The Role of IT in Forecasting in a Supply Chain

There is a natural role for IT in forecasting, given the large amount of data involved, the fre­quency with which forecasting is performed, and the importance of getting the highest quality results possible. A good forecasting package provides forecasts across a wide range of products that are updated in real time by incorporating any new demand information. This helps firms respond quickly to changes in the marketplace and avoid the costs of a delayed reaction. Good demand planning modules link not only to customer orders but often directly to customer sales information as well, thus incorporating the most current data into the demand forecast. A positive outcome of the investment in ERP systems has been a significant improvement in supply chain transparency and data integration, thus allowing potentially better forecasts. Although this tech­nical improvement can help produce better forecasts, firms must develop the organizational capabilities required to take advantage of this improvement.

Besides providing a rich library of forecasting methodologies, a good demand planning module should provide support in helping select the right forecasting model for the given demand pattern. This has become particularly important as the available library of forecasting method­ologies has grown.

As the name demand planning suggests, these modules facilitate the shaping of demand. Good demand planning modules contain tools to perform what-if analysis regarding the impact of potential changes in prices on demand. These tools help analyze the impact of promotions on demand and can be used to determine the extent and timing of promotions. This link is discussed in greater detail in Chapter 9 under sales and operations planning.

An important development is the use of demand correlated data (e.g. price, weather, other purchases, social data) to improve forecast accuracy or, in some cases, spur demand. In a well- publicized case, Target predicted that women were pregnant based on other products they were purchasing. A purchase of “cocoa-butter lotion, a purse large enough to double as a diaper bag, zinc and magnesium supplements and a bright blue rug” was a strong predictor of the woman’s pregnacy.2 Target then used this information to send suitable coupons to entice these women or their husbands to visit Target and purchase baby-related products. Sophisticated systems such as this can be used to not only improve forecast accuracy but also identify suitable marketing oppor­tunities to spur future demand.

Keep in mind that none of these tools is foolproof. Forecasts are virtually always inaccu­rate. A good IT system should help track historical forecast errors so they can be incorporated into future decisions. A well-structured forecast, along with a measure of error, can significantly improve decision making. Even with all these sophisticated tools, sometimes it is better to rely on human intuition in forecasting. One of the pitfalls of these IT tools is relying on them too much, which eliminates the human element in forecasting. Use the forecasts and the value they deliver, but remember that they cannot assess some of the more qualitative aspects about future demand that you may be able to do on your own.

A detailed list of forecasting software vendors is reported in the OR/MS Today forecasting software survey, and a discussion of each vendor is available at orms/surveys/FSS/fss-fr.html.

Source: Chopra Sunil, Meindl Peter (2014), Supply Chain Management: Strategy, Planning, and Operation, Pearson; 6th edition.

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