Demand Forecasting

In this chapter, we will first explore the three basic laws of forecasting and then several forecasting models before we will see how to select the best forecasting model using quantitative measures. After that, we will look at ARIMA and Seasonal ARIMA (SARIMA) forecasting models in detail. For each forecasting model, an example will be used to illustrate its application. Finally, we will look at two case studies, a luxury-watch inventory imbalance in a travel retailer case and an ATM ad hoc failure case, to understand how each case study applied aggregate and disaggregate forecasting to solve their respective problems.