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Intuitionistic Fuzzy Score and Distance-Based Hybrid Decision Framework for Analysing Sustainable Lean Six Sigma Enablers in the Manufacturing Sector
Mohamed Mansour   Pratibha Rani   Naif Almakayeel   Jurgita Antucheviciene  

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https://doi.org/10.15388/26-INFOR631
Pub. online: 18 May 2026      Type: Research Article      Open accessOpen Access

Received
1 February 2026
Accepted
1 May 2026
Published
18 May 2026

Abstract

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.

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Biographies

Mansour Mohamed
momansor@kku.edu.sa

M.A.A. Mansour is an assistant professor of industrial engineering at King Khalid University, Saudi Arabia, with a concurrent appointment as associate professor at Zagazig University, Egypt. He earned his PhD in industrial & systems engineering from Zagazig University in 2005. With over 20 years of academic and industrial experience, his research expertise spans optimization and metaheuristic algorithms, satellite scheduling, flexible manufacturing systems, ergonomics and human factors, sustainable manufacturing, and deep learning applications in industrial systems. He has authored 28 peer-reviewed publications in prestigious international journals. Dr. Mansour served as a visiting professor at the University of Southern California (2007–2008) and has led multiple funded research projects in transportation safety, ISO standards implementation, and environmental performance evaluation. His pioneering work includes developing anthropometric databases for Saudi populations and advancing genetic algorithms for space mission planning.

Rani Pratibha
pratibha138@gmail.com

P. Rani received her PhD in mathematics, and she is an adjunct professor in Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai, Tamil Nadu, India. Her main research interests are fuzzy sets theory, decision making, multi-criteria decision making, fuzzy set and its extensions, information measures, soft computing and mathematical modelling. She has published more than 170 peer-reviewed papers, many in high-quality international journals including IEEE Transactions on Fuzzy Systems, Journal of Cleaner Production, Information Sciences, Engineering Applications of Artificial Intelligence, Expert Systems with Applications, International Journal of Intelligent Systems, Journal of Enterprises Information Management, Applied Soft Computing, Automation in Construction, Computers and Industrial Engineering, International Journal of Fuzzy Systems, Group Decision and Negotiation, Neural Computing and Applications, Soft Computing, Proceedings of National Academy of Sciences, India, Section A: Physical Sciences and others. According to Stanford university, she is among world’s 2% scientists in the field of artificial intelligence.

Almakayeel Naif
halmakaeel@kku.edu.sa

N. Almakayeel is an associate professor of industrial engineering at King Khalid University, Saudi Arabia. He holds degrees in industrial engineering from King Fahd University (Dhahran, Saudi Arabia), Saint Mary’s University (Texas, USA), and a PhD from North Carolina A&T State University (North Carolina, USA). His research focuses on total quality management, quality control, lean manufacturing, Six Sigma, AI, IoT, and optimization. Since 2019, he has made significant contributions to academia and research at King Khalid University.

Antucheviciene Jurgita
jurgita.antucheviciene@vilniustech.lt

J. Antucheviciene is a professor in the Department of Construction Management and Real Estate at Vilnius Gediminas Technical University, Lithuania. She received her PhD in civil engineering from Vilnius Gediminas Technical University in 2005. She is a member of IEEE SMC Technical Committee on Grey Systems, and of two EURO Working Groups: Multicriteria Decision Aiding (EWG-MCDA) and Operations Research in Sustainable Development and Civil Engineering (EWG-ORSDCE). She is an associate editor of Applied Soft Computing, and Decision Analytics Journal, deputy editor-in-chief of Journal of Civil Engineering and Management, editorial board member of Sustainability, Buildings and others. Her main research interests include multi-criteria decision-making, civil engineering and management, and sustainable development. She has more than 200 scientific papers indexed in SSCI, SCI, Scopus. According to Stanford/Elsevier’s rankings, she is among the world’s top 2% scientists in the field of engineering.


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Lean Six Sigma sustainability manufacturing decision-making intuitionistic fuzzy set

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INFORMATICA

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