Oct 5 2020
20 years ago, Honda stood out through its reputation for quality. Outsiders were studying Honda’s approach and Youtube now offers several videos shot at that time about it. Today, quality is no longer the differentiator among carmakers that is used to be but the practices of a company like Honda — past and present — remain a worthwhile object of study.
Sep 26 2020
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.
Sep 21 2020
As part of a discussion started by Lance Richardson on LinkedIn, I stated as a fact that SPC was not part of the Toyota Production System (TPS), which prompted several contradictors to tell me I didn’t know what I was talking about. The “evidence” they provided, however, does not refute my statement. It confirms it instead.
Sep 10 2020
Factories are controlled environments, designed to put out consistent products in volumes according to a plan. Controls, however, are never perfect, and managers respond to series of events of both internal and external origin.
An event is an instantaneous state change, with a timestamp but no duration. An operation on a manufacturing shop floor is not an event but its start and its completion are. Machine failures and quality problem reports from customers are all events. Customer orders and truck arrivals are also events.
For rare events, you measure times between occurrences and mark each occurrence on a timeline. For frequent events, you count occurrences per unit of time and plot these numbers over time. On individual machines, you record the times between failures. For incoming online orders for a product, you count how many you receive each hour or each day. Every 200,000 years, compass needles reverse directions. That is a rare event, recorded in basalt oozing from the mid-Atlantic ridge as it cools down, as on a magnetic tape. (See Paleomagnetism.)
Once a year, an American TV viewer tuning in to the Academy Awards is a frequent event. For this, we care how many millions do it, but not how much time elapses between two tune-ins.
Responding to series of events is central to many businesses. Stores respond to customers coming in, airlines to passengers with reservations showing up to fly — or not, maintenance crews to machine failures, social networks to subscriber activities,… The challenges posed by the randomness of the arrival and service processes has given rise to queuing theory and commonly used results like Little’s Law about lead times, throughput and inventory in steady-state, or Kingman’s Rule about the way lead times explode when systems saturate.
We are concerned here with a different and simpler topic: monitoring series of events to detect changes in their rates of occurrence and tell fluctuations apart from shifts with assignable causes. If the arrival rate of quality problem reports from the field suddenly doubles, a sophisticated analysis is not needed. If it increases by 10%, the conclusion is not so obvious.
Aug 19 2020
Last Monday, SESA Systems invited me to give a webinar on my books and posted the video on Youtube:
#manufacturingbooks, #lean assembly, #leanlogistics, #workingwithmachines, #manufacturingsystemsanalysis, #introductiontomanufacturing
Oct 30 2020
Who Uses Statistical Design Of Experiments In Manufacturing?
Next to SPC, Design of Experiments (DOE) is the most common topic in discussions of Statistical Quality. Outside of niches like semiconductors or pharmaceuticals, however, there is little evidence of use, particularly in production.
At many companies, management pays lip service to DOE and even pays for training in it. You must “Design experiments” if you pursue continuous improvement.
In manufacturing, DOE is intended to help engineers improve processes and design products. It is a rich but stable body of knowledge. The latest major innovation was Taguchi methods 40 years ago. Since then, Statistics has been subsumed under Data Science and new developments have shifted in emphasis from experimentation to Data Mining.
Experimentation in science and engineering predates DOE by centuries. Mastering DOE is a multi-year commitment that few manufacturing professionals have been willing to make. Furthermore, its effective use requires DOE know-how to be combined with domain knowledge.
Six Sigma originally attempted to train cadres of engineers called “Black Belts” in a subset to DOE. They then served as internal consultants to other engineers within electronics manufacturing. Six Sigma, however, soon lost this focus.
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By Michel Baudin • Technology • 22 • Tags: DOE, Experiment, Experimental Design, Fisher, Lean, Statistical Design of Experiments, Taguchi, TPS