Intermittent Demand Forecasting: Context, Methods and Applications 1st Edition Syntetos

Intermittent Demand Forecasting: Context, Methods and Applications – Ebook PDF Instant Download/Delivery

Product details:

• ISBN-10 ‏ : ‎ 1119976081
• ISBN-13 ‏ : ‎ 978-1119976080
• Author(s): Syntetos, Aris A.; Boylan, John E

Intermittent Demand Forecasting is for anyone who is interested in improving forecasts of intermittent demand products, and enhancing the management of inventories. Whether you are a practitioner, at the sharp end of demand planning, a software designer, a student, an academic teaching operational research or operations management courses, or a researcher in this field, we hope that the book will inspire you to rethink demand forecasting. If you do so, then you can contribute towards significant economic and environmental benefits.

No prior knowledge of intermittent demand forecasting or inventory management is assumed in this book. The key formulae are accompanied by worked examples to show how they can be implemented in practice. For those wishing to understand the theory in more depth, technical notes are provided at the end of each chapter, as well as an extensive and up-to-date collection of references for further study. Software developments are reviewed, to give an appreciation of the current state of the art in commercial and open source software.

Table contents:

1 Economic and Environmental Context

2 Inventory Management and Forecasting

3 Service Level Measures

4 Demand Distributions

5 Compound Demand Distributions

6 Forecasting Mean Demand

7 Forecasting the Variance of Demand and Forecast Error

8 Inventory Settings

9 Accuracy and Its Implications

10 Judgement, Bias, and Mean Square Error

People also search:

croston’s method intermittent demand forecasting

intermittent demand forecasting python

croston’s method intermittent demand forecasting python

bayesian intermittent demand forecasting for large inventories

intermittent demand forecasting context methods and applications

intermittent demand forecasting machine learning