Components of a Forecast and Forecasting Methods in a Supply Chain

Yogi Berra, the former New York Yankees catcher who is famous for his malapropisms, once said, “It’s tough to make predictions, especially about the future.” One may be tempted to treat demand forecasting as magic or art and leave everything to chance. What a firm knows about its customers’ past behavior, however, sheds light on their future behavior. Demand does not arise in a vacuum. Rather, customer demand is influenced by a variety of factors and can be predicted, at least with some probability, if a company can determine the relationship between these factors and future demand. To forecast demand, companies must first identify the factors that influence future demand and then ascertain the relationship between these factors and future demand.

Companies must balance objective and subjective factors when forecasting demand. Although we focus on quantitative forecasting methods in this chapter, companies must include human input when they make their final forecast. Seven-Eleven Japan illustrates this point.

Seven-Eleven Japan provides its store managers with a state-of-the-art decision support system that makes a demand forecast and provides a recommended order. The store manager, however, is responsible for making the final decision and placing the order, because he or she may have access to information about market conditions that are not available in historical demand data. This knowledge of market conditions is likely to improve the forecast. For exam­ple, if the store manager knows that the weather is likely to be rainy and cold the next day, he or she can reduce the size of an ice cream order to be placed with an upstream supplier, even if demand was high during the previous few days when the weather was hot. In this instance, a change in market conditions (the weather) would not have been predicted using historical demand data. A supply chain can experience substantial payoffs from improving its demand forecasting through qualitative human inputs.

A company must be knowledgeable about numerous factors that are related to the demand forecast, including the following:

• Past demand
• Lead time of product replenishment
• Planned advertising or marketing efforts
• Planned price discounts
• State of the economy
• Actions that competitors have taken

A company must understand such factors before it can select an appropriate forecasting methodology. For example, historically a firm may have experienced low demand for chicken noodle soup in July and high demand in December and January. If the firm decides to discount the product in July, the situation is likely to change, with some of the future demand shifting to the month of July. The firm should make its forecast taking this factor into consideration.

Forecasting methods are classified according to the following four types:

1. Qualitative: Qualitative forecasting methods are primarily subjective and rely on human judgment. They are most appropriate when little historical data are available or when experts have market intelligence that may affect the forecast. Such methods may also be neces­sary to forecast demand several years into the future in a new industry.

2. Time series: Time-series forecasting methods use historical demand to make a fore­cast. They are based on the assumption that past demand history is a good indicator of future demand. These methods are most appropriate when the basic demand pattern does not vary sig­nificantly from one year to the next. These are the simplest methods to implement and can serve as a good starting point for a demand forecast.
3. Causal: Causal forecasting methods assume that the demand forecast is highly cor­related with certain factors in the environment (the state of the economy, interest rates, etc.). Causal forecasting methods find this correlation between demand and environmental factors and use estimates of what environmental factors will be to forecast future demand. For example, product pricing is strongly correlated with demand. Companies can thus use causal methods to determine the impact of price promotions on demand.
4. Simulation: Simulation forecasting methods imitate the consumer choices that give rise to demand to arrive at a forecast. Using simulation, a firm can combine time-series and causal methods to answer such questions as: What will be the impact of a price promotion? What will be the impact of a competitor opening a store nearby? Airlines simulate customer buying behavior to forecast demand for higher-fare seats when no seats are available at lower fares.

A company may find it difficult to decide which method is most appropriate for forecast­ing. In fact, several studies have indicated that using multiple forecasting methods to create a combined forecast is more effective than using any one method alone.

In this chapter, we deal primarily with time-series methods, which are most appropriate when future demand is related to historical demand, growth patterns, and any seasonal patterns. With any forecasting method, there is always a random element that cannot be explained by his­torical demand patterns. Therefore, any observed demand can be broken down into a systematic and a random component:

Observed demand (O) = systematic component (S) + random component (R)

The systematic component measures the expected value of demand and consists of what we will call level, the current deseasonalized demand; trend, the rate of growth or decline in demand for the next period; and seasonality, the predictable seasonal fluctuations in demand.

The random component is the part of the forecast that deviates from the systematic part. A company cannot (and should not) forecast the direction of the random component. All a com­pany can predict is the random component’s size and variability, which provides a measure of forecast error. The objective of forecasting is to filter out the random component (noise) and estimate the systematic component. The forecast error measures the difference between the fore­cast and actual demand. On average, a good forecasting method has an error whose size is com­parable to the random component of demand. A manager should be skeptical of a forecasting method that claims to have no forecasting error on historical demand. In this case, the method has merged the historical random component with the systematic component. As a result, the forecasting method will likely perform poorly.

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