“There are no good lean consultants. I’m not saying there are no good consultants. Of course there are; same bell curve as in every profession…”
See it in Gemba Coach
Bodo Wiegand heads the Lean Management Institute, which is the German affiliate of the Lean Enterprise Institute. In his latest two newsletters, on Wiegand’s Watch, he explains how management should not coddle organizations but instead lead them.
“One of the misconceptions about lean thinking is that it automatically leads to flattening the organization. Many people think that layers of management are always a bad thing and start removing layers as a way to empower employees, speed up decision-making, and improve innovation. While there is no shortage of organizations that suffer from too many layers, it should be noted that flattening does not necessarily lead to improved performance. Many organizations that flattened their structures have experienced little more than burned out managers, frustrated employees, and high turnover.”
Sourced through Lessons in Lean
Michel Baudin‘s comments: For the second time in a week, I am clipping a post from Gregg’s blog but I can’t help it if I find his writings worth sharing. In my experience, “flattening the organization” is particularly harmful on the shop floor. I have heard managers brag about their structure being “lean” because they had only 1 supervisor for 100 operators. This isn’t what Toyota does in car assembly, where operators work in teams of 4 to 6 and you have a first-line manager for 4 to 6 teams. This means that the number of operators for a first-line manager ranges from 16 to 36, with a mean that is actually around 17. This low number is designed to allow the first-line managers to help operators in their professional development and to lead improvement projects. A supervisor with 100 direct reports can do neither.
#LeanManagement, #First-LineManager, #ShopFloor, #ContinuousImprovement
“[…] When starting an improvement effort, I usually ask about the minimum target the team is attempting to achieve. The answer is often something made up on the spot or a generalization, like as much as possible. Improvement efforts should generally be driven by the actual requirements of the business. For example, if a company determines that the time between a customer placing an order and receiving the product is too long, it should determine an improvement target based on what the business needs. If it currently takes 42 days and customers expect to receive the product in 22 days because of their needs or what competitors are offering, the minimum improvement needed is 20 days.[…]”
Sourced through Lessons in Lean
Michel Baudin‘s comments:
Gregg Stocker illustrates abstract principles with concrete examples, which makes his meaning clear and unambiguous. The above excerpt is meant to show the need for employees and managers to understand the consequences of local actions on the organization as a whole. As he points out in the rest of his post, it’s not always easy.
“My fully-loaded 2012 Audi A6 had an intermittent frustrating problem since the day I bought it. No diagnostic codes indicated a problem. Escalation to German engineering had me ready to move back to Lexus. Their response was ‘it must not really be happening. Our codes would indicate if it were.’ That obnoxious response was based on the assumption they had thought of every cause of failure in developing the diagnostic codes. FMEA is not 100% and never will be. Do you have customer data that you’re not actively using to improve your product Four years after I first reported the issue, Audi issued an urgent safety recall for the problem that I had been experiencing. Why the delay?”
Sourced through AME Target
Michel Baudin‘s comments: I am sure many have had similar experiences to Becky’s with customer service in many companies. They tell you their product is used by millions and it’s the first time anyone reports this problem. You are probably using it wrong, or misreading its output,… This being said, it’s not really related to the concept of statistical significance.
The purpose of graphics for data visualization is communication, not decoration, which is often forgotten in publications as well as on company performance dashboards. A case in point is the chart on yesterday’s cover of the New York Times. It shows that solar energy currently accounts for more than twice as many jobs as coal. It also shows the numbers of jobs in different sectors and uses a color code to mark some as based on fossil fuels versus renewable and low-emission technologies.
Until recently, most publications would have used a pie chart. Now, graphic artists have found a way to square the pie chart into yet another style that will most likely trickle down to slideware and office walls, in spite of a low data-to-ink ratio and the use of two-dimensional shapes to display one-dimensional data.
Christoph Roser’s pulse line animation
“There are three different options on how to time the production lines.[…] The “easiest” one is an unstructured approach. The processes are still arranged in sequence; however, there is no fixed signal when to start processing a part. The pulse line is also a flow line, but now all parts move at the same time. […] When all processes are done, all parts move to the next process simultaneously. […] Another common way to structure the timing of flow lines is the continuously moving line.”
Sourced through All About Lean
Michel Baudin‘s comments: Christoph’s two posts are great for their rifle-shot focus on the single issue of flow line pacing and for their effective use of animation to illustrate principles. It makes the differences clear in a way you couldn’t on paper.
“Imagine going to work at 7:30 every night and spending the next 12 hours, including meals and breaks, inside a factory where your only job is to insert a single screw into the back of a smartphone, repeating the task over and over and over again. During the day, you sleep in a shared dorm room, and in the evening, you wake up and start all over again.That’s the routine that Dejian Zeng experienced when he spent six weeks working at an iPhone factory near Shanghai, China, last summer. […]. Unlike many of those workers, Zeng did not need to do the job to earn a living. He’s a grad student at New York University, and he worked at the factory for his summer project.”
Sourced through Business Insider
Michel Baudin‘s comments: Thanks to my colleague Kevin Hop for sending me this rare peek into the life of the people who assemble iPhones by hand in Chinese factories each employing tens of thousands of workers. We need to keep in mind that this is the perspective of Dejian Zeng, an American student who was there for 6 weeks, not someone who works there for a living, but it is still informative.
While his account wouldn’t make anyone want to embrace iPhone assembly as a career choice, it’s not a horror story. The work is dull and repetitive, and there is too much of it, but it’s not described as dirty or dangerous. I have seen worse in poorly ventilated paint shops and machine shops with slippery floors, and not only in China.
“This year is the 20th anniversary of the founding of the Lean Enterprise Institute (LEI). There will surely be a big celebration. But in my view, there is less to celebrate than meets the eye. Here’s why:
LEI has controlled the progressive management agenda for the last 20 years. That means they own the failures as well as the successes. By LEIs own reckoning (as well as its sister organization, the Lean Enterprise Academy in the U.K.), success has been much less than they had hoped for.”
Sourced through Bob Emiliani’s blog
Michel Baudin‘s comments: Overall, I agree with Bob’s assessment, but I think American manufacturers deserve more of the blame than the LEI, for faddishly latching on to one tool after another and mistaking it for a panacea. For example, in his introduction to “Learning to See,” Mike Rother explicitly warns the reader that, at Toyota, Materials and Information Flow Analysis (MIFA) is not a major tool. Yes, he repackaged it with the attractive but nonsensical name of “Value Stream Mapping” (VSM), but his audience didn’t have to elevate it to the status that it did.