Artificial Intelligence in Predictive Maintenance: Enhancing Machine Reliability and Industrial Productivity

Abstract

Artificial Intelligence (AI) has emerged as a transformative technology in modern manufacturing by enabling predictive maintenance strategies that enhance machine reliability and improve industrial productivity. This research investigated the effects Artificial Intelligence has on machine reliability and the productivity of the industrial sector, concerning predictive maintenance, and manufacturing companies located in Punjab and Sindh, Pakistan. A quantitative cross-sectional research design was used and an adopted, structured, online questionnaire was deployed. 467 online respondents located at various Pakistan based companies filled in the questionnaire. These respondents were maintenance engineers, production engineers, and plant managers, among others. Concerning the sample companies, the respondents hailed from Pakistan Steel Mills, Engro Corporation and Honda Atlas Pakistan, among others. The responses were downloaded into Microsoft Excel and then uploaded into SPSS Version 27 to perform the statistical analysis. Descriptive statistics were used in preliminary analysis of the study sample. To analyze the study variables, Pearson correlation analysis, multiple linear regression and Independent Samples t-test were employed, and One-Way ANOVA was used to establish the predictive effects and differences among the study variables. The study found that AI and predictive maintenance positively impacted the machine reliability, industrial maintenance, and the productivity of the industry. The study adds to the existing research of AI and predictive maintenance in the context of the Pakistan manufacturing industry. It also offers practical use for industry managers and policy makers.