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The science part uses information technology to generate demand forecasts using existing data from a variety of sources, e.g., distribution channels, factory outlets, value-added resellers, historical sales data, and macroeconomic data. A passive response would be to reduce output because of a predicted decrease in demand, while an active response would be to advertise in an effort to offset the predicted decrease in demand.Р”Вemand forecasting (DF) is part science and part art (intuition) for estimating what future demand for a product or service will be. Planning is often in response to a forecast. Finally, it is important to point out the difference between forecasting and planning. On the other hand, long-term estimates of overall or aggregate demand can be used in determining company strategy, planning longterm capacity and establishing facility location needs of the firm. Short-term demand estimates for individual products are necessary to determine daily or weekly management of the firmРІР‚в„ўs activities such as scheduling personnel and ordering materials. In most cases, a firm will need a long-term estimate of overall demand as well as a shorter-run estimate of demand for each individual product or service.

However, we may need aggregate estimates of demand as well as estimates for individual products. Most often, the basic need for forecasting arises in estimating customer demand for a firmРІР‚в„ўs products and services. In operations management, we forecast a wide range of future events, which could significantly affect the long-term success of the firm.

I try to emphasize an intuitive approach to forecasting, with frequent reference to the importance of plotting the data to assist the decision-maker in determining which forecasting technique may be more appropriate to use.

Other optional materials that can be mentioned briefly, but not explored in detail, include trend-adjusted exponential smoothing (mentioned so that students will realize that exponential smoothing does not work well if there is trend present) and computation of seasonal relatives (you may want to explain how relatives are used without getting into how they are derived).

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Another is that time series models are used more than associative models are. One is that students often are exposed to regression in their stat course(s). I tend to devote more time to the time series methods than I do to regression analysis, for several reasons. Teaching Notes This is a long chapter, so you may want to be selective about the topics covered to shorten the time devoted to it. However, forecasting is an important input for both, and for that reason, it is presented as early as possible. Other instructors prefer to blend the two. Some instructors like to emphasize the operations part of operations management and de-emphasize the design part. CHAPTER 03 FORECASTING Forecasting is placed early in the text mainly because it is a point of departure.

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