Logistics

Jan 2 2014

Lean and Management Processes

An online sparring partner of 15 years, Bill Waddell, concluded our latest exchange with the following:

“Lean is comprised of three elements: Culture, management processes and tools. While you obviously have a keen awareness of the culture and tools, you continually under-appreciate the management processes, Michael.”

It is a 3-step progression: first, Bill makes a general statement of what Lean is, then he points out a serious shortcoming in my thinking, and finally he misspells my name.

As I am not trying to go global cosmic with Lean but instead remain focused on Manufacturing, rather than Bill’s three elements, I see Lean as having the four dimensions identified by Crispin Vincenti-Brown. Whatever you do has some content in each of the following:

  • Engineering, in the design, implementation, and troubleshooting of production lines.
  • Logistics and Production Control, covering both physical distribution and the processing of all information related to types and quantities of materials and good.
  • Organization and People, covering the structure, sizing, responsibilities and modes of interaction of departments in production and support, to run daily operations, respond to emergencies, and improve.
  • Metrics and Accountability. How results are measured and how these measurements are used.

Attention must be appropriately balanced in all of these dimensions and, if one is under-appreciated in the US, it is Engineering, not Management. Metrics and organization issues hog the attention; what little is left over goes towards Logistics and Production Control, and Engineering is taken for granted. The tail is wagging the dog, and reality bites back in the form of implementation failures.

What is a management process, and how does it differ from a tool? The term sounds like standard management speak, but, if you google it, the only unqualified reference to it that comes up is in Wikipedia, where it is defined as “a process of planning and controlling the organizing and leading execution of any type of activity.”

Since Henri Fayol, however, we have all been taught that the job of all managers is to plan, organize, control, and lead. In those terms, there doesn’t seem to be any difference between a “management process” and just “management.” All other Google responses are for the processes of managing different functions, like the “Project Management Process,” “Performance Management Process,” “Change Management Process,” or the “A3 Management Process.” The corresponding images are a variety of box-and-arrow diagrams, pyramids, wheel charts, dish charts, and waterfalls/swim lanes, as in the following examples:

A manufacturing process is the network of tasks to make a product from materials — with routes that merge, branch, and sometimes even loop. A business process, likewise, is a network of tasks to turn inputs into outputs, like the order fulfillment process that turns customer orders into deliveries. A political process  is also a network of tasks leading to a particular result, like the election of a president or the approval of a budget. So, what about a management process? And what is the level of appreciation that it deserves?

Bill is the one who should really explain it, but, if I were to use this term, at the most basic level it would be for what I have been calling protocols, by which I mean the part of management work that is done by applying sets of rules or procedures rather than making judgement calls. They are pre-planned responses to events that might occur but are not part of routine operations. It can be the arrival of a new member into a team, the failure of a truck to show up, or a quality emergency.

This is the spirit of Toyota’s Change Point Management (CPM), in which the pre-planned responses are prepared by the teams that are potentially affected by the events and posted in the team’s work place. When the event occurs. all you have to do is retrieve the plan and you know what to do. And it is usually a better plan than what you would have improvised in the heat of the moment.

At a higher level, I would call process a protocol used to organize the way you make judgment calls. You can’t set the strategy of a company by applying rules, but you can use Hoshin Planning to organize the way you do it. A process like Hoshin Planning is akin to the rules of a game; it doesn’t determine how well the managers play. If they just comply with a mandate and go through the motions, they will produce a certain result. If, on the other hand, they understand what they are doing, connect it to their own work, and see the value in it, then they will produce a different result.

A good process does not guarantee a good outcome, and great teams have been able to coax performance out of dysfunctional processes. What is the proper level of appreciation for these management processes? Clearly, there is more to management than processes, and the best managers are those who excel at endeavors for which there is no script.

I learned to appreciate the relationship between management and engineering in Manufacturing from working with my mentor, Kei Abe. When he took me on as a junior partner in 1987, one of the first things I learned from him was to approach problems in a holistic manner, simultaneously at the technical and the managerial levels. I saw him coach a shop floor team on the details of SMED in the morning, and the board of directors on company strategy in the afternoon. It’s not a common mix of skills, but I believe it is what a manufacturing consultant should have.

By • Management • 9 • Tags: , , , , , ,

May 17 2013

Lean Assembly, Lean Logistics, and Euclides Coimbra’s Changes

My fellow consultant and author Euclides Coimbra has only written two reviews on Amazon, both on July 3, 2006, giving five stars to my books Lean Assembly and Lean Logistics, and commenting as follows:

  1. About Lean Assembly: “Very good book. Full of details. Useful for implementers. Knowledgeable readers can find many info between the lines. A wonderful contribution for Kaizen and Lean knowledge.”
  2. About Lean Logistics: “Following Lean Assembly Lean Logistics is a natural continuation. The style is the same and the information as valuable as Lean Assembly. A must have for any Kaizen and Lean implementer. Lots of details and useful information.”

A few months later, I went to work for him, and grew to appreciate his consulting talents. We parted later on good terms and I considered him a friend.

I just received a copy of his 2013 book, Kaizen in Logistics & Supply Chains, and found much overlap in subject matter with the two books of mine that he previously considered a “wonderful contribution” and a “must have.” I assume he changed his mind because they are not in the bibliography, and I couldn’t find my name anywhere in his book.

By • Book reviews • 1 • Tags: , , , , ,

Jul 23 2012

Another article that confuses Lean with reckless

See on Scoop.it – lean 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 www.ebnonline.com

By • Press clippings • 0 • Tags: , , ,

Apr 18 2012

Should Lean efforts focus on the supply chain or within the plant? Comments on an Industry Week article

See on Scoop.it – lean manufacturing

This article quotes Paul Myerson as saying that manufacturers preferred to “lean out within their four walls before working heavily with customers and suppliers.”

While I have heard this from many sources, I do not believe it is true. Having worked both within the four walls of plants and on their supply chains, I have repeatedly seen manufacturing managers conclude that their manufacturing needed no improvement, and that all the problems were with suppliers.

Before Paul Myerson, I also wrote a book on Lean Logistics. In 2005, it was the first on this subject. But I also wrote books on Lean Assembly (2002) and Working with Machines (2007), both of which deal with what happens “within the four walls.” Guess what? Lean Logistics sells more copies than the other two combined, and I don’t think it is a better book. To me, it just means that its subject is getting more attention.

Actually, it is getting a disproportionate amount of attention, and too early. Manufacturers should  focus on what happens within their walls first, and fix it. The vast majority, including many claiming to be Lean, have not. Until they do, they have no credibility with their suppliers and no business telling them how to improve.

See on www.industryweek.com

By • Press clippings • 71 • Tags: , , , ,

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.

By • Technology • 38 • Tags: , , ,

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