About Michel Baudin

Consultant, trainer, author on Lean Manufacturing

Posts by Michel Baudin:

Feb 27 2012

How natural disasters test Lean supply chains

Via Scoop.it – lean manufacturing

The floods in Thailand are the latest. Before, there was the Fukushima earthquake and, going back further in time, the Aisin Seiki fire of 1997 in Japan and the Mississippi flood of 1993…   Each time, the press has faulted Lean for making the economic disruptions caused by theses events worse. The actual record is that the vigilance inherent in Lean Logistics and the strength of customer-supplier relationships in a Lean Supply Chain are in fact key to a rapid recovery.

In 1993, Toyota logisticians in Chicago reserved all the trucking available in the area a few days before the flood cut off the rail lines to California, thereby allowing the NUMMI plant to keep working during the flood.

In 1997, when the Aisin Seiki fire deprived Toyota in Japan of its single source of proportioning valves, other suppliers came to the rescue in what the Wall Street Journal a few months later called the business equivalent of an Amish barn raising.

You can, and should protect production against routine fluctuations. That is what tools like Kanbans are countermeasures for. But there is no way you can afford to protect your business against all possible, rare catastrophic events. What you can and must do instead is be vigilant and prepared to respond quickly and creatively to whatever nature or society might throw at you.
Via the Bangkok Post

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Feb 26 2012

Growth in Maintenance’s Share of Manufacturing Employment

Via Scoop.it – lean manufacturing

This article describes a method involving initial testing and extensive training used by an Alabama steel mill to increase Maintenance’s share of its work force to almost 30%.

Jim Peck drew my attention to it on NWLEAN through a post in which he questioned their approach to recruitment as training people who didn’t need it or turning down people with the right skills. This kind of information,  of course, is not in the article.

The article points out the growing of share of Maintenance in the work people do in a manufacturing operation as it evolves. Based on the numbers in the article, close to one in four employees of the mill works in Maintenance today, and they are trying to increase this ratio. Steel is an industry that has had enormous productivity increases in the past decades. As they point out in the article, they went from 45,000 employees in the 1940s to 2,100 today, who produce as much.

In today’s labor-intensive manufacturing activities, maintenance’s share of the labor force is on the order of 5%, and I believe we can expect that number to rise. For example, an auto plant that employs 5,000 today may produce the same amount with the same depth of manufacturing with 500 people 25 years from now — if cars are still around in 2037… And, out of these 500 people, 150 to 200 will be in Maintenance, the rest being primarily programmers of automatic machines.

Whether testing is appropriate or not depends on the relevance of what people are tested on. An organization has the right to decide what “qualified” means for its own needs. On the other hand, I find testing inappropriate if there is a hidden agenda.

Many Silicon Valley software companies, for example, subject applicants to “coding interviews,” in which they are tested on such topics as the details of sorting algorithms. A computer science student learns this in college but rarely uses it as a professional programmer, because 90% of the time you need to sort records, you just invoke a sort function without worrying about what is under the hood. As a consequence, this kind of test is an effective way to bias the interviews in favor of recent college graduates and filter the experienced programmers.
Via www.reliableplant.com

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Feb 24 2012

Zambia, Japan agree need for increased productivity

Via Scoop.it – lean manufacturing
“GOVERNMENT says the country needs a vibrant and productive workforce that will implement practical measures to raise the standard of living required to increase production.”

Don’t you usually increase production in order to raise the standard of living rather than the other way around? In any case, good luck to the Zambians in achieving prosperity.
Via www.daily-mail.co.zm

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Feb 24 2012

Bodo Wiegand on Shop Floor Management as Leadership Responsibility

Bodo Wiegand heads the Lean Management Institute, which is the German affiliate of the Lean Enterprise Institute. The following is a translation from German of a large excerpt from his  February, 2012 newsletter,  Wiegand’s Watch:

Last week I was invited to a visit a company to discuss the benefits of Lean management with its Board. On such occasions I always ask for a detailed factory tour first. This way, the discussion can be better focused on the company’s actual problems and not get stuck in theory.
My short audit begins before the actual visit. Before turning into the visitor parking lot, I drive around the facility to inspect the grounds. Is it tidy? What do I see? As there marked pathways? How much material lying around? How many employees, forklifts, trucks and cars are moving around? This is my very first impression.
In the actual plant tour, I know they will not show me the problem areas of the company, and that they will keep me as much as possible on a visitor path is. However,  by saying that I would like to go from customer to supplier, I usually get to see what I need. So we follow the value stream from back to front.
The way to Shipping usually reinforces what I have seen outside:  if it is messy outside, with no marked pathways or areas are selected, heaps of materials are piling up, and  cars and trucks randomly parked, what else can I expect in production?
Far too few pay attention and remember that this is the company’s calling card.
Now, in this case are with me the production manager and the Lean Leader. They explain with pride that they have been doing Lean for two years already for 2 years and have achieved huge success. They have  set-up times in half on several machines. But we were at Shipping and I just wanted to know what products were arriving  today to go out  today or tomorrow at the latest. After questioning the Shipping clerk then we found that two containers that were very important and urgent were just too late.
To my question on how often something like this happens, the production manager answered “Rarely”; the shipping clerk, “Every day.” After a short discussion, the production manager admitted to a delivery reliability of 80%, but he was not quite sure. To my question about lead time the Lean leader proudly answered “In general, about 3 weeks.”
“How long does it take to run through a super hot job” , I asked as a follow-up.
“2 days,” he shot back.
My next question about how many projects he had initiated to reduce the lead time demotivated further, as he had to admit there weren’t any.
Well, for me the lead time is one of the most important metrics in a company is and a priority in the execution of projects. The shorter the lead time, the higher the flexibility, the smaller the stocks, the more stable the process, the less time available to make mistakes, and the more efficient the organization.
But satisfaction with a lead time  ratio of 1 to 10 between hot and normal jobs in German companies is quite amazing. For the hot job to be completed in 2 days, it flows through the company without intermediate storage, is processed immediately and is  carried through without pause, without waste, except of course that the supervisor personally takes the matter in hand. But why is it not always like this for all jobs? Why is the exception and not the rule?
But we moved on. In assembly, the Lean leader explains that they have built up an assembly line, but that it still cannot work to the takt time, and that they have therefore built up behind the line an assembly rework shop for quality problems.
Hello? – Has he really understood Lean?
But even outside of the assembly line you could not overlook the signs of chaos. You saw pallets with several items pulled from the supermarket, but by the pallet-load rather than in the quantities necessary for assembly. The reason was simple. The storage space in the supermarket was insufficient and the supermarket was just too full.
The degree of Lean manufacturing and Lean understanding was close to zero.
Next, I turned my attention to the order fulfillment process. But there, also, they had no clue where to start with takt time, bottlenecks, and inventory. The information boards were full of outdated figures on revenue and absenteeism. Two departments were reasonably tidy and provided with standards that were not followed. Brooms and tools had assigned shadows, but were not actually available. Employees were running around for no apparent reason, or talking in small groups. The production manager didn’t know the supervisor’s name, the clocks were off, some windows broken and lamps without bulbs, etc., etc.
[…]
To avoid any misunderstanding, as I walk through a company, I don’t pretend to understand everything, but I try to get an overall impression. Those of you who walk through production daily must know how to see and should focus their attention on a different theme every day to be a good shop floor manager. But beware! It is a difficult, thorny path – but it’s worth it.

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Feb 18 2012

Peter Drucker ideas repackaged as “Personal Kaizen” in Dubai

Via Scoop.it – lean manufacturing

Back in 1968, in The Effective Executive, Peter Drucker exhorted his readers to keep a detail journal of their activities to measure what they actually spent their time on. When I tried it, I quickly found this record keeping itself on my list of activities to eliminate. 44 years later, Dubai’s Oksana Tashakova has repackaged the idea as Kaizen in personal life.
Via www.khaleejtimes.com

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The-Most-Complicated-Formula

Feb 16 2012

Safety Stocks: Beware of Formulas

A formula you find in a book or learn in school is always tempting. It is a “standard.” If you follow it, others are less likely to challenge your results. These results, however, may be worthless unless you take a few precautions. Following are a few guidelines:

  1. Don’t use a formula you know nothing about. Its validity depends on assumptions that may or may not be satisfied. You don’t need to know how to prove the formula, but you need to know its range of applicability.
  2. Examine your data. Don’t just assume they meet the requirements. Examine their summary stats, check for the presence of outliers, generate histograms, scatter plots, time series, etc.
  3. Don’t make up missing data. If you are missing the data you need to estimate a parameter, find what you can infer about the situation from other parameters, by other methods. Do not plug in arbitrary values.
  4. Make your Excel formulas less prone to error by using named ranges rather than cell coordinates. If a formula is even slightly complicated, referring to variables by names like “mean” or “sigma” makes formulas easier to proof-read than with names like “AJ” or “AK.”

The safety stock formula for the reorder point method

Safety stock is a case in point. The literature gives you a formula that is supposed to allow you to set up reorder point loops with just the minimum amount of safety needed to prevent shortages under certain conditions of variability in both your consumption rate and your replenishment lead time. It is a beautiful application of 19th century mathematics but I have never seen it successfully used in manufacturing.

Let us look more closely at what it is so you can judge whether you would want to rely on it. Figure 1 shows you a model of the stock over time when you use the Reorder Point method and both consumption and replenishment lead time vary according to a normal distribution. The amount in stock when the reorder point is crossed should be just sufficient to cover your needs until the replenishment arrives. But since both replenishment lead time and demand vary, you need some safety stock to protect against shortages.

Figure 1. The reorder point inventory model

If your demand is the sum of small quantities from a large number of agents, such as sugar purchases by retail customers in a supermarket, then the demand model makes sense. In  a manufacturing context, there are many situations in which it doesn’t. If you produce in batches, then the demand for a component item will be lumpy: it will be either the quantity required for a batch or nothing. If you use heijunka, it will be so close to constant that you don’t need to worry about its variations.

What about replenishment lead times? If in-plant transportation is by forklifts dispatched like taxis, replenishment lead times cannot be  consistent. On the other hand, if it takes the form of periodic milk runs, then replenishment lead times are fixed at the milk run period or small multiples of it. With external suppliers, the replenishment lead times are much longer, and cannot be controlled as tightly as within the plant, and a safety stock is usually needed.

Let us assume that all the conditions shown in Figure 1 are met. Then there is a formula for calculating safety stock that you can find on Wikipedia or in David Simchi-Levy’s Designing and Managing the Supply Chain (pp. 53-54).  Remember that it is only valid for the Reorder Point method and that it is based on standard deviations of demand and lead time that are not accessible for future operations and rarely easy to estimate on past operations. The formula is as follows:

Where:

  • S is the safety stock you need.
  • C  is a coefficient set to guarantee that the probability of a stockout is small enough. You can think of it a number of standard deviations above the mean item demand needed to protect you against shortages. In terms of Excel built-in functions, C is given by:

C = NORMSINV(Service level)

Service levelC
90.0% 1.28
95.0% 1.64
99.0% 2.33
99.9% 3.09

  • The other factor, under the radical sign, is the corresponding standard deviation.
  • μL and σL are the mean and standard deviations of the lead time.
  • μD and σD are the mean and standard deviation of the demand per unit time, so that the demand for a period of length T has a mean of μD xT and a standard deviation of σDx √T

Case study: Misapplication of the safety stock formula

This formula is occasionally discussed in Manufacturing or Supply Chain Management discussion groups, but I have only ever seen one attempt to use it,  and it was a failure. It was for the supply of components to a factory, and 14 monthly values were available for demand, but only an average for lead times.

The first problem was the distribution of the demand, for which 14 monthly values were available. This is too few for a histogram, but you could plot their cumulative distribution and compare it with that of a normal distribution with the same mean and standard deviation, as in Figure 2. You can tell visually that the actual distribution is much more concentrated in the center than the normal model, which is anything but an obvious fit.  Ignoring such objections, the analyst proceeded to generate a spreadsheet.

Figure 2. Actual versus normal cumulative distribution

The second problem is that he entered the formula incorrectly, which was not easy to see, because of the way it was written in Excel.  The formula in the spreadsheet was as follows:

C*SQRT((AJ4*AL4^2)+(AI4^2*AM4^2))

then, looking at the spreadsheet columns, you found that they were used as follows:

  • AJ  for Standard Deviation of Daily Demand, and
  • AL for Average Replenishment time.

And therefore the first term under the square root sign was σDxμL2 instead of μLxσD2.

The third problem was that the formula requires estimates of standard deviations for both consumption and replenishment lead times, but no data was available on the latter. To make the formula produce numbers, the standard deviations of replenishment lead times was arbitrarily assumed to be 20% of the average.

For all of these reasons, the calculated safety stock values made no sense, but nobody noticed. They caused no shortage, and the “scientific” formula proved that they were the minimum prudent level to maintain.

Sizing safety stocks in practice

There is no universal formula to determine an optimal size of safety stocks. What can often be done is to simulate the operation of a particular replenishment loop under specified rules. For a simulation of a Kanban loop using Excel, see Lean Logistics, pp. 208-213.

No calculation or simulation, however, is a substitute for keeping an eye on what actually happens on the shop floor during production. One approach is to separate the safety stock physically from the regular, operational stock and monitor how often you have to dig into it. If, say, six months go by without you ever needing it, you are probably keeping too much and you cut it in half. With a Kanban loop, you tentatively remove a card from circulation. If no shortage results, then the card was unnecessary. If a shortage occurs, you return the card and look for an opportunity to improve the process so that the card can be removed.

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