Demand forecasting for managers / Stephan Kolassa, Enno Siemsen.
Kolassa, Stephan., author.; Siemsen, Enno., author.
2016
HD30.27 .K653 2016
Available Online

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Title Demand forecasting for managers / Stephan Kolassa, Enno Siemsen.
Author Kolassa, Stephan., author.
Edition First edition.
ISBN 9781606495025 paperback
9781606495032 (electronic bk.)
Published New York, New York (222 East 46th Street, New York, NY 10017) : Business Expert Press, 2016.
Language English
Description 1 online resource (158 pages)
Call Number HD30.27 .K653 2016
Dewey Decimal Classification 658.40355
Summary 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.
Bibliography, etc. Note Includes bibliographical references (pages 147-153) and index.
Access Note Access limited to authorized users.
Source of Description Title from PDF title page (viewed on August 29, 2016).
Added Author Siemsen, Enno., author.
Series Supply and operations management collection. 2156-8200
Available in Other Form Print version: 9781606495025
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Record Appears in Online Resources > Ebooks
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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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