Impact of Forecasting Techniques and Market Demand Scenarios on Multi-Product, Multi-Period Aggregate Production Planning
Customer demands fluctuate over a different time horizon and if forecasted with same forecasting method shows errors in production planning. A linear programming mathematical model is reformulated in this paper for aggregate production planning (APP) to find the best-suited forecasting techniques for different market demand scenarios. The model is reformulated as a linear programming model and solved using excel solver to minimize relevant costs (backorder cost, inventory cost, and regular time production cost) while meeting the forecasted demand. The system performance is evaluated on the basis of service level (SL) and inventory level (IL). A case studied from a silk industry of Bangladesh used here to define three demand scenario High, Peak and Few. For each of them, the service level and inventory level was compared with the inclusion of simple moving average (SMA), weighted moving average (WMA) and simple exponential smoothing (SES) forecasting methods in the APP model. We found from the computations that for High and Few scenarios SMA is best in terms of SL and IL but for Peak scenario, WMA is best in terms of IL.