Nov 4 2011
The pursuit of concurrent improvement in all dimensions of manufacturing performance through projects involving both the production floor and support services.
Nov 3 2011
Via Scoop.it – lean manufacturing
It is a curious fact that in industry after industry there is at least one company that appears to succeed not by doing the same thing better than everyone else but by playing a completely different game.
Nov 3 2011
In Woody Allen’s Midnight in Paris, the hero’s nemesis is an academic who constantly lectures on historical details that he often gets wrong. Introductions to Lean, nowadays, often include a section on history, but no source is quoted, there are many inconsistencies with otherwise known facts, and some of the interpretations are confusing.
Manufacturing practices are like life forms. Some appear and go extinct, while others endure forever. Some 2-billion-year old fossils on the shore of Lake Superior match living organisms in Australia’s Great Barrier Reef today. Likewise, some of the oldest ideas on making things are still practiced today. Knowing who developed what techniques when and why is not just about giving credit. Not only does it occasionally make us rediscover a lost art, like TWI, but it also helps us understand its current relevance.
Getting the timeline right matters because of causality; causality, because it explains motivation; motivation, because it determines current relevance. People invent solutions because they have problems. If we are still facing the same problems, we can adopt or adapt their solutions. The people of Toyota found solutions to overcome crises throughout the life of the company, which eventually coalesced into a system, as explained by Takahiro Fujimoto. Their techniques are easiest to understand within their historical context.
The history of manufacturing is poorly documented. We know the exact wording of speeches made by Cicero in the Roman senate in 63 BCE, but we don’t know how the Romans made standard swords, spears, helmets, and other weapons to sustain hundreds of thousands of legionnaires in the field (See Figure 1). Documenting how things were made has never been a priority of historians, and they rarely have the technical knowledge needed.
Figure 1. Cicero and a Roman soldier
Official histories are not to be trusted. School children throughout the world sit through classes where they hear an official account of history intended to create shared narratives. With titles like “Call to freedom,” the manuals make no pretense at objectivity (See Figure 2). In business, it is even worse: official histories are spun by the Public Relations departments of the companies that became dominant in their markets.
The real stories are found in the products, facilities, and documents left over from operations. Jim Womack can still visit today the hall where Venetians assembled galleys 500 years ago. Examining sewing machines at the Smithsonian, David Hounshell noticed that Singer stopped engraving machine serial numbers on parts around 1880, from which he deduces that they mastered interchangeable parts at that time. From memoirs, memos, drawings, specs, photographs and movies we can also infer the methods that were used and the conflicts that took place.
Most of us cannot do this research; we rely on professional historians. They quote their sources, infer cautiously from the facts, and don’t attempt to answer all questions. By contrast, white belts at history produce glib narratives, make up dialogs among historical figures, and presume to know their inner thoughts. As readers, we should tell the difference.
Did Sakichi Toyoda visit Ford in 1911? Several of the historical notes on Lean claim that he did, but there is no mention of such a visit in Mass and Robertson’s essay on the life of Sakichi Toyoda. According to their account, Sakichi Toyoda did visit the US and the UK in 1910, to see textile plants and apply for patents, and was back in Japan by January, 1911. Even if he did come in 1911, we may wonder what he might have been impressed with, considering that the first assembly line didn’t start until two years later.
Some of these accounts also state that Sakichi Toyoda invented an automatic loom in 1902. According to other accounts, his work at that time was on narrow steam-powered looms, and his first successful automatic loom was the Type G in 1924, which included a shuttle-change system developed by his son Kiichiro, who later founded the Toyota car company with the proceeds from the sale of the Type G patent in the UK.
Did Henry Ford invent Lean? Many accounts claim he did. This is puzzling because the term Mass Production was coined specifically to describe the Ford system. If Ford invented Lean, then Lean Manufacturing and Mass Production are the same, and we are wasting our time explaining how they differ. If Henry Ford invented Lean, then Issac Newton came up with relativity.
Nov 1 2011
Oct 27 2011
We have all seen the absurd situation in the featured picture above of a line of customers waiting for taxis while a line of taxis next to them is waiting for customers, with a barrier separating them. This particular instance is from The Hopeful Traveler blog. The cabs are from London, but the same scene could have been shot in many other major world cities.
I am sure we have all encountered similar situations in other circumstances, which may or may not be easy to resolve. One particular case where it should be easy is the restaurant buffet. Figure 1 shows a typical scene in buffet restaurants, with a line of people waiting to get food all on the one side of the table, while food is waiting and accessible on the opposite side.
Figure 1. A typical buffet
I think the fundamental mistake is the assumption that a buffet is like an assembly line, providing sequential access to dishes. This means that you cannot get to the Alo Gobi until the person in front of you is done with the Tandoori. The ideal buffet would instead provide random access, meaning that each customer would have immediate access to all dishes at all times. While it may not be feasible, you can get much closer to it than with the linear buffet. The following picture shows an alternative organization of a buffet in circular islands that is non-sequential.
Figure 2. A buffet island at the Holiday Inn in Visalia, CA
The limitation of this concept is that replenishment by waiters can interfere with customers. To avoid this, you would want dishes to be replenished from inside the circle while customers help themselves on the outside, as in the following sketch:
Figure 3. A buffet island with replenishment from inside
One problem with the circular buffet island, however, is its lack of modularity. You can add or remove whole islands but you cannot expand or shrink an island, which you can if you use straight tables arranged in a U-shape, as in Figure 4.
This buffet island may superficially look like a manufacturing cell, but it is radically different. Its purpose is random access to food as opposed to sequential processing of work pieces, and the materials do not flow around the cell but from the inside out.
Such are the thoughts going through my mind while munching on the Naan at Darbar.
Oct 26 2011
Data mining, in general, is the retrieval of information from data collected for a different purpose, such as using sales transaction histories to infer what products tend to be bought together. By contrast, design of experiments involves the collection of observations for the purpose of confirming or refuting hypotheses.
This perspective on data mining is consistent with the literature in expressing purpose, but most authors go further. They include in their definitions that data mining is done with computers, using large databases and specific analytical tools, which I think is too restrictive. The tools they list are the ones they have found useful in analyzing the behavior of millions of users of search engines or commerce websites, and they are not obviously applicable in other areas, such as manufacturing.
During World War II, British analysts used the serial numbers of captured or destroyed German tanks to estimate the numbers produced. Because serial numbers were not attached for this purpose, it was data mining. It used clever statistical models but, obviously, no computers.
Today, PhD-level data miners at Google, eBay, or Amazon sift through the page views and click-throughs of millions of users for clues to patterns they can use. The data, automatically collected, is accurate and collected by the terabytes every day. This “big data” requires parallel processing on clusters of computers and lends itself to the most advanced analytical tools ever developed.
Compared to this fire hose of data, what manufacturing produces is a trickle. In a factory, the master data/technical specs, plans and schedules, status of operations and work in process, and the history of production over, say, 12 months, usually adds up to a few gigabytes. It doesn’t fit on one spreadsheet, but it often does on a memory stick. On the other hand, much of it is still manually generated and therefore contains errors, and it is often structured in ways that make it difficult to work with.
Even if manufacturing companies could hire the data miners away from their current jobs, their experience with e-commerce or web search would not have prepared them well for the different challenges of manufacturing data mining.
There is an opportunity for data mining to contribute to competitiveness in manufacturing, but the approach must start from the needs. It must not be an e-commerce cure in search of manufacturing diseases.