Informatica

Determining criteria importance is a crucial task in multi-criteria decision-making problems, and the Best-Worst Method (BWM) has emerged as an effective weighting technique due to its reduced number of pairwise comparisons. Although BWM has been extended to handle group decision-making and linguistic information, existing approaches do not adequately address disagreements among evaluators, which may lead to dissatisfaction with the resulting criteria weights. To address this limitation, this paper proposes a consensus-based BWM for linguistic multi-criteria group decision-making. The proposed approach obtains consensual collective weights by minimally modifying the evaluators’ initial linguistic preferences. The resulting solution can support moderators and evaluators during consensus-reaching processes by facilitating the identification of disagreements and the generation of appropriate recommendations. The proposal is developed within the 2-tuple linguistic framework and provides both numerical and linguistic representations of the resulting weights. In addition, a novel stochastic consistency index is introduced to assess the reliability of evaluators’ preferences. The feasibility and effectiveness of the proposal are illustrated through a real-world case study and analysed by means of comparative and sensitivity analyses.

Lean Six Sigma (LSS) is defined as an innovative business strategy for achieving operational excellence through continuous improvement in the manufacturing sector. By embracing LSS principles, manufacturers can create an adaptable and capable system to preserve a competitive positioning, while reducing waste and defects in the business processes. The integration of sustainability with LSS has contributed to the upward attention among scholars and practitioners worldwide by advancing knowledge of how manufacturers can improve their sustainable performance through LSS practices. For any manufacturing firm, the challenge lies in exploring enablers that support successful adoption of sustainable LSS. Consequently, this study aims to develop an intuitionistic fuzzy decision-making framework for identifying and assessing the enablers influencing an integrated sustainable LSS in electric manufacturing companies. The proposed framework integrates the Weight by Envelope and Slope (WENSLO) and Modified Preference Selection Index (MPSI) models taking into account the developed score and distance formulae under the setting of intuitionistic fuzzy sets. Using an integrated intuitionistic fuzzy WENSLO-MPSI model, this study further evaluated thirteen sustainable LSS enablers of five electric manufacturing companies, followed by sensitivity and comparative analyses. The findings indicated that “Linking SLSS to business strategies”, “Green design principles” and “Effective scheduling” are the most significant enablers to implement sustainable LSS in an electrical manufacturing company.