UID:
almahu_9949561338502882
Format:
1 online resource (128 pages)
Edition:
First edition
ISBN:
3-631-75386-1
Series Statement:
Forschungsergebnisse der Wirtschaftsuniversitaet Wien.
Content:
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 often done today requires more sophisticated approaches explicitly considering stochastic influences. This book reviews current literature, presents a framework that addresses revenue management and demand fulfillment at once and introduces new stochastic approaches for demand fulfillment in make-to-stock manufacturing based on the ideas of the revenue management literature.
Note:
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.
,
English.
Additional Edition:
ISBN 3-631-59409-7
Language:
English
