Forecasting Methods

Forecasting Methods

Forecasting demand is not an easy task. The market is constantly changing and it makes the product demand difficult to predict. Therefore, there is not such as perfect product forecast of what customers will need in the future. However, there are several methods that help attenuating the uncertainty of forecasting demand. Since, the forecast methods or techniques differ from one another; the objective is to compare and contrast several forecasting methods, and how they are used by organizations to the best advantage under conditions of uncertainty.

One of the forecasting techniques typically used by organizations is the historical analogy. Chase et al. (2005), define that historical analogy "ties what is being forecast to a similar item" (p. 514). This technique is used when the company is planning to launch a new product to market. Since there is no data available for the new product, the organizations try to compensate the uncertainty by using data from product with similar characteristics. Similarly, the market research technique also uses data collection to forecast demand. The data collection is primarily done through direct surveys and interviews. Companies use this technique to be able to come up with better products than the existing ones. The uncertainty of what customers want or dislike is reduce by collecting data directly from them. It is common for organizations to hire external companies to conduct this investigation and to provide the forecast. Since the external organizations are solely dedicated to the forecasting business; they usually provide adequate and accurate information.

The collaborative or consensus forecast technique has several similarities with the historical analogy. In their article, Helms et al. (2000) mentioned that, "Collaborative forecasting is one of the ways that many companies have found to overcome some of the inherent problems with traditional forecasting and at the same time support the supply chain management initiative of their companies. Collaborative forecasting is a method in which the knowledge and information that exists internally and externally is brought together into a single, more accurate, forecast that has the support of the entire supply chain" (p. 395). In the consensus method, all members from the supply chain have an active participation in the development of an adequate forecast. The panel consensus method uses open meetings where all participants provide or exchange ideas. In addition, instead of only relying on historical data, the consensus method goes one step further. The historical data is enhanced with the current market trends and events to ensure that the demand uncertainty is reduced.

Another technique similar to the consensus method is the Delphi method. According to Chase et al. (2005), the Delphi method consists of a "group of experts that responds to questionnaire. A moderator compiles results and formulates a new questionnaire that is submitted to the group. Thus, there is a learning process for the group as it receives new information and there is no influence of group pressure or dominating individuals" (p. 514). The organizations try to reduce the level of forecast demand uncertainty by providing more freedom to the participants. Contrary to the consensus method, in the Delphi technique every participant is concealed. Therefore, every participant has the same weight and all opinions are taken in consideration. This is considered a good forecast technique that usually obtains excellent results.

Sometimes the demand is influenced by external demand. In this case, the demand can be identified as dependent