A Review of Quality Improvement Framework for Industry 4.0

Abstract

In the dynamic landscape of modern organizations, the pursuit of continuous improvement is paramount for sustaining competitiveness amidst evolving market demands. Quality programs, particularly Six Sigma, have emerged as indispensable tools for achieving organizational excellence by emphasizing defect reduction and process variation control. However, challenges arise in quality 4.0 environments, especially when confronting issues unrelated to Six Sigma principles, unstructured data, or large datasets, necessitating additional data-driven methodologies. This article aims to review the evolving needs of quality 4.0 by investigating complementary data-driven methodologies to Six Sigma and exploring untapped opportunities through their integration. Employing the Methodi Ordinatio as a systematic literature review method, we organize and synthesize existing literature on the integration of Six Sigma and industry 4.0 in the context of quality 4.0. Our findings reveal a critical gap in integration, emphasizing the necessity for a comprehensive framework. We identify a collection of methodologies that enhance each stage of the Six Sigma process, including Execution Framework (Agile), Define (Value Stream Mapping), Measure (Process Mining), Analysis (Simulation), Improvement (MCDM), and Control (Big Data). This article contributes to the Held by proposing a functional framework for quality improvement in the context of quality 4.0. By synthesizing diverse data-driven methodologies, it offers organizations a road map to industry 4.0 concepts, enhancing efficiency, effectiveness, and overall excellence. The identified collection of methodologies provides a nuanced approach to addressing challenges in each stage of the Six Sigma process, filling a vital gap in the current literature.