Advantages And Disadvantages Of Forecasting

Planning is essential for proper and effective management, and forecasting is an important subset of the planning function (Choi, 1999). Rahmlow and Klimberg (2002) identified some of the most important decision areas as well as the impact that forecasting has on these areas within an organization, the results are displayed in Table 1.

In the food retail industry, a major contributing factor to the successful operation and optimal stock management is forecasting (Arunraj and Ahrens, 2015). Kokkinou (2013) states that, as restaurant operators deal with highly perishable products, overestimation of sales can lead to unnecessary labor costs and stock wastage. Underestimation of sales can lead to unsatisfactory customer service and loss of revenue

Qualitative forecasting methods are often applied when limited data is available or when time is insufficient. These forecasting methods usually rely on the judgment of experts within a certain field. They usually take less time to construct and can be relatively inexpensive and easy to understand. A major disadvantage of qualitative forecasting is that it can be largely opinionated and as a result be subjective.

Quantitative forecasting methods rely on historical data to predict the future by finding trends and relationships in the historical data. Quantitative methods can further be classified into time series and causal methods. Time series methods are based on the assumption that past occurrences and behavior has some relevance in the future. They do not focus on what caused this behavior but rather assume that whatever caused this behavior will continue doing so in the future. Predictions are made by determining the impact of trends and seasonal factors on past data and extrapolating this behavior into the

These models are known as Hybrid Models. Hybrid Models are used to improve forecast accuracy by combining two or more forecasting methods with alternative capabilities to accommodate for the limitations that may be present when only one of the methods is used (Arunraj and Ahrens, 2015).

Selecting the appropriate forecasting method(s) is a crucial part of the forecasting process. As made clear in the preceding section, a wide variety of methods is available, each with its own limitations and capabilities. Armstrong (2001) identified some key principles and factors to consider in the selection of appropriate forecasting methods.

The principles are listed as follows:

• Use forecasting methods that contain methodical and detailed steps that can be explained and replicated.

• If sufficient data is available, use quantitative rather than qualitative methods.

• If large changes in the forecasts can be expected, use causal methods instead of time-series methods.

• Unless considerable proof is present that a complex method will improve forecasts, use simple forecasting methods.

Some factors to consider during the decision process of selecting an appropriate method