Physics

Credibility

Operations research

Gene

Multiple-criteria decision analysis

Lean manufacturing

Manufacturing engineering

Engineering

Political science

Artificial intelligence

Six Sigma

Chemistry

Computer science

TOPSIS

Management science

Ideal solution

Biochemistry

Fuzzy logic

Robustness (evolution)

Selection (genetic algorithm)

Thermodynamics

IEEE Transactions on Engineering Management

Project selection has a critical role in the successful execution of the lean six sigma (LSS) program in any industry. The poor selection of LSS projects leads to limited results and diminishes the credibility of LSS initiatives. For this reason, in this article, we propose a method for the assessment and effective selection of LSS projects. Intuitionistic fuzzy sets based on the weighted average were adopted for aggregating individual suggestions of decision makers. The weights of selection criteria were computed using entropy measures and the available projects are prioritized using the multiattribute decision making approach, i.e., modified TOPSIS and VIKOR. The proposed methodology is validated through a case example of the LSS project selection in a manufacturing organization. The results of the case study reveal that out of eight LSS projects, the assembly section (A8) is the best LSS project. A8 is the ideal LSS project for swift gains and manufacturing sustainability. The robustness and reliability of the obtained results are checked through a sensitivity analysis. The proposed methodology will help manufacturing organizations in the selection of the best opportunities among complex situations, results in sustainable development. The engineering managers and LSS consultants can also adopt the proposed methodology for LSS project selections.

Lean Six Sigma Project Selection in a Manufacturing Environment Using Hybrid Methodology Based on Intuitionistic Fuzzy MADM Approach