Forecasting Methods

Topics: Forecasting, Time series, Prediction Pages: 5 (1707 words) Published: April 2, 2006

All businesses are confronted with the general problem of having to make decisions under conditions of uncertainty. Management must understand the nature of demand and competition in order to develop realistic business plans, determine a strategic vision for the organization, and determine technology and infrastructure needs. To address these challenges, forecasting is used. According to Makridakis (1989), forecasting future events can be characterized as the search for answers to one or more of the following questions: „XWhat new economic, technical, or sociological forces is the organization likely to face in both the near and long term? „XWhen might these forces impact the firm¡¦s objective environment? „XWho is likely to be first to adapt to each competitive challenge? „XHow much change should the firm anticipate both in the short run and the long run? In this paper, I will provide an overview of forecasting methods and compare and contrast these various methods. The paper will then focus on how Mattel, one of the nations largest toy manufacturers, uses demand forecasting under conditions of uncertainty ¡V most specifically those relating to the pattern and rate at which customers demand products. What is Forecasting?

In Operations Management, demand forecasting is defined as ¡§the business process that attempts to estimate sales and the use of products so that they can be purchased, stocked, or manufactured in appropriate quantities in advance to support the firm¡¦s value adding activities.¡¨(Ross, 1995). Forecasting is a process that transforms historical time-series data and/or qualitative assessments into statements about future events. This process can produce either qualitative or subjective projections. Note that no forecasting process can consistently provide perfect forecasts. Any forecast that perfectly estimates subsequent events should raise cause for alarm, as this is probably indicative of improprieties such as ¡§cooking the books¡¦ or reporting performance data that shows conformance with plans versus actual events (Makridakis, 1989). Forecasting Methods

There are four basic types of forecasting methods: qualitative, time series analysis, causal relationships, and simulation. Qualitative Techniques
Qualitative techniques are subjective or judgmental and based on estimates and opinions (Chase, 2005). These forecasts reflect people¡¦s judgments or opinions and suggest likely conditions, such as people¡¦s opinion about whether it will rain today. These forecasts are preferred when there is a desire to engage individuals within the organization with a key business process. A potential pitfall of this technique is that some individuals base their judgments of future events on historical data, which may not provide relevant demand patterns that are stable enough to warrant their use to forecast future events. Additionally, emerging demand patterns may be too unstable for a numeric approach. Consequently, intimate knowledge of the market should be the data source of choice. There are numerous qualitative approaches to demand forecasting, following are some of the more common approaches: „XGrass-Roots Forecasting seeks input from people at the level of the organization that gives them the best contact with the event under study (Chase, 2005). This technique may consist of conducting a marketing study of sales representatives for their readings on current market conditions. The potential fault with this tool is that it is subject to the short-term perspectives of the sources. The source of the data may be unduly influenced by recent events. For example, a sales person who has had a good day may provide an overly-optimistic forecast for the future that does not accurately represent market conditions on the whole. „XHistorical Analogy: Forecasting based on historical analogy explores the possibility that past events can provide insights into the prediction of future related events....

References: Chase, R. et al. (2005). Operations Management for Competitive Advantage, 11th edition. McGraw-Hill Companies: New York.
Makridakis, S. et al. (1998). Forecasting Methods and Applications, 5th edition. John Wiley & Sons: New York.
Pereira, J. (1997). To these Youngsters, Trying Out the Toys is Hardly Kids Play. Wall Street Journal, 12/17/97.
Ross,D. (1995). Distribution Planning and Control. Chapman & Hall: New York.
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