Role of Machine Learning and Industry 4.0 Technology in Enhancing Manufacturing
Harsh Pradeepkumar Sharma , Durgavajhala Pragnya
¹Parul Institute of Management & Research, Parul University Vadodara, Gujarat, India
Under the Guidance of
Mrs. Aparna Vishal Bhargava
Assistant Professor, Faculty of Management Studies Parul University, Vadodara
ABSTRACT
The global manufacturing sector is undergoing a radical transformation through the convergence of Machine Learning (ML) and Industry 4.0 technologies. Traditional manufacturing processes often face challenges such as operational inefficiency, high downtime, legacy system integration, and skill gaps that hinder their competitiveness. This study investigates the role of ML and Industry 4.0 technologies—including Industrial Internet of Things (IIoT), robotics, digital twins, cyber-physical systems, and predictive maintenance—in enhancing manufacturing productivity, operational efficiency, and resilience. A mixed-methods research design was employed, combining structured questionnaires distributed to 126 manufacturing professionals with semi-structured interviews and focus group discussions. Quantitative data were analysed using descriptive statistics, Pearson correlation, multiple regression, and Structural Equation Modelling (SEM). The results indicate that adoption of smart technologies leads to moderate-to-high improvements in production efficiency (H1 partially supported), while high implementation costs, skill gaps, and legacy integration challenges are the dominant barriers to full adoption (H2 strongly supported). The SEM results confirm that barriers exert a stronger negative effect on performance than readiness factors alone, underscoring that technology deployment must be accompanied by strategic investment in workforce upskilling and change management.
Keywords: Machine Learning, Industry 4.0, Smart Manufacturing, Predictive Maintenance, Digital Twins, Operational Efficiency, Adoption Barriers, Indian Manufacturing
