Sullivan, T. T., Reid, R. A., and Cartier, B. 2007. TOCICO Dictionary. http://www.tocico.org/? page=dictionary.

Suggested Reading

www.inherentsimplicity.com/warp-speed is a site that allows downloading of the MICSS simulator including analysis files and more related materials.

See also Schragenheim, Dettmer, and Patterson. 2009. Supply Chain Management at Warp Speed. Chapters 6 and 7 are especially relevant.

About the Author.

In the last 25 years, Eli Schragenheim has taught, spoken at conferences, and consulted in more than 15 countries, including the United States, Canada, India, China, and Japan. He has also developed software simulation tools especially designed to experience the thinking of TOC, and consulted with several application software companies to develop the right TOC functionally in their own packages.

Mr. Schragenheim was a partner in the A.Y. Goldratt Institute and he is now a Director in The Goldratt Schools.

He is the author of Management Dilemmas. He collaborated with William H. Dettmer in writing Manufacturing at Warp Speed. He also collaborated with Carol A. Ptak on ERP, Tools, Techniques, and Applications for Integrating the Supply Chain, and with Dr. Goldratt and Carol A. Ptak on Necessary But Not Sufficient. In March 2009, a new book titled Supply Chain Fulfillment at Warp Speed, with William H. Dettmer and Wayne Patterson was published. The new book contains much of the new developments of TOC in operations.

Mr. Schragenheim holds an MBA from Tel Aviv University, Israel, and a BSc in Mathematics and Physics from Hebrew University in Jerusalem. In-between his formal studies, he was a TV director for almost 10 years. He is a citizen of Israel. The author's personal email is elyakim@netvision.net.il. Readers should feel free to write to Eli Schragenheim to discuss matters related to the chapters on MTO and MTA.

CHAPTER 11.

Supply Chain Management1

Amir Schragenheim

Introduction: The Current Practice of Managing Supply Chains

It is Wednesday afternoon. I am entering the grocery store and want to purchase some green peppers. However, they don't have any in stock. I can't find any good looking tomatoes either. I'm continuing to the Office Depot store. I heard great reviews about a new mouse that Microsoft issued and I would like to get one. However, I come to an empty shelf with only the item description stating "out of stock."

How many times have you gone to a shoe store to purchase a pair of wonderful shoes you wanted but they didn't have any in your size?

Why don't stores keep the right stocks to fulfill their demand? They seem to have a lot of stock. Why can't they do this simple task right?

Supply chains in our modern age operate in a way that seems to make a lot of sense. Manufacturers have robotic machinery to automate processes; many manufacturers have already installed new state-of-the-art Enterprise Resources Planning (ERP) systems to help them manage their shop floors.

Distributors and manufacturers have very sophisticated forecasting software to predict exactly how many items will be sold of each product and even each stock keeping unit (SKU).2 Therefore, they should know how many units they would like to send the retail stores (consumption points) and when.3 How is it that organizations still experience problems in managing the supply chains? Is technology not enough?

Problems with the Current System

Typical problems4 of supply chains are low inventory turns, high inventory investment, stockouts causing lost sales at some locations and at the same time excess inventories of the same items at other locations, high inventory obsolescence, lack of responsiveness to customer needs, etc. Let us examine some potential causes of these problems.

The Natural Tendency for Push Behavior

The vast majority of supply chains today are push systems. A push system in the APICS Dictionary (Blackstone, 2008, 112) is defined as ". . . 3) In distribution, a system for replenishing field warehouse inventories where replenishment decision making is centralized, decisions are usually made at the manufacturing site or central supply facility." ( APICS 2008, used by permission, all rights reserved.). Given this definition, the centralized position in the supply chain is the manufacturer that supplies his regional warehouse or consumers directly or a distributor that purchases items from several manufacturers and distributes them to his regional warehouses or directly to the customer.

What is the manufacturer/distributor5 (M/D) point of view when he is deciding on how much stock to keep at each location? He has two parameters in mind: 1. How much to keep upstream (closer to the manufacturer) in the supply chain.

2. How much to keep downstream (closer to the consumer) in the supply chain.

The natural tendency is to keep the stock as close to the consumers as possible. If a product is not at the consumption point, then there is a (much) smaller chance the item will be sold. Immediate consumption is the name of the game. Therefore, it is only logical that the M/D should keep most of the stock as close to the consumer as possible-as far downstream as he can manage-usually at the retail level. Figure 11-1 shows how the inventories are distributed across a typical traditional supply chain. Most of the stock is located at the end of the chain (the shops) and little at the hub (the plant/central warehouse [PWH/CWH]).

The traditional supply chain displays a push behavior: pushing the products downstream toward the retailer (shop) in hopes of increasing its consumption. However, the push behavior requires a good forecasting model in order to predict what, where, and when specific stocks will be needed at a specific stock location (shop). We must have the right item (what) at the specific location (where) at the right time (when).

Why Is It Impossible to Find a Good Forecasting Model?

The advanced forecasting modules existing today try to model the demand and create a good answer to the availability puzzle: What product to hold at which place (where) and when. Notice that this puzzle has three questions: what, where, and when. To be a good forecast of demand, forecasting has to answer each of these questions. The forecasting mechanism, no matter how good it is, cannot really predict what the demand would be like.

FIGURE 11-1 A typical push supply chain.

With respect to forecasting, one must consider some fallacies regarding statistics. These fallacies and a discussion of each are provided in the following sections.

1. The fallacy of disaggregation.

2. The fallacy of the mean.

3. The fallacy of the variance.

4. The fallacy of sudden changes.

The Fallacy of Disaggregation

The first fallacy is that aggregation or disaggregation has no impact on variation. The fact is that the more disaggregated the data is, the higher the variation is of those data elements. In our distribution environment, for the question of "How much demand for this product?" the answer for the M/D location is very accurate with low variability but the answer to this same question for a specific retail location is quite inaccurate with high variability. This phenomenon stems from the fact that fluctuations average out on the aggregated events (assuming they are independent events). If we predict the sales at 100 different locations, we might get an answer that sales in an average location will range from 10 to 25 units a day. If we ask the same question on the overall quantity that we need to manufacture, we will get a much narrower range as an answer-probably something ranging from 1650 to 1850. If we would just take the lows (10) and highs (25) of each of the 100 consumption points and aggregate them, we will get a much worse answer-from 1000 to 2500. This point is demonstrated in Fig. 11-2. Note the high variation at the retailer versus the lower variation at the M/D warehouse. The rule then becomes the higher the aggregation, the better the forecast.

FIGURE 11-2 The mathematical effect of aggregation.

The Fallacy of the Mean

The second phenomenon relates to the wrong interpretation of data-people using statistics must have a good enough understanding of the mathematical logic that stands behind the forecast. Huge mistakes are made daily in almost every organization because of a lack of understanding of statistics. For example, the average demand in the previous example is 17.5 (assuming a normal distribution and a high and low of 10 and 25). Suppose that we stocked 17.5 units at each retail location. Do you think we would sell 1750 units? Never! There are stores that would have demand less than 17.5 units a day and we would have excess inventory (not sold) in these stores. There are other stores where we stocked 17.5 units and the demand was greater than that amount. We can only sell the 17.5 units we have that day. Therefore, overall we would sell far less than the 1750 aggregate demand. A clever man not experienced in statistics might deduce from this example that the consumption will be between 1650 and 1850 for all consumption points, that each consumption point will have a consumption between 16.5 and 18.5, keeping 19 units for each location, and running out of stock in a fairly large number of them, while others will be left with a lot of stock they can't sell. The fact that we got an aggregated range does not mean that it can be applied to the points that make up this sum. Forecasting algorithms are getting more and more complex (software companies need to justify to the client that the new version will bring "better" results this time). One basic fact related to this complexity is important: The more sophisticated the algorithm, the more sophisticated the end user has to be in order to use the forecast correctly.

The Fallacy of the Variance

A related fallacy involves the understanding of variance. Most forecasting algorithms present the data as an average demand and if one really insists, then the standard deviation is given. The number of people who really understand the meaning of a standard deviation is very limited because this is a mathematical object that does not have any intuitive translation to real-life scenarios. Try to ask a salesperson not just how much he is going to sell, but also what the standard deviation is. This again calls for very sophisticated people interpreting the forecasting results in order to get some benefits from the forecast.

Suppose the salesperson estimates the average consumption of a product at a specific retail location as 17.5 with a standard deviation of 2. How much inventory should be kept at this site? If you stock exactly 17.5 units (assuming that is possible), then you would think you had a 50 percent customer service level. Recall the problem with means stated previously. However, suppose you wanted to satisfy 95 percent6 of the customers requesting this product. How much should you stock? The answer is provided by the following calculations: 17.5 + 1.645(2) = 20.8 units. Stocking only 2 units above the mean (19.5) would provide a customer service level of approximately 86 percent. The critical point is, few people conceptually can estimate a standard deviation and determine its impact on sales without a computer.

The Fallacy of Sudden Changes

Many forecasting methods7 can track changes in demand, but the more sudden the change the worst the forecast will be. An example follows. A very enthusiastic article in a paper has just appeared that suddenly changes the consumption pattern in the whole region. Suppose that the article summarizes a breakthrough study in cancer prevention and stated that if a person drinks one glass of cranberry juice a day, then this product and quantity will prevent cancer.8 What would happen to the demand for cranberry juice? On the other hand, suppose that a television report stated that the botulism epidemic currently spreading in our region is caused by peanut products and products containing any peanut derivative from a very large manufacturing plant in the region. What would happen instantly to the demand for these products? In today's dynamic market, such events happen frequently.

These fallacies severely affect the forecast of a single SKU (what item, where located, and when in time) and therefore provide a very poor base for determining the required stock level of that SKU. It is clear that another approach (instead of a better forecast) is needed in order to make this stocking decision.

The TOC Way-The Distribution/Replenishment Solution

The Theory of Constraints (TOC) analyzes the impact of the supply together with the demand to compute the right level of stocks throughout the supply chain, with the emphasis on the supply side. In an extreme case, where it is possible to respond instantly to demand, there is no need to rely on a forecast at all. While this situation is, of course, unattainable in almost all business environments, a step in this direction should be considered. In the case of keeping the right amount of stock in the supply chain, the TOC objective in responding to the three questions (what, where, and when) is to have very good availability of the items at all the consumption points (the end users). This objective is limited by the availability of cash and space, which means that it is impossible to keep high stocks of all items at all locations, even when obsolescence is not an issue. Not only that, but also as will be explained later in this chapter, keeping too high stocks of low demand SKUs will lower the total sales overall.

The TOC distribution/replenishment solution in the TOCICO Dictionary (Sullivan et al., 2007, 17) is defined as "(a) pull distribution method that involves setting stock buffer sizes and then monitoring and replenishing inventory within a supply chain based on the actual consumption of the end user, rather than a forecast. Each link in the supply chain holds the maximum expected demand within the average replenishment time, factored by the level of unreliability in replenishment time. Each link generally receives what was shipped or sold, though this amount is adjusted up or down when buffer management detects changes in the demand pattern." ( TOCICO 2007, used by permission, all rights reserved.) I will elaborate on this definition.

To respond to the three questions (what, where, when), the TOC distribution/replenishment solution is based on constant renewal of the consumed stocks from strategically placed stock buffers. The solution is comprised of six steps: 1. Aggregate stock at the highest level in the supply chain: the PWH/CWH.

2. Determine stock buffer sizes for all chain locations based on demand, supply, and replenishment lead time.