Optimizing Industrial Training in Industry 4.0: A Mixed-Methods Validation of an Integrated LMS and Six Sigma 4.0 Framework

Abstract

Industrial training programs face persistent challenges due to the lack of industry-specific contextualization in Learning Management Systems (LMS) and the digital disconnect in Six Sigma methodologies, limiting their effectiveness in Industry 4.0 environments. This study addresses this gap by proposing a novel integration of LMS with Six Sigma 4.0, aiming to enhance knowledge retention, project outcomes, and operational efficiency through data-driven training optimization. Employing a mixed-methods quasi-experimental design, the research combines quantitative pre-/post-intervention assessments (n = 110 trainees) with qualitative interviews (n = 8 trainers), analyzed via statistical testing (paired t-tests) and thematic coding. Results demonstrate statistically significant improvements in knowledge retention (34%, p < 0.001) and project outcomes (27%, p < 0.001), alongside two key qualitative benefits: real-time analytics enabling agile corrective actions, and a 40% reduction in manual audits through automated Six Sigma tools. The study concludes by validating a scalable framework for industrial training innovation, contributing actionable insights for workforce development in the industry 4.0 era.