When enough is… enough

cho-in-azoneThis is a behavior I’ve noticed quite often in food industry, in chemical or pharmaceutical plants: cleaning and sanitation processes (mainly their duration) are extended beyond the standard procedures at the expense of costs and production capacity.

Fear of harming

In the regulatory-constraint industries like food, chemical or pharma, people on shopfloor are trained and qualified to perform cleaning and sanitation operations. They follow procedures and work instructions, based on standards.

They usually also have frequent training about the importance of sanitation or sterilization and the possible consequences if badly done. Working in food, healthcare or pharma is embracing the sacred mission to bring something good, to cure or relieve customers and/or patients and do everything to prevent hurting them in any way.

They are also reminded what consequences for the organization in case of problem beyond failing to: losing the customers’/patients trust, losing the licence to produce, being sued, being exposed to scandals…scary enough for shopfloor people to take things seriously.

Yet the people on shopfloor seldom have the scientific background to fully understand what is required for good sanitation or sterilization, when doing more is useless or even counterproductive. They also are often left on their own, without expert supervisors to reassure them, answer possible question or take decisions in case of doubt.

Furthermore, the results of sanitation/sterilization is most often only known after a sample of rinsing water or the swabbing of the tool/equipment has been analyzed by some remote lab.

Fearing to harm the organization, or worse the customers / patients or possibly to have to go over the whole lengthy sanitation process again if it is not satisfactory, the sanitation is performed longer than procedures require it. This is base on the belief the more the better.

This seemingly logical and well-intentioned assumption is never challenged, leading to waste detergents, acids, water… and time, simply because over-sanitation is not noticed by management.

Changeovers are even longer

Changeovers in such environments can be long and painstaking due to regulatory constraints and all the paperwork associated. Ignoring the over-sanitation habits can extend the changeover duration even more.

Besides adding costs for no additional value, the additional time spent on sanitation may be needed on critical equipment (bottlenecks) and the time lost will not only never be recovered but the true cost is to be counted in minutes of turnover. And this one can be skyrocketing!


When looking for additional productive capacity or a way to get more out of the current process, check the changeovers’ content and take a closer look on sanitation.

Give the shopfloor personnel clear indication when enough is enough, without risk to harm anybody nor to endanger quality. If necessary, have a real qualified subject matter expert attending these critical phases, ready to support the team and answer any question.

Not only will it take some concerns off the team, but may be a great payback in terms of additional yield.

Feel free to share your thoughts and experience in the comments and share the post if you liked it.

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Where I could have used a Goal Tree but didn’t know about the tool then

During the June 2016 Logical Thinking Process alumni reunion, Bill Dettmer asked the participants to share their “War Stories”, i.e. experience with the Logical Thinking Process (LTP) and LTP tools.
I came up with several short stories. In this excerpt, I recall I could have used a Goal Tree but didn’t know the tool at that time.

The story I tell is the one that inspired my post Goal tree chronicles – The pharma plant.

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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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Performance improvement: simple things can earn big results

Silly things can cost a lot in terms of productivity and output.  In this video interview, Philip Marris  asks me about lessons learnt while helping a pharmaceutical plant to improve productivity and deliver drugs to patients faster.

It is about how simple actions solve those silly small problems and bring big results at literally no cost.

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Why SMED is quick win in pharma – Episode III

Improving changeovers in pharma industry is a relatively quick and easy way to… quick wins, faster and easier than usually assumed. This series tells you why.

Episode III: How to and Why it works

In the previous Episode I explained the background of the lag of many traditional pharma makers in regards to Industry Best Practices (mainly Lean) and operational performance. I highlighted faster changeovers as leverage for recovering wasted capacity and improve performances. In Episode II I gave examples of  gains that can be expected and why.
In this Episode, I explain how and why it works.

The first approach is the least “risky” one, which will not change anything, thus not jeopardizing compliance to procedures nor quality. It is based on the fact that changeovers take frequently more time than initially allocated and the assumption that the changeover procedure is sound and the time allocation is reasonable, i.e. changeover doable during the allocated time.

Reminder: pharma is regulation-constrained

As I explained in Episode II, in pharma industry all steps of a changeover are prescribed in procedures and traced. Lots of information and proofs are captured, paperwork filled because a great deal of these procedures are mandatory in order to comply with Good Manufacturing Practices (GMP) and/or local regulation.

This means there is a solid reference base available against which to compare actual way of changing over as well as a reference time allocation (standard time).

In other, lesser regulation-constrained industries, such detailed procedures and capture of data might simply not exist or will be much “lighter”.

The procedure fallacy

Industrial engineering, quality assurance and management assume that when procedures are written and approved, they are the tables of the law that personnel will follow thoroughly.

This is not always the case. People on shopfloor are pretty much on their own as management is more likely sitting behind a screen, on a desk in a remote location. So there is room for doing things slightly differently than the procedure prescribes. Often it is about swapping some tasks’ order because it is more convenient or the people’s preference.

Procedures are written for the standard (perfect world) case and won’t help if something unexpected happens, e.g. some material is not available or late. As unexpected events, big or small are likely to happen, people in charge of changeover will have to adapt or wait for instructions.

Doing things in a different order and/or in a different way will impact the changeover duration. It might speed up or delay it. The problem is that people on shopfloor may not have sufficient knowledge/insight about the possible overall impact, like negative side effects, of their even so small changes.

Chances are that procedures will be followed globally but variations will be found in the details of execution. That’s one of the reasons for the differences between allocated time and actual changeover duration.

And chances are that actual duration exceeds the usually generous allocated time, reducing the productive capacity, overall effectiveness and efficiency.

The easy way to reduce changeover duration

Let’s be clear: the easiest way to reduce changeover duration in pharma industry will probably not be the most rewarding one in terms of production capacity recovery, but… it’s the easiest one.

It is easy because it is only about sticking to already agreed procedure and standard time, thus no risk assessment nor quality assurance validation required.

This approach is based on following assumptions:

  • Changeover procedure is sound
  • Allocated changeover time(Standard Time)  is reasonable, i.e. changeover can be done within allocated time
  • Excessive duration changeovers outnumber the shorter ones, hence there is a net capacity loss when summed up

Step 1: Gather data about changeover duration. If there was no data capture or what was in place does not serve your purpose, create a form and capture what data is necessary

Step 2: Start analyzing. Look for trends, correlations and if possible causation

Step 3: Display a graph with changeover durations compared to standard time. Update it real time. The simple display of the graph and the information to shopfloor teams that changeovers durations will be monitored is enough to improve the situation, because now there is some management’s attention on it.

Step 4: Go see, ask why and show respect (this is a Lean management mantra). In other words, go and spend some time observing reality on the shopfloor (gemba). Do not hesitate to ask why this or that to people, they are the Subject Matter Experts. While asking, do not lecture but listen truly, without judgement and without disturbing operations. Try to find the root causes of good AND poor performance.

Step 5: suggest or make the necessary changes (without compromising GMP/safety/quality rules) in order to reduce the duration. Chances are the improvement will require someone with the necessary authority and know-how to coordinate the whole changeover, from new material delivery to leftovers sending back to storage place, including paperwork and human resources allocation to roles. Stress the necessity not to exceed allocated time.

Step 6: when necessary, run problem solving kaizen events. Always have at least one operational personnel involved.

Step 7: Keep capturing data, analysing it and understand the causes of longer AND shorter changeovers. Changeovers that will take exactly the allocated time are highly suspect, but will be dealt later. Watch for trend: after a short while, changeovers should seldom exceed the Standard Time, although some accidents may happen.

Step 8: iterate to step 4.


The last time I used this soft approach (2015), we could recover the equivalent of one week of productive capacity within a period of 3 months, roughly 8% and about 170,000 additional units made available for sales. This was done at zero additional costs as all we needed was better organization, committed and refocused people and more management’s attention.

Of course this improvement was sustainable.

The changeovers done way under Standard Time were the proof of the excessive allocation and/or potential better way to proceed. With the relevant data, it was easy to convince management to have the procedure and Standard Time updated, giving the opportunity to improve further using SMED methodology.

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Why SMED is quick win in pharma – Episode II

Improving changeovers in pharma industry is a relatively quick and easy way to quick wins, faster and easier than usually assumed. This series tells you why.

Episode II: What Gain? Why?

In the previous Episode I explained the background of the lag of many traditional pharma makers in regards to Industry Best Practices (mainly Lean) and operational performance. I highlighted faster changeovers as leverage for recovering wasted capacity and improve performances.

In this Episode, I explain what gain can be expected and why.

A changeover – as the name tells – is changing over from one finishing series or production batch to the next one. This can mean completely resetting a production line, changing formats or “simply” change the batch and product-related information and paperwork.

Changeover in pharma is more thorough than in lesser regulated activities as most often the lines must be totally cleared from anything related to the finished batch, sometimes cleansed, inspected, approved, reset and restarted.

All steps are traced, lots of information and proofs are captured, paperwork filled. A great deal of these procedures are mandatory in order to comply with Good Manufacturing Practices (GMP) and/or local regulation.

Many (additional or redundant) steps and procedures are the maker’s choice, often as a countermeasure of former problems or deviations. They can also be a consequence of regulation misinterpretation or fear not to be compliant. I will not discuss challenging these self-imposed constraints in this post, but please note these self-inflicted constraints are improvement potentials per se.

The changeover procedure is therefore a relatively lengthy one, closely monitored (mostly afterwards and through paperwork though) by Quality Assurance (QA).

As the big fear is to leave something from previous batch contaminating the new one (it can be a physical part, an information leaflet, etc. in case of packaging) the changeover durations are generously allocated, based on the assumption the more time to perform the changeover, the fewer the risks.

I emphasize; allocated time is much more than strictly necessary to changeover and remaining compliant to procedures and requirements.

On top of that, nobody in management would dare stressing the operators to speed up a changeover in fear of making them forget something or ending up with a quality deviation.

The later leads shop floor operators to extend the changeover duration beyond already generous time allocation, wasting even more productive capacity.

The reasons for the drift may vary:

  • On the positive side, the constant fear to do something wrong or forget something.
  • On the negative side I noticed frequent lack of discipline: operators do not stick to procedures and management is not following-up and monitoring closely enough.

Over time the extended duration tends to be accepted as the new standard, planners including the actual changeover time in their schedule. Nobody questions, nobody challenges.

This is Parkinson’s law: a task will always take all allocated time. Extend the allocation and the task will never again finish before the new allocated time.


During changeover production is stopped, which means changeover is non-productive and reduces the production capacity. Changeovers tend to be more frequent as batch sizes shrink, so the challenge is to change over quick in order to minimize the production stop.

Changeover duration often exceed what is really necessary to perform a changeover in good conditions, without taking chances with safety nor quality nor GMP/regulatory compliance.

The main reasons for this are:

  • Generous time allocation
  • Lack of rigor / discipline
  • Immaturity regarding industrial best practices (Lean, SMED…)

Reducing changeovers durations is a way to recover recover wasted capacity and improve productivity by earning more output with the same resources.

Changeovers waste capacity

How Much?

I just explained why a significant part of wasted capacity can be recovered, but how much is this?

Detailed data are not always available to estimate the recovery potential, therefore a rule-of-thumb can come in handy.

Experience told me the “Lean rule-of-thirds”, which means about 30% (at least) of wastes can be turned into savings. This is relatively scale-invariant but of course much easier to achieve at the beginning of a continuous improvement journey than later, in a more mature state.

Thus, a changeover duration of 2 hours or 120mn could be reduced fairly easily to 80mn or 1hr20, especially if the drift from initial standard time happened.

Example in primary or secondary pharma packaging

Based on the same example as above, with a production rate of 60 units a minute (not very high-speed), forty minute capacity recovered means 40mn x 60 units = 2,400 additional units.

Changeovers may occur several times in a shift or at least in a 24 hour timeframe, multiplying the gain.

Now with highly automated, high-speed equipment, these additional gains may be far higher. Think about how fast tablets are packed into blisters or vials filled with liquid.

Big money

Even after patents have fallen into public domain, some original molecules still sell well, especially when customers are somewhat reluctant to use generics. Therefore if each additional unit yields a net profit of one monetary unit ($, €, £, etc.), which is not uncommon for ex-blockbusters, the additional profit is worth the improvement effort and the Return On Investment fairly quick.

Next Episode: How to and Why it works?

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Why SMED is quick win in pharma – Episode I

Improving changeovers in pharma industry is a relatively quick and easy way to quick wins, faster and easier than usually assumed. This series tells you why.

Episode I: The Background

There are roughly two cases to consider when addressing operational performance in pharma industry :

  1. the traditional (big) ones
  2. the tollers and generic makers.

Traditional pharma makers used* to be protected by their patents, granting them several years (about 20) of monopoly in order to payoff the huge initial investments in R&D.

*Many blockbusters drugs have lost patent protection, allowing generic makers to produce and sell them much cheaper. The dramatic drop in revenue for the original makers brought up the term “patent cliff”, a metaphor for the sudden fall of incomes. For manufacturers without other blockbusters to compensate, it is a sudden exposure to leaner and meaner competitors.

Tollers (or subcontractors) and generic makers are not investing in hazardous R&D and will not have patent protection in return. They manufacture for others or “copy” drugs after they fell into public domain and sell at much lower price.

On the operational side, during the blockbusters years – when huge incomes were secured – the (big) pharmas did not care much about capacity exploitation and efficiency. When more capacity was required, new equipment, lines or even whole factories were bought/built.

The payoff was such that it was faster to setup a new facility and run it at relatively low productivity level than to try to improve already installed capacities.

Falling off the cliff

The consequences years after, once most of the blockbusters fell off the patent cliff into public domain and related revenues plummeted, are:

  • huge overcapacities, often whole plants,
  • low productivity* compared to other industries,
  • lower maturity regarding industrial best practices (e.g. Lean Manufacturing, Lean Management),
  • no real sense of urgency** to improve in operations,
  • lack of agility,
  • a “sudden” and unprepared facing of leaner and meaner competitors, meaning ordinary competition,

*OEE (Overall Equipment Effectiveness), is often in the range 15-35% when in other industries it is rather in the 50-65% range.

**this lax posture of well paid pharma workers, even when “the platform is finally burning”, make them the “spoiled children” from the perspective of others, struggling in harsher competition with lesser compensation.

Tollers and generic makers must be lean and efficient at once because of their business model. They don’t have secured incomes nor a patent shielded-off competition. They compete with makers in low wage countries, with lower sales prices, hence lower profit per unit.

They nevertheless have to invest and carry costs related to regulatory compliance.

Given the circumstances these makers understood much sooner the importance to close the gap with Best Practices in more mature industries. This does not mean that generic makers are all best in class, but they had powerful and early drivers to push them up the performance ladder.

Now that the scene is set, what is the link to quick changeovers?

For traditional pharma experiencing normal competition, investments are no more that easy they once were. Given the many remaining overcapacities, when delivery is poor, the solution is no more installing additional capacity but make better use of the one installed.

When looking closer how the installed capacity is wasted, changeover duration most often pops up as a major cause. And as changeovers tend to multiply with the shortening of runs and smaller, more frequent batches, they become good candidates for capacity / performance improvement.

In the next Episode, we’ll see how much can be gained and why.

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Be prepared for success!

Some improvements in operations can be so effective that management should better be prepared for success, otherwise it can ultimately turn out as a splendid blunder.

This post is a kind of sequel of “Your next bottleneck is elsewhere (and in the future)” and based on a true experience in pharma industry.

The story takes place in a primary and secondary packaging workshop in an European (big) pharma plant. This workshop’s poor performance is allegedly the cause of stock outs and delays on the market. Given the increasing competition for this drug – now no more patent protected – it is mandatory to restore on-time deliveries to resist the generic makers’ aggressive attacks.

The packaging is the last step before shipping and unblocking it suddenly means releasing a lot of goods as well as draining the queueing material before packaging.

Therefore the warnings to management as we begun our assignment: your next bottleneck is elsewhere and be prepared for success.

Management did not take it seriously. I assume first because in pharma nothing goes really fast, second because the silo mentality still prevails. The upstream process (called manufacturing) was another department with a different team struggling to improve. Off limits to us.

Our efforts on packaging paid off soon: +50% output within a few weeks, leading to restock the dispatch centers in various countries and… drain the huge buffer in front the packaging.

Now with the capacity recovered and high spirit of the team, we wanted more material to keep supplying the market and possibly regain lost market shares.

Alas, these results caught management totally unprepared for success and blind to the new bottleneck, which was not manufacturing but.. sales!

It turned out that manufacturing was not very good to supply packaging indeed, but could do. What was most shocking was that there were no more orders! Not only did the drastic packaging performance increase drain the buffer of raw material, but it drained the order book as well.

Being used to years-long delayed supplies, management (?) nor sales teams paid attention to the warning and did not anticipate the exploitation of the recovered capacity.

As a result, not only did the drastic performance increase in packaging remain only a local success, it did not yield the huge revenues it could have and disappointed all highly energized packaging team members, now waiting idle for occupation.


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Trouble with manual data capture

Asking people to fill out forms in order to monitor performance, track a phenomenon or try to gather data for problem solving, too often leads to trouble when data is ultimately collected and analysed.

The case is about manual data capture into paper forms and logbooks on production lines. A precious source of information for a consultant like me. Potentially.

Alas, as I started to capture the precious bits of information from the paper forms into a spreadsheet, I soon realized how poorly the initial data were written:

Most of the forms were not thoroughly filled out, some boxes not filled, fields left blank, totals not calculated or wrong, dates not specified and a lot of bad handwriting leading to possible misinterpretation, among other liberties taken.

It seems obvious that the production operators do not understand the importance of the data they are supposed to capture nor the reasons for desired accuracy and completeness.

To them it’s probably a mere chore and not understanding the future use of the stuff they are supposed to write, they pay minimum attention to it.

It is also obvious that management is complacent about the situation and does not use the data, otherwise somebody else would have pointed out the mess before me, and hopefully acted upon.

Well, we can’t change the past and all data lost are definitely lost. The poorly input ones is all I could get, so I’ll had to make with what I had.

Thanks to a relatively important (I dare not write big) amount of data, flaws do not have too much impact, the big picture remains truthful. For me the importance is the big picture, not the accuracy of each single data point. (A takeaway from my exposure to big data!)

I noticed that most of the worse filled forms related to “special events”, when production suffered a breakdown, shortages and the like. These dots on the performance curve would anyhow been regarded as outliers and discarded for the sake of a more significant trend.

So it was not a big deal to disregard them from the beginning.

However, the pity was that no robust and deeper analysis could be conducted on these “special events”, not that unusual over a six-month period.

Some incomplete data could be restored indirectly, for example calculating durations from start and end time or conversely a missing timestamp could be restored from another date and duration for example. Sometimes, these kind of fixes introduced some uncertainty on the values, but again I was not after accuracy but trying to depict and understanding the big picture.

In order to be fair with personnel on the lines, I have to agree that some of the forms had poor design. A better one could have led to less misunderstanding or confusion. This acknowledged, the data reporting was not left to everybody’s choice, as it is mandatory by regulation.

Because to my great surprise and disappointment, this happened in pharma industry.

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