Basic Approach to Demand Forecasting in a Supply Chain

The following five points are important for an organization to forecast effectively:

1. Understand the objective of forecasting.
2. Integrate demand planning and forecasting throughout the supply chain.
3. Identify the major factors that influence the demand forecast.
4. Forecast at the appropriate level of aggregation.
5. Establish performance and error measures for the forecast.

1. Understand the Objective of Forecasting

Every forecast supports decisions that are based on it, so an important first step is to identify these decisions clearly. Examples of such decisions include how much of a particular product to make, how much to inventory, and how much to order. All parties affected by a supply chain decision should be aware of the link between the decision and the forecast. For example, Walmart’s plans to discount detergent during the month of July must be shared with the manu­facturer, the transporter, and others involved in filling demand, as they all must make decisions that are affected by the forecast of demand. All parties should come up with a common forecast for the promotion and a shared plan of action based on the forecast. Failure to make these deci­sions jointly may result in either too much or too little product in various stages of the supply chain.

2. Integrate Demand Planning and Forecasting Throughout the supply chain

A company should link its forecast to all planning activities throughout the supply chain. These include capacity planning, production planning, promotion planning, and purchasing, among others. In one unfortunately common scenario, a retailer develops forecasts based on promo­tional activities, whereas a manufacturer, unaware of these promotions, develops a different fore­cast for its production planning based on historical orders. This leads to a mismatch between supply and demand, resulting in poor customer service. To accomplish integration, it is a good idea for a firm to have a cross-functional team, with members from each affected function responsible for forecasting demand—and an even better idea is to have members of different companies in the supply chain working together to create a forecast.

3. Identify Major factors That influence the demand forecast

Next, a firm must identify demand, supply, and product-related phenomena that influence the demand forecast. On the demand side, a company must ascertain whether demand is growing or declining or has a seasonal pattern. These estimates must be based on demand, not on sales data. For example, a supermarket promoted a certain brand of cereal in July 2014. As a result, the demand for this cereal was high, whereas the demand for other, comparable cereal brands was low in July. The supermarket should not use the sales data from 2014 to estimate that demand for this brand will be high in July 2015, because this will occur only if the same brand is promoted again in July 2015 and other brands respond as they did the previous year. When making the demand forecast, the supermarket must understand what the demand would have been in the absence of promotion activity and how demand is affected by promotions and competitor actions. A combination of these pieces of information will allow the supermarket to forecast demand for July 2015, given the promotion activity planned for that year.

On the supply side, a company must consider the available supply sources to decide on the accuracy of the forecast desired. If alternate supply sources with short lead times are available, a highly accurate forecast may not be especially important. However, if only a single supplier with a long lead time is available, an accurate forecast will have great value.

On the product side, a firm must know the number of variants of a product being sold and whether these variants substitute for or complement one another. If demand for a product influences or is influenced by demand for another product, the two forecasts are best made jointly. For example, when a firm introduces an improved version of an existing product, it is likely that the demand for the existing product will decline because customers will buy the improved version. Although the decline in demand for the original product is not indicated by historical data, the historical demand is still useful in that it allows the firm to estimate the combined total demand for the two versions. Clearly, demand for the two products should be forecast jointly.

4. Forecast at the Appropriate Level of Aggregation

Given that aggregate forecasts are more accurate than disaggregate forecasts, it is important to forecast at a level of aggregation that is appropriate, given the supply chain decision that is driven by the forecast. Consider a buyer at a retail chain who is forecasting to select an order size for shirts. One approach is to ask each store manager the precise number of shirts needed and add up all the requests to get an order size with the supplier. The advantage of this approach is that it uses local market intelligence that each store manager has. The problem with this approach is that it makes store managers forecast well before demand arises at a time when their forecasts are unlikely to be accurate. A better approach may be to forecast demand at the aggregate level when ordering with the supplier and ask each store manager to forecast only when the shirts are to be allocated across the stores. In this case, the long lead time forecast (supplier order) is aggregate, thus lowering error. The disaggregate store-level forecast is made close to the sales season, when local market intelligence is likely to be most effective.

5. Establish Performance and Error Measures for the Forecast

Companies should establish clear performance measures to evaluate the accuracy and timeliness of the forecast. These measures should be linked to the objectives of the business decisions based on these forecasts. Consider a mail-order company that uses a forecast to place orders with its suppli­ers, which take two months to send in the orders. The mail-order company must ensure that the forecast is created at least two months before the start of the sales season because of the two-month lead time for replenishment. At the end of the sales season, the company must compare actual demand to forecasted demand to estimate the accuracy of the forecast. Then plans for decreasing future forecast errors or responding to the observed forecast errors can be put into place.

In the next section, we discuss techniques for static and adaptive time-series forecasting.

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