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.
“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.
‘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.
“Is Lean a set of tools – or a set of principles? If the latter, we’ll fall far short of our potential”
Sourced through LinkedIn
Michel Baudin‘s comments:
Because of the way the meaning of Lean has changed over the past 25 years, I think it’s too late to ask this question. On the other hand, it is relevant about TPS or about the art of designing and improving manufacturing operations, whatever shorter name you want to give it.
“[…]Organizations dealing repeatedly with projects will soon develop templates of Work Breakdown Structures (WBS) holding the most current tasks and milestones. These canvasses speed up somewhat the project initiation and ensure some degree of standardization.
Over time though, the copy-pasting from one project to the next, the addition of “improvements” and requirements as well as countermeasures to problems kind of inflate the templates and the projects. This, in turn, extends the project’s duration as every additional task not only adds its allocated time to completion, but also the safety margin(s) the doer and/or project manager will add on top.[…]”
Sourced through Chris Hohmann
Michel Baudin‘s comments:
The project management literature astonishingly fails to provide guidance on the art of breaking a project down into tasks. The “Body of Knowledge” tells you what a Work Breakdown Structure (WBS) should look like but not how you actually break a project down into meaningful pieces, whether it is a dinner party, the construction of a bridge, or a moon shot. For a manager who has to make a plan, this makes templates irresistible: instead of thinking, you just fill in the blanks.
Chris’s questions are certainly relevant but I would like to go further and propose a few rules for generating a WBS.
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.