May 3 2012
See on Scoop.it – lean manufacturing
A great article by Lonnie Wilson, pen-pal of over 10 years.
Focus on lean and get those cost reductions, and more.
See on www.industryweek.com
Apr 26 2012
Long flights are opportunities to whittle down my stack of books to read about Lean. The one I took from San Francisco to Shanghai this time made heavy use of what the author calls a “value curve,” but I have to admit that it was a bit much for my jet-lagged brain. As I couldn’t quite make sense of this curve, it made me want to explore in more detail exactly what it is, what can be learned from it, and what actions it may support.
It plots parameters the author calls “Value available” and “Value required” against volume for a set of yearly data about a company. The author claims that companies that are not Lean have U-shaped profiles of “value required” that make them profitable only in the vicinity of an optimal volume of activity, while Lean companies have flatter value-required profiles that allow them to prosper under a broader range of volumes.
Following is the example he uses to introduce the value curve:
Figure 3.2 is about GM from 1926 to 1936, and, the parameter on the x-axis is a common measure of automobile industry volume, albeit one that commingles everything with wheels and an engine that goes on the road, from cars to trucks and buses.
Since the word “value” appears seven times on the chart, I had to check what the author meant by it. In the book’s glossary, he defines it as “A product or service that satisfies a customer need or desire even as it changes over time.” Since service organizations call their offerings “products,” products are always intended for customers, and products are upgraded over time, I can’t see any difference between Value and Product, but why not? The next time I buy a haircut or a fish, I will know I should call them values rather than products. On the chart, however, replacing “Value” with “Product” doesn’t makes it easier to understand, as seen below, so I must still be missing something:
I was also puzzled by the use of the expression “Behind the Value Curve” inside a chart called “Value Curve,” making the chart refer to itself. Clearly, it was not as simple as I hoped it would be. Looking further in the Glossary, I found the following:
But Value Available is a flat curve on the chart, and therefore it can’t mean Sales, because GM’s sales were anything but flat between 1926 and 1936. The author explains that he makes it flat to show “the relationship between value available and value required.” Another way of saying this is that he wants to show the ratio of Net Income to Sales, usually known as Profit Margin. It seems that the unit on the y-axis should be “%,” as befits a ratio, rather than “Dollars.” It really doesn’t matter whether Sales and Net Income are reported in Dollars or Yens.
In the book’s appendix, the author provides the data he used to generate his chart, and I used them to plot profit margin as a function of volume. I also labeled each point by year so that we could follow the company’s trajectory on the plane of Profit Margin versus Vehicles Produced. To see if this chart supported the author’s assertion, I also retrieved and plotted the corresponding data for Toyota between 2000 and 2011, with the following results:
These charts clearly tell stories. The first shows GM through the growth of the twenties and the great depression; the second, Toyota through its 2001-2008 boom, followed by the financial crisis, the mass recalls of 2010, and the Fukushima earthquake and Thailand floods of 2011. It also shows how the economics of the auto industry changed in 80 years. In good times, today’s mature automobile industry yields profit margins that are barely 1/3 of what they used to be, on volumes that are many times higher. In the worst year of the great depression, 1932, GM made only 28% as many vehicles as in 1929. If the worst of the current crisis was in 2009-2010, Toyota’s drop in volume, while similar in absolute terms to GM’s in the great depression, was much smaller in relative terms, at barely 15% off from the 2008 peak.
But do these curves support the author’s assertion that Lean companies are better at coping with volume changes? I don’t see it. I happen to think it’s true, but I don’t see these particular charts as making this point.
Apr 15 2012
The aggregate metric for equipment most often mentioned in the Lean literature is Overall Equipment Effectiveness (OEE). I first encountered it 15 years ago, when a client’s intern who had been slated to help on a project I was coaching, was instead commandeered to spend the summer calculating the OEEs of machine tools. I argued that the project was a better opportunity than just taking measurements, both for the improvements at stake for the client and for the intern’s learning experience, but I lost. Looking closer at the OEE itself, I felt that it was difficult to calculate accurately, analyze, and act on. In other words, it does not meet the requirements listed in Part 1.
Jun 28 2012
Metrics on the web versus manufacturing
Mingled last night with 209 internet operations “ninjas” at the meetup of the Large Scale Production Engineering (#lspe) group, on Actionable Metrics, hosted by Yahoo! in Sunnyvale, CA, and heard speakers from collectibles marketplace site etsy, web performance testing service SOASTA, and video streaming service Netflix describe how they used metrics in their activities.
Both in style and content, the presentations were radically different from what I have heard in manufacturing on that subject. The speakers went fast, as if they were in a great hurry to give us as much information as possible prior to returning to work. They were also extraordinarily open about the tools they used and how they used them.
The first feature of their work that struck me was the simplicity of their dashboards. Almost all the charts they monitor are simple line plots of time series, free of the jumble of 3D pie charts, stacked bar charts, and other complicated displays commonly found on manufacturing dashboards. The most elaborate display showed a time series of, for example, login response times, with a dynamically adjusted confidence band based on a statistical model to help operations engineers tell significant changes from fluctuations. Yet, even with these features, the meaning of the chart was obvious, even to an outsider to their business. It is not difficult, for example, to understand a plot of the evolution of login times as the number of users grows or additional security is added.
As engineers tweak and enhance the functions provided on their servers everyday, they monitor metrics to see if it does any good, and take immediate action if it doesn’t. The time within which they can and must react is measured in seconds, not hours or days. Their metrics fall into the following categories:
Customer experience translates as is to a manufacturing context but Infrastructure, on the other hand, corresponds to Logistics, and Application to Production all with different time scales. While one of the speakers described response time as “one metric to rule them all,” none of the organizations presenting imposed any standard set of metrics on their engineers. Instead, they provide them with tools to capture the data, compute the metrics, display them on charts, and generate alerts, but it is their choice and their responsibility to define the metrics.
Response time is to their world what order fulfillment lead time is in manufacturing, but its importance is much greater. There are sectors in manufacturing where short order fulfillment lead times are a competitive advantage with customers, but also many, particularly with big ticket items, where other factors trump short lead times. If you are a farmer buying a tractor, for example, you will take one with the features you want over one you can have sooner that doesn’t have them. The pursuit of short production lead times has to do with other considerations, such as reducing inventory, detecting quality problems faster, or reducing the obsolescence cost of engineering changes. When providing services on the web, on the other hand, any slowdown in response causes customers to balk, and, internally, any increase in transaction processing time can cause servers to saturate and customer response times to explode.
For these engineers, capturing one metric is a matter of adding one line of code in their software, and they use open-source tools to generate plots and dashboards. It is not difficult to do. The hard part is identifying the right metrics. The Netflix speaker was quite aware of this. After someone had him “No matter what you measure, it’s useful,” he charted the evolution over one year of the proportion of user IP addresses ending with an even number.
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By Michel Baudin • Metrics • 1 • Tags: Metrics