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S-Model Perspectives on Aggregation of Preferences and Decision-Making Joseph A. Donndelinger General Motors R&D Center Warren, MI.

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Presentation on theme: "S-Model Perspectives on Aggregation of Preferences and Decision-Making Joseph A. Donndelinger General Motors R&D Center Warren, MI."— Presentation transcript:

1 S-Model Perspectives on Aggregation of Preferences and Decision-Making Joseph A. Donndelinger General Motors R&D Center Warren, MI

2 The Foundation of the S-Model The Customer is the Arbiter of Value Used with permission of H. E. Cook

3 Overview of the S-Model Customer-Perceived Value Drives Market Demand Price Sales Volume Baseline Value Competitive Advantage Increased Value Product Specifications Drive Customer-Perceived Value Product Value Product Spec Improved Function Increased Value (e.g Time, Turning Circle)

4 S-Model Aggregation of Value Baseline Value Product Value “Critical” Specifications: Interdependent “Optional” Specifications: Independent

5 An S-Model Influence Diagram   Value Price Cost Margin Profit Volume Competitors Product VV Specifications Subsystem

6 A Multi-Criteria Application...   Value Profit VV Specifications Price Cost Margin Volume Competitors Product Subsystem Product Value is an aggregation of value ratios for multiple product specifications

7   Value Price Cost Margin Profit Volume Competitors Product VV Subsystem …and a Single Criterion Application Profit is the single criterion used in decision making Specifications

8 Management of Uncertainty Sources of Uncertainty –Evaluation of Product Performance –Translation of Performance to Value –Marketplace Changes Over Time Competitive Action Changes in Customer Preferences Exogenous Economic Factors Strategy for Uncertainty Management –Quantify and Propagate Uncertainties –Computational Efficiency Becomes Important

9 Top Research Issues Development of Rigorous Methods for Aggregation of Engineering Data, addressing: –Propagation of Uncertainty –Combination of Subjective and Objective Information –Ramifications for Decision Quality –Bounds / Limits of hierarchical approaches Commonality between Decision Support and Optimization Systems –Are common problem formulations feasible and practical? –How should aspirational (or “stretch”) targets be employed? Formalized, bi-directional mapping of decision scenarios to Decision Support Tools


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