House of quality: A fuzzy logic-based requirements analysis

Cecilia Temponi, John Yen, W. Amos Tiao

Research output: Contribution to journalArticlepeer-review

194 Scopus citations

Abstract

House Of Quality (HOQ) is one of the matrices of an iterative process called Quality Function Deployment (QFD). The foundation of the HOQ is the belief that products should be designed to reflect customers' desires and taste. HOQ is performed by a multidisciplinary team representing marketing, design engineering, manufacturing engineering, and any other functions considered critical by the company. In general, it provides a framework in which all participants can communicate their thoughts about a product. More specifically, HOQ is often used to identify the relationships between requirements based on different viewpoints. There are two issues in analyzing these requirements using HOQ. First, requirements are often described informally using vague terms. However, lack of formal way in interpreting the semantics of these requirements makes it difficult to determine if a realization of the system meets its customer's needs. Second, identifying relationships between requirements is often time consuming. Sometimes, it is difficult to arrive at a group consensus on a particular relationship between requirements. To address these issues, we have developed a fuzzy logic-based extension to HOQ for capturing imprecise requirements to both facilitate communication of team members and have a formal representation of requirements. Based on this representation, we developed a heuristic inference scheme to reason about the implicit relationships between requirements. We illustrate our approach using a textile mill supply business application.

Original languageEnglish (US)
Pages (from-to)340-354
Number of pages15
JournalEuropean Journal of Operational Research
Volume117
Issue number2
DOIs
StatePublished - Sep 1 1999

All Science Journal Classification (ASJC) codes

  • General Computer Science
  • Modeling and Simulation
  • Management Science and Operations Research
  • Information Systems and Management

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