A novel belief-based QFD-AHP model in interval type-2 fuzzy environment for lean after-sales service in automotive industry

Author:

Baskir Mukerrem Bahar

Abstract

Purpose The purpose of this paper is to propose a novel lean management tool to provide a comprehensive and flexible evaluation model while converting customer voices into technical characteristics in lean implementations. Design/methodology/approach For this purpose, the proposed model was constructed by belief space-evaluations, quality function deployment (QFD) and analytic hierarchy process (AHP) in interval type-2 fuzzy (IT2F) environment. This model involves three phases: determining the linguistic weights and belief-based relations with their IT2F-sets, processing information about IT2F-based belief-evaluations and ranking the technical characteristics using the defuzzified belief-based relative importance values. Findings The proposed model was applied to automotive after-sales service in Turkey to demonstrate its use in lean service-decisions. This model was compared with its classical and type-1 fuzzy versions. The ranking-results of the proposed model differed from those of the other versions. The reason is that the IT2F-environment offers a sensitive and flexible evaluation of the model’s linguistic scales. Research limitations/implications Calculations in the proposed model may be quite involved for practitioners. An Excel-dashboard was created to simplify the computational complexity. Practical implications Researchers/practitioners can apply this model to any lean manufacturing/service implementation. Social implications Company managers/employees/customers can recognize their perception-mechanisms via belief space-evaluations and experience how uncertainty in the perception-mechanism affects their decisions. Originality/value The proposed model provides a new lean tool due to the Bayesian model combined with QFD-AHP in IT2F-environment. This model eliminates the ambiguity in conceptual change-based lean decisions.

Publisher

Emerald

Subject

General Medicine

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