Forecasting: Practice and Process for Demand Management
by Hans Levenbach and James P. Cleary (2006). Thomson/Brooks-Cole.
Book comes with Excel Add-in PEERForecaster.xla, problem sets, case studies on an enclosed CD ROM
This text introduces students to the forecasting principles, applications, and methods of demand forecasting.
If you are an academic, you can receive a complimentary review copy by visiting online at http://servicedirect.thomsonlearning.com, using the promotion code 6TPST160.
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, called PEERForecaster.xla and data sets on CD.
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