Qualitative forecasting is an estimating method that involves the application of expert judgment; instead of relying on numerical analysis. Consequently, the forecast depends on the experience and knowledge of the staff and even consultants to predict the future outcomes ( Nenni, Giustiniano, & Pirolo, 2013). The method substantially differs from the qualitative approach, which relies on the analysis of compiled historical data to determine the future trends. Qualitative forecasting is ideal in circumstances where it is believed that future outcomes are likely to depart to a greater extent from the trends in earlier days, and which cannot be estimated by quantitative approaches. For instance, the historical trends in regards to a corporation’s sales may reveal that there would be an increase in sales in the forthcoming year, which may normally be measured by trend line analysis; but an industry expert may say that there would be a shortage of production inputs from a core supplier that would compel the sales to go downwards.
Qualitative forecasting has various pitfalls that may make the forecasts made inaccurate. When an individual uses this method to predict the future trends, he or she has to use a rating scale that reveals the likelihood of any specific outcome ( Nenni, Giustiniano, & Pirolo, 2013). For instance, one may rate his or her new marketing strategy’s probability of doubling sales. He or she may rate it on a scale of one to ten. One may think that figures are highly accurate; however, such figures are mere estimates, and they may be inaccurate since prognosticators mostly assume that previous trends would persist. Moreover, qualitative forecasting has the challenge of people being overly optimistic ( Nenni, Giustiniano, & Pirolo, 2013). For example, one may poll the salespeople on the prospects for growth in the sales of the company products. Since salespeople handle market forces daily, they may have good instincts on where the market is headed. However, such personnel may be overly optimistic since they tend to believe that sales must be ever-increasing.
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There are numerous consequences of using an inaccurate forecast. An inaccurate forecast means that the organization has the wrong information regarding its operations. With irrelevant data, there would be over-production or underproduction ( Guizzardi & Stacchini, 2015). Over-production would lead to losses since some goods would go bad. Under-production would lead to the company failing to meet the demands of its customers, who may opt to move to its competitors. Consequently, this would affect working capital and inventory. Excess inventory resulting from over-estimating of the demand often leads to high inventory (working capital), which in turn lowers the company’s profits. Additionally, decision-making would be difficult, and this may affect the various stakeholders in the business, including suppliers and stockholders ( Guizzardi & Stacchini, 2015). Poor stakeholder relations would put the business in the risk of failure since there would be little or no trust.
Forecasts are always inaccurate since there is a lot of randomness and instability in the demand for goods and services, which even sophisticated forecasting approaches cannot help one to attain the desired level of accuracy. This means that there are some issues that one cannot accurately predict ( Green & Armstrong, 2015). For example, one may predict that the demand for a given product would increase by a half in the first quarter of a given year based on the previous trends and statistics. However, if there is a change in government policy, say an increase in personal tax, the demand for such a product would not increase as initially anticipated. The selection of different forecasting approaches might assist mitigate the forecast error. This is because the limitations of one method would be alleviated by the other methods ( Green & Armstrong, 2015). Moreover, the various methods can combine their strengths to become stronger.
References
Green, K. C., & Armstrong, J. S. (2015). Simple versus complex forecasting: The evidence. Journal of Business Research , 68 (8), 1678-1685.
Guizzardi, A., & Stacchini, A. (2015). Real-time forecasting regional tourism with business sentiment surveys. Tourism Management , 47 , 213-223.
Nenni, M. E., Giustiniano, L., & Pirolo, L. (2013). Demand forecasting in the fashion industry: a review. International Journal of Engineering Business Management , 5 , 37.
