How Does This All Play Out?

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.

Continue reading

Bridging the Gap between Buyers and Suppliers | Robert Moakler | IndustryWeek

“Creating high performance, collaborative alliances between buyers and U.S. suppliers will ensure rebuilding a strong and sustainable American supply chain.”



Michel Baudin‘s comments:

Robert Moakler reiterates the well known fact that collaboration between suppliers and customers is a win/win, and offers an e-sourcing platform as the better mousetrap that will make it happen.

As COO of an “online marketplace exclusively developed for the American manufacturing industry,” Moakler is forthright about where he is coming from. But is lack of technology the reason why adversarial, arm’s length relations between suppliers and customers remain the norm?

My own findings on this matter — summarized in Lean Logistics, on pp. 342-350 — is that each side stands to gain a short-term advantage from unilaterally breaking a collaborative relationship, and that the business history of the past 25 years shows examples of this happening.

On the customer side, a new VP of purchasing can instruct buyers to use the information suppliers have shared to force price concessions. Conversely, suppliers can leverage intimate, single-sourcing, collaborative relations with a customer to charge above-market prices.

None of these behaviors is viable in the long term, but not all managers care about the long term, and the toughest challenge in establishing collaborative relations is defusing well-founded fears about the future behavior of the other side.

While wishing Mr. Moakley the best of luck in his business, I don’t believe technology is the problem.

See on Scoop.itlean manufacturing

The GM Toyota Rating Scale | Bill Waddell

See on Scoop.itlean manufacturing

“In a survey of suppliers on their working relationships with the six major U.S. auto makers – Toyota, Honda, Nissan, Ford, Chrysler and GM – GM scored the worst.  But of course they did.  They are GM and we can always count on such results from them. […] Toyota scored highest with a ranking of 318, followed by Honda at 295, Nissan at 273, Ford at 267, Chrysler at 245, with GM trotting along behind the rest with an embarrassing 244.”


Michel Baudin‘s comments:

While I am not overly surprised at the outcome, I am concerned about the analysis method. The scores are weighted counts of subjective assessments, with people being asked to rate, for example, the “Supplier-Company overall working relationship” or “Suppliers’ opportunity to make acceptable returns over the long term.”

This is not exactly like the length of a rod after cutting or the sales of Model X last month. There is no objective yardstick, and two individuals might rate the same company behavior differently.

It is not overly difficult to think of more objective metrics, such as, for example, the “divorce rate” within a supplier network. What is the rate at which existing suppliers disappear from the network and others come in? The friction within a given Supplier-Customer relationship could be assessed from the number of incidents like the customer paying late or the supplier missing deliveries…

Such data is more challenging to collect, but supports more solid inferences than opinions.

See on

“The Fragility of Lean is its Strength” | Michael Ballé

See on Scoop.itlean manufacturing

“Can a pull system with zero stock make the company more fragile? Sure it can, that’s the whole point: fragile, but flexible. The pull system doesn’t make the company fragile: it reveals the weak points. Top performance is about keeping a ball on top of a round hill it needs everybody’s engagement to make sure it stays there.”

Michel Baudin‘s insight:

Pull systems don”t have zero stocks, but low stocks, and the experience of disasters — from the Mississippi flood of 1993 in the US, to the Aisin Seiki fire of 1997 and the Fukushima earthquate of 2011 in Japan — has shown Toyota’s pull system to be robust and able to recover much faster than anyone expected, including the Japanese press.

“Fragility is strength” is a cute paradox, but not applicable here. The true paradox is that stocks generally do not protect you against shortages. They do for crude oil and similar commodities, but not for  the thousands of different items you need in manufacturing. You end up with full warehouses that are somehow out of the item you need today.

What protects you more effectively is the combination of low stocks with the kind of vigilance that makes you reserve all the available trucking in Chicago a few days before a flood, as Toyota did in 1993. A pull system supports the vigilant monitoring of stocks in two ways:

  1. Self-adjustment in the speed of circulation of pull signals automatically absorbs the routine fluctuations in volume and mix.
  2. When major disruptions occur, they are quickly detected and acted on.

See on

Article presenting team-building games as “best practice” | AME

See on Scoop.itlean manufacturing

In his 1951 novel Player Piano, Kurt Vonnegut describes team building games that were eerily similar to the ones in this article. This approach has therefore been around US corporations for at least 60 years. But does it work?

We know that simulation games are effective as a Lean training tool, for example, but they are direct metaphors for the production work the participants do. The idea that generic games, unrelated to work, would be effective at developing teamwork is anything but obvious.

A promoter of this approach is quoted in the article as citing “research from MIT,” which I couldn’t find on Google. Experimental proof would require two groups of similar teams engaged in similar projects, with one group using these exercises and the other not. Then it would compare their performance on work projects.

We are also supposed to show respect for people. How respectful is it to an employee’s expertise to put him or her through this kind of experience? With the same time and money, you could send a machinist to a seminar on new cutting tools, with the duty to report on learnings to colleagues, or you could send a warehouse manager to learn about, say, RFID.

See on

Deming’s Point 4 of 14 – End the practice of awarding business on the basis of a price tag…

(Picture from TYWKIWDBI)

The complete wording of Deming’s Point 4 is as follows:

“End the practice of awarding business on the basis of a price tag. Instead, minimize total cost. Move toward a single supplier for any one item, on a long-term relationship of loyalty and trust.”

Today, you will encounter no one involved with supply chain management who would argue against the idea of basing purchasing decisions on the total cost of having the item on hand when needed for production and developing collaborative relationships with suppliers. The idea of single-sourcing every item, on the other hand, makes many managers nervous, but, without such a committed relationship, you cannot have information exchange at the depth required for collaboration to pay off.

As of 2012, however, very few companies have followed through on this recommendation. What we have seen instead in the past 20 years instead is “We’ll skip Lean and go straight to China,” based exclusively on temporarily cheap labor, without due consideration to local infrastructure, quality and productivity issues, and logistics. Companies that are “reshoring” after being burned at this now have an opportunity to implement this most specific and least controversial of Deming’s 14 points.

It breaks down into the following specific recommendations on what is now called supply chain management:

  1. End the practice of awarding business on the basis of a price tag.
  2. Minimize total cost.
  3. Move toward a single supplier for any one item.
  4. Develop long-term relationships of loyalty and trust with suppliers.

1. Stop awarding business on the basis of a price tag

In this area,  companies don’t behave like individuals. Whether you buy food, clothing, household appliances, or the services of a plumber, you don’t systematically choose the lowest price. Like the astronaut on the launch pad, you do not want every part in the rocket to have been made by the lowest bidder. Even if you are hunting for bargains, you also consider quality, delivery, and the availability of support. You willingly pay more for appliances that are reputed for working well, lasting long, operating quietly, match the design of your house, and have spare parts and service readily available.

In principle, a company’s purchasing agents should think the same way. When they don’t, it is because they are evaluated on the prices they are able to negotiate and because they are not familiar with the actual use of the materials or equipment they buy. If you hired a third party to do your shopping, with instructions to find the lowest prices, you are unlikely to be happy with the results or even to same money over time.

Because they don’t use what they buy, purchasers rely on specs to decide whether a supplier’s product meets the company’s needs. As Deming points out, however, conformance to specs is never synonymous with fitness for use, no matter how carefully the specs are written. Specs only work as a one-way filter; if a product is out of spec, you know you can’t use it, but, if it is within specs, it does not guarantee that you can. Juran distinguished between true and substitute characteristics. The true characteristics are what you are really after, like the taste of a cake. Unfortunately, you cannot verify it without eating the cake, so you use substitute characteristics that you can measure, like the cake’s diameter or the sugar content of the ingredients. If they are out of specs, you know there is something wrong with the cake, but they can all be within spec and the cake still taste awful.

Relying on specs in purchasing is therefore taking necessary conditions and treating them as sufficient. But how do you avoid doing this? Deming does not say. I recommend the following:

  1. Avoid perverse incentives. Use metrics for purchasing that do not overemphasize the price.
  2. Implement Lean supply chain management. It is a broader subject than just buying based on price, but it provides a context for a more balanced approach to evaluating suppliers.
  3. Rotate professionals in and out of Purchasing. This means treating purchasing as a skill employees should have rather than a career. If you have people in Purchasing who have previously worked in Production or Engineering, they will have a better understanding of the issues.
  4. Give end users a voice in Purchasing. Purchasing should not have the authority to switch suppliers without the approval of those who consume the materials or use the equipment.

2. Minimize total cost

For manufacturing, it means considering everything it takes to have good materials within arm’s reach of the production operator for as long as the line is running on this product, as opposed to the price on a purchase order. For equipment, it means looking at the total cost of ownership (TCO), also a term that was introduced after Out of the Crisis came out.

The only issue Deming raises is that of quality, but it is not the only one, particularly when you consider switching from a  supplier located 10 miles from your plant to one that is 6,000 miles and 10 time zones away in an unfamiliar country. You have to consider transportation, longer lead times, communications and travel.

Furthermore, discussing cost and quality in the same breath leads naturally to thinking about what the literature calls “cost of quality.” The literature on quality defines this cost as the sum of the direct costs of failure, appraisal and repair, and omits the impact of quality on sales, as being too “controversial” and difficult to measure. This “cost of quality,” however, is the tip of the iceberg; it grossly underestimates the business consequences of quality problems, as shown, for example by the Firestone tread separation issue in 2000 or Toyota sticky accelerator pedals in 2010. A car maker’s reputation for quality is its crown jewels, and the answer on how much effort it should put into nurturing is is whatever it takes.

While transportation costs are relatively easy to calculate, the cost of expanding lead times from days to weeks or months is, in some cases, much larger than the cost of having inventory in transit. For example, toys sold in the US during each Christmas season are made in China the previous summer, but you cannot tell bestsellers from duds until late in the fall, by which time there is nothing you can do to adjust the supply.

To follow Deming’s recommendation here, you consider not the unit price of the item but all the outflows of funds generated by the decision to buy it for a given supplier for as long as you intend to do it, knowing that this may vary from a few months for fashion-related items to several decades for airplane parts. The question is not the price of one unit but, for example, what it takes to make, say, 1,000 usable units available on your production line every day for the next four years. And you have to write at least a best-case, worst-case, and most likely scenarios of how it may unfold in terms of volumes, quality and delivery performance, and  technical support of the supplier. Each scenario results in cash flow schedules that can be compared.

Such an analysis cannot be done without making assumptions about product life, demand, and supplier capabilities. It is more complex than picking the lowest bidder, but the stakes are high.

3. Move toward a single supplier for any one item

What happens when your single supplier fails? It happened to Toyota with the Aishin Seiki fire of February, 1997. The plant was Toyota’s single source of proportioning valves for Toyota in Japan. Toyota’s factories shut down within four hours of the fire, the supplier network was mobilized, production was restarted within a week, and was back to full volume in 6 weeks.  In the Japanese press, the fire was initially viewed as a failure of Toyota’s system; by the time it ended, it was a vindication of it.

If you buy thousands of items, even with a single source for each, you will have hundreds of suppliers. If you have a policy of having at least two sources for each item, you will have even more suppliers and more complicated relationships to manage. Deming emphasizes the impact on quality, but it touches in fact every aspect of supplier relations. Juggling multiple suppliers for each item is playing the field; having a single source, a monogamous relationship.

If, for each item, you have a single source for whom you are a major customer, your plan for dealing with emergencies like the Aisin Seiki fire is to rely of the strength of your supplier network to come up with an appropriate response. The Wall Street Journal article about the Aisin Seiki fire in May, 1997 described the response of Toyota suppliers as the manufacturing equivalent of an  Amish barn raising.

Sudden surges in demand are not an issue in car manufacturing, but they are in other industries, like semiconductor production equipment. If you are a machine shop making components for this industry, you may see demand doubling overnight simply because  one semiconductor company placed a big order for machines in a new wafer fab. You know that sudden changes in the economy may cause this order to be cancelled, and you cannot count of other orders filling up you slack capacity once this order is filled. In this case, rather than investing in additional equipment that is unlikely to be permanently needed, suppliers have been known to make second-sourcing agreements with competitors to provide surge capacity. One consequence of such arrangements is that the parts arriving at the customer plant may come from different suppliers. From the customer’s perspective, however, it is still a single-sourcing arrangement,  because the primary supplier remains responsible for quality and delivery.

4. Develop long-term relationships with suppliers

A six-year contract representing 30% of your sales to be a customer’s sole supplier of a component sets the stage for a different working relationship than a one-year contract representing 10% of your sales, in which you are one of a stable of suppliers among which the customer splits the demand. Exclusive, long-term relationships are clearly a required foundation for the collaboration that the entire literature on supply chain management agrees should take place between suppliers and customers, but generally doesn’t.

“Arms around” is better for both sides than “arm’s length” and adversarial. So why is it so rare, and what can we do to make it more common? The abundant literature on supply chain management fails to see what I think is the elephant in the room: unlike a plant, a supply chain is ruled by the interaction of multiple, independent economic agents. This is discussed in Chapter 19 of Lean Logistics (pp. 341-352). The summary is as follows:

In the lean supply chain, the traditionally adversarial, arm’s length relationship between supplier and customer makes way for a collaborative approach, centered on long-term single-sourcing agreements, and extensive exchanges of business information and technical know-how. This approach increases the total payoff of the relationship, but transitioning to it is difficult because it requires behavior changes on both sides.
Sustaining it over time also requires management to consistently forgo the short-term windfalls that can be reaped through a unilateral return to the adversarial approach. That supplier and customer should collaborate to increase the total payoff does not prevent each one from negotiating aggressively with the other on sharing this payoff.

Once you acknowledge that a collaborative relationship takes a long time to build and are easily destroyed by either side, you can manage it accordingly and give it the attention it requires.

Cellular manufacturing for web-customized wedding dresses

See on Scoop.itlean manufacturing

“…rather than be part of a large group working on an assembly line, workers are divided into small teams, with each one headed by a high-skilled leader. The cells each produce three or four dresses a day according to very specific designs that require a lot of handwork. Taken in total, each company can produce about 200 dresses a day that way, with each cell producing a very different dress…”

See on

Another article that confuses Lean with reckless

See on Scoop.itlean manufacturing

This is yet another article that equates a Lean supply chain with one that is unprepared for disasters.

See Just-in-time and disasters for the actual Lean approach to disaster recovery.

See on

Safety Stocks: More about the formula

In a previous post on 2/12/2012, I warned against the blind use of formulas in setting safety stock levels. Since then, it has been the single most popular post in this blog, and commands as many page views today as when it first came out. Among the many comments, I noticed that several readers, when looking at the formula, were disturbed that three of the four parameters under the radical are squared and the other one isn’t, to the point that they assume it to be a mistake. I have even seen an attempt on Wikipedia to “correct that mistake.”

I was myself puzzled by it when I first saw the formula, but it’s no mistake.  The problem is that most references, including Wikipedia,  just provide the formula without any proof or even explanation. The authors just assume that the eyes of inventory managers would glaze over at the hint of any math. If you are willing to take my word that it is mathematically valid, you can skip the math. You don’t have to take my word for it, but then, to settle the discussion, there no alternative to digging into the math.

A side effect of working out the math behind a formula is that it makes you think harder about the assumptions behind it, and therefore its range of applicability, which we do after the proof. If you don’t need the proof, please skip to that section.

Math prerequisites

As math goes, it is not complicated. It only requires a basic understanding of expected value, variance, and standard deviation, as taught in an introductory course on probability.

In this context, those who have forgotten these concepts can think of them as follows:

  • The expected value E(X) of a random variable X can be viewed, in the broadest sense, as the average of the values it can take, weighted by the probability of each value. It is linear, meaning that, for any two random variables X and Y that have expected values,

E[X+Y] = E[X]+E[Y]

and, for any number a,

E[a\times X]= a \times E[X]

  • Its variance is the expected value of the square of the deviation of individual values of X from its expected value E(X):

Var(X) = E[X-E(X)]^{2}= E[X^{2} -E(X)^{2}]

Variances are additive, but only for uncorrelated variables X and Y that have variances. If

E[[X-E(X)] \times [Y-E(Y)]]= 0


Var(X+Y) = Var(X)+Var(Y)

  • Its standard deviation is

\sigma = \sqrt{Var[X]}

Proof of the Safety Stock Formula

Fasten your seat belts. Here we go:

As stated in the previous post, the formula is:


  • S is the safety stock you need.
  • C  is a coefficient set to guarantee that the probability of a stockout is small enough.
  • The other factor, under the radical sign, is the corresponding standard deviation.
  • μL and σL are the mean and standard deviations of the time between deliveries.
  • μD and σD are the mean and standard deviation rates for the demand.

  \sqrt{\mu{_{L}^{}}\times\sigma_{D}^{2}+\mu_{D}^{2} \times \sigma_{L}^{2}} is the standard deviation of the item quantity consumed between deliveries, considering that the time between deliveries varies.

μD and  \sigma_{D}^{2} are the mean and variance of the demand per unit time, so that the demand for a period of length T has a mean of \mu_{D} \times T , a variance of  \sigma_{D}^{2} \times T, and therefore a standard deviation of \sigma_{D} \times \sqrt{T}. See below a discussion of the implications of this assumption.

Note that the assumptions are only that these means and variances exist. At this stage, we don’t have to assume more, and particularly not that times between deliveries and demand follow a particular distribution.

If D(T) is the demand during an interval of duration T, since:

we have:

If we now allow T to vary, around mean μL with, standard deviation σL , we have:

and therefore:


That’s how the variance ends up linear in one parameter and quadratic in the other three!


 \sigma\left [ D \right ]= \sqrt{\mu _{L}\times\sigma_{D}^{2} + \mu _{D}^{2} \times \sigma_{L}^{2} }


Note that all of the above argument only requires the means and standard deviations to exist. There is no assumption to this point that the demand or the lead time follow a normal distribution. However, the calculation of the multiplier C used to calculate an upper bound for the demand in a period, is based on the assumption that the demand between deliveries is normally distributed.


The assumption that the variance of demand in a period of length T is \sigma_{D}^{2} \times T implies that it is additive, because if  T = T_{1} + T_{2}, then \sigma_{D}^{2} \times T = \sigma_{D}^{2} \times T_{1} + \sigma_{D}^{2} \times T_{2}.

But this is only true if the demands in periods T_{1} and T_{2} are uncorrelated. For a hot dog stand working during lunch time, this is reasonable: the demands in the intervals between 12:20 and 12:30, and between 12:30 and 12:40 are from different passers by, who make their lunch choices independently.

On the other hand, in a factory, if you make a product in white on day shift and in black on swing shift every day,  then the shift demand for white parts will not meet the assumptions. Within a day, it won’t be proportional to the length of the interval you are considering, and the variances won’t add up. Between days, the assumptions may apply.

More generally, the time periods you are considering must be long with respect to the detailed scheduling decisions you make. If you cycle through your products in a repeating sequence, you have an “Every-Part-Every” interval (EPEI), meaning, for example, that, if your EPEI is 1 week, you have one production run of every product every week.

In a warehouse, product-specific items don’t need replenishment lead times below the EPEI. If you are using an item once a week, you don’t need it delivered twice a day. You may instead receive it once a week, every other week, every three weeks, etc. And the weekly consumption will fluctuate with the size of the production run and with quality losses. Therefore, it is reasonable to assume that its variance will be \sigma_{D}^{2} \times T where T is a multiple of the EPEI, and it can be confirmed through historical data.

You can have replenishment lead times that are less than the EPEI for materials used in multiple products. For example, you could have daily deliveries of a resin used to make hundreds of different injection-molded parts with an EPEI of one week. In this case, the model may be applied to shorter lead times, subject of course to validation from historical data.