“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
Two weeks ago, 718,890 French 18-year olds spent four hours writing philosophical essays as part of the Bac nationwide exam they must pass to graduate High School. Among the topics offered to the humanities students was “Is observing enough to know?” They must elaborate on the meaning of the terms, argue for and against an answer, quote the philosophers they were taught, and draw conclusions. And it is written long-hand, without any illustrations or hyperlinks. I couldn’t compete with them, but this particular topic resonates with me because of my time observing factories.
Journalist Charles Duhigg has a new book out on the subject of productivity and was being interviewed about it on NPR. I heard him express as a general principle that new technology never increases productivity when first implemented because organizations and individuals use it as a new way of doing exactly what they were doing before. Over time, productivity does increases as users discover new tasks or methods that the technology enables but were beyond the imagination of its early adopters.
“Motion and transportation count among the 7 basic muda or wastes, that should be eliminated or at least reduced to their bare minimum in order to be leaner.
Now, with the probable rise of robotics, will robotic motion (and transportation) still be considered a waste?”
Sourced through Chris Hohmann’s blog
Michel Baudin‘s comments: It’s a valid question, but one that should be asked about handling and transportation automation in general, not just robots. It is also one that is not properly answered with the simplistic theory of value and waste that has been reiterated in the English-language literature on Lean for 20 years.
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
‘The cobot controversy” is the title of a short article published by and on the Hannover Messe (“Hannover Fair”, the industry exhibition) website. […]This article proposes a “balanced” view about the impact of the collaborative robots (cobots) on the jobs in industry. It caught my interest because most often the articles on those subjects, i.e. robots and future of jobs are single-sided.
On the one hand promoters of the factory of the future, industry 4.0 and robotics only highlight the alleged benefits of the new technologies. On the other hand, prophets of doom predict nothing else than mass extinction of jobs.”
Sourced through Christian Hohmann’s blog
Michel Baudin‘s comments: This is the first of a series of posts on Christian’s blog about cobots, a term I hadn’t heard before that designates robots that collaborate with people. According to Wikipedia, the term was coined in 1996 by tow academics, J. Edward Colgate and Michael Peshkin, and has been used to designate commercial products since 2012. The concept, however, has existed independently of the term both in science-fiction and in real life.
“[…] 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.