Nov 3 2011

The UK’s Financial Times on exceptional companies, including Toyota

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.
Via www.ft.com

By • Press clippings • 0 • Tags: , ,

Toyota Type-G loom 1926

Nov 3 2011

How well do we know the history of Lean?

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.

Figure 2. Cover of an 8th grade history textbook from the US

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.

By • History • 13 • Tags: , , , ,

Temple Grandin - The way I see it

Nov 2 2011

Lean Reflections on Temple Grandin

Via Scoop.it – lean manufacturing

I just wish we would talk more about Temple Grandin’s work than her autism. Like Sakichi Toyoda with looms, she focused on one of the oldest economic activities, raising cattle, and made it better  by observing details nobody else cared about. Even for a non-cowboy, her Humane Livestock Handling  is fascinating.   Professionally, autism may have hindered or helped her. In any case, it is something she was born with, not something she accomplished. Having >50% of all cattle handling facilities in the US based on her designs is an accomplishment.
Show original

By • Press clippings • 0 • Tags:

Nov 1 2011

Design First, Lean Second: A Case Study

Via Scoop.it – lean manufacturing

For when you are not stuck with a 10-year old design…
Show original

By • Press clippings, Technology • 0 • Tags: , ,

scrum-rugby

Nov 1 2011

Black belts, scrums, and other metaphors

To be useful, a metaphor must help understanding. For the promoters of Six Sigma to call their certification Black Belt was marketing genius. A more descriptive label might have been Staff Statistician, but what self-respecting manufacturing professional would want to be that? Borrowing a term from Japanese martial arts not only  appealed to their fighting spirit, but also gave the impression that an approach developed at Motorola in the US had a connection with Japan, ground-zero of manufacturing excellence. Even in Japan, Black Belt (“Kuroto”) and White Belt (“Shiroto”) have migrated from martial arts to everyday language, to designate respectively a real pro and an amateur.

As a metaphor,  Black Belt also made sense because there is a parallel between the Six Sigma and martial arts training programs. Traditional  masters in the martial arts of China trained one or two disciples at the Bruce Lee level in a lifetime, just as universities trained only a handful of experts in statistical design of experiments that could be effective in electronics manufacturing. One Karate instructor, on the other hand, can train hundreds of Black Belts, just as a Six Sigma program can teach a focused subset of statistical design of experiments to hundreds of engineers.

Scrum, in software development, is also a sports metaphor, a term borrowed from rugby, which few Americans know. The connection between a rugby scrum and what software people call by the same name, however, is not obvious. A rugby scrum involves the forward players of two teams locked in the pattern of Figure 1.

Figure 1. A rugby scrum

The ball is released in the middle of the scrum and both team try to take possession by kicking it backwards while pushing the other team forwards. It is exciting and bruising to participate in, as well as a great spectacle. For software developers, scrum is an approach to project management illustrated by the status panel in Figure 2.

Figure 2. A software development scrum

It leaves you wondering what plays the role of the opposing team, the ball, or the player positions. In other words, in what way is a rugby scrum a metaphor for this approach at all?

By • Management • 11 • Tags: ,

yardstick

Oct 28 2011

The staying power of bad metrics

A speaker I once heard on manufacturing metrics started with a quote from football coach Vince Lombardi: “If you’re not keeping score, you’re only practicing.” In a sport, your score or your rank is, by definition, the correct measure of success, and we assume too easily that this kind of thinking crosses over to every human endeavor, from national economies to plant performance or education. In this process, we begin using highly aggregated metrics as if they were physical measurements like mass or speed, and avert our eyes from how these sausages are made.

Following are a few of the egregious examples:

  • GDP. Gross Domestic Product (GDP), for example, is in the news every day. If you pollute and spend money to clean up your toxic waste, you contribute more to the GDP than if you produce cleanly. Because of this kind of absurdity, GDP as a metric has been criticized by many economists, including Joseph Stiglitz. In 2009, he even convinced French president Nicolas Sarkozy to seek alternatives. Yet, two years later, the same president is pushing to include in the country’s constitution a “Golden Rule” that caps budget deficits at a percentage of the same flawed GDP!
  • IQ. In the US, IQ  is still widely treated as a measure of intelligence. On its face, the notion that human intelligence is reducible to a number is an insult to its subject. In fact, all an IQ measures is the ability to take an IQ test. Psychologists recognize this, but many school teachers and the public at large don’t. (See Steven Jay Gould’s The Mismeasure of Man.)
  • Food calories. Calories are the most commonly used metric in nutrition. What this number actually represents is the heat generated by drying and burning a food item. But is digestion the same as combustion? Obviously not, for example, for fibers, which cross the human body unchanged. The absurdity of assigning calories to fibers has not escaped one dieter, who questioned it on a Calorie Count forum, and received, among other replies, the following:

Fiber calories are included in nutrition information, but only in come countries. In the US, it is legal to not put in fiber calories because they are not digestible. Therefore, they do not “count” as such. however, if you, like most people, tend to underestimate cals sightly, there is nothing wrong with including them to create a “buffer zone.”

In other words, it makes no sense but you should pretend it does.

Do we behave the same way in the manufacturing world? Yes. For example, many companies measure productivity in terms of Sales/Employee. There is an easy way to boost this metric: outsource all production, close all plants and become a trading company. It is not easy to find metrics for quality, cost, delivery, safety, and morale that are meaningful and cannot be gamed, but it can be done. For overall company productivity, for example, you can use Value added/Employee, where

Value-added = Sales – (Materials + Energy + Outsourced Services)

This is what Peter Drucker called Contributed Value. Value added/Employee is not a perfect metric, but at least it does not provide a perverse incentive to outsource, and the US census bureau publishes statistics on value-added and employment by industry, that are helpful for benchmarking.

Following are a few conditions that a good metric must meet:

  1. A good metric is immediately understandable. No training or even explanation is required to figure out what it means, and the number directly maps to reality, free of any manipulation. One type of common manipulation is to assume that one particular ratio cannot possibly be over 85%, and redefine 85% for this ratio as “100% performance.” While this makes performance look better, it also makes the number misleading and difficult to interpret.
  2. People see how they can affect the outcome. With a good metric, it is also easy to understand what kind of actions can affect the value of the measurement. A shop floor metric, for example, should not a be a function of the price of oil in the world market, because there is nothing the operators can do to affect it. Their actions, on the other hand, can affect the number of labor-hours required per unit, or the rework rate.
  3. A better value for the metric always means better business performance for the company. One of the most difficult characteristics to guarantee is that a better value of a metric always translates to better business performance for the company. Equipment efficiency measures are notorious for failing in this area because maximizing them often leads to overproduction and WIP accumulation.
  4. The input data of the metric should be easy to collect. Lead time statistics, for example, require entry and exit timestamps by unit of production. The difference between these times then only gives you the lead time is calendar time, not in work time. The get lead times in work time, you then have to match the timestamps against the plant’s work calendar. Lead time information, however, can be inferred from WIP and WIP age data, which can be collected by direct observation of WIP on the shop floor. Metrics of
    WIP, therefore, contain essentially the same information but are easier to  calculate. (See Little’s Law.)
  5. All metrics should have the appropriate sensitivity. If daily fluctuations are not what is of interest, then they need to be filtered out. A common method for doing this is to plot 5-day moving averages instead of individual values — that is, the point plotted today is the average of the values observed in the last five days. Daily fluctuations are smoothed away, but weekly trends stand out.

Peter Drucker sold corporate America on the idea that you can’t manage what you can’t measure, and this has led many managers to believe that employees would do whatever it takes to maximize their scores. Given flawed metrics, it if fortunate for the companies that these managers were wrong. If they had been right, all the companies that measure productivity in terms of  Sales/Employee would actually have outsourced all production. They didn’t, because metrics are only one of many factors influencing behavior. Most employees, at all level, will not maximize their metrics through actions they feel violate common sense or are inconsistent with their personal ethics.

By • Metrics • 12 • Tags: ,

«< 160 161 162 163 164 >»