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Oct 3 2014

A summary of mistakes about Lean

In an invitation to the Lean Enterprise Academy ‘s Lean Summit 2014, David Brunt included the following summary of Lean since 1990:

“Early implementations focused on empowered teams and continuous improvement (kaizen) or attempts to replicate a pre-defined box of tools such as 5S, SMED, SPC and kanban. For others lean became synonymous with kaizen events – that were actually kaikaku – radically reconfiguring individual operations. For some, this led to them developing their version of Toyota’s famed Production System (TPS) including their own schematic ‘house’ or ‘temple’ of lean along with departments of continuous improvement specialists.”

It is a pretty accurate account of what happened — the only major omission being the omnipresent VSMs — and it goes a long way towards explaining why the vast majority of these efforts failed. They were limited at best to superficial details of TPS, included elements that were not part of TPS, and misjudged implementation priorities. Let’s us go through the list:

  1. “Empowered teams.”  As a manager you have a team to work with. What decisions should you allow this team to make on its own? This is best subjected to the sleep-at-night test. Knowing that you are responsible for the outcome, what can you delegate to the team and still sleep at night? It obviously depends on the team. If it is a team of production operators with 10 years of TPS practice behind it, the answer will not be the same as if they are beginners. Implementations that start with empowering teams put the cart before the horse.
  2. “Continuous improvement (kaizen).” Lean, or TPS, are often described as approaches to continuous improvement (CI), when CI is in fact only one component of the system. You cannot convert a plant from mass production to Lean manufacturing by continuous improvement, because it is not about tweaking details. For example, if you have implemented cells in machining or assembly, you can make them perform better with CI, but you have to have cells first, and that is beyond the scope of CI.
  3. “Replicate a pre-defined box of tools.” It can work, if your situation is sufficiently similar to the one you are copying, you really know what the tools are, and you master them.
    • SMED and Kanban are tools of TPS but often misunderstood. For example, you often see SMED used to try to increase equipment utilization instead of flexibility, and Kanban is often confused with the two-bin system or even reorder-point.
    • SPC is not part of TPS. This is so shocking to American and European professionals trained by the Quality establishment that they just inserted it back in, regardless of what Toyota actually did. The latest examples of SPC control charts at Toyota are from the 1950s.
    • 5S is part of TPS, but is mistakenly assumed easy to implement because its technical content is trivial. In fact, the absence of technical content is what makes it difficult to implement and certainly unfit for an initial project.
  4. “Kaizen events” are an American invention and not part of TPS. As Brunt points out, the name is misleading, because what they do is not Kaizen. The popularity of this method over the past 25 years and the confusion created by the name have in effect prevented Lean implementation from including the real Kaizen.
  5. “Departments of continuous improvement specialists.” The creation of these departments has often made Lean implementation into a function alongside Production Control, Maintenance, or Quality Assurance, with the result of making it a professional specialty instead of part of everybody’s job. It works to make a good show for outside visitors, but not for much else. This department cannot be large enough to have the capacity to do all that needs to be done. Even if it did, it does not have the authority to make the changes take root in daily operations.

These efforts failed because the approach was simplistic. Both the technical and managerial content of TPS are deeper and take a while to learn. A successful implementation, particularly is a different industry, is not based on copying tools but on understanding underlying principles and deploying them as appropriate to the new context.

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By Michel Baudin • Management 0 • Tags: CI, Continuous improvement, Kaizen, Kanban, Lean, Lean implementation, SMED, SPC, Toyota, TPS

Sep 29 2014

Discipline And The Broken Windows Theory | Dumontis

“Over the last few years a lot has been written about Lean leadership. For instance about what the differences would be between Lean and traditional leadership. And what the characteristics are of a Lean leader. One of the aspects often missing, I feel, is “discipline”. I have always told my managers that they weren’t paid more because they would supposedly be more intelligent or because they studied for a longer period of time, but because I expected them to be the most disciplined in respecting standards. As without the manager’s respect – also interestingly described in the “broken windows” theory – the organization as a whole will flout its own rules.”

Source: www.dumontis.com

Michel Baudin‘s comments:

Is being disciplined in respecting standards truly the quality that justifies managerial pay? By this criterion, the Caine’s Captain Queeg and the Bounty’s Lt. Bligh were both excellent managers. Whatever happened to “plan, organize, control, and lead”?

Like the “Hawthorne effect” or “Maslow’s hierarchy of needs,” the broken windows theory is being accepted just because it sounds plausible, not because it is supported by experiments. Do clean walls and intact windows deter serious crime? Perhaps, but it has to be established, and the response of passers-by to flyers does not do the job.

See on Scoop.it – lean manufacturing

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By Michel Baudin • Blog clippings 6 • Tags: Broken-windows theory, Hawthorne effect, Maslow's hierarchy of needs

Sep 19 2014

The Creative Benefits of Boredom | HBR Blog Network | David Burkus

“[…]a certain level of boredom might actually enhance the creative quality of our work […]”

Source: blogs.hbr.org

Michel Baudin‘s comments:

It is one step away from claiming that boredom makes you creative, which would make no sense. The frustration of boredom may motivate you to use your creativity, but deliberately boring people in order to make them creative is not something I would recommend.

I think that creativity is innate, but much more widely spread than most managers and engineers believe. The example in the article is about sales;  I am more familiar with manufacturing, where most jobs are repetitive, tedious, and boring.

They jobs are also tiring, but most production operators will tell you that they don’t mind the tiredness as much as the slowness of the clock. Boredom is their number one enemy, and participation in improvement activities a welcome relief from it, as well as an opportunity to be creative.

People who are bored by repetitive tasks go “on automatic.” Their hands keeps executing the sequence of tasks with accuracy and precision, while their minds wander off to, perhaps, the lake they fish in on week-ends. While on automatic, you don’t think about improvements.

Changes in the routine, whether deliberate or accidental, refocus their minds on the workplace. This includes product changes, spec changes, rotation between work stations, or any breakdown like defects in the product, component shortages, or machine stoppages. During theses changes, while engaged, your mind is focused on responding as you were trained to, and avoiding mistakes. If you think of better ways to do this work, they go on the back burner in your mind, while you attend to immediate needs.

Depending on the management culture, operators may or may not be willing to share these ideas. They may be afraid of humiliation by a tactless manager, or they may fear that improving their job puts it in jeopardy,…

To put to use the operators’ creativity, you have to organize for this purpose, and it can’t be while the line is running. This is why continuous improvement requires structures, procedures, and leadership.

See on Scoop.it – lean manufacturing

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By Michel Baudin • Blog clippings 0 • Tags: boredom, Continuous improvement, creativity

Sep 7 2014

VSM Pitfall: unnecessary process | Chris Hohmann

Value Stream Mapping (VSM) is probably the main analysis tool and the most used in the lean toolbox. Easy to understand and handle, VSM is the starting point of improvement workshops and kaizen eve…

Source: hohmannchris.wordpress.com

Michel Baudin‘s comments:

Thoughtful comments, as usual from Chris Hohmann.

However, we need to go further and question the wisdom of reducing Lean implementation to Value-Stream Mapping and kaizen events when neither tool is central to the Toyota Production System.

“Value-Stream Mapping,” which is really materials and information flow mapping, is a minor tool at Toyota, used only with suppliers who have delivery problems. And “kaizen events” don’t exist at Toyota.

See on Scoop.it – lean manufacturing

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By Michel Baudin • Blog clippings 1 • Tags: Kaizen Events, Lean, Lean implementation, Toyota, VSM

Sep 2 2014

Are Part Numbers Too Smart for Their Own Good? | ENGINEERING.com

[…] technology experts are warning that the use of such descriptive part numbers is not necessarily so “smart,” and that they could drag down productivity in today’s fast-changing manufacturing environments. A smarter tactic, they assert, is to employ auto-generated “insignificant” or “non-intelligent” part numbers and let information about the part reside in a database. […]

Source: www.engineering.com

See on Scoop.it – lean manufacturing

Michel Baudin‘s comments:
For details on the reasons to get rid of so-called “smart” part numbers, see  Why “Smart” part numbers should be replaced with keys and property lists.

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By Michel Baudin • Press clippings 0 • Tags: Master Data, nomenclature, smart part numbers, Technical Data Management

Gauss with bell shape banknote

Aug 23 2014

The bell curve: “Normal” or “Gaussian”?

Most discussions of statistical quality refer to the “Normal distribution,” but “Normal” is a loaded word. If we talk about the “Normal distribution,” it implies that all other distributions are, in some way, abnormal. The “Normal distribution” is also called “Gaussian,” after the discoverer of many of its properties, and I prefer it as a more neutral term. Before Germany adopted the Euro, its last 10-Mark note featured the bell curve next to Gauss’s face.

The Gaussian distribution is widely used, and abused, because its math is simple, well known, and wonderful. Here are a few of its remarkable properties:

  1. It applies to a broad class of measurement errors. John Herschel arrived at the Gaussian distribution for measurement errors in the position of bodies in the sky simply from the fact that the errors in x and y should be independent and that the probability of a given error should depend only on the distance from the true point.
  2. It is stable. If you add Gaussian variables, or take any linear combination of them, the result is also Gaussian.
  3. Many sums of variables converge to it.  The Central Limit Theorem (CLT) says that, if you add variables that are independent, identically distributed, with a distribution that has a mean and a standard deviation, they sum converges towards a Gaussian. It makes it an attractive model, for example, for order quantities for a product coming independently from a large number of customers.
  4. Mint syrup diffusion in water
    Mint syrup diffusion in water

    It solves the equation of diffusion. The concentration of, say, a dye introduced into clear water through a pinpoint is a Gaussian that spreads overt time. You can experience it in your kitchen: fill a white plate with about 1/8 in of water, and drop the smallest amount of mint syrup you can in the center. After a few seconds, the syrup in the water forms a cloud that looks very much like a two-dimensional Gaussian bell shape for concentration, as shown on the right. And it fact it is, because the Gaussian density function solves the diffusion equation, with a standard deviation that rises with time. It also happens in gases, but too quickly to observe in your kitchen, and in solids, but too slowly.

  5. It solves the equation of heat transfer by conduction. Likewise, when heat spreads by conduction from a point source in a solid, the temperature profile is Gaussian… The equation is the same as for diffusion.
  6. Unique filter. A time-series of raw data — for temperatures, order quantities, stock prices,… — usually has fluctuations that you want to smooth out in order to bring to light the trends or cycles your are looking for. A common way of doing this is replacing each point with a moving average of its neighbors, taken over windows of varying lengths, often with weights that decrease with distance, so that a point that is 30 minutes in the past counts for less than the point of 1 second ago. And you would like to set these weights so that, whenever you enlarge the window, the peaks in your signal are eroded and the valleys fill up. A surprising, and recent discovery (1986) is that the only weighting function that does this is the Gaussian bell curve, with its standard deviation as the scale parameter.
  7. Own transform. This is mostly of interest to mathematicians, but the Gaussian bell curve is its own Laplace transform, which drastically simplifies calculations.
  8. …

For all these reasons, the Gaussian distribution deserves attention, but it doesn’t mean that there aren’t other models that do too. For example, when you pool the output of independent series of events, like failures of different types on a machine, you tend towards a Poisson process, characterized by independent numbers of events in disjoint time intervals, and a constant occurrence rate over time. It is also quite useful but it doesn’t command the same level of attention as the gaussian.

The most egregious misuse of the gaussian distribution is in the rank-and-yank approach to human resources, which forces bosses to rate their subordinates “on a curve.” Measuring several dimensions of people performance and examining their distributions might make sense, but mandating that grades be “normally distributed” is absurd.

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By Michel Baudin • Data science 1 • Tags: gauss, gaussian, measurement, measurement error, Normal distribution, scale-space filtering

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