A Structured Approach for Classifying and Prioritizing Product Requirements
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1999-08-04
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Abstract
New product development involves making a series of decisions that transform vaguely defined customer needs and desires into a final product. Two important, but often overlooked, product development decisions are (1) the classification of requirements as mandatory or optional, and (2) the prioritization of requirements. This research effort addresses the current lack of theoretically sound and practical methods for classifying and prioritizing product requirements by focusing on three primary objectives. The first objective is to develop a structured approach for classifying and prioritizing product requirements. The second objective is to use the structured approach to gather, analyze, and aggregate stakeholder input. The third objective is to use the structured approach to support both group and individual learning. The first objective was accomplished through the development and demonstration of a structured requirement analysis model (SRAM). SRAM's development involved integrating methods and concepts from the following knowledge domains: requirement analysis, Multi-Attribute Decision-Making (MADM), market orientation, organizational learning, and cognitive decision theory. The second and third objectives were accomplished through the implementation of SRAM to resolve two diverse case studies. The main case study involved classifying and prioritizing functional requirements for a proposed knowledge?based CAD engineering system. In contrast, the second case study was focused on evaluating alternative vision statements for a consulting group. After successful completion of both case studies, SRAM was formally evaluated by case study participants and a controlled group that did not participate in either case study. Thus, the three primary objectives of this thesis were verified and validated via case study implementation and impartial evaluation.SRAM uses MADM as a solution framework for classifying and prioritizing product requirements. Requirements are evaluated using market orientation based (market priority, risk, customer value, and performance) qualitative (fuzzy linguistic) and quantitative decision criteria. Within this MADM framework, the Analytical Hierarchy Process (AHP) and entropy weighting are used to derive attribute importance weights and define stakeholder preference structures. Where pairwise comparison inconsistencies are passively corrected using a geometric averaging procedure for constructing supertransitive approximation to binary matrices. Each stakeholder?s requirement classifications and priorities are derived via the hierarchical application of the Technique for Order Preference By Similarity to the Ideal Solution (TOPSIS). While the results for an aggregated group of stakeholders are determined using weighted Borda Scoring and heuristic decision rules.Through the first and second case studies, it was discovered that resolving real-world problems requires understanding both how decision-makers should ideally behave and how they actually behave. Accordingly, quantitative results generated using traditional decision analysis methods were qualitatively analyzed using the essential elements of good decision-making (framing, gathering intelligence, coming to conclusions, and learning from feedback) as a conceptual foundation. The systematic application of structured decision-making was utilized to resolve conflict, develop consensus, define preferences, correct inconsistencies, and highlight critical issues. Emphasis was placed on supporting individual and group learning through structured decision-making. Hence, regardless of the specific outcomes of classification and prioritization decisions, SRAM helps provide users with necessary knowledge and skill to address similar problems in the future. Results from the formal evaluation of SRAM indicate participants from both case studies and a controlled group that did not participate in either case study view SRAM as being effective, practical, valid, and supportive of group and individual learning. In addition, both the second case study and the evaluation process demonstrated SRAM?s ability to be utilized in a variety of applications.
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Degree
PhD
Discipline
Industrial Engineering
