Demand forecasting is one of the most fundamental tasks that a business must perform. It can be a significant source of competitive advantage by improving customer service levels and by reducing costs related to supply–demand mismatches. In contrast, biased or otherwise inaccurate forecasting results in inferior decisions and thus undermines business performance.
For example, the toy retailer Toys “R” Us made a huge mistake in demand forecasting for the 2015 Christmas season. For several days, the actual number of online orders was more than twice the company's forecasts, and the company's distribution centers were overwhelmed. As a result, the company was forced to throttle demand by terminating some online sales, resulting in lower demand and lower revenue (Ziobro, 2016).
The goal of the forecasting models discussed in this chapter is to estimate the quantity of a product or service that consumers will purchase. Most classical forecasting techniques involve time‐series methods that require substantial historical data. Some of these methods are designed for demands that are stable over time. Others can handle demands that exhibit trends or seasonality, but even these require the trends to be stable and predictable. However, products today have shorter and shorter life cycles, in part driven by rapid technology upgrades for high‐tech products. As a result, firms have much less historical data available to use for ...