Journalists and other authors who should know better routinely conflate productivity increase with automation and automation with the introduction of robots. “Productivity” covers a set of performance metrics that are increased by a variety of methods, many of which do not involve automation. Automation sometimes increases productivity, but not always. Finally, most of the time, automation does not involve robots. At last Tuesday’s Palo Alto Lean Coffee, I asked Tesla’s Omar Guerrero and Genentech’s Curtis Anderson for examples of changes that had increased productivity in their organizations.
“Research shows that over a million manufacturing jobs sit unfilled right now. That number is expected to increase to over 3 million by the end of this decade. A skills shortage is to blame, say most. ‘We need CNC operators, robot operators, and mechatronics skills’ say all too many manufacturing companies. […] How does a manufacturing company leader solve that problem? By emphasizing the only capability that truly matters: The willingness and ability to learn.”
Sourced through Industry Week
Michel Baudin‘s comments: As usual, I tend to agree with Becky Morgan. In the article’s featured image, I also noticed the learner’s gray hair and his obvious willingness to take instruction from a younger man. It reinforces Becky’s points. When you desperately need a CNC programmer, you are tempted to seek someone with just this skill to fill just this pigeonhole. What Becky says is that, not only are you unlikely to find this rare pearl but, even if you did, it wouldn’t serve you well because the skill in question would be obsolete in 5 years. Instead, she argues, you should recruit team members to learn and grow with the company.
The first article in Jill Jusko’s twice yearly “Top 10” Industry Week articles about Lean is her own Lessons in Lean Training, in which she quotes consultant Jon Armstrong as saying “individuals first need to know why before they know how. It’s important to start with the principles.” It sounds rational but it isn’t quite as obvious as it sounds. It’s an effective way to teach geometry but not English spelling. In geometry, you arrive at conclusions through logic; in spelling, you memorize arbitrary rules. You don’t learn to spell because of principles but because you won’t get the job you want with a misspelled resume.
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.
“I have long felt that people have listened too intently to the analysts who have not actually “played the game” – the interpreters of Toyota’s management system, not the people who actually created it. I think that it is easy for all to agree that someone who actually created something is a much better guide than someone who studied it second-hand.[…] Original sources are the best sources to learn from and should form the fundamental basis of your understanding of TPS and Lean. ”
Sourced through Bob Emiliani
Michel Baudin‘s comments: The originators of Toyota’s production and management system are all dead. This includes Sakichi, Kiichiro and Eiji Toyoda, Taiichi Ohno, Shigeo Shingo, and others, which makes it difficult to learn from them through personal communication. We can read what little they published, or rely on the generations that came after them. The people Emiliani shows to the right of Taiichi Ohno as “originators,” Fujio Cho and Chihiro Nakao, actually are disciples of the originators, which isn’t quite the same. As Emiliani sees it, the alternative to learning from these people is learning from “interpreters” who, as he implies in the title, don’t know what they are talking about because they had no hand in creating it. Are these really the only choices?
“It is disrespectful to workers for Management to make promises that they cannot deliver on. However there are presently some academics and authors in the Lean community who say that Lean transformation should provide ‘Meaningful Work’ for all workers. This phrase is setting too high an expectation for our workers…that we will not be able to deliver on…”
Sourced through LinkedIn
Michel Baudin‘s comments: I agree. Just Another Car Factory? Lean Production and Its Discontents is a chronicle of the early years of CAMI, a GM-Suzuki joint venture in Canada, which describes labor problems as due to management overselling Lean to production operators. As a manager, it’s one thing to overpromise to your superiors and another to shop floor operators. They don’t react the same way. Superiors reward you for setting “stretch goals,” and punish you if you only commit to what you can deliver. It’s the project game, as it has been played by generations in American managers. With shop floor operators, on the other hand, you lose your credibility and your ability to lead.
There is nothing you can do to turn a job in which you repeat the same 60 seconds of activity 400 times a day into “meaningful work.” You can make it easier and safer, you can mitigate the monotony by rotating operators between stations every two hours, and you can involve operators in Kaizen,… All of this improves both the performance of the production line and the experience of working on it, but it still won’t make working on an assembly line the kind of jobs kids dream of doing when they grow up. Dennis is right to say that overpromising to workers is disrespectful. They can handle the truth.
“Many topics in lean address how to deal with uncertainty and fluctuations (or mura for unevenness). There is a particularly neat trick for manual lines that self-organizes fluctuations in the workload: the Bucket Brigade! It does have some advantages, but it also has quite a few limitations and prerequisites for it to work. Most importantly it works best only for very short cycle times as for example picking materials. Unfortunately, these requirements are rarely mentioned in literature. Let me show you the basics work in this post before I go into some of the trickier details in the next post.”
Sourced through AllAboutLean
Michel Baudin‘s comments: The bucket-brigade system, also known as “bump-back,” is indeed a clever solution, often applied to mass-customization, as in the following examples of food service at Chipotle and Subway:
It is also used in the more complex process of custom bag assembly at Timbuk2 designs. See also John Bartholdi’s description and simulation of the system. The concept is discussed on pp. 141-142 of Working with Machines and, in this blog, as a sometimes preferred alternative to the baton-touch approach .
Incidentally, Christoph’s post-WW-II picture reminded me of a story I heard long ago about a hotel guest in Germany at that time complaining about hearing trains all night. “But there is no railroad near here,” said the innkeeper. Walking out, the guest saw a line of people passing bricks to each other, saying “Bitte schön, danke schön, bitte schön, danke schön,….”
In a previous post, I pointed out that manufacturing professionals’ eyes glaze over when they hear the word “probability.” Even outside manufacturing, most professionals’ idea of probability is that, if you throw a die, you have one chance in six of getting an ace. 2000 years ago, Claudius wrote a book on how to win at dice but the field of inquiry has broadened since, producing results that affect business, technology, science, politics, and everyday life.
In the age of big data, all professionals would benefit from digging deeper and becoming, at least, savvy recipients of probabilistic arguments prepared by others. The analysts themselves need a deeper understanding than their audience. With the software available today in the broad categories of data science or machine learning, however, they don’t need to master 1,000 pages of math in order to apply probability theory, any more than you need to understand the mechanics of gearboxes to drive a car.
It wasn’t the case in earlier decades, when you needed to learn the math and implement it in your own code. Not only is it now unnecessary, but many new tools have been added to the kit. You still need to learn what the math doesn’t tell you: which tools to apply, when and how, in order to solve your actual problems. It’s no longer about computing, but about figuring out what to compute and acting on the results.
Following are a few examples that illustrate these ideas, and pointers on concepts I have personally found most enlightening on this subject. There is more to come, if there is popular demand.
It is a seemingly simple question, but one that is not asked as often as it should be. It challenges managers to consider the responses of other stakeholders and think beyond immediate consequences. It checks their “bias for action,” and makes them take a pause to think farther than one move ahead.
If you outsource an item, for example, will the new supplier eventually morph into a competitor? What know-how might you lose? How will it affect employee morale? Are you putting your quality reputation at risk? The question is an invitation to work through multiple scenarios of responses by your suppliers, your work force, and your customers, reaching into the future.
“If there is ever a time to discuss the similarities between plant leadership and politics, perhaps during an election year is as fitting a time as any. Some time ago I was attending a class at Columbia University, and over a conversation at lunch with a professor, we discussed what a day in the life of a plant manager was like (I was a plant manager at the time). After a bit of conversation about my typical day, the professor said, ‘It’s like you really are running for election as town mayor, aren’t you?'”
Sourced through from: Plant Manager/Town Mayor
Michel Baudin‘s comments:
In my presentation on the Lean Leadership Role of the Plant Manager at the Lean Leadership Summit last month, I used the ship captain as a metaphor, but the plant manager as town mayor is enlightening as well. The abstract of my talk was as follows:
The plant manager is like a ship captain, in daily contact with a team that may range from a handful to thousands of people, and accountable to an organization that is remote and has entrusted him or her with a valuable asset. The plant manager is the voice of top management to the plant and of the plant to top management, and represents the company to the local community. Of course, the plant manager must know how to pay bills on time and let maintenance use qualified technicians to fix forklifts, but there is more to the job, particularly about Lean leadership. The plant manager implements corporate policy but does not make it. If top management has adopted Lean, the plant managers can make it succeed or fail.