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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If making money is your goal, throughput is your obsession

In a for-profit organization making money is the goal and the limitation to making more money is called a constraint.

Conversely, a constraint is a limiting factor to get more out of the system. There is only one constraint which is the most limiting factor restricting the Throughput.

Throughput is the rate at which the organization is making money.

If the constraint is limiting Throughput, it means the constraint controls all the money-making.

From this point, making the maximum money given the constraint, there are two (cumulative) options:

  • Elevate the constraint, which means get over the limitation of the constraint to allow more Throughput.
  • Keep Throughput at its maximum by avoiding anything limiting it more.

Elevating the constraint might be difficult or even impossible to do, simply because if it wouldn’t, chances are it would already have been done. More seriously a constraint can be something very difficult to get or to change, like a very expensive equipment, something very rare or something very difficult to influence/change like regulation or policy.

Keeping Throughput at maximum in the given conditions is called exploiting the constraint. It requires constant attention to prevent anything to choke the Throughput.

That’s why once the constraint is identified, it becomes the center of all attention. If the constraint is a resource, like a machine, an equipment, a department or some talented person, this resource deserves a special treatment to protect it against anything limiting its Throughput further.

As the constraint controls all the money-making, it is a good spot where to literally sit and constantly monitor the Throughput. Every decision should be made with regards to its influence to the Throughput:

  • if it is reducing the Throughput, it must be challenged
  • If it is increasing or a least securing the Throughput without adding more Operational Expenses (Net Profit = Throughput – Operational Expenses), it must be considered.

Therefore, if making money is your goal, Throughput is your obsession.

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Your next bottleneck is elsewhere (and in the future)

Theory of Constraints provides the five focusing steps, an iterative improvement process which aims to focus efforts on the sole system constraint (often a bottleneck).

These five steps are:

  1. Identify the constraint (bottleneck)
  2. Exploit the constraint; improve capacity utilization
  3. Subordinate all non-constraint resources to the constraint
  4. Increase the capacity of constraint if relevant
  5. Repeat step 1 if the constraint has changed

The final step is an invitation to continuous improvement, but also a warning: do not let inertia, passivity and acceptance of the status quo become the constraint.

Yet one other aspect of this warning remains mostly unknown.

While teams work hard to exploit the bottleneck resource and recover some wasted capacity, they do not anticipate that the next bottleneck is located elsewhere in the future.

Most teams working to elevate a capacity constraint do not imagine that the additional capacity that will be recovered requires immediate anticipated loading.

Indeed, most of the time, once the goal is reached and the bottleneck is no longer the constraint, they “expect” to see another bottleneck emerge in their area, as if they were playing whack-a-mole; hit one, wait for the next to pop-up.

Chance are that the next bottleneck will probably not be found within their perimeter. The next bottleneck can be upstreams, in another department or with some supplier.

The next bottleneck will instead most likely be found either in development, engineering, marketing or sales. And it will come as a surprise due to lack of anticipation.

The next bottleneck may be the order book, as sales nor marketing did not anticipate the loading of the recovered capacity. It may be development, unable to bring forward the launch of the next product.

It lies in the future is a warning about the necessity to anticipate it and the probable time lag before the anticipated efforts pay off.

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Constraint vs. bottleneck

In Theory of Constraints lingo, there is a subtle difference between a constraint and a bottleneck.

A bottleneck (resource) is a resource with capacity less or equal to demand while a constraint is a limiting factor to organization’s performance, an obstacle to the organization achieving its goal.

A constraint can be called bottleneck but a bottleneck is not always a constraint.

Let’s take an example of a plant with a subassembly workshop gathering resources A, B and C. The whole process needs another resource D and final assembly consisting of resources E and F. The capacity of each resource is displayed under their letter.

The demand is 100 units per day.

According to definitions we’ll find two bottlenecks: resource B limited to 80 units/day and resource E limited to 60 units/day. Each of these two have a capacity less than daily demand.

Resource B is handicap to resource C and for the whole subassembly workshop, but has little influence on the throughput of the plant. Plant’s throughput is limited by resource E, which is both a bottleneck and the constraint. It is primarily E which hinders the plant to deliver 100 units/day.

Imagine The subassembly is led by a foreman named Hector. Hector’s realm encompasses The resources A,B and C. The final assembly process is his customer.

Hector has significant experience within this company and is well aware B is a bottleneck. Even so Hector may not know anything about Theory of Constraints, his common sense made him discover some good rules to better exploit the bottleneck resource.

For example, Hector organized breaks so that B is never left unmanned and not running, manages to minimize changeovers.

If he knew about Theory of Constraints, he would probably squeeze more throughput from B, for instance placing the quality check before the bottleneck in order to insure only OK parts will be processed by the very limited B. Actually quality check is after C, which sometimes causes B to waste valuable time processing parts that will not pass the quality check, something that could be foreseen before B.

As it is the case in many companies, top management set local productivity objectives and is expecting Hector’s subassembly to run with best productivity. Logically Hector will complain about B’s limitations and keep asking for another investment in a second B. Waiting for this investment, Hector manages to produce daily around 80 units, the best subassembly can do.

In Hector’s eyes B is the constraint, which is true if we consider subassembly alone.

Production manager Isadora has to take care about the whole plant and thus considers the whole process. She doesn’t know either about Theory of Constraints, but her analytical skills and common sense focused her attention onto E, the bottleneck and constraint to the whole process.

Having limited means, she’ll explain Hector that working to increase the capacity of B would have little interest as long as E is the limiting factor for the whole system (the plant). What Isodora did not notice is that as long the daily limit is 60 units/day, some costs could be saved in subassembly if its daily production would be aligned to the capacity of E, for instance overtime and excess inventory carry over costs. But she’s blinded by local productivity objectives set by top management.

Nevertheless, Isadora came close to self-discover the five focusing steps of Theory of Constraints:

  1. Identify the constraint (E)
  2. Exploit the constraint
  3. Subordinate everything to the constraint (e.g. subassembly)
  4. Elevate the constraint
  5. Prevent inertia to become the constraint

If Isadora succeeds to elevate the constraint E, chances are that the B will be the next constraint!

Related: Schragenheim’s concise history of constraints

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