Demand forecasting for managers / Stephan Kolassa, Enno Siemsen.

Part 1. Introduction -- 1. Introduction -- 2. Choice under uncertainty -- 3. A simple example -- Part 2. Forecasting basics -- 4. Know your time series -- 5. Time series decomposition -- Part 3. Forecasting models -- 6. Exponential smoothing -- 7. ARIMA models -- 8. Causal models and leading indicators -- 9. Count data and intermittent demands -- 10. Human judgment -- Part 4. Forecasting quality -- 11. Forecast quality measures -- 12. Forecasting competitions -- Part 5. Forecasting organization -- 13. Sales and operations planning -- 14. Forecasting hierarchies -- References -- Index.

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Most decisions and plans in a firm require a forecast. Not matching supply with demand can make or break any business, and that is why forecasting is so invaluable. Forecasting can appear as a frightening topic with many arcane equations to master. We therefore start out from the very basics and provide a nontechnical overview of common forecasting techniques as well as organizational aspects of creating a robust forecasting process. We also discuss how to measure forecast accuracy to hold people accountable and guide continuous improvement. This book does not require prior knowledge of higher mathematics, statistics, or operations research. It is designed to serve as a first introduction to the nonexpert, such as a manager overseeing a forecasting group, or an MBA student who needs to be familiar with the broad outlines of forecasting without specializing in it.

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