Forecasting

Forecasting

Coni Robinson

University of Phoenix

Organization Management

MGT 554

Yolanda Phipps

March 15, 2008

Forecasting

Business forecasting is used by most businesses as a way of predicting the future activities of the business. “Business forecasting can simply be defined as a process of utilizing formulated methods and tools that will enable certain predictions to be made concerning future occurrences within the business cycle, with the primary aim of making decisions and planning courses of action for the future” (Adeyemi, 2006, 1). There are many different ways of forecasting business futures. This paper will describe some examples of forecasting such as; qualitative, time series analysis, and causal. Within each of these forecasting techniques are common models which are different ways of predicting demands of supplies and services within a business.

Qualitative

Qualitative forecasting models are “subjective, judgmental and are based on estimates and opinions” (Chase, Jacobs, & Aquilano, 2005, 514). The first forecasting example will be the grass roots approach meaning the person dealing most closely with the customers is able to forecast the future needs. Using the bottom-up approach information from the customer will be relayed to storage warehouses all the way up to the production lines. (Chase et al., 516) “This traditional approach certainly is a sound basis to develop a forecast, but gives little recognition to important implicit and explicit trends embedded in the historical data” (D'Attilio, 1989, 1).

Market research is used to collect data based on surveys to evaluate existing products. Market research uses many techniques for obtaining information from phone surveys to written surveys. The evaluation of the products can be “used to forecast long-range and new product sales” (Chase et al., 2005, 514).

Delphi forecasting is developing a questionnaire and distributing it to participants. This method evaluates a certain product and the longevity of the product. Panels of experts develop the questionnaire to be sent out. This method was developed to solicit honest valid answers to questions about certain products. “The Delphi technique of forecasting is more appropriate for strategic than operational forecasting…because we estimate the sales several periods before its product introduction” (Goldfisher, 1992-1993, 1).

Time Series Analysis

“Time series forecasting models try to predict the future based on past data” (Chase et al., 2005, 518). Time series analysis is “based on the idea that the history of occurrences over time can be used to predict the future” (Chase et al., 514). Time series analysis is a quantitative evaluation of the history of a particular product. “A time series is a set of ordered observations on a quantitative characteristic of a phenomenon at equally spaced time points” (Arsham, n.d.).

Simple moving average is used “when demand for a product is neither growing nor declining rapidly, and if it does not have seasonal characteristics, a moving average can be useful in removing the random fluctuations for forecasting” (Chase et al., 2005, 518). Simple moving averages are used when forecasting is needed for a certain time based on the information preceding that time. Predicting demand based on past needs and averages can be smooth for a company the longer the period that is being evaluated.

Regression analysis is the forecast of a variable based on another variable. For example, the purchase of products can be directly related to a family’s income. When the income is down the purchase power will also go down. Regression analysis “fits a straight line to past data generally relating the data to time” (Chase et al., 2005, 514).

Seasonal analysis can yield a solid forecasting tool for sales of certain products. For example, when winter brings generous amounts of snow the sales of snow shovels and snow blowers will rise. In contrast when spring comes along the sales of flowers and lawn mowers will be the products to buy. Seasons can determine product purchases based on what is needed during that season. Another example is the increase in consumer spending during the Christmas season. Seasonal forecasting for the purchase of products can be based on the sales in the previous season.

Causal

Causal relationship forecasting “tries to understand the system underlying and surrounding the item being forecast”

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