Management of Stochastic Demand in Make-To-Stock Manufacturing.

• Superior document: Forschungsergebnisse der Wirtschaftsuniversitaet Wien Series ; v.37
• Author: Quante, Rainer.
• Place / Publishing House: Frankfurt a.M. : : Peter Lang GmbH, Internationaler Verlag der Wissenschaften,, 2009.; Ã2010.
• Year of Publication: 2009
• Edition: 1st ed.
• Language: English
• Series: Forschungsergebnisse der Wirtschaftsuniversitaet Wien Series
• Physical Description: 1 online resource (134 pages)

Table of Contents

• Cover
• List of Figures
• List of Tables
• Nomenclature
• 1 Introduction
• 1.1 Research Topic and Motivation
• 1.2 Organization, Objectives and Contributions
• 2 Demand Fulfillment in Make-to-Stock Manufacturing
• 2.1 Make-to-Stock and the Customer Order Decoupling Point
• 2.2 Structure of Advanced Planning Systems
• 2.3 Available-to-Promise
• 2.3.1 Definition
• 2.3.2 Dimensions of ATP
• 3 A Framework for Demand Management
• 3.1 Demand Management Defined
• 3.2 General Model Types for DM
• 3.2.1 Classifying Demand Management Models
• 3.2.2 Single-Class Exogenous Demand Models
• 3.2.3 Price-Based Demand Models
• 3.2.4 Quantity-Based Demand Models
• 3.3 General Software Types for DM
• 3.3.1 Classifying Demand Management Software
• 3.3.2 Single-Class Exogenous Demand Solutions
• 3.3.3 Price-Based Solutions
• 3.3.4 Quantity-Based Solutions
• 4 Demand Management Models in MTS Manufacturing
• 4.1 Matching of Model and Software Types
• 4.2 Quantity-Based DM in Manufacturing
• 4.2.1 Traditional Revenue Management
• 4.2.2 Allocated Available-to-Promise
• 4.2.3 Inventory Rationing
• 4.3 A Selected Allocation and Order Promising Model
• 4.3.1 Models Without Customer Segmentation
• 4.3.2 Models With Customer Segmentation
• 4.3.3 Search Rules for ATP Consumption
• 4.4 Summary
• 5 New Demand Management Approaches
• 5.1 A Revenue Management Approach
• 5.1.1 Model formulation
• 5.1.2 Structural properties and optimal policy
• 5.1.3 A Numerical Example
• 5.2 Approximations Based on Linear Programming
• 5.2.1 Deterministic Linear Programming
• 5.2.2 Randomized Linear Programming
• 6 Simulation Environment
• 6.1 Technical Settings and Implementation Issues
• 6.1.1 Test Environment
• 6.1.2 Implementation Issues
• 6.2 Simulation Issues
• 6.2.1 Data Generation
• 6.2.2 Simulation Options.
• 6.2.3 Output and Key Performance Indicators
• 7 Numerical Analysis
• 7.1 SOPA in Stochastic Environments
• 7.1.1 Base Case Analysis
• 7.1.2 Impact of Customer Classes
• 7.1.3 Impact of Customer Heterogeneity
• 7.1.4 Impact of Forecast Errors
• 7.1.5 Impact of Backlogging Costs
• 7.2 Analysis of the Revenue Management Approach
• 7.2.1 Base Case Analysis
• 7.2.2 Impact of Demand Variability
• 7.2.3 Impact of Customer Heterogeneity
• 7.2.4 Impact of Supply Shortage
• 7.3 Analysis of Randomized Linear Programming
• 7.4 Summary
• 8 Conclusion
• Appendix
• A Proofs of the Structural Properties of the RM approach
• B Data Tables.