More about Toyota and SPC

The post on Does Toyota Use SPC? elicited many comments on LinkedIn. Some suggested that it was scoping SPC too narrowly when contrasting it with Toyota’s approach. In fact, SPC as referenced in the post is the body of knowledge described in the American literature on quality and taught in professional courses.

As to why Toyota is not using SPC, the answer is simple: SPC is about process capability and the quality problems Toyota addresses in 2020 are not due to lack of process capability. In industries that lack process capability, modern data science outguns the old SPC toolkit but that is a different discussion. The most vital question raised in the comments was why we have been not learning Toyota’s approach to quality. In the past 30 years, American industry has learned “Lean Six Sigma” instead.

The comments also enriched the public sources of information cited in the post with corroboration by current and former employees of Toyota.

Scope of SPC

Inside an organization, members can use any label that suits their purpose to describe their activities. In an open forum, on the other hand, participants must agree on a common meaning for any discussion to be possible. Otherwise, it’s the tower of Babel and they all talk past each other.

In the Quality Literature

If you search Google Images for “SPC,” you see Control Charts. In the literature, Control Charts make up the bulk of SPC:

  • In Douglas Montgomery’s Introduction to Statistical Quality Control, Part II, about SPC, covers only Control Charts, in 350 pages. DOE is a separate topic and the subject of Part III.
  • In Juran’s Quality Control Handbook, 5th Edition, Section 45 about SPC covers exclusively Control Charts.
  • Tom Pyzdek, in The Handbook for Quality Management, covers this ground in Chapter 9, Quantifying Process Variation using 55 pages but does not call it SPC.

A process capability study is Step 1 in setting up Control Charts, and the C_{p} and C_{p_k} are ratios based on control limits and tolerances. It’s all about the same model of process behavior. I see other items under the same heading but you are welcome to view them differently.

If a manufacturer doesn’t use this specific body of knowledge, for the purpose of this discussion, it’s not using SPC. It may well be using data science to develop and stabilize processes. At a sufficiently high level of abstraction, an abacus and a computer are both digital calculation devices but you don’t see abacus training offered as part of Industry 4.0.

SPC as Taught

SPC is taught as a specific set of tools. Students pay to get trained to use them and receive a belt as certification from various organizations. Sign up for a class called “SPC” and you are not going to learn in general about eliminating variation. You are going to be indoctrinated into specific and ancient statistical techniques, including Shewhart charts, Western Electric rules, Cusum charts, and XmR charts. The syllabus may even include scatterplots and histograms, at the level taught in Middle School.

You will not learn any of the tools and techniques a company like Google requires from data scientists, some of which are relevant to process control.

The American Society for Quality (ASQ) has an extensive course catalog that includes five entries with “SPC” in the title. SPC Basics is all about Control Charts. Advanced SPC adds the Normal Distribution, and Design Of Experiments (DOE).

The Union of Japanese Scientists and Engineers (JUSE), known locally as Nikkagiren (日科技連) is the organization that hosted Deming and Juran in the 1950s and awards the Deming Prize. In Japanese, they offer many courses on quality, but none focused on SPC. They have separate courses on TQM, QC-circles, Problem-Solving and Statistical Quality Control (SQC). Under SQC, SPC appears part of their course on the 7 tools of QC. Their other SQC courses cover testing and estimation, correlation and regression, design of experiments, multivariate analysis, and Taguchi methods.

Quality versus SPC

One participant in the discussion, Tony Burns, cites a post from his own blog that opens with “The control chart is at the heart of the very definition of quality.” With all due respect, the control chart is just a tool. Manufacturers have pursued quality by other means since before Shewhart invented it and today as well.

We can summarize Juran’s perspective by saying that quality is the agreement of reality with expectancy. This koan-like statement can be unpacked in different ways, as expectancy can be interpreted both as customer expectations and as expected value from probability theory. Juran sets a goal but does not mandate any particular tool to reach it.

If not SPC, then what?

If you lack process capability, you need to get it. You may achieve it just based on your engineers’ experience and knowledge of physics and chemistry. This is the way it was done pre-SPC, and many companies still do it, including successful ones. The promise of data science is that you will do it faster if you know how to collect and analyze data, and communicate about the results, even if you take into consideration the learning required.

Changes since 1950

In 1950, SPC would have provided the best available tools for this. Today, data science does, encompassing data acquisition from sensors, data storage and retrieval through a variety of databases, analysis with a variety of tools, and communication via infographics and other methods…

Shewhart et al. developed SPC around the limitations of the information technology of the 1920s, which no longer apply. The boosters of old-school SPC like to point out failures of machine control systems to eliminate the need to take measurements and tally attributes on workpieces after they complete each operation.

Of course, you can shoot yourself in the foot with fancy toys. What matters is what you can do when using them wisely. Unfortunately, all the time you spend learning old methods is no longer available to become effective with new ones.

Compared with the pre-WWW-II era technology that SPC was developed for, a few things have changed. Sensors, with data acquisition systems give you more and better data than manual collection, including photographs and videos. Cyber-physical systems, for example in additive manufacturing of metal parts, run a process simulation along with the actual process and respond when the two diverge. In semiconductors, Run2Run controls tweak the next operation based on the present one.

SPC versus TWI

It’s not just a matter of vintage. Other concepts from the same era as SPC, like TWI, have better stood the passing of time. TWI is about human behavior and cognition, which have not changed. You can use a smartphone app in lieu of 3×5 cards, and instructional videos but the concept remains the same.

Quality Strategy and Why Toyota doesn’t need SPC

Shingo’s rejection of all statistical methods, together with Mikel Harry’s embrace of them got me thinking about why they held such diametrically opposed views, which they both expressed as context-free absolutes. In reality, they worked in different contexts, Shingo’s in mechanical industries and Harry’s in electronics. Once you clarify this, they can both be right.

Toyota doesn’t need these techniques because the automotive industry is so mature that its quality problems are not due to process capability but to discrete events like machine failures and to human errors, like wrong part picks. One-piece flow lines let you promptly detect and respond to the discrete events and poka-yoke/mistake-proofing addresses human errors. This is explained in the first article in the “For the Curious” section of the blog post.

Honda is similar. As backed up by initial quality surveys, they have the highest quality engines in the industry. Doing some training at their engine plant in Ohio, I was surprised to find out that they had not a single engineer trained in DOE or any other kind of statistical method. Honda relied exclusively on process knowledge.

Sources of Information

I have known about this for decades, from personal communications I can’t cite. In the post, I am citing publicly available sources so that it is clear I am not asking anyone to take my word. Since the post came out, current and former Toyota members have corroborated its main point through their comments.

Public Sources

About the origins of this information that you find amusing, the internet and YouTube are just channels. Through these channels, I am citing the following sources:

  • A Toyota web page about the history of the company.
  • Art Smalley, an ex-Toyota employee turned consultant whom I have met and respect
  • A video from a Toyota forklift plant in the UK. What you see in the video involves no sampling and no statistics.

Toyota People

Multiple current employees and alumni of Toyota confirmed the main point:

  • David Fleming “My experience is that Toyota does not use SPC in their processes. They have a long and successful history of understanding product design and equipment capability in research and development.”
  • Art Smalley “By the time I worked for Toyota in the latter part of the 1980’s these manual charts were essentially gone.”
  • David Martin. “TOYOTA does NOT specifically use SPC to assure quality…”
  • Don Kersey.


Michael Bremer reported that a friend of his, a plant manager at a Toyota subsidiary, used SPC as of 5 years ago. It would be great to have this friend explain what they were doing. From Michael Bremer’s account, it’s hard to tell whether they were (1) plotting parameters over time, or (2) taking samples from batches, computing stats from samples drawn from the batches, and checking these stats against control limits set in a process capability study.

Plotting time series of critical parameters is generally useful and (almost) everyone does it. If they did (1), they responded to what they saw in these plots, and it may have served them well. Unless they did (2), however, it wasn’t “SPC” as in this discussion, which is what you learn when you take a class  from an organization like the ASQ.

Learning about Quality from Toyota

Robert Nugent asked: “If Toyota does not use SPC and yet still meets or exceeds its competitors quality why aren’t we learning from them?”

Good question! I have been trying my best to learn from them and to pass on what I learned. As a quality improvement strategy, it is obvious to me that you should use statistics/data science to make processes capable, then move on to using one-piece flow for rapid problem detection and response, and, finally, to Poka-Yoke to eliminate human error. The data science gets you from 30% defectives to 3%, one-piece flow to 0.3%, and Poka-Yoke to 1 ppm.

I presented this concept at the SME Lean Management Solutions Conference in 2001 and taught it with Kevin Hop in our Lean Quality course but I have never seen it in the literature of the quality profession. The presentation was on September 11, a day we remember for other events. Since then, “Lean Six Sigma” has drowned out our message.

##toyota, #spc, #tps, #asq, #juse