Information from the abstract
Purpose Lean 4.0 tools have been developed by integrating digital technologies with lean philosophy to support autonomous process improvement. This development addresses the limitations of traditional lean tools, which rely on manual observation and human effort and may be insufficient for modern manufacturing that requires automation, connectivity, and real-time information. However, selecting lean 4.0 tools is a complex decision-making problem that involves various alternatives, different criteria, decision-makers’ preferences, and uncertainty. Therefore, this study proposes a hybrid multi-criteria decision-making (MCDM) approach for lean 4.0 tool selection. Design/methodology/approach The proposed approach integrates two Pythagorean fuzzy MCDM methods and seven criteria that cover waste reduction, quality improvement, and performance enhancement. The Pythagorean fuzzy analytic hierarchy process enables decision-makers to compare the criteria using Pythagorean fuzzy sets and determine their weights. The Pythagorean fuzzy technique for order preference by similarity to ideal solution enables decision-makers to evaluate lean 4.0 tools and obtain the final results based on Pythagorean fuzzy sets and the weights. The sensitivity and comparative analyses validated the proposed approach. Findings The proposed approach provided reliable results in different conditions and identified Six Sigma 4.0 as the most appropriate tool. Its three-month implementation reduced defects by 65% and idle time by 83%, increased the process capability index by 120.93%, and enhanced the digital maturity level. Originality/value This study proposes a new framework that supports organizations in selecting the most appropriate lean 4.0 tool. The proposed methodology demonstrates theoretical advancement in decision-making research and provides managerial implications for the selection of tools to guide organizations toward digital manufacturing.
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Related topics: Multi-Criteria Decision Making · Digital Transformation in Industry · Quality and Supply Management
Thai researcher and institutional participation
Nitidetch Koohathongsumrit · Wasana Chankham · Ramkhamhaeng University
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