Abstract:

Demand forecasting is a topic of paramount importance in order to reduce inventory costs and maximize client service level. Typically, this function is accomplished by means of a Forecasting Support System (FSS), which integrates a statistical tool with judgemental adjusted forecasts introduced by managers. In principle, the forecast employed by the company is the result of two sources of information. On the one hand, the FSS provides a statistical forecast, known as either system forecast or baseline forecast. Usually, this forecast comes from a univariate statistical tool as the exponential smoothing. On the other hand, several meetings are arranged among the managers that use such forecasts (production, finance and marketing staff) to agree a final forecast by modifying the baseline forecast. The reason of this adjustment is to incorporate key information that it is very difficult to include in a statistical model. The aim of this work is to design a workshop that reproduced this kind of decisions to train students in planning production skills. It should be noted that traditional books in business administration do not usually implement tools that allow students to integrate judgemental opinions and statistical forecasts. Actually, most of the fundamental bibliography just rely on basic statistical tools to introduce the area of demand planning.

The workshop will utilize the data collected from a real chemical company. Therefore, the first objective of this work is to employ an Excel sheet to implement the statistical forecasting algorithm, in our case study it will be an exponential smoothing on the basis of the company past sales. Secondly, the forecasts implemented by students will be compared to the forecasts made by the company managers. Lastly, after analysing potential discrepancies between student and managers responses, a final model that combines statistical and judgmental forecasts will be implemented as well to have a set of forecasts that minimize the company inventory costs.

Keywords:

Case study approach, workshop, forecasting, business, chemical engineering.