Lean Manufacturing Assessment: Dimensional Analysis with Hesitant Fuzzy Linguistic Term Sets

Author:

Chitiva-Enciso William Alexander1ORCID,Pérez-Domínguez Luis Asunción1ORCID,Romero-López Roberto1ORCID,Luviano-Cruz David1ORCID,Pérez-Olguín Iván Juan Carlos1ORCID,Méndez-González Luis Carlos1ORCID

Affiliation:

1. Departamento de Ingeniería Industrial y Manufactura, Universidad Autónoma de Ciudad Juárez, Av. Plutarco Elías Calles 1210 Fovissste Chamizal, Ciudad Juárez 32310, Mexico

Abstract

Lean Manufacturing has become, in recent years, one of the most important philosophies for improving production and organizational systems. The literature shows that Hesitant Fuzzy Linguistic Terms Sets (HFLTSs) are highly capable of manipulating the uncertainty that the judgments made by evaluators carry and that they are subject to their perception, especially when used in combination with multicriteria decision making (MCDM) for the measurement of indicators in this type of system, as well as their general performance. However, it is still of interest to researchers to develop techniques and instruments that facilitate the measurement of the results obtained after applying this philosophy in organizations. This article proposes a model for the evaluation of the Lean Manufacturing performance through the Analytic Hierarchy Process (AHP) and Dimensional Analysis (DA) using HFLTSs. The results obtained show that the proposed model is a solid tool for the evaluation of Lean Manufacturing systems from a different perspective and that it can be integrated into the issuance of evaluations in a better way by considering human subjectivity. At the same time, it offers a strategy to create priorities in the action plans that Lean system managers propose after evaluating. However, it is important to apply the proposed model to multiple organizations and analyze the results obtained to maximize its benefits.

Funder

Universidad Autonoma de Ciudad Juárez and the National Council of Humanities Science and Technology

Publisher

MDPI AG

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