Consumer Demand: Demand Forecasting As A Scientific Process

Consumer Demand
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Consumer Demand
Forecasting has become necessary for businesses to survive; it is a technique that requires accurate and practical ways of determining the future demands of goods and services. Forecasting means attempting to predict the future demand, hence requires comprehensive information before making important decisions (Albright, Winston & Zappe, 2010). A set of techniques is used to predict the overall levels of future demand based on specific facts. Several techniques may differ, meaning that estimation techniques used for demand forecasting may not be precise (Albright, Winston & Zappe, 2010).
Demand forecasting is a scientific process used to estimate product demand within a particular period to determine how much of what product is needed and when and where. Forecasters need to be aware that demand forecasting using demand statistic equation is a difficult exercise because making the estimate for future under changing condition is an enormous task (Albright, Winston & Zappe, 2010). Factors like consumer behavior are unpredictable because it is motivated and influenced by several other factors. There is not an easy or accurate formula for forecasting on future demands (Albright, Winston & Zappe, 2010).
The statistician has come up with several methods of demand forecasting, but each method has its advantages and disadvantage, selecting the right method is essential to make an accurate demand forecasting (Provost & Fawcett, 2013). In making demand forecasting using a statistical demand equation, a well thought-out combination of statistical skill and rational judgment is imperative. Mathematical and statistical demand forecasting techniques are helpful because they classify relationships and provides clear analysis, however, they do not present sound judgment. A good demand forecasting should be based on facts (Provost & Fawcett, 2013).
The main challenge faced by forecasters when conducting demand forecasting is the selection of effective technique, there is no specific method that would enable the organization to anticipate uncertainty or risk in the future, but statistical methods offer more accurate forecasting results (McGuigan, Moyer & Harris, 2013). Statistical forecasting methods are elaborate sets of methods used for prediction in the long term. Using this method, demand is predicted by historical and cross sectional data gathered (McGuigan, Moyer & Harris, 2013).
Historical data are the past trends obtained from various reliable sources like previous year's financial statement and survey reports. Sectional data are current data collected from individual interviews when conducting market surveys. Statistical methods are cost effective and reliable with minimal subjectivity (McGuigan, Moyer & Harris, 2013). |For instance, regression analysis is an example of a statistical technique used to estim...