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S-Model Perspectives on Aggregation of Preferences and Decision-Making

This article delves into the foundation of the S-Model, illustrating how customer-perceived value drives market demand and competitive advantage. It explores the aggregation of value ratios for product specifications, emphasizing the role of profit in decision-making amid uncertainty. Top research issues include uncertainty management, rigorous data aggregation methods, and decision support optimization.

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S-Model Perspectives on Aggregation of Preferences and Decision-Making

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  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. Product Specifications Drive Customer-Perceived Value Customer-Perceived Value Drives Market Demand Increased Value Product Value Competitive Advantage Sales Volume Increased Value Improved Function Price Product Spec Baseline Value (e.g. 0-60 Time, Turning Circle) Overview of the S-Model

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

  5. An S-Model Influence Diagram Profit Margin Volume Value Price Cost u u u DV Competitors Product Specifications Subsystem Subsystem Subsystem

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

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

  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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