Date of Award

2019

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Industrial and Systems Engineering and Engineering Management

Committee Chair

Paul D Collopy

Committee Member

Bryan Mesmer

Committee Member

Sampson Gholston

Committee Member

James Swain

Committee Member

L. Dale Thomas

Subject(s)

Systems engineering, Social choice, Quality function deployment

Abstract

Much of Systems Engineering (SE) involves heuristic tools and rules of thumb to aid the engineer in making decisions. Practicing engineers rely on these heuristic methods to manage risk, plan and perform tests, plan projects and design products. Product design is the focus of this research. Numerous product design methods in practice utilize heuristics and simplifying assumptions to ease the burden on the design engineer. However, do engineers pay a price for these simplifications? To understand the impact of design methods, a normative design framework is developed based on a rigorous consideration of Decision Theory and Social Choice Theory. Engineers strive to make the best choice from available design alternatives. Engineers and engineering organizations who make rational design decisions are expected to be more competitive and successful than those who make irrational design decisions. Decision Theory and Social Choice Theory provide the mathematical tools to determine how often a design method leads to a rational decision and evaluate the impact of irrational design selections. In this research, two specific engineering design methodologies are evaluated—the Pugh Method of Controlled Convergence (Pugh Method) and Quality Function Deployment (QFD). The evaluation uses a normative framework for engineering design. Although the Pugh Method and QFD are substantially different design methods, the form of the decision-making structure causes both to exhibit similar emergent behavior that leads to irrational design selections. The causes of these emergent behaviors are identified, and the impact of irrational design selection is estimated.

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