Introduction
Forecasting refers to the method of being able to predict what is going to happen in the future, and in business, the future is narrowly defined by the existing economic conditions. When organizations want to develop a forecast associated with their immediate performance, they collect data on previous experiences and occurrences. The analysis of this data allows developing an accurate picture of the present economy to make predictions about future conditions.
For example, retail clothing companies, such as H&M, use forecasting as the critical strategic tool for predicting demand and making a profit. According to Kalaoglu, Akyuz, Ecemiş, Eryuruk, Sümen, And Kalaoglu (2015), clothing brands use forecasting to determine future targets of products, business, and industry in general. To make a forecast for a specific time period (e.g., March and April sales), a retail company collects data on the sales for the same period in the previous year as well as on the sales in during January and February. Using the average number for the periods, the company can make estimations for sales. Therefore, forecasting uses quantitative methods to facilitate a better understanding of strategic decisions and advantages (Anderson et al., 2016).
When it comes to improving organizational processes or strategic decisions, companies can also use forecasting. For example, in the case when a specific cost variable increases unexpectedly, an organization can predict its effect on targets and forecasts. To address the issue, a company can compensate for rising costs by adjusting its price to reflect the change. Therefore, strategic forecasting is essential for making the operations of an organization sensitive to the characteristics of a market on a regular basis. Companies can decide whether additional resources should be used to facilitate corrective action or strategy changes are needed.
Apart from making a strategic decision associated with resources or costs, forecasting can be used to make predictions in the context of specific environments. Company strategies are associated with influencing their environments to ensure that they correspond to those used in forecasts. This is necessary because targets can change in accordance with shifts in environments. External environments are important in strategic forecasting because of their influence on the performance of companies (Chindia, 2016). As illustrated in the study by Chindia (2016), regression analysis can use the data collected by companies to influence the changes in strategies as related adjusting to shifting environments. The more accurate the forecast is, the more effective an organization is in terms of being effective in changing.
Summary
When exploring the topic of strategic forecasting, the study by Kalaoglu et al. (2015) is essential to mention. The scholars explored the multi-dimensional nature of forecasting in the context of a clothing industry. The article is concluded with the finding that businesses could tremendously benefit from appropriate forecasting before creating new products or product lines. Such forecasting prevents them from spending too much time and resources to develop products that can fail in the marketplace.
The researchers explored various quantitative forecasting models, “such as the simple moving average model, weighted moving the average model, and linear trend model”, to determine how clothing companies in Turkey forecast their sales (Kalaoglu et al, 2015, p. 173). The research aligns with the quantitative focus of the course and can be of great value when studying the methods that businesses use to become profitable and competitive in a particular market.
References
Anderson, D. R., Sweeney, D. J., Williams, T. A., Camm, J. D., Cochran, J. L., Fry, M. J., & Ohlmann, J. W. (2016). Quantitative methods for business with CengageNOW (13th ed.). Boston, MA: Cengage Learning.
Chindia, E. (2016). Forecasting techniques, external operating environment and accuracy of performance forecasting. Advances in Economics and Business, 4(8), 468-475.
Kalaoglu, Ö., Akyuz, E., Ecemiş, S., Eryuruk, S., Sümen, H., & Kalaoglu, F. (2015). Retail demand forecasting in clothing industry. Tekstil Ve Konfeksiyon, 25(2), 173-178.