Business Forecasting Techniques

 Business Forecasting Techniques

Forecasting is an essential business technique for anticipation and predicting vital data and information regarding the future of an organization. Forecasting is the foundation for all planning and organizing activities that are fundamental to any business’ success. This process involves gathering relevant and appropriate information about the past and present and using the data to estimate the future of the business. Rivera-Castro et al. (2019) assert that forecasting is at the core of every management practice. The success of any organization depends upon effective forecasting and preparation for future activities. There are many techniques that business leaders utilize to forecast the future of the business. These methods include judgmental or opinion forecasts, opinion surveys, time series, extrapolation, and similarity event methods, among others.

Judgmental Forecasts

This technique involves the use of intuition and opinions by managers and organizational leaders. Human judgment and opinions are majorly relied upon to predict customer demands in the future. The management or executives utilize the views of others and their own anticipations to tell the direction that the business is headed in (Kros & Rowe, 2016). Many times, managers also use the opinions of external consultants. Managers also consult salesmen to give information about consumer tastes, preferences, and attitudes, as well as the activities of the competitors.

Consumer Opinion Surveys

This method is effective when making short term forecasts for sales. Leaders directly contact the consumers through surveys and interviews to gauge their preferences and attitudes towards certain products or services. The information collected from the surveys is then used to project future demands and sales. This technique is appropriate in businesses where the consumers are industrial manufacturers who make large purchases (Abou Maroun, Zowghi, & Agarwal, 2019). However, this method is not appropriate for making long term predictions or for forecasting consumer purchase of household goods, which are rapidly affected by fashions and attitudes.

Time Series Technique

This technique bases on the assumption that the past is an accurate prediction of future trends. This assumption is useful, particularly when a lot of historical data is available and when the trends remain stable through the historical course. The technique identifies patterns that represent a combination of seasonal and cyclical factors, as well as customer trends over some time. Time series try to identify the best predictions by eliminating the probability of uncertain fluctuations in preferences and attitudes (Cabedo & Tirado, 2015). There are three methods of using time series, namely:

Trends: These exhibit the overall tendency of customer activities and are always downward-moving depending on customer behavior.

A seasonal variation uses seasonal elements to provide data about cyclical or seasonal behavior of customers. Seasonal variations are often repetitive and easy to predict.

Random fluctuations

Time series eliminates the use of random fluctuations to predict the future. However, leaders often use this technique to make predictions about the future of the practice, as compared to the future of customer behavior.

Extrapolation

Extrapolation is the estimation or prediction of future customer behavior from unknown historical data. Here, managers and leaders take into consideration the effects of various factors, such as fashions, tastes, and preferences that are continuously changing. The technique assumes that the effect of such factors is stable and has a constant pattern, thus assuring this continuity in the future.

Similar Events Method

This method is also called the historical analogy technique. Here, predictions about the business are made solely based on past events that are almost similar to the current events in the business (Tax, Verenich, La Rosa, & Dumas, 2017). For instance, assessing the attitudes of employees regarding workplace equality, managers can predict the attitudes and behaviors of employees by examining how employees reacted to such changes in the past. The manager appropriately analyzes past events that are similar to the current ones and go ahead to make predictions.

Conclusion

Techniques for forecasting the future of the business are essential for any business. Forecasting stands at the centre of every effective planning and organizing. Depending on the nature and size of the business, managers should choose a technique that will produce successful outcomes for the business in the future.

References

Abou Maroun, E., Zowghi, D., & Agarwal, R. (2019). Challenges in forecasting uncertain product demand in the supply chain: A systematic literature review. 32nd annual Australian and New Zealand Academy of Management.

Cabedo, J. D., & Tirado, J. M. (2015). ROUGH SETS AND Discriminant Analysis Techniques for Business Default Forecasting. Fuzzy Economic Review, 20(1).

Kros, J. F., & Rowe, W. J. (2016). Business School Forecasting for the Real World’, Advances in Business and Management Forecasting (Advances in Business and Management Forecasting, Volume 11).

Rivera-Castro, R., Nazarov, I., Xiang, Y., Pletneev, A., Maksimov, I., & Burnaev, E. (2019, July). Demand forecasting techniques for build-to-order lean manufacturing supply chains. In International Symposium on Neural Networks (pp. 213-222). Springer, Cham.

Tax, N., Verenich, I., La Rosa, M., & Dumas, M. (2017, June). Predictive business process monitoring with LSTM neural networks. In International Conference on Advanced Information Systems Engineering (pp. 477-492). Springer, Cham.