Forecasting Analysis for an Auto Parts Manufacturer using Least Inaccuracy Cost Criteria
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Abstract
Although forecasting resembles fortune telling, businesses plan and execute all plans based on informal or formal forecasts about the demand for their products and services. In this study, we will present a summary of the forecasting analysis project that we conducted in an automobile parts manufacturer in the mid-west for seven of their critical products. For each product, several forecasting models were evaluated on a four-year monthly data. However, instead of focusing on the standard error measures such as Mean Square Error and Mean Absolute Deviation, we developed a unique criteria based on least cost to measure the "most desirable" forecasting model. The results of this process will be discussed for example products.
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