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.

 

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.

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.

image

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?

————–

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 dealt lawsuits. Don’t even go there!

Looking at some old notes…

I am cleaning out some old notepads. One page has the notes I took during a 4pm production status meeting during my first “get to know you” visit to this company.

In a box on my notes page it says:

image

“More information is not going to help until you begin to define how you expect things to run.”

What I had observed is their focus on the previous 24 hours metrics, with lots of speculation about “what happened” to account for the lost production. They were focused on incidents and, honestly, assigning those incidents as causes. Where they came up short, they were seeking more information about lost production incidents.

But everything I have written is just to give you a little context for the scribbled box on my notes a few years ago.

Today? They fixed it. This was one of the most dramatic culture shifts – from “find who to blame” to “let’s solve the problem” I have ever witnessed.

Reflection:

What are your daily meetings like? Are they focused on the stuff that went wrong yesterday? Or are they focused on where you are trying to go in the near future?

The Lure of Rapid Lean Transformation

image

Lean In A Box

I can see where the promise of a rapid “lean transformation” is appealing. We shop around, listen to presentations, read proposals, and find someone with a good “solution.”

For the next few weeks, maybe a couple of months, things move in a flurry. Lines are connected, visual inventory controls are established, your team is taught about the lean tools with simulations demonstrating the power of flow. Thus convinced, the team is expected to buy into the changes being made.

At the end, with much hoopla and pizza, we are done, and the new system is running.

The Grass is Greener – For a While

Now let me tell another story. It isn’t my story, I heard it during a presentation by Jim Sorensen in Seattle. I’m sure he tells it much better than I will. Jim talks about his neighbor who has a fantastic, immaculate lawn. Jim recounts that, one day as he was chatting with his neighbor, shared a little envy – “I wish my lawn looked as good as yours.” The neighbor’s response was “Jim, if you had a lawn like mine, in three months it would look like your lawn does now.” His neighbor works on his lawn. The immaculate appearance was important to him. For Jim? He chose to spend his time doing different things, which is fine. But at the same time, he, we, shouldn’t expect an immaculate lawn to stay that way unless we are willing to do the work.

Likewise with these rapid implementations. To continue to metaphor, we rip out your old grass, put down beautiful fresh sod, take the photos, and move on to the next house. But if you don’t fundamentally change the way you maintain that new lawn, by the end of the year, it will look like your old one.

You Are Transforming Your Culture

Continuous improvement as “the way we do things” is a fundamental shift in the way people think, and the way they spend their time. There is no “set it and forget it.” Your processes are either improving or eroding. You must exert continuous active control by the people who are closest to the work just to keep up with the entropy. Their “buy in” is not even close to being enough to make it work. They need different leadership.

Many years ago I was interviewing at a Big Household Name Company that looking to accelerate their “lean deployment.” They already had a guy with incredible qualifications working very hard to teach the things they needed to understand.

Every person that talked to me asked “How can we go faster?” My reply was “Listen to what [your lean director] says and do it. He knows what he is talking about.” Unfortunately, he was telling them that there were no easy shortcuts, and that they had to begin thinking about production in a fundamentally different way. My feeling was they were looking for someone to tell them there was an easier way to do it.

I run into that a lot. An organization wants the results, they want the immaculate lawn, but they want someone else to do the work to make it that way, then they want the outcome.

The problem is you can’t outsource your own thinking.

More Cycle Time Questions from Search Results

A couple of more exam questions showed up in search results.

4. what can happen if the cycle time is much shorter than the takt time

The searcher didn’t even bother typing this one – just cut and paste the entire thing, including the question number.

Fortunately the answer is really easy:

Raiders of the Lost Ark warehouse

If you keep this up, you’ll need another building.

OK, here’s one that is a little more subtle:

185 units in 14 hours is what cycle time

Hmmm. My first thought is to go back to Who is Grading the Questions?

think whoever wrote this question is looking for something like 14 hours  (840 minutes) / 185 units = 272 seconds / unit (more or less). But that’s an average, it isn’t a cycle time.

The Average Rate of Output is not “Cycle Time.”

The average doesn’t give you any more information than the original question. This is simply a rate of output: 185 units in 14 hours. Cycle time is the measured time to complete one cycle.

There might be a single cycle that produces a batch of 185 units. Let’s say, for example, in a cure oven process that takes 14 hours to load, run and unload. Then the cycle time is 14 hours.

If the pieces are moving in a sequence of operations, but in a lot (which is different from a batch), where Operations 1 is completed on all 185, then Operation 2 is completed on all 185, etc. and maybe that all takes 14 hours? I have no idea what the cycle time(s) for the operations are. Most of the time the units are waiting in queue for the other 184 items to be done at each operation.

If we have true one-by-one flow then I have at least 185 cycle times. Hopefully they are all about the same, but likely that isn’t the case.

Are we measuring exit cycles (the cadence of output at the end of the process), or operator or machine cycle times?

By taking the average we are obscuring all of this information. Calling the average the “cycle time” just makes it worse because it gives people the impression that they are the same.

Cycle time is not calculated. It is measured. You cannot determine cycle time by applying mathematical operations on numbers. You have to go observe with a stopwatch.

And finally:

within each four hours worked, workers can take a total of 48 minutes in allowance (so allowance factor is based on workday). if the observed time is 6 minutes, and performance rating is 95%, what is the standard time (in minutes; give three significant digits after the decimal point)?

Even if I could understand the question, down to thousandths of a minute? Seriously?

</rant> <!– Until next time –!>

 

 

Who is Grading the Questions?

Once again the search terms have revealed an interesting test question. Let’s parse it and see what we can learn.

2. the cycle time to complete a final step on an assembly line is normally distributed, with a mean of 4 minutes and a standard deviation of 1 minute.1) at the end of an 8-hour shift, how many units will have been assembled on average?

The question is about exit cycles – the cadence at the output of a production process of some kind, in this case an assembly line.

Measuring and plotting the variation of exit cycles at the customer end of your production process is a great way to get a handle on how much variation there is.

This question, however, reveals a level of simplistic understanding in the mind of the person who wrote the question of how these things work.

Answering the Question

First, we have to assume that this assembly line (which implies manual work) takes no breaks during their 8 hour shift. We have no information that suggests otherwise.

That means we have 8 hours x 60 minutes = 480 minutes of working time.

Intuitively, if the exit cycles are normally distributed (which implies the distribution is symmetric), with a mean of 4 minutes, then we should be able to simply divide to get the average number of units built during each shift.

480 / 4 = 120 units

Which in the long-haul is close, though it will take a lot of iterations to converge on that. Running about 65 iterations of a random number set with a mean of 4 and standard deviation of 1, I got an average daily production of 120.1, a high of 127 units, and a low of 115.

But Exit Cycle Times Aren’t Normally Distributed

This is a problem with a lot of the “statistics” I see being kicked around today.

This is a histogram of 36 actual exit cycle times from a real production line.

image

The “Cycle Time” bucket numbers on the horizontal axis represent the top of the range. So the high bar says there were 10 cycle times between 25 and 30 seconds.

The mean of the data set is 42, which is actually one of the less frequent values.

The first thing that jumps out is that this data set might have two peaks (be bimodal). I would want a lot more data before I drew this conclusion, however let’s assume it is for the discussion because this is quite common.

A bimodal distribution implies there are two different processes running. If we looked at a run chart of individual cycles, we would probably see one of two things:

  • Clusters of longer times spaced between clusters of shorter times. This would be the case if the line is running lots or batches, then switching over.
  • A repeating cadence like “short-short-short-long, short-short-short-long” which would indicate really good production leveling.

But all of that is a sidebar. The real issue I have with this “homework question” simple: Cycle times are (almost) never normally distributed.

There is a minimum time that can elapse between the completion of one unit and the completion of the next, but there is no theoretical limit on the length of delay. These distributions (almost) always have a tail to the right.

So once again, be very careful about blindly using averages (arithmetic means) unless you understand the whole story.

The Test Question Misleads the Students

And this is my problem with questions like this on exams and homework, especially in classes that purport to be teaching how factories run. Even when using a distribution, the problems almost always default to a symmetric distribution when real world factory-related data sets are rarely symmetrically distributed.

You are teaching your students a simple solution that won’t work in the real world without explaining why.

The Lean Plateau

Many organizations trying to deploy lean get great results for the first couple of years, then things tend to stall or plateau. This is in spite of continued effort from the “lean team.”

image

We Still Don’t Have a Lean Culture

This was the comment by the Continuous Improvement director of a pretty large corporation. They had been running improvement events for several years, everyone had pretty much been through one.

Each of the events had made pretty good strides during the week, but the behavior wasn’t changing. Things were eroding behind the events, even though everyone agreed things were better.

It was getting harder and harder to make more progress. They had hit the plateau.

What Causes the Lean Plateau?

While it might not be universal, what I have seen happen is this:

The implementation is led by a small group of dedicated technical experts. They are the ones who are looking for opportunities, organizing the kaizen event teams, leading workshops, and overseeing the implementation of lean techniques.

While this works in the short term, often the last implemented results begin to erode as soon as the lean experts shift their attention elsewhere.

At first, this isn’t noticed because the implementation is proceeding faster than the erosion.

However the more areas that are implemented, the faster the erosion becomes. There is simply more “surface area” of implemented areas.

At some point, the rate of erosion = the rate of implementation, and the lean team’s efforts start to shift from implementing new areas to going back and re-implementing areas that have eroded.

The lean team’s capacity becomes consumed re-implementing, and they spend less and less time going over new ground. They are spending all of their time “spinning the plates” and no time starting new ones.

Key Point: The lean plateau occurs when the level of implementation effort and the rate of erosion reach an equilibrium.

In the worst scenario, sooner or later financial pressures come into play. Management begins to question the expense of maintaining an improvement office if things aren’t getting significantly better on the bottom line. What they don’t see is that the office is keeping things from getting worse, but they aren’t called the “maintain what we have office” for a reason.

Breaking the Lean Plateau

When I was a lean director in a large company, we were confronting this very question. We had a meeting to talk about it, and quickly started blaming “lack of management commitment.”

Leaders Weren’t Stepping Up

In any given area, after education and planning, our last step was always to have a major effort to put flow production into place. Since the performance of the area would be substantially better, we expected the leaders to work hard to continue that performance.

What actually happened in an area was “implemented,” was the line leaders in that area – supervisors, managers, senior managers – weren’t working to look for erosion and correct it.

Instead, when a problem was encountered, they were making some kind of accommodation that compromised flow. The effect of the problem went away, but things had eroded a bit.

What we thought we learned: The weren’t “supporting the changes.”

What we really learned – though it was only realized in hindsight: This is the mechanism of “erosion.”

Flow production is specifically designed to surface small problems quickly. If there is no mechanism to detect those problems, respond, correct, and learn, then the only thing leaders can do is add a little inventory, add a little time, add an extra operation.

As Hirano put it so well decades ago:

All waste is cleverly disguised as useful work.

But Our Current Condition was Incomplete

There were outliers where it was working.

As we talked, we realized that each of us had experience with an outlier – one or two areas that were actually improving pretty steadily. Trying to understand what was different about these bright spots, we looked for what they all had in common. Surprisingly:

  • They were areas with no dedicated improvement teams.
  • They ran few, if any, 5-day kaizen events.
  • They were geographically close to one of us (senior “Directors”).
  • One of us had decent rapport with the area management team.
  • We each had an informal routine with them: We would drop by when we had time, and walk the work area with the area leader. We could discuss the challenges they were facing, how things were operating, go together to the operations concerned, and look at what was happening. We could ask questions designed to “sharpen the vision” of the leader. Sometimes they were leading questions. Most of the time they were from genuine curiosity.
  • By the time we left, there was generally some action or short term goal that the leader had set for himself.

Even though we “lean directors” had never worked together before, our stories were surprisingly consistent.

The Current Condition (Everywhere Else)

aka Dave’s Insight

The next logical question was “If that is what we do, what happens everywhere else? What do the lean staff people do?”

Now we were trying to understand the normal pattern of work, not simply the outcome of “the area erodes because the leaders don’t support the changes.”

Dave confidently stood up and grabbed the marker. He started outlining how he trained and certified his kaizen leaders. He worked through the list of skills he worked to develop:

  • Proficiently deliver the various topical training modules – Waste vs. Value Add; Standard Work; Jidoka; Kanban and Pull;
  • “Scan” an area to find improvement opportunities.
  • Establish the lean tools to be deployed.
  • Organize the workshop team.
  • Facilitate the “Vision”
  • Manage the “Kaizen Newspaper” items
  • etc

and at some point through this detailed explanation he stopped in mid sentence and said something that brought all of us to reality (Please avert your eyes if you are offended by a language you won’t hear on network TV):

“Aw… shit.”

What we realized more or less simultaneously was this:

Management wasn’t engaged because our process wasn’t engaging them.

Instead, our experts were essentially pushing them aside and “fixing” things, then turning the newly “leaned” area over to the supervisors and first line managers who, at most, might have participated in the workshop and helped move things around.

Those critical front line leaders were, at best left with a to-do list of ideas (kaizen newspaper items) that hadn’t been implemented during the 5 days.

There was nothing in the structure to challenge them to meet a serious business objective beyond “Look at how much better everything runs now.” The amount of improvement was an after-the-fact measurement (or estimate) rather than a before-we-begin imperative.

So it really should be no surprise that come Monday morning, when the inevitable forces of entropy showed up, that things started to erode. The whole system couldn’t have been better designed for that outcome.

Why the Difference in Approach?

In retrospect, I don’t know. Each of us senior “lean directors” had been taught, or heavily influenced by, Toyota-experienced Japanese mentors, teachers, consultants.

When we engaged the “outlier” areas, we were following a kinder, gentler version of what they had taught us.

On the other hand, what we were teaching our own people was modeled more on what western consultants were doing. Perhaps it is because it is easier to use forms and PowerPoint for structure than to teach the skills of the conversations we were having.

Implement by Experts or Coached by Leaders

That really is your choice. The expert implementation seems a lot easier.

Unfortunately the “rapid improvement event” (or whatever you call them) system has a really poor record of sustaining.

Perhaps our little group figured out why.

There are no guarantees. No approach will work every time. But a difficult approach that works some of the time is probably better than an easy path that almost never works.

Using Takt Time to Compute Labor Cost

How can I use takt time in computing labor cost?

Sometimes the searches that lead here give us interesting questions.

While simple on the surface, this question takes us in all kinds of interesting directions.

Actually the simplest answer is this: You can’t. Not from takt time alone.

Takt Time

Takt time is an expression of your customer’s requirement, leveled over the time you are producing the product or service. It says nothing about your ability to meet that requirement, nor does it say anything about the people, space or equipment required to do it.

Cycle Time

Cycle time comes in many flavors, but ultimately it tells you how much time – people time, equipment time, transportation time – is required for one unit of production.

Takt time and cycle time together can help you determine the required capacity to meet the customer’s demand, however they don’t give you the entire story.

In the simplest scenario, we have a leveled production line with nothing but manual operations (or the machine operations are trivially short compared to the takt time).

If I were to measure the time required for each person on the line to perform their work on one unit of the product or service and add them up, then I have the total work required. This should be close to the time it would take one person to do the job from beginning to end.

Let’s say it takes 360 minutes of work to assemble the product.

If the takt time says I need a unit of output every 36 minutes, then I can do some simple math.

How long do I have to complete the next unit?  36 minutes. (the takt time)

How long does it take to complete one full unit?  360 minutes (the total manual cycle time)

(How long does it take) / (How long do I have) = how many people you need

360 minutes of total cycle time / 36 minutes takt time = 10 people.

But this isn’t your labor cost because that assumes the work can be perfectly balanced, and everything goes perfectly smoothly. Show me a factory like that… anywhere. They don’t exist.

So you need a bit more.

Planned Cycle Time (a.k.a. Operational Takt Time and “Actual Takt”)

How much more? That requires really understanding the sources of variation in your process. The more variation there is, the more extra people (and other stuff) you will need to absorb it.

If we don’t know, we can start (for experimental purposes) by planning to run the line about 15% faster than the takt time. Now we get a new calculation.

85% of the takt time = 0.85 x 36 minutes = ~31 minutes.  (I am rounding)

Now we re-calculate the people required with the new number:

360 minutes required / 31 minutes available = 11.6 people which rounds to 12 people.

Those two extra people are the cost of uncontrolled variation. You need them to ensure you actually complete the required number of units every day.

“But that cost is too high.”

Getting to Cost

12 people is the result of math, simple division that any 3rd grader can do. If you don’t like the answer, there are two possible solutions.

  1. Decide that 360 / 30 = something other than 11.6 (12). (or don’t do the math at all and just “decide” how many people are “appropriate” – perhaps based on some kind of load factor. This, in fact, is a pretty common approach. Unfortunately, it doesn’t work very well for some reason.
  2. Work to improve your process and reduce the cycle time or the variation.

Some people suggest slowing down the process, but this doesn’t change your labor cost per unit. It only alters your output. It still requires 360 minutes of work to do one unit of assembly (plus the variation). Actually, unless you slow down by an increment of the cycle time, it will increase your labor cost per unit because you have to round up to get the people you actually need, and/or work overtime to make up the production shortfall that the variation is causing.

So, realistically, we have to look at option #2 above.

This becomes a challenge – a reason to work on improving the process.

Really Getting to Cost

Challenge: We need to get this output with 10 people.

Now we have something we can work with. We can do some more simple math and determine a couple of levers we can pull.

We can reverse the equation and solve for the target cycle time:

10 people x 30 minute planned cycle time-per-unit = 300 minutes total cycle time.

Thus, if we can get the total cycle time down to 300 minutes from 360, then the math suggests we can do this with 10 people:

300 minutes required / 30 minutes planned cycle time = 10 people.

But maybe we can work on the variation as well. Remember, we added a 15% pad by reducing the customer takt time of 36 minutes to a planned cycle time (or operational takt time, same thing, different words) of 30 minutes. Question: What sources of instability can we reduce so we can use a planned cycle time of 33 minutes rather than 30?

Then (after we reduce the variation) we can slow down the process a bit, and we could get by with a smaller reduction in the total cycle time:

330 minutes required / 33 minutes planned cycle time = 10 people.

(See how this is different than just slowing it down? If you don’t do anything about the variation first, all you are doing is kicking in overtime or shorting production.)

So which way to go?

We don’t know.

First we need to really study the current process and understand why it takes 360 minutes, and where the variation is coming from. Likely some other alternatives will show themselves when we do that.

Then we can take that information, and establish an initial target condition, and get to work.

Summarizing:

  • You can’t use takt time alone to determine your labor cost. Your labor cost per unit is driven by the total manual cycle time and the process variation.
  • With that information, you can determine the total labor you need on the line with the takt time.
  • None of this should be considered an unalterable given. Rather, it should be a starting point for meeting the challenge.

And finally, if you just use this to reduce your total headcount in your operation, you will, at best, only see a fraction of the “savings” show up on your bottom line. You need to take a holistic approach and use these tools to grow your business rather than cut your costs. That is, in reality, the only way they actually reach anywhere near their potential.

 

 

 

Prediction Doesn’t Equal Understanding

Lunar Eclipse over Everett, WA. Photo by Mark Rosenthal, © 2015Sometimes people fall into a trap of believing they understand a process if they can successfully predict it’s outcome. We see this in meetings. A problem or performance gap will be discussed, and an action item will be assigned to implement a solution.
Tonight those of us in the western USA saw the moon rise in partial eclipse.

We knew this would happen because our understanding of orbital mechanics allows us to predict these events… right?

Well, sort of. Except we have been predicting astronomical events like this for thousands of years, long before Newton, or even Copernicus.

The photo below is of a sophisticated computer that predicted lunar eclipses, solar eclipses, and other astronomical events in 1600BC (and earlier). Click through the photo for an explanation of how Stonehenge works:

Photo of Stonehenge
Creative Commons flickr user garethwiscombe

Stonehenge represented a powerful descriptive theory. That is, a sufficient level of understanding to describe the phenomena the builders were observing. But they didn’t know why those phenomena occurred.

Let’s go to our understanding of processes.

The ability to predict the level of quality fallout does not indicate understanding of why it occurs. All it tells you is that you have made enough observations that you can conclude the process is stable, and will likely keep operating that way unless something materially changes. That is all statistical process control tells you.

Likewise, the ability to predict how long something takes does not indicate understanding of why. Obviously I could continue on this theme.

A lot of management processes, though, are quite content with the ability to predict. We create workforce plans based on past experience, without ever challenging the baseline. We create financial models and develop “required” levels of inventory based on past experience. And all of these models are useful for their intended purpose: Creating estimates of the future based on the past.

But they are inadequate for improvement or problem solving.

Let’s say your car has traditionally gotten 26 miles-per-gallon of fuel. That’s not bad. (For my non-US readers, that’s about 9 liters / 100 km.) You can use that information to predict how far a tank of fuel will get you, even if you have no idea how the car works.

If your tank holds 15 gallons of fuel, you’ll be looking to fill after driving about 300 miles.

But what if you need to get 30 miles-per-gallon?

Or what if all of a sudden you are only getting 20 miles-per-gallon?

If you are measuring, you will know the gap you need to close. In one case you will need to improve the operation of the vehicle in some way. In the other case, you will need to determine what has changed and restore the operation to the prior conditions.

In both of those cases, if you don’t know how the car operated to deliver 26 miles-per-gallon, it is going to be pretty tough. (It is a lot harder to figure out how something is supposed to work if it is broken before you start troubleshooting it.)

Here’s an even more frustrating scenario: On the last tank of fuel, you measured 30 miles per gallon, but have no idea why things improved! This kind of thing actually happens all of the time. We have a record month or quarter, it is clearly beyond random fluctuation, but we don’t know what happened.

The Message for Management:

If you are managing to KPIs only, and can’t explain the process mechanics behind the measurements you are getting, you are operating in the same neolithic process used by the builders of Stonehenge. No matter how thoroughly they understood what would happen, they did not understand why.

If your shipments are late, if your design process takes too long, if your quality or customer service is marginal, if the product doesn’t meet customer’s expectations, and you can’t explain the mechanisms that are causing these things (or the mechanisms of a process that operates reliably and acceptably) then you aren’t managing, you are simply directing people to make the eclipse happen on a different day.

“Seek first to understand.”

Dig in, go see for yourself. Let yourself be surprised by just how hard it is to get stuff done.