THE ACCURACY OF DEMAND FORECAST MODELS AS A CRITICAL FACTOR IN THE FINANCIAL PERFORMANCE OF THE FOOD INDUSTRY
Every organization needs to balance their production capacities with demand. The role of demand forecasting is to assist in the organization's strategic planning; this process allows administrators to anticipate the future and plot an appropriate course of action. On its own, however, a system of demand forecasting is not enough. It is the quality of information obtained by this system which enables the organization to achieve better operational planning. In this context, this paper presents case study research to: (a) define the quantitative model to forecast demand with greater accuracy; and (b) to verify the influence of accuracy in demand forecasting on financial performance. This is an ex-post facto descriptive inquiry with a time series in which we made use of historical data from five groups of products over the period 2004–2008. The results suggest that if a company employs the ARIMA model for groups A, B, and E; the Holt model for group D; and the Winter model for group C, revenues will increase by approximately $1,600,000 annually.
Key-words: Accuracy. Demand forecasting. Financial performance.
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