Quality function deployment: exploiting interrelationships for progressive prioritization

Abstract

PurposeThe purpose of this paper is twofold: to incorporate the symbolic relationships among the attributes of customer requirements (CRs) and engineering characteristics (ECs) as well as to factor in the values numerically to enhance the prioritization process for an improved, comprehensive quality function deployment (QFD) analysis. The aim is to develop the concept of assimilating and factoring in the often-ignored interrelationships among CRs and ECs utilizing the weighted average method for the CR and EC correlations with overall calculations.Design/methodology/approachAfter a brief literature review of the methods utilized, the research paper discusses the framework for the correlation triangle challenge and introduces a novel mathematical solution utilizing triangle values in conjunction with computed initial raw weights for CRs and initial priority scores for ECs. The capability and applicability of the proposed model are demonstrated with a real-life example.FindingsThrough the proposed technique, the roof and the interrelationship triangle's signs and symbols are translated into numerical values for each permutation of ECs and CRs, and then the prioritization values are processed and finalized. The proposed model successfully modifies and removes vagueness from an otherwise overlooked part of the QFD process.Practical implicationsThe illustrated case study aptly proves that the proposed methodology yields more revealing and informative outcomes for engineers and designers, thus adding much-needed reliability to the outcome and its analysis. The validation conducted through the rank comparison endorses the premise, and the results obtained reflect the strength and accuracy of the progressive QFD as a product planning tool.Originality/valueThe research article proposes a fresh and unique QFD approach that solves typical procedural complications encountered in a regular QFD. Whereas the traditional methods neglect the interrelationships among CRs and ECs, this new methodology employs them in an improved, numerical way by incorporating them in quantitative analysis, which leads to judicious and improved decision-making.