Forecasting plays a crucial role during business planning. It is the act of estimating the demand of products and services of the future and the resources necessary for the production of these outputs.(Ching-Chin et al, 2010) describe forecasting as the art and sciences through which future events can be predicted. The prediction can be achieved through historical data. In the event historical data is missing, intuitive prediction can be applied.
Risk and uncertainty are crucial in forecasting and prediction: It is a good practice in the indication of uncertainty. For accurate results, the data should be up to date. In some cases the data used to predict the variable of interest is itself forecasted.
Unfortunately, moving averages is not 100% accurate in establishing trends as they present significant risk to investors(Arunraj & Ahrens, 2015). In addition to that, Moving average applies to specific companies and industries. Some of the common drawbacks of the model include;
1.The average forecasting model assumes that same forecasting model are applied in varying period since they do not take into account the historical data. This is likely to lead to the risk of errors displayed in the moving trend over a given period of time.
2. The model can be spread out over a given period of time. It is however associated with challenges because the general trend is subject to change depending on the time period used. Short time frames are volatile whereas longer time frame has less volatility but they do not give correct information in relation to the changing market.
3. The trend does not take into account changes that may affect security of the future performance such as new competitors or variation in product demand in the industry.
To sum it up, average forecasting model is an important analytical tool for indication of future trend, but for any tool to be effective, its functionality must first be understood, the appropriate time it’s to be used or not. The perils discussed above indicate scenarios when the model is not an effective tool.
References
Arunraj, N. S., & Ahrens, D. (2015). A hybrid seasonal autoregressive integrated moving average and quantile regression for daily food sales forecasting. International Journal of Production Economics. https://doi.org/10.1016/j.ijpe.2015.09.039
Ching-Chin, C., Ka Ieng, A. I., Ling-Ling, W., & Ling-Chieh, K. (2010). Designing a decision-support system for new product sales forecasting. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2009.06.087