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Demand Forecasting for Managers.

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 pro...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Kolassa, Stephan
Otros Autores: Siemsen, Enno
Formato: Electrónico eBook
Idioma:Inglés
Publicado: New York : Business Expert Press, 2014.
Temas:
Acceso en línea:Texto completo

MARC

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505 0 |a Cover; Demand Forecasting for Managers; Contents; Acknowledgments; PART I: Introduction; CHAPTER 1: Introduction; CHAPTER 2: Choice Under Uncertainty; CHAPTER 3: A Simple Example; PART II: Forecasting Basics; CHAPTER 4: Know Your Time Series; CHAPTER 5: Time Series Decomposition; PART III: Forecasting Models; CHAPTER 6: Exponential Smoothing; CHAPTER 7: ARIMA Models; CHAPTER 8: Causal Models and Leading Indicators; CHAPTER 9: Count Data and Intermittent Demands; CHAPTER 10: Human Judgment; PART IV: Forecasting Quality; CHAPTER 11: Forecast Quality Measures. 
505 8 |a CHAPTER 12: Forecasting CompetitionsPART V: Forecasting Organization; CHAPTER 13: Sales and Operations Planning; CHAPTER 14: Forecasting Hierarchies; References; Index; Ad Page; Back Cover. 
520 3 |a 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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