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
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
References: Aquilano, N. J., Chase, R. B., & Jacobs, F. R. (2005). Operations management for competitive advantage. (11th ed.). New York: McGraw Hill Irwin. Chapman, S., Ettkin, L. P., & Helms, M. M. (2001). Supply chain forecasting: Collaborative forecasting supports supply chain management. Golden, J. E., Herbig, P., Milewicz, J., & Smith, H. C. (1996). Differences in forecasting behavior between large and small firms. Sohal, A. S., & Waddell, D. (1994). Forecasting: The key to managerial decision making.