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

• Author: Quante, Rainer.
• Place / Publishing House: Frankfurt am Main : : Peter Lang GmbH, Internationaler Verlag der Wissenschaften,, 2009.; ©2010.
• Year of Publication: 2009
• Edition: First edition
• Language: English
• Series: Forschungsergebnisse der Wirtschaftsuniversitaet Wien.
• Physical Description: 1 online resource (128 pages)

Up to now, demand fulfillment in make-to-stock manufacturing is usually handled by advanced planning systems. Orders are fulfilled on the basis of simple rules or deterministic planning approaches not taking into account demand fluctuations. The consideration of different customer classes as it is o...

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.