Achieving operational excellence in manufacturing supply chains using lean six sigma: a case study approach

Purpose Achieving operational excellence in supply chain management (SCM) is essential for enhancing customer satisfaction, improving efficiency, and sustaining industrial competitiveness. This paper aims to develop an integrated Lean Six Sigma (LSS) framework to optimize SCM by minimizing waste, enhancing process reliability and improving responsiveness. The framework’s effectiveness is validated through a case study in a spare parts manufacturing company in Egypt, demonstrating its practical application and impact. Design/methodology/approach The study identifies critical success factors (CSFs) for effective LSS implementation in SCM. A structured DMAIC (Define-Measure-Analyze-Improve-Control) approach is applied, offering a data-driven methodology for optimizing supply chain performance and ensuring sustainable improvements. Findings The proposed framework significantly enhances SCM efficiency, operational effectiveness and customer satisfaction. Key performance improvements include product quality (85%–89%), sigma level (2.5–2.7) and processing time reduction (645–370 h/ton), Overall Equipment Effectiveness (OEE) increases (75% to 81%), value-added activity enhancement (50% to 54%), and customer satisfaction growth (87% to 89%). These improvements, achieved through LSS methodologies, drive defect reduction, workflow optimization, enhanced equipment utilization and a culture of continuous improvement, ensuring long-term operational excellence. Research limitations/implications This study’s scope was limited to a single product in a specific industrial context. Future research should explore the broader applicability of the LSS-SCM framework across various sectors, including services, to assess its versatility and generalizability. Expanding the research scope will provide valuable insights into the framework’s adaptability and potential impact across different industries. Practical implications This research provides a structured and replicable methodology for managers and engineers to enhance SCM performance, streamline production processes, and strengthen operational resilience. It offers practical, data-driven strategies for integrating LSS into SCM, enabling organizations to optimize processes, reduce waste and achieve long-term competitive advantage. Originality/value This study presents a comprehensive, empirically validated LSS framework for SCM optimization. By demonstrating the structured application of LSS tools, provides a novel, systematic approach to enhancing efficiency, effectiveness and resilience in modern supply chains, fostering sustainable continuous improvement and industrial competitiveness.

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Publication Details

Journal
International Journal of Lean Six Sigma
Published
2025-03-20
DOI
https://doi.org/10.1108/ijlss-03-2024-0045
Citations
13
Primary Topic
Quality and Supply Management
Type
article
Field-Weighted Citation Impact
29.69
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article

Achieving operational excellence in manufacturing supply chains using lean six sigma: a case study approach

Attia Hussien Gomaa
13 citations
International Journal of Lean Six Sigma
Quality and Supply Management
29.69
article

Achieving operational excellence in manufacturing supply chains using lean six sigma: a case study approach

Attia Hussien Gomaa
article en
13 citations

Abstract

Purpose Achieving operational excellence in supply chain management (SCM) is essential for enhancing customer satisfaction, improving efficiency, and sustaining industrial competitiveness. This paper aims to develop an integrated Lean Six Sigma (LSS) framework to optimize SCM by minimizing waste, enhancing process reliability and improving responsiveness. The framework’s effectiveness is validated through a case study in a spare parts manufacturing company in Egypt, demonstrating its practical application and impact. Design/methodology/approach The study identifies critical success factors (CSFs) for effective LSS implementation in SCM. A structured DMAIC (Define-Measure-Analyze-Improve-Control) approach is applied, offering a data-driven methodology for optimizing supply chain performance and ensuring sustainable improvements. Findings The proposed framework significantly enhances SCM efficiency, operational effectiveness and customer satisfaction. Key performance improvements include product quality (85%–89%), sigma level (2.5–2.7) and processing time reduction (645–370 h/ton), Overall Equipment Effectiveness (OEE) increases (75% to 81%), value-added activity enhancement (50% to 54%), and customer satisfaction growth (87% to 89%). These improvements, achieved through LSS methodologies, drive defect reduction, workflow optimization, enhanced equipment utilization and a culture of continuous improvement, ensuring long-term operational excellence. Research limitations/implications This study’s scope was limited to a single product in a specific industrial context. Future research should explore the broader applicability of the LSS-SCM framework across various sectors, including services, to assess its versatility and generalizability. Expanding the research scope will provide valuable insights into the framework’s adaptability and potential impact across different industries. Practical implications This research provides a structured and replicable methodology for managers and engineers to enhance SCM performance, streamline production processes, and strengthen operational resilience. It offers practical, data-driven strategies for integrating LSS into SCM, enabling organizations to optimize processes, reduce waste and achieve long-term competitive advantage. Originality/value This study presents a comprehensive, empirically validated LSS framework for SCM optimization. By demonstrating the structured application of LSS tools, provides a novel, systematic approach to enhancing efficiency, effectiveness and resilience in modern supply chains, fostering sustainable continuous improvement and industrial competitiveness.

International Journal of Lean Six SigmaVol. 17(2)
Benha University (EG)
Openalex Percentile: Top 0%
Quality and Supply Management
29.69
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