Managing Flow Variability

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Chapter 7: managing flow variability: safety inventory
7.1 Objective
In the previous chapter on inventory, we focused on economies of scale as the major driver for inventory. The purpose of this chapter is to introduce the notion of safety inventory as a buffer against stochastic variability in supply / demand and discuss various levers for reducing it. The chapter is covered over two classes each of duration 100 minutes. In the first class, we first motivate the need for forecasting as a way of estimating demand. We emphasize the four key characteristics of forecast without getting into any details of forecasting methodologies. This is key since the strategies for managing inventories critically exploit these characteristics. The first two characteristics of a forecast emphasize the need to estimate the variability of demand in addition to its mean. Building up on the examples of economic order quantity model in chapter 6, we discuss the notion of stock-outs (when demands become uncertain), introduce the cycle service level measure, and derive the safety stock as a buffer against uncertain demand to provide a certain service level. The key determinants of safety stock – demand variability and the mean and the variability of replenishment lead time - are emphasized. These are positioned as primary levers. Subsequently, the third characteristic of forecast (the aggregation principle) is used to discuss the concept of physical centralization of stocks as a way to reduce safety stock without affecting the cycle service level. Other manifestations of this principle in terms of virtual centralization, substitution, specialization, and component commonality are discussed qualitatively. Discussion of periodic review models can also be introduced here. Either this can be done soon after the basic model for continuous review is done (so there is a direct contrast) or after completing the discussion of centralization concepts. In the second class, we emphasize the last characteristic of forecasts (the effect of forecast horizon on the accuracy of forecasts) as a lever to reduce safety stock investment and build flexibility to react to market changes through postponement or delayed differentiation. We use the Hewlett Packard and the Benetton case. (While discussing the Benneton case, for lack of time, we ignore the U.S. entry issue). We also introduce the classical newsvendor problem in this class. Thus the supply chain module occupies three 100 minutes classes, one based on chapter 6 and two based on chapter 7. 7.2 Additional Suggested Readings

We continue with the Hewlett-Packard case to illustrate the notion of safety stock and the concept of centralization (across DCs in Europe). * “Hewlett-Packard: DeskJet Printer Supply Chain (A)”. Stanford Case 1993. Authors: Laura Kopczak and Hau L. Lee. Suggested assignment questions (continued from chapter 6):

1. Are there any other factors that need to be considered when deciding on the inventory stocking policy of different types of printers in Europe? How would you come up with appropriate levels of safety stock? 2. How would you recommend that Hewlett-Packard structure its supply chain to best match supply with demand? Do you find it worthwhile for DCs to start supporting manufacturing? * Benetton (A), Harvard Business School case # 9-685-014. Suggested questions:

3. Summarize the important elements of Benetton’s marketing, logistics, manufacturing and financial strategies. Identify the presence or absence of interdependencies among these functional strategies. 4. Is being a Benetton retailer a worthwhile business? Explain. 5. How does Benetton gain advantage over its European competition? * Palu Gear, Author: Jan van Mieghem (email: permissions@vanmieghem.us). This case can be used to all of the concepts in Chapters 6 and 7 including EOQ, centralization, as well as the newsvendor model. The questions are part of the case. 7.3...
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