What data for changeover monitoring and improvement?

CapacityMaximizing the exploitation of critical Capacity Constraint Resources (CCRs), so called bottlenecks, is crucial for maximizing revenue. Changeovers usually have a significant impact on productive capacity, reducing it with every new change made on those resources that already have too few of it.

Yet changeovers are a necessary evil, and the trend is going for more frequent, shorter production runs of different products, so called high mix / low volume. Consequently, changeovers must be kept as short as possible in order to avoid wasting the precious limited productive capacity of Capacity Constrained Resources (CCRs).

Monitoring changeover durations at bottlenecks is a means to:

  • reinforce management’s attention to the appropriate CCR management
  • analyze current ways of changing over
  • improving and reducing changeovers duration

Management’s obsession should be about maximizing Throughput of the constraints.

To learn more about this, read my post “If making money is your Goal, Throughput is your obsession”.

What data for changeover monitoring?

When starting to have a closer look at how capacity is lost during changeovers, the question is: besides direct periodic observations, what data are necessary and meaningful for such monitoring?

Before rushing into a data collecting craze, here are a few things to take into account:

In the era of big data, it is admitted now that one never has enough data. Yet data must be collected somewhere and possibly by someone. The pitfall here is to overburden operators with data collection at the expense of their normal tasks.

I remember a workshop manager so passionate with data analysis that he had his teams spent more time collecting data than to run their business.

Chances are that your data collection will be manual, by people on shop floor. Keep it as simple and as short as possible.

This a matter of respect for people and a way to insure data capture will be done properly and consistently. The more complicated and boring the chore, the more chances people will find ways to escape it.

Take time to think about the future use of data, which will give you hints about the kind of information you need to collect.

Don’t go for collecting everything. Essential fews are better than trivial many!

Be smart: don’t ask for data that can be computed from other data, e.g. the day of the week can be computed from the date, no need to capture it.

Example of data (collected and computed)

  • Line or machine number
  • Date (computed)
  • Week number (computed)
  • Changeover starting date and hour
  • Changeover ending date and hour
  • Changeover duration (computed)
  • Changeover type
  • Shift (team) id.

Explain why you need these data, what for and how long presumably you will ask for data capture. Make yourself a note to give data collectors regular feedback in order to keep people interested or at least informed about the use of the data.

Data relative to resources with significant excess productive capacity can be ignored for the sake of simplicity and avoid overburdening data collectors. Yet chances are that some day you’ll regret not having captured those data as well, and soon enough. Make your own mind about this.

Monitoring: what kind of surveys and analyses?

There are roughly two types of analyses you should be looking for: trends and correlations. Trends are timely evolutions and correlations are patterns involving several parameters.


One key trend to follow-up is changeover duration over time.

Monitoring by itself usually leads to some improvement, as nobody wants to take blame for poor performance i.e. excessive duration. As frequently things tend to improve spontaneously as soon as measurement is put in place, I use to say measurement is the first improvement step.

The first measurements set the crime scene, or original benchmark if you will. Progress will be appraised by comparing actual data against the original ones, and later the reference will shift to the best sustained performance.

In order to compare meaningful data, make sure the data sets are comparable. For instance certain changeovers may require additional specific tasks and operations. You may therefore have to define categories of changeovers, like “simple”, “complex”, “light”, “heavy”, etc.

Over time the trendline must show a steady decrease of changeover durations, as improvement efforts pay off. The trendline should fall quickly, then slow down and finally reach a plateau* as a result of improvements being increasingly difficult (and costly) to achieve, until a breakthrough opens new perspectives: a new tool, simplified tightenings, another organisation…

Changeover duration

*See my post Improving 50% is easy, improving 5% is difficult

>Consider SMED techniques to recover capacity


Looking for correlations is looking for some patterns. Here are some examples of what to check:

Is there a more favorable or unfavorable day of the week? If yes, understanding the cause(s) behind this good or poor performance can lead to a solution to improve everyday performance.

Does one team outperform underperform? Is one team especially (un)successful? The successful team may have better practices than the lower performing ones. Can those be shared and standardized?

For instance if one team consistently outperforms, it could be this team found a way to better organize and control the changeover.

If it is the case, this good practice should be shared or even become the standard as it proved more efficient.

I happen to see the performance data from a night shift in a pharma plant being significantly better than the day shifts. Fewer disturbances during the night was the alleged cause.

Be critical: an outstanding team may “cut corners” to save time. Make sure that all mandatory operations are executed. Bad habits or bad practices should be eradicated.

Conversely, poor performing teams may need to be retrained and/or need coaching.

Is one type of changeover more difficult to master? Search for causes and influencing factors. Some engineering may be required to help improving.

These are only some examples of patterns that can be checked. Take time to consider what factor can have some influence on changeover ease and speed, than check how to test it with data and how to collect these data.

Note that correlation is not causation. When finding a pattern, check in depth to validate or invalidate your assumptions!

Speak with data

All the data collection and analysing is meant to allow you and your teams to speak with data, conduct experiments in a scientific way and ultimately base your decisions on facts, not beliefs or vague intuitions.

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Is Lean about eliminating waste or not?

Some thought leaders and Lean promoters stress the fact that Lean is about eliminating waste while others seem to get away from this idea.

Could some have been wrong? Is there a shift in Lean Thinking? What is Lean finally about? Is Lean about waste elimination or not?

Well, yes and no.

Defining waste

Waste is an outcome of problems, the result of processes not delivering what is expected but Undesirable Effects instead. In order to avoid the same consequences occurring again in future, something has to be corrected and/or improved.

So when someone mentions eliminating waste, in a Lean Thinking context, it means (should mean) solving problems.

Lean for everyone

As Lean is a philosophy for everyone, not for experts only, it is necessary for people on the shopfloor, manning machines or doing routine administration tasks to develop and hone their Lean awareness and culture by eliminating waste and solving problems.

In order to do that, they have to be trained and coached to identify problems and learn how to solve them. They will do so in their familiar environment first and yes it can turn out as a kind of systematic waste hunting.

On the other end, senior management need to setup the “True North” a far away and visible reference, a Goal to achieve for the organization. It is then necessary to solve the various problems hindering the organization to achieve its Goal and improve the processes accordingly.

This again can be called waste hunting, yet it is (should be) focused onto most important problems (and wastes) standing in the way of the organization’s attempt to achieve its Goal.

Once the True North is defined, everyone is expected to align his/her contribution to the achievement of the organization’s Goal. This means pick and work on the problems necessary to be solved.

So Lean is about waste!?

A Lean transformation is not an all-out elimination of waste, but focusing limited resources on the most important leverage points to let value flow faster to the customer.

For instance, if a machine critical to timely deliver goods to the customer has very few spare capacity and often this capacity is wasted by some problems (e.g. late raw material supply or quality issues), then solving the problems in order to reduce the waste of capacity is meaningful.

If a machine in the same process has a lot of spare, unused capacity, it may be seen as a waste of capacity too, but it would only be counterproductive to reduce this waste by running the machine more than necessary. It would end up with overproduction of unecessary parts, excess inventory and transforming raw material that can no longer be used for producing anything else.

Lean is not about waste when it means optimizing every process step by eliminating waste, simply because the sum of the local optima cannot lead to the system optimum.

Wrapping up

When some Lean promoters state Lean is not about waste, they probably mean Lean is not solely about eliminating waste, as waste elimination is a means, not a goal.

Striving to eliminate all waste will not likely end up with a Lean organization.

Yet solving problems that hinder the organization to achieve its Goal is mandatory and as waste is the result of problems, Lean is about waste.

I hope this helps.

Readers are welcome to share their thoughts.

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What is Kaikaku?

Kaikaku is one of these Japanese words which found their way into the Lean lingo. Kaikaku is usually translated into “radical change” or breakthrough.

my tiny Japanese dictionary proposes “reform”, “renovation” and “reorganization”.

“Doing” kaikaku means introducing a major change in a process in order to drastically improve it (quantum leap). Kaikaku is therefore “opposed” to Kaizen, which is incremental, small steps, improvement.

Kaizen is often praised for being a safe and low-cost improvement way. By changing only one thing at a time and trying allows to observe the effect of the change and to learn from this experience.

Kaikaku will discard much if not all from the existing solution and introduce big change(s). The usual set of parameters and previous accumulated learning may not be useful anymore. The new process is likely to be unstable until all new influencing parameters are fully understood and under control. Therefore Kaikaku is feared as risky.

Yet Kaikaku is not all bad. Once Kaizen has given all that can be reasonably achieved (timely and in terms of Return Of Investment), a radical change may be the only option to improve further.

Kaikaku is often understood as innovation, bringing in some high-tech or top-notch technology.

Indeed, if a manufacturer changes his production way from cutting away material to additive manufacturing (3D printing to make it simple), it is a disruption and potentially a quantum leap in productivity, efficiency, lead time, customizing, etc.

Kaikaku can be more mundane than that, like reorganising the way of operating for instance.

I remember working for Yamaha music, assembling home cinema receivers and CD players, when we heard the headquarter was planning a switch from long linear conveyor belt assembly lines into small autonomous cells, it was kaikaku because it was disrupting decades of streamlined production.


Chris Hohmann in Yamaha’s headquarter, Hamamatsu city

Many Kaizen events (also called kaizen blitz) are in fact small kaikakus where drastic changes are made in short time. Those events are not the best way for try-and-learn, it’s more often one expert moderating a workgroup and leading it to a disruptive solution, hence kaikaku.

If you’d like to share your thoughts or experience, use the comments below.

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What is a spaghetti diagram?

A spaghetti diagram, spaghetti plot or spaghetti chart is the drawing depicting the physical flow or route of:

  • a part, raw material in a workshop or factory
  • a human worker in his/her work environment
  • a patient in his/her journey in a hospital
  • nurses in their station
  • a file or paperwork being handed over across offices
  • etc.

The drawing of the journey will show how intricate the route is, looking like a plate of spaghetti, hence the name.

Spaghetti chart, what for?

Spaghetti (1)These charts are used to analyze the distance covered, the going back and forth to some place, the wasted time in motion and/or transportation (muda).

People are often unaware what distances they walk in a day and management is unaware of the time spent moving around the place wasting time and energy.

Spaghetti chart are useful to redesign a layout or reposition some equipment in order to reduce the unnecessary walking time and fatigue, which is only waste.

Sometimes it is the order of steps in a process that can be changed for the sake of efficiency.

A spaghetti diagram is a welcome sidekick to Value Stream Mapping, as the later maps the conceptual route through a process while the spaghetti chart shows the actual (or future) physical one.

How to draw a spaghetti chart?

Spaghetti charts depicting the actual situation should be based on real observation. On a prepared sheet with outlines of the facility, machines, equipment, etc. the observer traces the lines as the observed object/person moves from one spot to the next.

Tips and tricks

  • The drawing should be more or less on scale, so that it is easier to estimate the total distance covered.
  • If scale is unknown, count the steps when walking and estimate an average stride length. This will help estimate all the distances and the accumulated distance.
  • When the trail is going and coming forth, draw each line separately in order to count the frequency per time unit (e.g. per quarter, per hour, etc.). it will also help to estimate the total distance by multiplying the segment length with frequency.
  • Try to depict the route faithfully. Do not draw straight lines through walls as I saw once because my explanation was not specific enough!

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5 reasons 5S make the world a better place

5S is usually seen as very basic, simple methodology, easy to get through. The reality is totally different and most companies fail to implement a significant and sustainable maturity level of 5S.

For those not familiar with and wanting to learn more about 5S, check my Quick Beginner’s Guide to 5S.

Here are 5 reasons and few examples why 5S make the world a better place.

Please note they are nothing else than some high level consideration and there is much more to 5S than that!


A tidy clean place is usually a safer place. Compared to dark, dirty, cluttered workspace, a 5S one provides better visibility and overall safety.

Better visibility reduces risks related to hidden hazardous items and situations. It improves the perception of potential risks and helps people to behave, like walking on the pedestrian reserved lanes.

Cleanliness make abnormalities visible, thus prevent risks like slipping on spills.

Decluttering prevents tripping or piled items falling down.


5S came (back) to the West in the heyday of Total Quality Management, when improving quality was a matter of keeping up with japanese competitors. Quality work and making quality product is not compatible with messy, dirty workplace.

In a cluttered and dirty place it is more likely to have quality defects, like for instance scratches or stain on a panel. Many such quality issues require painstaking rework or even part replacement, which are both wastes.

A screw, nut or bolt can go unnoticed on a dirty and cluttered workbench, leading easily to be forgotten on (re)assembly.


In a 5S environment, items and information can be found immediately without lengthy searches. Saving time is important for reactivity and for adding more valuable and/or enjoyable activities.

In a true 5S work environment, it is possible to share more tools, jigs and fixtures or files because everything is better organized and made visible. More sharing means less buying, thus saving unnecessary expenses.

True 5S workplace requires less space, which in turn requires less walking or transportation and possibly monetary savings.

In 5S places, fewer material gets lost and there is no need for frequent replacement or duplicate inventories.


“High performance companies are beautiful” says my boss, suggesting a tight correlation between the care to keep the company tidy, clean – and generally speaking good looking – and operational performance.

A good 5S image gives confidence to customers, partners, investors and talents, while it provides pride to employees and representatives.

The outlook of a company accounts much more than generally thought in suppliers’ assessments or audits. Poor condition will make auditors suspicious and look closer to details.

Customers will probably be reluctant to buy from a poor looking supplier, fearing that quality or even safety of the goods or services will reflect the company’s look.


5S helps to use just required quantities, which consumes lesser raw materials and energy, reduces waste, sewage, pollution and the like.

5S workplace are less likely to have pollution issues by accident or lack of rigor/discipline.

Tidy smaller workplaces require fewer air conditioning or heating, less lighting.

Constantly cleaned areas are easier to keep clean and require less aggressive chemicals to remove smear and stains.

This is not greenwashing but concrete actions.

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What is Little’s law? What is it good for?

Little’s law is a simple equation explaining how Waiting Time, Throughput and Inventory are related.

Wait Time = Inventory (or WIP) / Throughput

Here is a video about Little’s law:

Fine, what is Little’s law good for?

Well, if a process lead time is too long, chances are that work-in-progress (WIP) is too high. For a given processing rate (Throughput), the lead time will be equal to WIP/Throughput. To reduce the lead time the process Throughput must be increased or the WIP reduced.

Throughput is usually limited by some constraint: machining speed, resources available and so on. It may not be easy to increase Throughput.

WIP on the other hand can generally be controlled by limiting the inventory or the work to be done entering the system upfront.

In this video, Philip Marris explains how to reduce WIP by controlling the flow of work entering the system. Even he does not mention Little’s law, it is indeed used to reduce Inventory.

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5S in hospital

5S are as well an approach, philosophy and methodology to better workplace organization, foundations for efficient and safe work, as well as insuring quality and continuous improvement. They originated in their current form* in industrial workshops in Japan, leading many people to think “this is a production thing“.

The following video shows a good example of the application of the 5S principles in a Toronto hospital.

*I believe 5S preexisted in different forms, especially in the TWI cards during WWII.

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Trial and learn approach with PDSA

PDSA is a variant of Shewhart’s PDCA (Plan Do Check Act) cycle where Check is replaced with Study. It is used to structure a series of trials or experiments and learn from the results.

Let’s look closer at each phase:

Plan means:

  • describe what is to be achieved, the goal, the outcome
  • frame the improvement efforts and
  • align efforts onto the goal
  • prepare a set of “experiments” supposed to improve the situation
  • prepare the “how” the collected data will be analyzed

Do means:

  • carry out the planned actions
  • collect data

Study means:

  • analyse collected data
  • compare results to predicted outcome
  • capture the lessons learned (influencing vs. neutral factors)

Act means:

  • according to latest results and accumulated experience, what is the next cycle about, i.e. what are the next assumptions?
  • start a new PDSA cycle with a new set of assumptions


  • limit the ambition for each cycle and prefer several small incremental steps rather than quantum leaps. “Learning will be improved if fed in small bits”
  • keep the experiences simple by limiting the number of variables

Smaller steps allow to adjust better and faster to small (environmental or system) parameter changes. Smaller steps and limited number of parameter i.e. simpler experimentation is easier to carry out with non-specialists like shop floor personnel.

During the Study (S) phase:

  • look for consistency of actions carried out vs. those planned
  • check if conditions, support, etc were as planned, no biases
  • is the result an improvement?
  • is the system as a whole improved or is it just a local improvement?
  • can we conclude the original assumption(s) is/are valid?

PDSA compared to PDCA

With PDCA, the Check phase suggest an existing benchmark, reference or standard against which to assess the result of the Do phase. It is a compliance check. In PDSA the importance is building knowledge and improve by trial-and-learning. In PDSA, the failure to improve is as valuable than the improvement, as there is a lesson to be learnt behind it.

Any comments?

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Why 5S fail? We’re done!

Most of 5S assessment or audit systems I’ve seen are built upon a maturity scale, usually from 1 (poor or insufficient) to 5 (complete, satisfactory or the like) for each of the 5Ss and a schedule for the assessments or audits.

The way these systems are built is most often misleading, letting people believe that once the minimum level achieved to pass the audit is reached for every S, they’re done.

In fact they had one first turn of the PDCA wheel. Yet achieving this is sometimes painstaking enough that facilitators in charge are not eager to explain it was just a start and the whole has to be repeated over and over at higher level each time.

Management sees a good enough improvement of the situation and is not really willing to sponsor an activity that does not directly yield more output.

So there is a hypocrite general agreement that “they’re done” with 5S and it’s time to move on to something else.

This post is part of the Why 5S fail series

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Why 5S fail? Nobody is interested in housekeeping

This post is part of the series “why 5S fail”.

Nobody is interested in housekeeping.

This is particularly true in heavier industries with more male workforce, “housekeeping” is incompatible with (macho) pride and way beneath their dignity.

Lego_018aThey may not hesitate to manually lift heavy weights and handle greasy dirty parts, but would be reluctant to mop up spilled lubricant or dry-wipe a machine casing.

In most machist workshops they complain about being turned into “maids”.

Therefore announcing decluttering and cleaning up is never the best way to start, even in environments with lesser testosterone levels.

What is far most appealing is the call to join a challenge, for which 5S are a necessary condition but not telling it.

My way would be to find such a challenge and while searching how to achieve the goal, gently lead the participants to express themselves the need for order, cleanliness and suitable work environment.

When facilitating 5S deployment, I put myself in a learning posture and ask lots of questions, in a smart way. People usually love to share their knowledge, especially because on shop floor it is not that common that somebody pays attention to their work and experience.

A bit of flattering “you are the subject matter experts, you know best” is usually welcome and sweetens their day, which is also true and is what I really think.

Of course my questioning is not that candid but a way to surface the required basic conditions to achieve our goal.

Once these basic conditions listed, I manage to go through the 5S rollout quickly in order to start the next level of tasks, usually more appealing, like problem solving or technical improvements.

Of course, I do not hesitate to iterate back to sorting, arranging, cleaning and redefining the standards if the 5S maturity is not at desired level.

I simply explain why it is common to iterate and if my audience don’t know about, I’ll explain the Deming wheel (PDCA cycle) and its wedge.

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