Demand Forecasting

Topics: Forecasting, Supply and demand, Exponential smoothing Pages: 61 (11432 words) Published: April 9, 2012



Learning Objectives
After reading this chapter, you will be able to:

1. Understand the role of forecasting for both an enterprise and a supply chain. 2. Identify the components of a demand forecast. 3. Forecast demand in a supply chain given historical demand data using time-series methodologies. 4. Analyze demand forecasts to estimate forecast error.


orecasts of future demand are essential for making supply chain decisions. In this chapter, we explain how historical demand information can be used to forecast future demand and how these forecasts affect the supply chain. We describe several methods to forecast demand and estimate a forecast's accuracy. We then discuss how these methods can be implemented using Microsoft Excel.


Demand forecasts form the basis of all supply chain planning. Consider the push/pull view of the supply chain discussed in Chapter 1. All push processes in the supply chain are performed in anticipation of customer demand, whereas all pull processes are per­ formed in response to customer demand. For push processes, a manager must plan the level of activity, be it production, transportation, or any other planned activity. For pull processes, a manager must plan the level of available capacity and inventory but not the actual amount to be executed. In both instances, the first step a manager must take is to forecast what customer demand will be. \ For example, Dell orders PC components in anticipation of customer orders, whereas it performs assembly in response to customer orders. Dell uses a forecast of future demand to determine the quantity of components to have on hand (a push process) and to determine the capacity needed in its plants (for pull production). Farther up the supply chain, Intel also needs forecasts to determine its own produc­ tion and inventory levels. Intel's suppliers also need forecasts for the same reason. When each stage in the supply chain makes its own separate forecast, these forecasts are often very different. The result is a mismatch between supply and demand. When all stages of a supply chain work together to produce a collaborative forecast, it tends to be much more accurate. The resulting forecast accuracy enables supply chains to be both more responsive and more efficient in serving their customers. Leaders in many supply chains, from PC manufacturers to packaged-goods retailers, have



Planning Demand and Supply in a Supply Chain

improved their ability to match supply and demand by moving toward collaborative forecasting. For example, consider the value of collaborative forecasting for Coca-Cola and its bottlers. Coca-Cola decides on the timing of various promotions based on the demand forecast over the coming quarter. Promotion decisions are then incorporated into an updated demand forecast. The updated forecast is essential for the bottlers to plan their capacity and production decisions. A bottler operating without an updated fore­ cast based on the promotion is unlikely to have sufficient supply available for Coca­ Cola, thus hurting supply chain profits. Mature products with stable demand, such as milk or paper towels, are usually eas­ iest to forecast. Forecasting and the accompanying managerial decisions are extremely difficult when either the supply of raw materials or the demand for the finished prod­ uct is highly unpredictable. Fashion goods and many high-tech products are examples of items that are difficult to forecast. Good forecasting is very important in these cases because the time window for sales is narrow. If a firm has over- or underproduced, it has little chance to recover. For a product with stable demand, in contrast, the impact of a forecasting error is less significant. Before we begin an in-depth discussion of the components of forecasts and fore­ casting methods in the supply chain, we...
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