Big Data Analytics Approach to Quality, Reliability, and Risk Management: Enhancing Results in Industry 4.0 - Kaviyarasu, Renukrishna (Digital Repository of Theses, 2026)
In recent years, organisations across industries have increasingly adopted Industry 4.0 to enhance productivity, operational stability, and competitiveness in national and global economies. While digitalisation, automation, and data-driven operations offer significant potential, evidence suggests that many Industry 4.0 initiatives fail to deliver sustained value due to fragmented decision-making, organisational silos, and weak integration between analytics and operational management. These challenges are particularly pronounced in Industry 4.0 IT product environments, where quality, system reliability, and operational risk critically influence performance outcomes. In practice, quality, reliability, and risk (QRR) are frequently managed as separate functions, despite their strong interdependence. Although large volumes of operational data are generated through digital systems, Big Data Analytics is often applied in isolation, limiting its ability to support integrated and proactive managerial decision-making. This fragmentation restricts early detection of deviations and contributes to delayed responses to escalating operational risks. This research addresses this gap by examining how Big Data Analytics can support integrated decision-making across quality, reliability, and risk in Industry 4.0 IT product environments. Guided by a pragmatic research philosophy, the study adopts an applied and evaluative research design using a mixed-method approach. Descriptive and diagnostic analytics are employed to identify patterns, trends, and interdependencies across QRR dimensions, while qualitative interpretation is used to translate analytical findings into decision-oriented managerial insights. The analysis is based on secondary, publicly available datasets relevant to digitally intensive operational contexts. The findings indicate that quality deviations frequently act as early signals of reliability degradation and increased operational risk, reinforcing the interconnected nature of QRR dimensions. The study further demonstrates that analytics delivers greater organisational value when embedded within a structured decision-making framework rather than applied as a standalone technical capability. Building on these insights, the research proposes an integrated Quality–Reliability–Risk (QRR) decision-making framework and a corresponding implementation approach to support continuous monitoring and informed managerial action in Industry 4.0 environments. The contribution of this study lies in bridging the gap between data availability and actionable decision-making by providing a practical, analytics-driven framework that enhances operational visibility, decision consistency, and organisational resilience.