Theorising and testing the underpinnings of Lean Six Sigma : a thesis submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Engineering, School of Food and Advanced Technology, Massey University, Manawatū, New Zealand

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Massey University
Listed in 2024 Dean's List of Exceptional Theses
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Lean Six Sigma (LSS) is a widely used business process improvement method that combines Lean and Six Sigma. Despite its popularity and large volumes of research, the theoretical underpinnings of LSS remain underdeveloped. This thesis explores the theoretical foundations and practical implications of LSS, using an LSS project as the unit of analysis. Research objectives include: (i) identifying and operationalising the determinants of LSS, (ii) hypothesising the relationships between the determinants of LSS in predicting and explaining LSS project performance and testing the hypothesis empirically, (iii) assessing the impact of residual risks on LSS project performance, (iv) interpreting theoretical relationships from a practical perspective, and (v) testing whether LSS fits to nonmanufacturing as well as it would to manufacturing at a theoretical level. To achieve the objectives, a conceptual model was first framed by conducting a comprehensive literature review on available theories of SS/LSS and a novel approach (machine learning) to extract essential elements from the literature on critical success factors (CSFs). The conceptual model was then developed into a testable theoretical model through case research, which facilitated the operationalisation of the theoretical constructs. The overall hypothesis underpinning the theoretical model states, “Leadership engagement drives LSS Project Initiation and the Continuous Improvement Culture to execute an LSS project to yield the desired outcomes, but the Project Execution → Project Performance causal link would be moderated by the project residual risk”. Finally, the theory was empirically tested using partial least squares structural equation modelling based on data from 296 organisations worldwide. Although the data supported the overall hypothesis, some individual paths failed to support the model (p > 0.05). For example, project residual risk did not moderate the impact as anticipated, indicating that risk assessment is given significant attention during LSS project initiation. The total effect of Leadership Engagement on LSS Project Outcomes was 0.216 (p < 0.001), implying its practical importance (medium effect). The model fitted to nonmanufacturing equally well as manufacturing, supporting the hypothesis. Although case studies suggested that LSS projects are defined differently in manufacturing and nonmanufacturing and LSS structure differs from context to context, the model is robust enough to provide a solid theoretical foundation for LSS. The study adds to the current body of knowledge as a theory extension to the field of quality and operations management.
Reengineering (Management), Mathematical models, Case studies, Information technology, Management, Machine learning, Lean Six Sigma, project lens, continuous improvement, partial least squares structural equation modelling, Dean's List of Exceptional Theses