ADVANCING OPERATIONAL EFFICIENCY: AN IN-DEPTH STUDY OF MACHINE LEARNING APPLICATIONS IN INDUSTRIAL AUTOMATION

Abstract

Machine learning (ML) has emerged as a transformative force in industrial automation, optimizing operational efficiency, reducing costs and enhancing decision-making across U.S. industries. This study provides a quantitative analysis of ML's adoption, applications, benefits and barriers, at a sector level in manufacturing, energy, logistics and construction sectors. To assess key parameters including adoption rates, primary applications, operational improvements and challenges such as data quality issues, lack of workforce skill to utilize the technology and fitting in with legacy systems, a structured survey spanning 200 participants was conducted. Statistical analysis of our data (chi- square tests, ANOVA, regression models) showed us important predictors of ML success including the availability of training and the quality of the data. The extensive usage of ML in predictive maintenance and supply chain optimization helps save costs and gain productivity through significant costs savings and productivity gain. While that was true at the time, there are still many problems, particularly in areas like logistics where the data has been fragmented and regulated on the back end. This study highlights the key barriers to scale ML adoption and highlights investing in data infrastructure, workforce development and supportive policies to address them. The results provide lessons for policymakers and industry leaders on how to enhance the U.S. competitive edge in industrial automation.