often don’t see just how long it takes to stabilize a “quick, big change,” we tend to think that quick small changes are slower. I disagree. In my experience the opposite is true.

When there is a clear Challenge and Direction, and frequent check-ins via coaching cycles (or less formal means) on what changes are being made, no time is wasted working on the wrong things.

When small changes are made and tested as part of experiments vs. just being implemented, then there is less chance of erosion later. Rather than overwhelming people with all of the problems at once from a bunch of changes, one-by-one lets them learn what problems must be dealt with. They have an opportunity to always take the next step from a working process rather than struggling to get something that is totally unfamiliar to work at all.

That, in turn, builds confidence and capability.

In a mature organization that has practiced this for years, an outside observer might well see “big changes” being made. But that organization is operating from a base of learning and experience, and what might look big to you might not be big to them. It is all a matter of perspective.

What Do You Think?

I’m throwing this out there, hoping to hear from practitioners. What have you struggled with getting changes made that actually shift people’s behavior (vs. just implementing tools and techniques). What has worked? What hasn’t worked? I’d love to hear in the comments.

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Push Improvement vs. Pull Improvement

I’m writing up a proposal for a benchmarking and study week, and just typed the term “push improvement” as a contrast to a true “continuous improvement culture.” I wanted to explore that a bit here, with you, and perhaps I’ll fill in the idea in my own mind.

Push Improvement

A few years ago I was working with a few sites of a multi-national corporation. In one of their divisions, their corporate lean office was pushing various programs into place. Each site was required to implement, and was graded on:

  • 5S
  • Jidoka
  • Heijunka
  • Toyota Kata

plus a few other things. Those are the ones I really remember. Each of these programs was separate and distinct, they had been deployed on some kind of phased time-table over a few years. They also had requirements to maintain some number of Six-Sigma Black Belts and Green Belts in their facilities, with the requirement to report on a number of projects.

They brought me in to teach the Toyota Kata stuff.

In reality, while people taking the class generally thought it was worthwhile, the leadership teams were also a little resentful that all of this stuff was being, in the words of one plant manager, “pushed down our throats.”

Even the Toyota Kata “implementation” had a specific sequence of steps that the site was expected to check off and report their progress on. In addition, there were requirements for how the improvement boards would look, including the position of the corporate logo and the colors used – all in the name of “standardization.”

At the same time, of course, the plants were also measured on their performance – financial, delivery, quality, etc. These metrics were separate and distinct from their audit scores on all of the lean stuff.

On the shop floor, they had the artifacts in place – the boards, the charts, the lines on the floor, but were struggling with making it all work. There was no integration into the management system. Each of these was a program.

I should also point out that, ironically, the plant that was getting the most traction with continuous improvement at the time was the one that was pushing back the hardest against this rote, “standard” approach.

A few years before that, I had been working (as an employee) in another multi-national company. There was a big push from the executives at the very top to develop a set of metrics they could use to determine if a site was “doing lean correctly.” They wanted a set of measurements they could monitor from corporate headquarters that would ensure that any business performance improvement was the result of “doing lean” rather than something else.

Both of these organizations were looking at this as an issue with compliance rather than developing their leaders.

Implementations like these typically involve things like:

  • Developing some kind of “curriculum” and rolling it out.
  • Requiring sites to have a “value stream map” and a “lean plan.”
  • Audits and “lean assessments” against some kind of checklist.
  • Monitoring the level of activity, such as how many kaizen events sites are running.

I have also seen cases of an underlying assumption that “if only we can explain it well enough, then managers will understand and do it.” This assumption drives building models and diagrams that try to explain how everything works, or the relationships between the tools. I’ve seen “pillar models,” puzzles, gears, notional value stream maps, and lots of other diagrams.

In summary, “push improvement” is present when implementing an improvement program is the goal. Symptoms are things such as a project plan with milestones for specific “lean tools” to be in place.

The underlying thinking is that you are doing improvement because you can, not because you must.

This is, I believe, what Jeff Liker and Karyn Ross refer to as mechanistic lean in their book The Toyota Way to Service Excellence.

Relationship to the Status Quo

One of the obstacles that organizations face with this approach is the traditional relationship to the status quo. Most business leaders are trained to evaluate any change against a financial return based on the cost. In other words, we look at the current level of performance as a baseline; evaluate the likely improvement; look at what it would cost to get to that new level and ask “Is it worth doing this?”

If the answer comes up short of some threshold of return, then the answer is “no,” and the status-quo remains as a rational optimum.

Implication: The status-quo is OK unless there is a compelling reason to change it.

For the lean practitioner, this presents a problem. We have to convince management that there is a short-term return on what we are proposing to do in order to justify the effort, time and expense. Six Sigma black belt projects are intently focused on demonstrating very high cost benefit, for example. And, honestly, if you are spending the money to bring in a consultant from Japan and an interpreter, I can see wanting some assurance there is a payback.1

If the discussions are around “What improvements can we make?” and then working up the benefit, you are in this trap. Those benefits, by the way, rarely find their way to the actual P&L unless there is already a business plan to take advantage of them.

The root cause of this thinking may well be disconnection of continuous improvement from whatever challenges the organization is facing; coupled with assumptions that a “continuous improvement program” can be implemented as a project plan on a predictable timeline.

Pull Improvement

Solving Problems

The purpose of any improvement activity is to solve problems. Real problems. In my classes, I often ask for a show of hands from people who have a shortage of problems on a daily basis. I never get any takers. Since there are usually lots of problems – too many to deal with all of them – we need to be careful to work on the right ones. The ROI approach I talked about above is a common way to sort through which ones are worth it. We can also get locked into “Pareto Paralysis”

So which ones should we work on?

Inverting this question, when someone wants to make a change, put in a “lean tool,” etc., my question is “What problem are you trying to solve?” And “we don’t have standard work” is not a problem, it is lack of a proposed solution. In these cases I might ask “…. and therefore?” to try to understand the consequence of this “lack of…” The problem might be buried in there somewhere. But if the issue at hand is trying to raise an audit score for its own sake, we are pack to “Push Improvement.”

A meeting gets derailed pretty fast when people are debating which solution should be applied without first agreeing on the problem they are solving. The Coaching Kata question “Which one [obstacle] are you addressing now?” is intended to get help the learner stay clear and focused on this.

But long before we are talking about problems, we need to know where we are trying to end up. This is why the idea of a clear and compelling challenge is critical.

Challenge First

Organizations that are driven by continuous improvement have a different relationship with the status-quo. Those organizations always have a concrete challenge in front of them. In other words, the status-quo is unacceptable. “Today is the worst we will ever be.”

Cost enters into the discussion when looking at possible ways to reach that destination. But it does not drive the decision about whether or not to try. That decision has already been made. The debate is on how to do it.

The Challenge with Challenges

“Wait a minute… we have objectives to reach too!” Yes, lots of organizations set out objectives for the year or longer. Here are some of the scenarios I have seen, and why I think they are different.

The goal setting is bottom-up. The question “What can we improve?” cascades down the organization, and individual managers set their goals for the year and roll them back up. There may be a little back-and-forth, and discussion of “stretch goals,” but the commitment comes from below. In most of the cases I have seen these goals are carefully worded, the measurements delicately negotiated, with bonuses riding on the level of attainment of these objectives. I have used the word “goal” here because these are rarely challenges or even challenging.

The goal is based strictly on hitting metrics. I have discussed the dangers of “management by measurement” a few times in the past.

A pass is given if there is a strong enough justification made for missing the goal. Thus the incentive to carefully define the metrics, and include caveats and loopholes.

There are measurements but not objectives. Any improvement is OK.

Any true challenge is labeled as a “stretch goal” meaning “I don’t really expect you to be able to reach it.”

And, sometimes the end of the year is an exercise in re-negotiating the definition of “success” to meet whatever has been achieved.

None of this is going to drive continuous improvement, nor align the effort toward achieving something remarkable or “insanely great.”

But here is the biggest difference: FEAR.

Fear of failure. Fear of committing to something I don’t already know how to achieve. Fear of admitting “I don’t know.”

And fear breeds excuses and other victim language that makes sure success or failure was beyond my control.

Fearless Challenges

So maybe that’s it. The key difference is fearless challenge. So what would that look like?

Here I defer to Jeff Liker and Gary Convis’s great book The Toyota Way to Lean Leadership. But I have seen this in action elsewhere as well.

Some key differences here:

  • The challenge comes from above. It isn’t a bottom-up “what can we improve?” It is a top-down “This is what we need to be able to do.”
  • It is an operational need, not a process specification. In other words, it isn’t the “lean plan.” The process specification is what is created to meet the challenge.
  • The challenge is an integral part of developing people’s capability. Perhaps this is the key difference.
  • There is no fear because the challenge comes with active support to meet it. That support, if done well, is both technical and emotional. We’re in this together – because we are.

Improvement = Meeting the Challenge

Given that there is a business or operational imperative established, we are no longer trying to push improvement for its own sake. We have an answer to “Why are we doing this?” beyond “to get a higher 5S score.”

Now there is a pull.

In my Toyota Kata class, I give the teams a challenge that, in the moment, is seemingly impossible. That is intentional. I hear air getting sucked in through teeth. “No way!” is usually the reply when I ask how it feels.

Then, for the next few hours, the teams are methodically guided through:

  • Grasping the current condition – understanding their process as a much deeper level.
  • Breaking down the problem into pieces, taking on one at a time. First a target condition, then specific obstacles.

Depending on how much time they have, 1/4 to 1/3 of the teams crack the problem, a few excel and go beyond, and those that don’t usually acknowledge they are close.

The teams that get there fastest are the ones who get into a quick cadence of documented experiments and learning. They see the problems they must solve much quicker, and figure out what they need to do. I tell them at the start, as I hold up my blank Experiment Records, “The teams that get it are the ones who burn through these the quickest.”

In the process of tackling the challenge, they learn what continuous improvement is really about. I don’t specify their solution. Any hints I give are about what to pay attention to, not what the solution looks like. I don’t deploy tools or give them a template for the solution. At the same time, most teams converge on something similar, which isn’t surprising.

Taking this to the real world – I see similar things. Teams taking on similar challenges on similar processes often arrive at similar looking solutions. But each got there themselves, for their own reasons, often following quite different paths.

While it seems more efficient to just tell them the answers, it is far more effective to teach them how to solve the problem. That is something they can take beyond the immediate issue and into other domains.

Pull Improvement = Meeting a Need

In my post Learning to See in 2013, I posed the question nobody asks: Why are you doing this at all? I point out that, in many cases, value-stream mapping is used as a “what could we improve?” tool, which is backwards from the original intent.

If there is a clear answer to “Why are we doing this?” or, put another way, “What do we need to be able to do that, today, we can’t?” or even “What experience do we aspire to deliver to our customers that, today, we cannot?” then everything else follows. Continuous Improvement becomes a daily discussion about what steps are we taking to get there, how are we doing, what are we learning, what do we need to do next (based on what we learned)?

This is pull. The people responsible for getting a higher level of performance are pulling the effort to get things to flow more smoothly. The mantra here is “not good enough,” but that must be the form of a challenge that inspires people to step up, not punitive.

Then it’s easy because “they” want to do it.

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Epilogue: For the Practitioner

If you are reading this, you are likely a practitioner – someone on staff who is responsible for “continuous improvement” in some form, but not directly responsible for day-to-day operations. I say that because I know, in general, who my subscribers are.

This concept presents a dilemma because while you are challenged with influencing how the organization goes about improving things, the challenge of what improvements must be made (if it exists at all) is disconnected from your efforts.

That leaves you with trying to “drive improvement into the organization” and “be a change agent” and all of those other buzzwords that are probably in your job description.

Here are some things to at least think about that might help.

Let go of dogma. If you think continuous improvement is only valid if a specific set of tools or jargon are used, then you are already creating resistance for your efforts.

Focus on learning rather than doing. You don’t have all of the answers. And even if you did, you aren’t helping anyone else by just telling them what to do. No matter how much sense it makes to you, logical arguments are rarely persuasive, and generally create a false “yes.”

Seek first to understand. Listen. Paraphrase back. Try to get the words “Yeah, that’s right.” to come out of that resistant manager you are dealing with. Remember your purpose here is to help line leadership meet their challenges. Often those challenges are vague, are negative – as in trying to avoid some consequence – or even expressed as implied threats. You don’t have to agree, but you do need to “get it.”

That’s the first step to rapport, which in turn, is necessary to any kind of agreement or real cooperation. As a friend of mine said a long time ago: “You can always get someone’s attention by punching them in the nose, but they likely aren’t going to listen to what you have to say.” Making someone wrong is rarely going to increase their cooperation.

All of this, by the way, is harder than it sounds. I’m still learning these lessons, sometimes multiple times. I’ve been on my own journey of explicit / deliberate learning here for a couple of years.

We have a couple of generations now of improvement practitioners who have been trained with the idea that “lean is good” (or Six Sigma is good, or Theory of Constraints is good, or…). Therefore, it follows, that these things need to be put into place for their own sake – because all of the best companies do them.

This approach, though, reflects (to me) a shallow understanding of what continuous improvement is all about. It skips the “Why?” and goes straight to “How” and “What.” My experience has also been that relatively few of the practitioners steeped in this can actually articulate how the mechanics of these systems actually drive improvement on a daily basis after all of the mechanics are in place beyond a superficial statement like “people would see and remove waste.”

It seems that implementing the mechanics is equated with improvement, when in reality, those mechanics are simply an engine for starting improvement.

Yes, the mechanics are important, but the mechanics are not the reason. We are leaving out the people when we have these discussions. What are they doing every day (other than “following their standard work”)? How do these mechanics actually help them move, as a team, toward a goal they cannot otherwise attain?

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1In its worst manifestation, this thinking can be a cancer on the integrity of the company, for example, GM has had a couple of scandals where it has been revealed that they calculated the ROI of fixing a safety defect vs. the cost of paying off wrongful death lawsuits. Don’t even go there!

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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.

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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.

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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:

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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?

 

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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.

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The Importance of Prediction for Learning

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One of the things, perhaps the thing, that distinguishes “scientific thinking” from “just doing stuff” is the idea of prediction: When we take some kind of action, and deliberately and consciously predict the outcome we create an opportunity to override the default narrative in our brain and deliberately examine our results.

The Toyota Kata “Experiment Record” (which also goes by the name “PDCA Cycles Record”) is a simple form that provides structure for turning an “action item” into an experiment.

Why Is It Important to Make a Prediction?

Explicit learning is driven by prediction.

Explicit Learning

“The most exciting phrase to hear in science, the one that heralds new discoveries, is not ‘Eureka!’ (I found it!) but ‘That’s funny …’ “

— Isaac Asimov

Curiosity is sparked by the unexpected. “I wonder what that is…”

The only way to have “unexpected” is to have “expected.”

When we consciously and deliberately make a prediction, we are setting ourselves up to learn. Why? Because rather than relying on happening to notice things are a little unusual, we are deliberately looking for them.

Deliberate Prediction: The Key to a “Learning Organization?”

Steve Spear, in his book The High Velocity Edge, makes the case that what all high-performance organizations have in common is a culture of explicitly defining their expected result from virtually everything they do.

He studied Toyota extensively for his PhD work, and discovered that rather than exploiting a “lean tool set,” what distinguished Toyota’s culture was deliberately designing prediction mechanisms into all of their processes and activities. This was followed up by an immediate response to investigate anything that doesn’t align with the prediction.

This is the purpose behind standard work, kanban, takt time / cycle time, 1:1 flow, etc. All of those “tools” are mechanisms for driving anomalous outcomes into immediate visibility so they can say… “Huh… that’s funny. I wonder what just happened?”

The High Velocity Edge extends the theory into a more general one, and we see a common mechanism in other high-performance organizations.

OK… that’s one data point on the higher-level continuum.

 

Building 214

Back in 2009 I wrote about a culture change in a post titled A Morning Market. That story actually took place around 2002-2004, and I have just re-verified (Spring 2017) that it still holds.

But it really wasn’t until this afternoon as I was discussing that story with Craig that it finally hit me. The last step in their problem-solving process was “Verification.” To summarize a key point that is actually buried in that post, they could not say a problem was cleared until they had a countermeasure, and had verified that it works.

What is that? It’s a prediction.

Rather than simply putting in a solution and moving on, their process forced them to construct a hypothesis (this countermeasure will make the problem go away), and then experimentally test that hypothesis.

If it worked, great. If it didn’t work then… “Huh, that’s funny. I wonder what just happened?”

This, in turn, not only made them better deliberate problem solvers, it engaged deliberate learning.

What is critically important to understand here is this: That verification step was not included in the problem solving process they trained on. We added it internally as part of our (then kind of rote) understanding of “What would Toyota do?” But it worked, and I believe added a level of nuance that was instrumental in keeping it going.

 

The Improvement Kata

Mike Rother’s work extends what we learned about Toyota. Going beyond “How do they structure their processes?” he went into “How do they structure their conversations?” (And “How can we learn to structure ours the same way?”)

A hallmark of the Improvement Kata, especially (but not exclusively) the “Starter Kata” around experiments, is a deliberate step to make a prediction, test it, and compare the actual outcome with the prediction.

This, in turn, is backed up in Steve Spear’s HBR articles, especially Learning to Lead at Toyota and Fixing Health Care From the Inside, Today,”  both of which should be mandatory reading for anyone interested in learning about continuous improvement.

 

You are Always Making a Prediction Anyway

Any action you take, anything you do, is actually a hypothesis. You are intending or expecting some kind of outcome.

What time do you leave for work? Why? Likely because you predict that if you leave at a particular time, and follow a particular route, you will arrive by a specified time. You might not think about it, but you have made a prediction.

If you are running to any kind of plan, the plan itself is a prediction. It is saying that “If these people work on these tasks, starting at this time, they will complete them at this later time.” It is predicting that the assigned tasks are the tasks that are required to get the bigger job done.

A work sequence is a prediction. If these people carry out these tasks in this order, we will get this outcome in this amount of time at this quality level.

A Six Sigma project is a prediction. If we control these variables in this way, we will see this aspect of the variation stay within these limits.

An “action item” is a prediction. If we take this action then that will happen, or this problem will be solved.

In all of these cases you don’t know, for sure if it will work until you try it and look for anomalies that don’t fit the model.

But in the difference in day-to-day life is we aren’t explicit about what we expect. We don’t really think it through and aren’t particularly aware when an outcome or result differs from what we expected. We just deal with the immediate condition and move on, or worse, assign blame.

What About Implicit Learning?

The human brain (and all brains, really) is a learning engine. Our experience of learning typically comes from what we perceive as feelings.

Take a look at Destlin Sandlin’s classic “Backwards Bicycle” video here, then let’s talk about what was happening.

 

There is nothing special about a “backwards bicycle.” If Destin (or his son) had no prior experience with a regular bicycle, this would simply be “learning to ride a bicycle.” What makes it hard is that, in addition to building new neural pathways for riding a backwards bicycle, he must also extinguish the existing pathways for “riding a bicycle.”

The Neuroscience of Learning (As I understand it.)

Destin has a clear (very clear) objective (Challenge) in his mind: Ride the bicycle without falling down.

As he tries to ride, he knows if he feels like he is losing his balance then he is about to fall.

He (his brain) doesn’t know how to control the wheel to keep the bike upright as he tries to ride. His arms initially make more or less random movements in an attempt to stay upright. This is instinctive, he isn’t thinking about how to move his arms. (This is what he calls the difference between “knowledge” and “understanding.”)

Whatever neurons were firing to move his arms when he loses his balance are a little less likely to fire again the next time he attempts to ride.

Whatever neurons were firing to move his arms when he stays upright for a little while are a little more likely to fire again the next time he attempts to ride.

This actually starts with increased levels of excitatory or inhibitory neurotransmitters in those neural synapses. No physical change to the brain takes place. But this requires a lot of energy. IF HE PERSISTS, over time (often a long time), the brain grows physical connections in those circuits, making those new pathways more permanent. (It also breaks the connections in the pathways that are being extinguished.)

Destin’s six year old son’s brain is optimized for this kind of learning. He creates those new physical neural connections much faster than an adult does. His brain is set up to learn how to ride a bicycle. His father’s brain is set up to ride a bicycle without thinking too much about it. Thus, Destin has a harder time shifting his performance-optimized brain back into learning mode.

All of this is implicit learning. You have something you want to learn, and you are essentially trying stuff. Initially it is random. But over time, the things that work eventually overpower the things that do not. This is also how machine learning algorithms work (not surprisingly).

 

What does this have to do with prediction?

Destin’s brain is running a series of initially random trials and comparing the result of each with the desired result. The line between a “desired result” and a “predicted result” can be kind of blurry in this type of learning. But what is critical here is to understand that learning cannot take place without some baseline to compare the actual result against. There must be a gap of some kind between the outcome we want and what we got. Without that gap, we are simply reinforcing the status-quo.

The weakness with implicit learning is it can reinforce behaviors and beliefs that correlate with a result without actually causing it. We aren’t actually testing whether our actions caused the outcome. We are just repeating those actions that have been followed by the outcome we wanted whether that is by causation or coincidence.

In the case of something like learning to ride a bicycle, that is generally OK. We may learn things that are unnecessary to stay upright on the bicycle1, but we will learn the things that are required.

In athletics, once the basics are in place, coaches can help shift this learning from implicit to explicit by having you practice specific things with specific objectives.

Moving from Implicit to Explicit

Bluntly, the vast majority of organizations are engaged in implicit, not explicit, learning. They repeat whatever has worked in the past without necessary examining why it worked, or if “now” even is similar to “the past.”

These are organizations that operate on “instinct” and “feel.” That actually more-or-less works as long as conditions are relatively stable. They may do things that are unnecessary but are also doing things that are required.

… Until conditions or requirements change.

When the organization has to accomplish something that is outside of their current domain of knowledge – beyond their knowledge threshold – those anecdotes break down. The narrative of cause-and-effect in our minds is no longer accurate.

That is when it is critical to step back, become deliberate, and ask “Where, exactly, are we tying to go?” and “What do we need to learn to get there?”

The alternative is “just trying stuff” and hoping, somewhere along the way, you get the outcome you want. The problem with that? You’re right back where you were – it works, but you don’t know why.

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1Sometimes we develop beliefs that things we do can influence events that, in reality, we have no control over whatsoever. Once we develop those beliefs, we bias heavily to see evidence they are true, and exclude evidence that they are not true.