Characteristics of Forecasts in a Supply Chain

Companies and supply chain managers should be aware of the following characteristics of forecasts.

1. Forecasts are always inaccurate and should thus include both the expected value of the forecast and a measure of forecast error. To understand the importance of forecast error, consider two car dealers. One of them expects sales to range between 100 and 1,900 units, whereas the other expects sales to range between 900 and 1,100 units. Even though both dealers anticipate average sales of 1,000, the sourcing policies for each dealer should be very different, given the difference in forecast accuracy. Thus, the forecast error (or demand uncertainty) is a key input into most supply chain decisions. Unfortunately, most firms do not maintain any estimates of forecast error.
2. Long-term forecasts are usually less accurate than short-term forecasts; that is, long­term forecasts have a larger standard deviation of error relative to the mean than short-term fore­casts. Seven-Eleven Japan has exploited this key property to improve its performance. The company has instituted a replenishment process that enables it to respond to an order within hours. For example, if a store manager places an order by 10 a.m., the order is delivered by 7 p.m. the same day. Therefore, the manager has to forecast what will sell that night only less than 12 hours before the actual sale. The short lead time allows a manager to take into account current information that could affect product sales, such as the weather. This forecast is likely to be more accurate than if the store manager had to forecast demand a week in advance.
3. Aggregate forecasts are usually more accurate than disaggregate forecasts, as they tend to have a smaller standard deviation of error relative to the mean. For example, it is easy to forecast the gross domestic product (GDP) of the United States for a given year with less than a 2 percent error. However, it is much more difficult to forecast yearly revenue for a company with less than a 2 percent error, and it is even harder to forecast revenue for a given product with the same degree of accuracy. The key difference among the three forecasts is the degree of aggregation. The GDP is an aggregation across many companies, and the earnings of a company are an aggregation across sev­eral product lines. The greater the aggregation, the more accurate the forecast.
4. In general, the farther up the supply chain a company is (or the farther it is from the consumer), the greater the distortion of information it receives. One classic example of this phe­nomenon is the bullwhip effect (see Chapter 10), in which order variation is amplified as orders move farther from the end customer. Collaborative forecasting based on sales to the end cus­tomer helps upstream enterprises reduce forecast error.

In the next section, we discuss the basic components of a forecast, explain the four classi­fications into which forecasting methods fall, and introduce the notion of forecast error.

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