A Bayesian Best-Worst approach for assessing the critical success factors in sustainable lean manufacturing

In recent years, Industry 4.0 (I4.0) has been a recurrent theme in the literature on Lean Six Sigma (LSS), given the synergies that can arise from their combination. However, their joint implementation presents several challenges. In this article, a systematic literature review (SLR) of research on I4.0 and LSS integration was performed. This review involved five database platforms and included seventy-four articles providing state-of-the-art knowledge on the topic, focusing on the barriers to and enablers of integration. As a result, 20 integration barriers were identified, highlighting the high implementation cost, long learning curve, and technology incompatibility as the main barriers. Seventeen enablers were found to facilitate and guarantee implementation success, highlighting investment in IT infrastructure and employee training, stakeholder involvement, and top management support. In addition, the article discusses actions to facilitate I4.0 and LSS integration in practice, determined by connecting the identified enablers to their corresponding barriers. Finally, the SLR identifies several avenues for future research.

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Industry 4.0 and Lean Six Sigma Integration: A Systematic Review of Barriers and Enablers

In the Industry 4.0 era, healthcare services have experienced more dual interventions that integrate lean and six sigma with simulation modeling. This systematic review, which focuses on evidence-based practice and complies with the PRISMA guidelines, aims to evaluate the effects of these dual interventions on healthcare services and provide insights into which paradigms and tools produce the best results. Our review identified 4018 studies, of which 39 studies met the inclusion criteria and were selected. The predominantly positive results reported in 73 outcomes were mostly related to patient flow: length of stay, waiting time, and turnaround time. In contrast, there is little reported evidence of the impact on patient health and satisfaction, staff wellbeing, resource use, and savings. Discrete event simulation stands out in 74% of the interventions as the main simulation paradigm. Meanwhile, 66% of the interventions utilized lean, followed by lean-six sigma with 28%. Our findings confirm that dual interventions focus mainly on utilization and access to healthcare services, particularly on either patient flow problems or problems concerning the allocation of resources; however, most interventions lack evidence of implementation. Therefore, this study promotes further research and encourages practical applications including the use of Industry 4.0 technologies.

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Lean, Six Sigma, and Simulation: Evidence from Healthcare Interventions

The aim of this study is to identify the readiness factors of Lean Six Sigma (LSS) for the food manufacturing industry. A multi-method qualitative approach was used. Semi-structured interviews were conducted with twelve quality practitioners and three case food companies to identify and explore the LSS readiness factors in the food manufacturing industry. Twenty-eight factors were identified and classified into six LSS readiness factors (RF) in the food manufacturing industry context, namely, management support and leadership, organisational culture readiness, process management, project management, employee involvement, and external relations. This study could benefit managers in the food business as a diagnostic tool to evaluate their readiness to implement LSS prior to investing in the programme. The proposed framework identifies LSS readiness dimensions and their attributes to enable food businesses to conduct readiness level self-assessments, thereby contributing to the successful implementation of LSS in the food manufacturing industry. The determined readiness factors will potentially enable an organisational transformation to be better understood, making the implementation of LSS practices in food businesses more successful and sustainable.

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Uncovering Readiness Factors Influencing the Lean Six Sigma Pre-Implementation Phase in the Food Industry

Abstract Talent training has a strong social constraint and economic dominance, and is closely related to regional economic development with two-way interaction. Context is any information that can be used to describe the situation and characteristics of an object, including time, location, social relationships, natural conditions, and project characteristics. Regional economic development is influenced by the process of multiple types of contextual elements, but traditional development models do not consider or only take into account a single contextual element, ignoring the combined influence of multiple contextual elements. To this end, the paper proposes a model for analyzing the correlation between talent training quality and regional economic development that integrates context-awareness and random forest algorithms, modeling contextual elements as feature attributes to be considered when splitting decision trees in random forests. The experimental results show that when conducting the analysis, assigning corresponding weights to various contextual elements according to the degree of importance can improve the accuracy of the recommendations. The prediction accuracy of the random forest model is higher under different data sampling ratios.

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Correlation analysis between talent training quality and regional economic development based on multivariate statistical analysis model

The Lean Six Sigma (LSS) philosophy and sustainability have become topics of interest since the 1990s; they have generally been analyzed together since 2012. Numerous professionals, managers, and researchers have sought methodologies by which to assess their impact and know their effectiveness within companies. During the past decade, the application of partial least squares structural equation modeling (PLS-SEM) has been widely accepted in various modeling, prediction, or multivariate analyses as a way to measure the impact of LSS on sustainability. This study conducts a literature review to identify the use of PLS-SEM in measuring the impact of LSS on sustainability. A systematic review methodology has been employed, applying five search criteria to three scientific database platforms. This approach has been helpful to identify PLS-SEM as a valuable methodology for measuring the impact of LSS on sustainability. One of the research findings is that LSS practices positively impact 83% of economic indicators, 78% of environmental indicators, and 70% of social indicators. This article creates a theoretical foundation for future research on these issues, outlining research opportunities to generate future studies. It also allows researchers and managers who are interested in improving sustainability indicators to access valuable knowledge regarding what types of LSS tools could be used.

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Jorge Abad-Moran

Papers

Lean Six Sigma Impact Analysis on Sustainability Using Partial Least Squares Structural Equation Modeling (PLS-SEM): A Literature Review

Industry 4.0 and Lean Six Sigma Integration: A Systematic Review of Barriers and Enablers