Smart manufacturing, grounded in Industry 4.0 and advancing toward Industry 5.0, is increasingly discussed in the literature in relation to the adoption of advanced digital technologies and data-driven analytics. This review provides a structured synthesis of industrial analytics by classifying methodological approaches, application domains, and adoption barriers based on peer-reviewed research. The reviewed literature shows that data-driven methods are most frequently reported in manufacturing operations, quality management, and maintenance reliability, particularly in relation to predictive modeling and performance-oriented applications. Algorithmic trends indicate a dominant reliance on established methods such as regression, support vector machines, genetic algorithms, and neural networks, while more advanced deep learning and hybrid frameworks are reported less frequently. Despite reported predictive capability, challenges related to data integration, system interoperability, legacy infrastructure, and workforce readiness are frequently discussed in the literature in relation to analytics applications remaining limited in their translation into sustained operational decision-making and execution. By consolidating insights across technological, methodological, and organizational dimensions, this review synthesizes how industrial analytics is discussed as a socio-technical capability in relation to the transition from efficiency-oriented Industry 4.0 practices toward human-centric, resilient, and sustainability-focused Industry 5.0 objectives.
Khan et al. (2026) studied this question.