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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3-color system for Goal Trees

In this 5 minute excerpt from the Logical Thinking Process (LTP) Alumni reunion with Bill Dettmer, June 2016 in Paris, France, I explain my 3-color system for assessing the current reality with a Goal Tree.

The 3-color system is a visual management tool to assess the organization’s readyness to achieve its Goal and shows where to act in priority.

You’ll find several articles on this topic here on my blog, for instance:


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Does Value Stream Mapping apply to Product Development?

Value Stream Mapping (VSM) is a great tool to map processes. It started in manufacturing where it is used to understand physical and information flows and quickly spread to administrative processes. It is even used in hospitals.

As Product Development is a process, so yes VSM can be used.

However, development activities have some specificities compared to manufacturing which require to adapt VSM to Development and also bring some limitations to VSM used in Development.

>Lisez-moi en français

The first limitation is similar to manufacturing if the activity is high mix / low volume. In such a case, the specificities may outweigh the commonalities, thus drastically reduce the interest of VSM as the time spent to map the process vs. valuable informations to get from the map isn’t worth it.

If this isn’t the case, and if the Product Development process is underperforming and needs improvement, VSM “manufacturing-style” can be used to map and analyse the development process despite other limitations. It is “good enough” to surface the biggest obstacles to better performance.

Having used VSM to describe and analyze an automotive equipment maker product development process, I could identify improvements leading to a potential 30 to 40 % Lead Time reduction depending of the nature of the project. This is consistent with what I call the Lean rule of thirds, i.e. reducing wastes or improving performance 30%.

Later on, with a more mature Product Development process this type of VSM may show its limitations.

VSM pitfalls and limitations in Development

There are many differences between manufacturing and development. For instance the definition of “value-added” is relatively easy in manufacturing while more elusive in development. Takt time is a key concept for production but does not make sense in development*. Loops are wastes in manufacturing but iterations are valuable in development.

*Takt time in manufacturing is the rate of customers’ demand. In development takt time can be the rate of new projects or product launches decided by the company.

Concurrent activities are seldom in manufacturing but common in Lean Development, and so on.

Therefore the transposition of Lean Manufacturing methods and tools is possible to some extend but with great care and adaptation. One warning about this is to be found in “The Lean Machine” Productivity Press. pp. 131–132:

Key learning about the difference between TPS and LPD is summarized in the advice Jim Womack gives Harley Davidson’s Dantaar Oosterwal; “Don’t try to bring lean manufacturing upstream to product development. The application of Lean in product development and manufacturing are different. Some aspects may look similar, but they are not! Be leary of an expert with experience in lean manufacturing that claims to know product development”.

On the other hand, Allen Ward and Durward Sobek recommend to “learn from Lean Manufacturing to improve labs and prototype shops”, in Lean Product and Process Development, Lean Institute Inc, 2014 second edition p.42.

Other resources about VSM for Product Development exist. Here are only few chosen examples:

Ronald Mascitelli discusses the usage of VSM in his book “Mastering Lean Product Development”, Technology Perspectives 2011, pp. 187-190.

There is a paper of interest by Darwish, Haque, Shehab, and Al-Ashaab, “Value stream mapping and analysis of product development (engineering) processes”  that can be downloaded here:  https://www.researchgate.net/publication/272565743

Finally, the Lean Aerospace Initiative (LAI, MIT) proposed the Product Development Value Stream Mapping (PDVSM) specifically designed for Product Development. The Manual version 1.0 (Sept. 2005) can be downloaded for free from several sources, including MIT:

https://dspace.mit.edu/bitstream/handle/1721.1/83453/PDM_1003_McMan_PDVSM.pdf?sequence=1

Wrapping-up

Value Stream Mapping does apply to Product Development with limitations in mind and/or adaptation to the specificities of development activities. Before rushing to map such a process, give yourself time to consider if the time invested will really be worth it, especially if the process is not likely to be common to many new developments.

VSM is great but is only one tool among others. The value of the analysis does not come from the map but from the “brain juice” the analyst(s) throw in to sift out improvement potential and identify issues and obstacles to overcome.

Feel free to comment and share your thoughts and experience. If you liked this post, share it!


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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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