Demand Forecasting for Executives and Professionals
Chapman & Hall/CRC (Verlag)
978-1-032-50773-6 (ISBN)
This book surveys what executives who make decisions based on forecasts and professionals responsible for forecasts should know about forecasting. It discusses how individuals and firms should think about forecasting and guidelines for good practices. It introduces readers to the subject of time series, presents basic and advanced forecasting models, from exponential smoothing across ARIMA to modern Machine Learning methods, and examines human judgment's role in interpreting numbers and identifying forecasting errors and how it should be integrated into organizations.
This is a great book to start learning about forecasting if you are new to the area or have some preliminary exposure to forecasting. Whether you are a practitioner, either in a role managing a forecasting team or at operationally involved in demand planning, a software designer, a student or an academic teaching business analytics, operational research, or operations management courses, the book can inspire you to rethink demand forecasting.
No prior knowledge of higher mathematics, statistics, operations research, or forecasting is assumed in this book. It is designed to serve as a first introduction to the non-expert who needs to be familiar with the broad outlines of forecasting without specializing in it. This may include a manager overseeing a forecasting group, or a student enrolled in an MBA program, an executive education course, or programs not specialising in analytics. Worked examples accompany the key formulae to show how they can be implemented.
Key Features:
While there are many books about forecasting technique, very few are published targeting managers. This book fills that gap.
It provides the right balance between explaining the importance of demand forecasting and providing enough information to allow a busy manager to read a book and learn something that can be directly used in practice.
It provides key takeaways that will help managers to make difference in their companies.
Dr. Bahman Rostami-Tabar is an Associate Professor in Data and Management Science, at Cardiff University, UK. Dr. Stephan Kolassa is a Data Science Expert at SAP, Switzerland and Honorary Researcher at Lancaster University, UK. In 2023 Dr. Kolassa was named a Fellow of the International Institute of Forecasters. Prof. Enno Siemsen is the Patrick A. Thiele Distinguished Chair in Business, University of Wisconsin-Madison, USA.
Part 1. Introduction 1. Introduction 2. The forecasting workflow 3. Choice under uncertainty 4. A simple example Part 2. Forecasting basics 5. Know your time series 6. Time series components 7. Time series decomposition Part 3. Forecasting models 8. Low hanging fruit: simple forecasts 9. Exponential Smoothing 10. ARIMA models 11. Causal models and predictors 12. Count data and intermittent demand 13. Forecasting hierarchies 14. Artificial Intelligence and Machine Learning 15. Long, multiple and non-periodic seasonal cycles 16. Human judgment Part 4. Forecasting quality 17. Error measures 18. Forecasting competitions Part 5. Forecasting organisation 19. Leading forecasters and forecasting teams 20. Sales and operations planning 21. Why does forecasting fail? Part 6. Learning More 22. Learning more
| Erscheinungsdatum | 03.10.2023 |
|---|---|
| Zusatzinfo | 6 Tables, black and white; 64 Line drawings, black and white; 64 Illustrations, black and white |
| Sprache | englisch |
| Maße | 156 x 234 mm |
| Gewicht | 653 g |
| Themenwelt | Mathematik / Informatik ► Mathematik |
| Wirtschaft ► Betriebswirtschaft / Management | |
| Wirtschaft ► Volkswirtschaftslehre ► Ökonometrie | |
| ISBN-10 | 1-032-50773-X / 103250773X |
| ISBN-13 | 978-1-032-50773-6 / 9781032507736 |
| Zustand | Neuware |
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