Demand forecasting is an essential corporate practice that enables managers to plan ahead and make sound business-related decisions. The long-term forecasts predict corporate trends for periods of over two years, while the short-term ones may describe periods of several months. The two types of projections also vary in terms of use. The long-term outlook facilitates operational decisions, whereas the short-term approach supports a strategic resolution at the top management. Although the most common qualitative and quantitative business forecasting methods are substantially beneficial to corporations, they are also subject to various limitations.
Qualitative forecasting is used for long-term business prediction. The approach is subjective and often utilizes the opinion and judgments of industrial experts (Chindia, 2016). Delphi method, market research, and historical life-cycle analogy are the most common approaches for qualitative forecasting (Chindia, 2016). One of the benefits associated with the above technique is its utilization in the absence of historical data. However, studies reveal that the method may be unreliable, especially during changes in product and market demand (Ezeliora et al., 2014). In such instances, an alternative methodology may be required.
On the other hand, the quantitative technique is used for short-term forecasts. Under this method, future demand is calculated as a function of past data (Chindia, 2016). For instance, future sales for a given product may be computed using available historical data. Last-period demand and simple exponential smoothing are some of the methods used in quantitative forecasting (Chindia, 2016). However, the benefits of this technique are limited to ever-changing consumer behavior and market patterns, which may affect the legibility of historical data (Ezeliora et al., 2014). When such issues arise, a business can combine both forecasting methods.
Predicting techniques are essential in organizations despite their limitations. For instance, I can use a qualitative method to forecast the state of e-commerce sales. In the second quarter of 2018, the sector recorded $127.3 billion in sales (Agrawal, 2018). In my opinion, the transactions will be higher in 2022. Such a prediction is based on an informed judgment of the steady upward trend in the industry. I prefer such a technique because historical data on the consistent growth rate of e-commerce sales is unavailable.
References
Agrawal, P. (2018). Five things your e-commerce business needs to thrive. Forbes. Retrieved from https://www.forbes.com/sites/forbescoachescouncil/2018/09/07/five-things-your-e-commerce-business-needs-to-thrive/#3e70c5a5587b
Chindia, E.W. (2016). Forecasting techniques and accuracy of performance forecasting. International Journal of Management Excellence, 7(2), 813-820.
Ezeliora, C.D., Umeh, M.N., Mbeledeogu, N., & Okoye, U.P. (2014). Application of forecasting methods for the estimation of production demand. International Journal of Science, Engineering and Technology Research, 3(2), 184-202.
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