An intuitionistic fuzzy Choquet integral approach for quality function deployment: an application in the medical devices industry

Abstract

Purpose This study bridges a critical literature gap by proposing a novel quality function deployment (QFD) framework that integrates the intuitionistic fuzzy Choquet integral (IFCI) with group decision-making (GDM) for the medical devices industry. The purpose is to develop an objective and robust decision-support tool that not only handles the uncertainty and hesitation inherent in linguistic assessments but also explicitly models the nonlinear interactions among technical criteria, which are often overlooked in traditional QFD. Design/methodology/approach The study proposes an IFCI approach for the QFD process with GDM. The proposed methodology is explained in detail, and a case study in the medical devices industry is presented. The proposed model establishes the theoretical framework, while the case study offers empirical validation in a real-world context. Findings By integrating the intuitionistic fuzzy integrated determination of objective criteria weights (IF-IDOCRIW) method and IFCI approach into QFD, the methodology effectively captures diverse perspectives and manages uncertainties in GDM. Unlike traditional QFD models that assume linear, additive relationships among criteria, the proposed method captures nonlinear interdependencies and synergy effects. A key finding was the proposed method's ability to reveal non-additive synergies between criteria, leading to a prioritization that diverges from what traditional linear models would produce. The results were validated through a sensitivity analysis, which confirmed the stability of the top-ranked characteristics while identifying others whose priority is highly dependent on decision-maker weights, providing crucial strategic insights for resource allocation and risk management. Originality/value This paper contributes to the literature by integrating the IF-IDOCRIW method and IFCI with QFD, extending traditional frameworks to better handle GDM under uncertainty. The approach introduces a decision-support tool that improves the accuracy of resource allocation and prioritization in quality management. By capturing both linear and nonlinear relationships among key criteria, it offers a more comprehensive and dynamic assessment method, enhancing strategic decision-making in complex settings like the medical devices industry.