The fallacy of maturity assessments

Maturity assessments are a kind of qualitative audit during which the current “maturity” of an organization is compared to a maturity reference model and ranked accordingly to its score.

As explained in the wikipedia article about maturity model (https://en.wikipedia.org/wiki/Maturity_model), the implementation is either top-down or bottom-up, but from my experience it is mostly top-down. The desired maturity score is set by the corporate top management in its desire to bring the organization to a minimum level of maturity about… Lean, Supply Chain practices, project management, digital… you name it.

The maturity assessment is usually quite simple: a questionnaire guides the assessment, each maturity level being characterized by a set of requirements. It is close to an audit.

The outcome of such an assessment is usually a graphic summary displaying the maturity profile or a radar chart, comments about the weak points / poor scores and maybe some recommendation for improvement.

The gap between the current maturity and the desired maturity state is to then to be closed by an action plan or by following a prescribed roadmap.

What’s wrong with maturity models/assessments?

1 – The fallacy of maturity assessments

A maturity assessment would be ok if it would be considered for what it is: a maturity assessment. But the one-dimensional assessment is too often used as a two-dimensional tool by assuming that the level of operational performance is positively correlated to maturity.

In other words: the better the maturity, the better the operational performance.

Indeed, such a correlation can frequently be found, but correlation isn’t causation, which means that there is no mechanical nor systematic link between the maturity and performance level.

Even so the high level of maturity matches a high level of performance and vice-versa, there is no guarantee that performance will raise if maturity is raised.

Furthermore, studies have shown that there are exceptions and organizations with low maturity perform better than some high maturity ones. You may be interested reading my post How lean are you part 2, about Awareness / performance matrix about this subject.

Therefore the belief in the positive correlation between maturity and performance that makes it a kind of law is flawed or is nothing more than wishful thinking.

Many organizations boast about their high maturity, the number of kaizen events, number of workshops, number of colored belts, the number of training sessions or worker’s suggestions but there is nothing impressive to be noticed on the gemba.

Now I can understand why most managers and improvement champions like the sole maturity assessment:

  • it is much easier to do
  • the assessment items can be common to very different units with different activities
  • the general roadmap and global target are easy to set
  • maturity objectives are qualitative

On the other hand:

  • measuring overall performance that can be compared can be more tricky, especially in an organization with several different core businesses
  • it is annoying to admit that all efforts to raise maturity are not paying-off in terms of performance and painful to explain why

2 – The one-fits all maturity targets

Another problem with maturity assessment is that some corporations dictate a minimum maturity level regardless to local realities.

That’s how some subsidiaries doing well with regards to performance get bad maturity scores because they do not apply SMED (Single Minute Exchange of Die, an approach to reduce the changeover duration). The point is these subsidiaries have more or less continuous production processes with huge batch sizes that barely change. Why would they go for SMED when they don’t need it? The same case can be told with one-piece flow or heijunka (load levelling) enacted as a must do.

Others are scoring poor because they didn’t Value Stream Map (VSM) their processes. The fact is that those units had no problems a VSM could help to solve. The example list can go on and probably, dear reader, you have faced such situations yourself (leave your testimony in the comments..!)

3 – Doing it to be compliant, not because it makes sense

This third point is a corollary to the previous one. Because the objectives have been set at higher level and in order to be compliant, most unit manager will pay lip service to the dictated targets, get the scores good enough and be left alone once the assessment is done.

The local staff recognizes the nonsense of the demanded score, yet goes for the least effort and instead of fighting against the extra unnecessary work, chose to display what top management wants.

This the typical “tell me how you’re measured, I tell you how you behave” syndrome inducing counterproductive behaviors or practices.

While top management will be pleased with the scores enforcing its flawed belief, the local units managers did not embrace at all the practices, tools or methods prescribed. They only camouflaged the reality.

Wrapping up

Maturity assessment are not a bad thing per se, but their practicality and simplicity are often misused to assess more than just maturity (or awareness). This is most often misleading because of the false underlying assumptions and promoting wrong behaviors and practices.


PS: You may be interested to read Michel Baudin’s comments on his own blog about this post: http://michelbaudin.com/2017/08/22/the-fallacy-of-maturity-assessments-chris-hohmann/


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Doing wrong things much better

I sincerely believe that experimenting with Lean tools was key to spread Lean awareness, ease the principles and tools acceptance and contribute to the Lean popularity.

This was particularly okay in the “tools age”, when Lean was understood as a nice and handy toolbox.

Yet limited and non sustainable successes were hints that Lean could not be “just a toolbox”. Jim Womack, Dan Jones, John Shook and others decoded and explained Lean’s underlying philosophy, the craftsmanship making tools even more powerful, able to transform organizations, save companies and yield significant and sustainable results. So much more than tools.

Unfortunately very few people and organizations understood and embraced Lean Management. This leaves most of Lean tool users stick to their favorite tools, and like kids fascinated by the hammer still run around looking for nails to hit. Any nails.

Ironically the most “successful” organizations with Lean succeed to do wrong things much better.

“successful” here means seemingly good with implementing Lean tools, most probably scoring good on maturity or awareness checks, yet not getting full benefits of Lean in terms of true performance.

What do I mean with “doing wrong things much better”?

Take 5S. The workplaces are neat, clean, free from clutter and with lots of visual indications about where to put things, how to behave and so on. The janitor kit is top notch and the daily a day weekly cleaning schedule is displayed. This good condition is maintained for years now.
That’s all good, but 5S is not about cleaning.

What would be expected after achieving to maintain a clean and neat environment is to eliminate the need for cleaning. Reinforcing cleaning discipline and improving cleaning tools is just doing the wrong thing (keeping on cleaning) much better.

Example number two: rolling out SMED for quick changeovers on all machines seems to be a good practice as the changeovers are necessary evils, do not add value and drain some productive capacity.

Eliminating all the wastes during changeovers is therefore a Lean driven organization’s objective, right?

No it’s not.

Machines with excess capacity vs. customer demand are no good candidates for SMED. The excess capacity should be used to change over more frequently, allowing batch size and Lead Time reduction (this is Little’s law) as well as enhancing flexibility.

Further reducing the changeover duration on machines with excess capacity for the sake of rolling out SMED and “be Lean” will burn up limited resources without benefits for the system as a whole.

  • How many additional widgets can be sold thanks to a global SMED rollout?
  • How much Operating Expenses can be reduced?
  • How much inventories can be reduced?

If these questions are left without convincing answers, the system will not have any benefits but will incur the costs associated with the global SMED rollout.

Applying SMED on a machine with excess capacity is doing the wrong thing (changing over faster a machine that does not require it) much better (it is faster indeed, probably to let the machine idle a longer time).

Example number three: Value Stream Mapping

Its ability to reveal the wastes and obstacles to smooth and quick flow made Value Stream Mapping (VSM) a highly praised and favorite Lean tool. It is used by waste hunters to surface the hidden wastes and improvement points in any process. This is typically a beautiful and strong hammer looking for nails to hit.

Not so seldom do the Value Stream Mappers map a process in search for improvements without consideration of the process’ usefulness. Spending time and using up resources to analyse and improve a useless or very secondary process is nothing more than doing the wrong things much better.

So, what’s missing?

Two things are usually missing in Lean-tools savvy organizations that would bring them to a next level of performance: a system-wide understanding of causes-and-effects and focus.

A system-wide understanding of causes-and-effects means stopping to believe that the system-wide optimum is the sum of all local optima. in other words, getting rid of wastes everywhere will end up with a waste-free system.

Systems are complex, with many subsystems interacting dynamically. Local improvements will not automatically improve the system as a whole because many local optima will compete against each others. An improvement here can severe performance there.

Without understanding the system’s physics and how the subsystems operate, the local improvement initiatives are very likely to end up unnoticed, or worse counterproductive from a broader perspective.

Once the system’s physics are understood, it is key to identify the few leverage points where an action will have significant effect on the system as a whole. Once these leverage points identified, the limited resources must focus on them and not be wasted anywhere else.

How can it be done?

The answer is simple: Theory of Constraints.

Theory of Constraints (ToC) is a body of knowledge that is all about finding and leveraging the limiting factor within a system: the constraint.

Once the constraint identified, the Lean toolbox as well as Lean Management principles and even Six Sigma come in handy to leverage it and get more out of the system.

Used in a synergy cocktail ToC puts Lean on steroids and yields incredible results.

As a focusing “tool” ToC avoids burning up precious and limited resources on the wrong subjects and wrong spots, avoids “doing wrong thing much better!”.

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The man-machine system performance

When looking for performance improvement of a man-machine system, too often management puts emphasis onto machine or technology at large, ignoring the fact that humans associated with equipment, machines or technology form an interrelated system and consequently humans are the discriminating factor.

The fallacy of trusting the latest technology

There is a strong belief, backed up by vendor’s marketing, that the latest state-of-the-art high-tech equipment will bring a breakthrough in performance. This is welcome news for executives struggling to keep their organization up with competition and seeking a significant performance uplift.

Production managers, industrial engineers or system designers are big kids loving high-tech expensive toys, geeks of their own kind and dreaming to get the latest, biggest, fastest piece of equipment.

Once investment made, performance does not skyrocket though.

What happened?

Management blindness

Management ignored the human factor, i.e. people put in front or in charge of the new machine, the latest technology. An operator and his machine for instance are a system.

The overall performance of this system is determined by the human-machine pair, and guess what, the most variable and hardest to control is the human factor.

Unlike machines, humans have their moods, their worries, variable health and morale, private concerns and motivation issues. One day fine, the other day down.

Humans are not equal in competencies and skills. Some learn fast, some learn slow and some never really get it.

So what’s the point giving the latest top-notch technology to someone not competent or not motivated?

Yet this is most often what happens. Management assumes that the best of machines will make the difference, totally ignoring the influence of the people in charge.

The irresistible appeal of technology

Most decision makers and managers have some kind of hard-science background, got their degrees in engineering or business management. They were taught the robustness of math, the beauty of straightforward logic and to trust only facts and data.

When puzzled facing in real-life the highly variable and elusive nature of humans, they have a natural tendency to prefer hardware. This is something that can be put into equations and eventually controlled. This is what they are most familiar with or at least the most at ease with.

Humans are only trouble. No equation helps to understand their intrinsic drivers nor to reduce their variabilities. This is all about soft skills and psychological factors. Nothing for engineers and hard science-minded people.

Instead, they put a strong hope that the best and latest technology will trump the human factor, reduce it to a neglectable pain. But this never happens.

So again: what’s the point giving the latest top-notch technology to someone not competent or not motivated?

Leveraging performance

In order to improve a man-machine system, it is key to first have a look on the human factor, the most important one. Make sure competency is granted. If someone lacks the necessary competencies, performance is nothing than a matter of luck.

Beware of incompetent but highly motivated people though. In their desire to do well, they may have unknowingly potentially dangerous behaviours and/or take bad decisions. These motivated ones are likely to learn, do thing right but need training and guidance.

Not motivated incompetents are not likely to take any initiatives. They are the manager’s pain and burden and giving them better, faster machines won’t help. What’s worse with not motivated incompetents is passive aggressive behaviors that can lead to potentially dangerous situations as well.

Competent but not motivated people need and probably deserve management’s attention in order to get them into the winning quadrant of the competency-motivation matrix, aka skill-will matrix (top right).

There are the competent and motivated people who do their job effectively, often efficiently and without bothering anybody.

Competent Find a driver or a whip No worries
Incompetent Long way to go… Potentially dangerous good will
Not motivated Motivated

Competency-motivation matrix from a supervisor perspective

It is with these competent and motivated people that the limits of machines or technology can be found, as they will use them properly and purposely. Even when these performance limits are reached, it’s not certain that better planning and/or better organization cannot get more performance out of the system.

Think about quick changeovers and all capacity that can be regained applying SMED methodology, or rethinking maintenance in Total Productive Maintenance (TPM) style.

Wrapping up

When facing the challenge for improving performance, considering the way operations are done should be the first step. The second is to remember than investing in people is usually cheaper and more effective than investing in technology in first place, because a well utilized outdated machine will have better yield and be way cheaper than a poorly utilized state-of-the-art new one.

“Unfortunately” for tech-lovers who would prefer new “toys”, this investment in humans has to be a substantial part of their manager’s daily routine.


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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.

Trends

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

Correlations

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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Why No One Talks About TPM Anymore?

Total Productive Maintenance (TPM) was a big thing in the late 1980s, got lot of attention, tried to go from “maintenance” to “management” and finally faded out into oblivion.

This analysis is my own, you may respond in comments.

Total Productive Maintenance (TPM) originated in Japan with Nippondenso in the 1960s and is an evolution from Preventive Maintenance principles introduces to Japan from the USA.

TPM grow popular in the west with other “Japanese methods” in the 1980s when Toyota Production System (TPS) was not known nor the word “Lean” used for manufacturing.

TPM in a nutshell

TPM proposed a framework of 8 pillars aiming at getting the most out of lines, machines and equipment especially by reducing machine downtime and increasing machine availability. The 8 pillars are:

  • Autonomous Maintenance
  • Planned Maintenance
  • Quality Maintenance
  • Focused Improvement
  • Early Equipment Maintenance
  • Education and Training
  • Health, Safety & Environment
  • TPM in Office

Their naming and order (may) vary according to sources. “Six big losses” were identified as obstacles to better machine effectiveness:

  1. Breakdown losses
  2. Setup and adjustment losses
  3. Idling and minor stoppage losses
  4. Speed losses
  5. Quality defects and rework losses
  6. Startup / yield losses

These six losses’ measurement were combined into a single KPI: Overall Equipment Effectiveness (OEE), still very popular and widespread in industry.

TPM was striving to improve OEE by developing the personnel’s maturity about the 8 pillars and reducing the 6 losses.

Early years till today

TPM brought tremendous improvements in operations and work conditions. Despite the machine-orientation TPM had also influence on operations management and at some point was supposed to translate into broader application, attempting to go Total Productive Management.

Alas, TPM was strongly machine shop and shopfloor connoted and never got much attention outside of these playgrounds. Furthermore, Lean became highly fashionable and easier to transpose in any activity, thus got all attention.

In my humble opinion TPM was bound to fail from the moment it was presented as a maturity-driven approach to improvement, requiring organizations to go through a multiyear program, step by step implementing every pillar. The pitch was that eventually performance will raise once personnel is trained and gets experienced with TPM. After some years of continuous effort, the organization will be mature enough to apply for one of the PM awards awarded by the Japan Institute of Plant Maintenance (JIPM) or local representative body.

Of course, the journey toward maturity would require consultants’ support, making it a long, costly and still risky one. With the competitive challenges getting tougher, few CEOs would commit to such a slow approach with questionable ROI, very much machine-effectiveness oriented.

Nowadays OEE, quick changeover and setup techniques like Single Minute Exchange of Die (SMED) are seen as part of the Lean Body of Knowledge and TPM very seldom mentioned.


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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.

Results

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.

Wrapping-up

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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Recover wasted capacity with SMED

SMED is a systematic approach to quick changeovers in order to minimize machine downtime. It is welcome to recover wasted capacity on a bottleneck resource.

  • A bottleneck is a resource with a capacity, in average, lower or equal to customer’s demand. A true bottleneck runs 24 hours 7 days a week and still cannot supply what is required.
  • A bottleneck is usually very expensive and/or difficult to get. If this is not the case, the solution is obvious: buy additional capacity!

More about bottlenecks.

As a bottleneck is the limiting factor hindering getting more out of the process, hence making money in case of for-profit organizations, and as the bottleneck resource is not easy to duplicate, the best option is to get more through it.

Analyzing the capacity

Almost every resource has a maximum capacity that can seldom be utilized, because of some reasons that waste some of it. The resource’s capacity can be can be depicted by a lab beaker as shown below, with a maximum capacity of 100 (%).

Several events during production will hinder the utilization of the full capacity:

  • Machine downtime
  • Lack of supplies
  • Human rest time
  • Etc.

These events are wasting the precious capacity, just as if our beaker was leaking. Changeovers usually are an important cause of capacity loss.

Reducing drastically the changeover duration with SMED is like fixing the leak and recovering a part of the wasted capacity.

In case of a bottleneck this is very important because the recovered capacity is converted in additional sales without (or delaying) investment in scarce or highly expensive additional resource.


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SMED explained while doing laundry – part two

In part one I explained SMED is a systematic approach to quick changeovers in order to minimize machine downtime.

In order to explain what SMED is good for to non-specialists, I used the laundry example, in which the mundane washing machine is a very precious resource and it should wash (add value) as much as possible in 24 hours a day. Conversely, such a precious resource should be protected from any downtime, especially when changing (laundry) batch.

After describing a typical lengthy and far from efficient changeover, this second episode will show how to drastically improve the performance by reducing the changeover duration.

Distinguish internal and external setup

The very first thing to understand when striving to reduce changeover duration is to distinguish internal and external setup operations.

Internal Setup (IS) operations are done within the machine or so close to it that safety requires to have it stopped to achieve them. Typically changing a tool fixed on a machine or adjustment inside the machine.

External Setup (ES) operations either have nothing to do with the machine itself, like filling a tracking record form or bringing raw material from warehouse near to the machine, either can be achieved without endangering oneself. ES operation can be achieved while the machine keeps running.

Many times, some operations and task are first seen as External Setups, but after closer analysis are totally unnecessary. This happens often when countermeasures to some old problem have not been suppressed after the problem was settled.

Internal and external setup with laundry

Back to our laundry problem; what are internal and external setups?

Considering the chart of a typical changeover as displayed hereunder,


The time wasted until “operator” noticed the end of washing cycle is no Internal Setup nor is it External, it’s just plain waste and must not happen.

“Searching for empty clean basket” to receive the washed laundry and “searching for detergent” and “sorting laundry” should not happen at the expense of machine time. These operations should be included in preparation prior to changeover, which is External Setup.

If work environment, here laundry room, is well organized and tidy, searching for items like basket and detergent should be unnecessary. Even so in preparation – External Setup – this time is not impeding the washing machine performance, it can be saved for more valuable occupation.

The changeover duration after separating ES and IS should be drastically reduced to almost bare minimum. Almost, because the remaining IS operation are likely to be optimized (reduced).

The changeover process after improvement should look like this:

This simplified laundry example is very similar to the changeover duration optimization done in production lines, machining cells, etc.

Important notice

When working to reduce the machine downtime during changeovers, most of people try to improve some technical aspect like fittings, fastenings, jigs and so on. As this fictional example shows, most of the improvement potential is found in organization.


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