Forecasting: Practice and Process for Demand Management

        by Hans Levenbach and James P. Cleary (2005). Cengage Learning

 

 

This book introduces students and practitioners to the principles, applications, and methods of demand forecasting.

 

 

 

Forecasting: Practice and Process for Demand Management focuses on how managers and planners predict future customer demand for their business’s products and services, emphasizing that forecasting is a structured process, rather than a series of disconnected techniques, that predicts the right quantity of the right product to be in the right place at the right time for the right price.

 

Levenbach and Cleary stress applications in their book, present­ing concepts in the context of real examples drawn from their own broad experience as forecasting practitioners in industry, consultants to organizations, and educators. And where appropriate, they also use time series from real-world sources in order to illustrate forecasting methods and compare or contrast results. The text addresses the macroeconomic forecasting procedures used by economists as well as the specific product-level forecasting techniques now widely used by corporate sales and operations planning organizations—providing comprehensive coverage of traditional and advanced forecasting tools. Throughout, the authors focus more on training students to perform accurate data analysis than on modeling sophistication. The text incorporates computing throughout the book, featuring Microsoft® Excel applications and including a professional Excel add-in and data sets on CD.

 

 

Brief Contents

 

Part I. Introducing the Forecasting Process

1 – Forecasting As A Structured Process

2 - Classifying Forecasting Techniques

 

 

Part II. Exploring Time Series      

3 – Data Exploration For Forecasting

4 - Characteristics of Time Series

5 - Assessing Accuracy of Forecasts

Part III. Forecasting the Aggregate       

6 - Dealing With Seasonal Fluctuations

7 – Forecasting the Business Environment

 

Part IV: Applying Bottom-up Techniques     

8 - The Exponential Smoothing Method

9 - Disaggregate Product-Demand Forecasting

 

 

Part V: Forecasting Models

10 – Creating and Analyzing Causal Forecasting Models

11 - Linear Regression Analysis

12 - Forecasting with Regression Models

13 – Building ARIMA Models: The Box-Jenkins Approach

14 - Forecasting With ARIMA Models

 

 

Part VI - Improving Forecasting Effectiveness      

15 – Selecting The Final Forecast Number

16 – Implementing The Forecasting Process

 

 

This book on Demand Forecasting is available With Excel Add-ins, problem sets, case studies on enclosed CD ROM


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