Press clippings

Oct 27 2023

Is SPC Still Relevant? | D.C. Fair & S. Hindle | Quality Digest

“Today’s manufacturing systems have become more automated, data-driven, and sophisticated than ever before. Visit any modern shop floor and you’ll find a plethora of IT systems, HMIs, PLC data streams, machine controllers, engineering support, and other digital initiatives, all vying to improve manufacturing quality and efficiencies.

That begs these questions: With all this technology, is statistical process control (SPC) still relevant? Is SPC even needed anymore? Some believe manufacturing sophistication trumps SPC technologies that were invented 100 years ago. But is that true? We the authors believe that SPC is indeed relevant today and can be a vitally important aid to manufacturing.”

Source: QualityDigest

Michel Baudin‘s comments: Of the changes affecting the relevance of 100-year-old SPC to today’s manufacturing, the authors only consider one: the availability of much larger data sets. They do not discuss neither how processes in electronics, pharmaceuticals, aerospace, automotive, and many other industries are more complex today than 100 years ago. They don’t think it relevant that thinking in Probability and Statistics has evolved into today’s Data Science in the past 100 years, producing many new tools that leverage today’s IT and OT.

Points about SPC

Of the eight points they make in the article, only two are truly about SPC:

  • Point 1 is that you can deploy it more easily with today’s IT than with paper and pencils. SPC courses, to this day, teach techniques developed specifically to work around the limitations of the paper-and-pencil age. Paperless SPC is a horseless carriage.
  • Point 7 is about “How to set up your processes more optimally to get the most out of them.” It raises the question of who uses SPC for this. Biotechnology doesn’t, and relies instead exclusively on genetic engineering. Neither do semiconductors. In the wafer process, they use Run2Run controls  to tweak the next operation based on the outcome of the present one.

Other Points

Point 2 puts forward SPC as a tool to manage “high-volume data streams,” which is interesting,  given that Shewhart and his followers designed SPC specifically to work with a trickle of manually collected data. The keywords you encounter in the context of high data volume in manufacturing include in-memory databases, data warehouses, data lakes, data lakehouses, and not SPC.

Point 3 presents SPC as a means of breaking down data silos. Data silos emerge when different groups in an organization use different information models. They use different names for the same data objects, store them in different formats, and represent their relationships differently. These are difficult issues to solve, and you won’t find any help on this in the SPC literature.

Point 4 is about “Repurposing SPC data.” If it means putting the technical data on processes collected on the shopfloor to all sorts of uses, it is a worthwhile effort. If you want guidance on how to do it, you will find it under data mining, not SPC.

Point 5, “measuring data bias and inaccuracies” is about data cleaning, Again, good luck finding guidance on how to do it in the SPC literature.

About Point 6, “instantly communicate shop floor issues across the enterprise,” other keywords, like Andon boards, come to mind.

Point 8 presents SPC as the “voice of the process.” Processes, however, speak in many other, more immediate ways than SPC.

Conclusion

The article concludes by saying: “your organization can benefit greatly by modernizing a 100-year old technology: statistical process control.” I couldn’t agree more.

#SPC, #statisticalprocesscontrol

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ElephantInTheRoom

Sep 13 2023

HR: The elephant in the room for psychological safety

In his latest column in Quality Digest, Mark Graban wrote the following about psychological safety:

“How do leaders cultivate the conditions in which employees feel safe enough to speak up and participate in continuous improvement? Clark argues that leaders need to: 1) model vulnerable acts; and 2) reward vulnerable acts.

For example, leaders must model the key behaviors they want to see, such as admitting that things aren’t perfect. Leaders can also model helpful behaviors by sharing an idea along with the words, “I might not be completely right, so let’s test our idea on a small scale and see.” When leaders model these vulnerable acts, some employees might choose to follow their lead.

When a person chooses to speak up, it isn’t a matter of courage or character; it’s a function of culture. The level of safety that’s felt by an employee is the end result of all of the interactions they’ve had with leaders and colleagues, past and present.”

Source: Graban, M. (2023) Stop Spending Money on Problem-Solving Training, Focus on Psychological Safety Instead Quality Digest

Continue reading…

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Oct 4 2022

Strange Statements About Probability Models | Don Wheeler | Quality Digest

In his latest column in Quality Digest, Don Wheeler wrote the following blanket statements, free of any caveat:

  1. “Probability models are built on the assumption that the data can be thought of as observations from a set of random variables that are independent and identically distributed.”
  2. “In the end, which probability model you may fit to your data hardly matters. It is an exercise that serves no practical purpose.”

Source: Wheeler, D. (2022) Converting Capabilities, What difference does the probability model make? Quality Digest

Michel Baudin‘s comments:

Not all models assume i.i.d. variables

Wheeler’s first statement might have applied 100 years ago. Today, however, there are many models in probability that are not based on the assumption that data are “observations from a set of random variables that are independent and identically distributed”:

  • ARIMA models for time series are used, for example in forecasting beer sales.
  • Epidemiologists use models that assume existing infections cause new ones. Therefore counts for successive periods are not independent.
  • The spatial data analysis tools used in mining and oil exploration assume that an observation at any point informs you about its neighborhood. The analysts don’t assume that observations at different points are independent.
  • The probability models used to locate a wreck on the ocean floor, find a needle in a haystack, and other similar search problems have nothing to do with series of independent and identically distributed observations.

Probability Models Are Useful

In his second statement, Wheeler seems determined to deter engineers and managers from studying probability. If a prominent statistician tells them it serves no useful purpose, why bother? It is particularly odd when you consider that Wheeler’s beloved XmR/Process Behavior charts use control limits based on the model of observations as the sum of a constant and a Gaussian white noise.

Probability models have many useful purposes.  They can keep from pursuing special causes for mere fluctuations and help you find root causes of actual problems. They also help you plan your supply chain and dimension your production lines.

Histograms are Old-Hat; Use KDE Instead

As Wheeler also says, “Many people have been taught that the first step in the statistical inquisition of their data is to fit some probability model to the histogram.” It’s time to learn something new, that takes advantage of IT developments since Karl Pearson invented the histogram in 1891.

Fitting models to a sample of 250 points based on a histogram is old-hat. A small dataset today is more 30,000 points, and you visualize its distribution with kernel density estimation(KDE), not histograms.

#donwheeler, #probability, #quality

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satellite-image-National-Oceanic-and-Atmospheric-Administration-August-28-2005.jpeg

Dec 25 2021

Always the Hurricanes Blowing

Atlantic hurricanes hurt people and destroy property around the Gulf of Mexico every year. Whether climate change is increasing their frequency is a serious question. Don Wheeler just had a column on this subject in the latest Quality Digest. It’s about Torturing the Data. He argues that we should be careful about not force-fitting models to arrive at pre-ordained conclusions.

His way of not torturing the history of hurricanes in the Atlantic is plotting yearly counts on, what else,  an XmR chart. It’s just as he would for hole diameters in metal plates coming off a production line in 1945. Hurricanes and holes in metal plates, however, have different backstories.

Continue reading…

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Oct 27 2021

Culture Change | MIT Sloan Management Review | Rose Hollister et al.

How the authors see it: “We define culture as a shared set of values (what we care about), beliefs (what we believe to be true), and norms of behavior (how we do things). […] We developed a set of culture transformation principles that maximize the likelihood of success:

  • Recognize that responsibility for culture can’t be delegated.
  • Start with the “why.”
  • Define the target cultural values and behaviors.
  • Engage and get input.
  • Build a bridge to the future desired culture.
  • Build a culture road map.
  • Reinforce the desired culture in all organizational systems.
  • Rapidly reward the emerging culture. “

Source: Hollister, R., Tecosky, K., Watkins, M, & Wolpert, C. (2021) Why Every Executive Should Be Focusing on Culture Change Now, MIT Sloan Management Review, Reprint 63137

Michel Baudin‘s comments: I define culture more simply as “the way we do things around here.” You don’t change it by making it the goal of a change program. To do it, first, you change the work, and then employees’ perceptions follow.

Redesign shop floor layouts to facilitate flow, develop multiskilled operators, hire employees for whole careers and retain them through thick and thin, engage them in solving problems,…

Over time, employees realize it’s not idle talk about lofty goals but concrete changes to their daily experience, including teamwork. Then it becomes “the way we do things around here,” in other words, a new culture.

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Jul 31 2021

Follett in 1924: Use Operators’ Knowledge

Mary Parker Follett

 

Almost 100 years ago, Mary Parker Follett wrote: “And our aim in the […] organization of industry should be […] so to organize the plant that the workmen’s experience can be added to that of the expert; we must see just where their experience will be a plus matter, and we must plan to have the workmen learn more and more of the industry as a whole.” 

Source: Follett, M. P. (2013 reprint). Creative Experience. United States: Martino Fine Books. (p. 20)

Michel Baudin‘s comments: Today, we would say “manufacturing” rather than “industry,” and “operator” rather than “workmen.” This is the earliest text I have seen that recommends engaging shop floor operators in improvement activities and training them to understand the broader picture. 

Taylor wanted to control operators so that they couldn’t collude to curtail output. The Gilbreths wanted to make their work easier. Follett wants to tap into their knowledge and combine it with the experts’ in order to achieve better outcomes. And she also believes in their ability to learn.

In these few words, she showed more respect for the humanity of the operators than I recall seeing from her immediate precursors and contemporaries. 

 

#maryparkerfollett, #respectforhumanity, #traceyrichardson

 

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