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Forecasting: Practice & Process for Demand Management
Forecasting: Practice and Process for Data Management focuses on how forecast managers and planners create forecasts for products and services for their business. The text addresses both the macroeconomic forecasting procedures used by economists as well as the specific product-level forecasting techniques that are now widely used by sales and operations
planning organizations in corporations.
Contents:
Introducing the Forecasting Process
1 - Forecasting as a Structured Process
2 - Classifying Forecasting Techniques
Exploring Time Series
3 - Data Exploration for Forecasting
4 - Characteristics of Time Series
5 - Assessing Accuracy of Forecasts
Forecasting the Aggregate
6 - Dealing with Seasonal Flunctuations
7 - Forecasting the Business Environment
Applying Bottom-Up Techniques
8 - The Exponential Smoothing Method
9 - Disaggregate Product-Demand Forecasting
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
Improving Forecasting Effectiveness
15 - Selecting the Final Forecast Number
16 - Implementing the Forecasting Process
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