Is SPC Obsolete? (Revisited)

Six years ago, one of the first posts in this blog — Is SPC Obsolete? — started a spirited discussion with 122 comments. Reflecting on it, however, I find that the participants, including myself, missed the mark in many ways:

  1. My own post and comments were too long on what is wrong with SPC, as taught to this day, and too short on alternatives. Here, I am attempting to remedy this by presenting two techniques, induction trees and naive Bayes, that I think should be taught as part of anything reasonably called statistical process control. I conclude with what I think are the cultural reasons why they are ignored.
  2. The discussions were too narrowly focused on control charts. While the Wikipedia article on SPC is only about control charts, other authors, like Douglas Montgomery or Jack B. Revelle, see it as including other tools, such scatterplots, Pareto charts, and histograms, topics that none of the discussion participants said anything about. Even among control charts, there was undue emphasis on just one kind, the XmR chart, that Don Wheeler thinks is all you need to understand variation.
  3. Many of the contributors resorted to the argument of authority, saying that an approach must be right because of who said so, as opposed to what it says. With all due respect to Shewhart, Deming, and Juran, we are not going to solve today’s quality problems by parsing their words. If they were still around, perhaps they would chime in and exhort quality professionals to apply their own judgment instead.

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There Is More To Data Than Just Numbers

Don Wheeler’s Understanding Variation starts with a chapter entitled “Data are random and miscellaneous” that contains no discussion of any part of its title. Implicit in Wheeler’s book, however, is the view that data consists of tables of numbers, representing either measured variables — lengths, weights, densities,… — or event occurrence counts — defective units, defects, machine failures,…

Many times, I have quoted computer scientist Don Knuth on this subject, saying that data is “the stuff that’s input or output,” meaning anything that can be read or written, and it includes much more than tables of numbers. The data we work with today includes, for example, the following:

  • Unstructured text, like 25,000 incident reports written by maintenance techs all over the world in their versions of English about problems with jet engines, or thousands of product reviews posted by consumers on e-commerce sites
  • Images, like photographs of visual defects on products, or electron-microscope images of integrated circuits.
  • Videos recordings of operations.

Analyzing data about a manufacturing process today means extracting information from all sources. The state of the art, based on automatic data acquisition and databases includes analytical techniques that were unthinkable in Shewhart’s day, known under the labels of data science, data mining or machine learning.

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Takt Time Concept Still Misunderstood | LinkedIn Discussion

Mark DeLuzio

“What does TAKT Time mean to you and how have you used it to better your business?”



Sourced through LinkedIn

Michel Baudin‘s comments: It’s 2017, and this question should be unnecessary, but the responses reveal that confusion about this concept is still widespread. As I belong to The Takt Times Group, I felt compelled to participate; at the same time, I didn’t want to repeat everything else I have written on the topic.

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Productivity, Automation, And Robots

Journalists and other authors who should know better routinely conflate productivity increase with automation and automation with the introduction of robots. “Productivity” covers a set of performance metrics that are increased by a variety of methods, many of which do not involve automation. Automation sometimes increases productivity, but not always. Finally, most of the time, automation does not involve robots. At last Tuesday’s Palo Alto Lean Coffee, I asked Tesla’s Omar Guerrero and Genentech’s Curtis Anderson for examples of changes that had increased productivity in their organizations.

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Using videos to improve operations | Part 8 – Video Repositories

The seven articles I posted four years ago on the art of using videos to improve operations included no pointers on what to do with the videos once you have them. This concern may seem premature in a manufacturing world where video recordings of operations are still rare, process instructions are in dusty binders and obsolete, customization specs come in the form of all-uppercase text from a 30-year old dot matrix printer with a worn-out ribbon, engineering project records reside in individual employees’ laptops, and management expects IT issues to be resolved by implementing a new, all-in-one ERP system.

In everyday life, on the other hand, videos are already in common use to explain how to pry loose a stuck garbage disposal, remove a door lock, change a special bulb in car headlight, or neatly cut a mango into cubes. You just describe your problem in a Youtube search, and up come videos usually shot and narrated by handy amateurs, and sometimes pros. It is particularly useful for tasks involving motion with key points that are difficult to explain with words or still images. The manufacturing world will eventually catch up.

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Acceptance Sampling In The Age Of Low PPM Defectives

Today, some automotive parts manufacturers are able to deliver one million consecutive units without a single defective, and pondering quality management practices appropriate for this level of performance is not idle speculation. Of course, it is only achieved by outstanding suppliers using mature processes in mature industries. You cannot expect it during new product introduction or in high-technology industries where, if your processes are mature, your products are obsolete.

While still taught as part of the quality curriculum, acceptance sampling has been criticized by authors like W. E. Deming and is not part of the Lean approach to quality. For qualified items from suppliers you trust, you accept shipments with no inspection; for new items or suppliers you do not trust, you inspect 100% of incoming units until the situation improves. Let us examine both what the math tells us about this and possible management actions, with the help of 21st century IT.

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Saturation In Manufacturing Versus Service

In Capacity Planning For 1st Responders, we considered the problem of dimensioning a group so that there is at least one member available when needed. Not all service groups, however, are expected to respond immediately to all customers. Most, from supermarket check stands and airport check-in counters to clinics for non-emergency health care, allow some amount of queueing, giving rise to the question of how long the queues become when the servers get busy.

Patients waiting in Emergency Room

At one point in his latest book, Andy and Me And The Hospital, Pascal Dennis writes that the average number of patients in an emergency room is inversely proportional to the availability of the doctors. The busier the doctors are, the more dramatic the effect. For example, if they go from being busy 98% of the time to 99%, their availability drop by half from 2% to 1%, and the mean number of patients doubles. Conversely, any improvement in emergency room procedures that, to provide the same service, reduces the doctors’ utilization from 99% to 98%, which cuts the mean number of patients —  and their mean waiting time — in half.

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Variability, Randomness, And Uncertainty in Operations

This elaborates on the topics of randomness versus uncertainty that I briefly touched on in a prior post. Always skittish about using dreaded words like “probability” or “randomness,” writers on manufacturing or service operations, even Deming, prefer to use “variability” or “variation” for the way both demand and performance change over time, but it doesn’t mean the same thing. For example, a hotel room that goes for $100/night in November through March and $200/night from April to October has a price that is variable but not random. The rates are published, and you know them ahead of time.

By contrast, to a passenger, the airfare from San Francisco to Chicago is not only variable but random. The airlines change tens of thousands of fares every day in ways you discover when you book a flight. Based on having flown this route four times in the past 12 months, however, you expect the fare to be in the range of $400 to $800, with $600 as the most likely. The information you have is not complete enough for you to know what the price will be but it does enable you to have a confidence interval for it.

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Probability For Professionals

dice In a previous post, I pointed out that manufacturing professionals’ eyes glaze over when they hear the word “probability.” Even outside manufacturing, most professionals’ idea of probability is that, if you throw a die, you have one chance in six of getting an ace.  2000 years ago, Claudius wrote a book on how to win at dice but the field of inquiry has broadened since, producing results that affect business, technology, science, politics, and everyday life.

In the age of big data, all professionals would benefit from digging deeper and becoming, at least, savvy recipients of probabilistic arguments prepared by others. The analysts themselves need a deeper understanding than their audience. With the software available today in the broad categories of data science or machine learning, however, they don’t need to master 1,000 pages of math in order to apply probability theory, any more than you need to understand the mechanics of gearboxes to drive a car.

It wasn’t the case in earlier decades, when you needed to learn the math and implement it in your own code. Not only is it now unnecessary, but many new tools have been added to the kit. You still need to learn what the math doesn’t tell you: which tools to apply, when and how, in order to solve your actual problems. It’s no longer about computing, but about figuring out what to compute and acting on the results.

Following are a few examples that illustrate these ideas, and pointers on concepts I have personally found most enlightening on this subject. There is more to come, if there is popular demand.

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If Talk Of Probability Makes Your Eyes Glaze Over…

Few terms cause manufacturing professionals’ eyes to glaze over like “probability.” They perceive it as a complicated theory without much relevance to their work. It is nowhere to be found in the Japanese literature on production systems and supply chains, or in the American literature on Lean. Among influential American thinkers on manufacturing, Deming was the only one to focus on it, albeit implicitly, when he made “Knowledge of Variation” one of the four components of his System of Profound Knowledge (SoPK).

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