A HYBRID BEST WORST - FUZZY TOPSIS METHODOLOGY FOR LEAN SIX SIGMA PROJECT SELECTION
DOI:
https://doi.org/10.4314/njt.v43i3.6Keywords:
Best Worst Method, Fuzzy TOPSIS, Lean Six Sigma, Multi Criterion Decision-Making, Project SelectionAbstract
Prioritizing Lean Six Sigma (LSS) projects that align with company objectives is crucial, yet traditional methods struggle with associated subjective criteria. This study proposed a hybrid Best Worst Method - Fuzzy TOPSIS approach to prioritize LSS projects for a project consulting company. The method integrates expert opinion from 3 decision-makers on 7 main criteria and 24 sub-criteria to select the optimal LSS projects in project management consulting company. Triangular fuzzy numbers were used to describe the responses. The fuzzy positive and negative solutions of the five alternatives were calculated. Results indicate project alternative 3 (ERP Deployment Project) is the optimal choice with the highest closeness coefficient (0.68651), while project alternative 2 (Warehouse Automation Project - 0.54077), project 1 (Data Warehousing Project – 0.46731), project 4 (Battery life improvement – 0.54077), and project 5 (Improvement of OEE – 0.34093) follow closely, thus ensuring efficient project selection. Emphasis should be placed on project 3 when considering the 7 criteria while the other projects are closely monitored in the ranking order. Future research can explore the combination of other multi-criterion decision making approaches that enrich criteria weights and address the subjectivity of decision-makers’ opinion. The hybrid methodology used in this work is applicable in other disciplines where selection and ranking problems exists.
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