Industry 4.0 Indicators and Sustainability Factors to Improve the Efficiency of Indian Process Industries: An Empirical Study

Abstract

This study examines the relationship between Industry 4.0 indicators and the sustainable production performance of Indian process industries using the PLS-SEM approach in SmartPLS. The study identifies five independent exogenous latent variables, including Smart Manufacturing Systems (SMS), Digital Twin Technology (DTT), Artificial Intelligence and Machine Learning (AIML), Advanced Robotics and Automation (ARA), and Sustainable Supply Chain Management (SSCM). SMS refers to a ‘production system’ characterized by the use and integration of advanced manufacturing technologies, such as digital technologies, automation, and data analytics, to improve  production processes. DTT uses digital representations of physical elements, structures, or procedures to improve performance and anticipate challenges, while AIML focuses on artificial intelligence and machine learning for algorithmic decision-making, predictive maintenance, and process improvement. ARA integrates state-of-the-art robotics and automation to increase production capacity, precision, and versatility. SSCM encompasses the bulk of contributions to integrating sustainability into supply chain management, with objectives such as resource efficiency, waste minimization, and green supply chain management. The structural latent dependent variable, Production Efficiency, Performance and Sustainability (PEPS), is defined as a measure of the efficiency, performance, and sustainability of production processes, based on ecological and resource-exploitation factors, as well as the quality of services provided. Empirical data were collected via 240 surveys of experts, managers, engineers, and other stakeholders involved in the Indian process industry sector. Several positive correlations were observed between the coherently defined Industry 4.0 indicators and  improved production efficiency and sustainability. The results indicate that the appropriate engagement with these sophisticated technologies and methodologies has the potential to enhance sustainable production and performance, which is useful for individuals and policymakers.