A Systematic Review of Predictive Maintenance and Production Scheduling Methodologies with PRISMA Approach

Predictive maintenance has been considered fundamental in industrial applications over the last few years. It contributes to improving reliability, availability, and maintainability of the systems and decreasing production efficiency in manufacturing plants. This article aims to explore the integration of predictive maintenance into production scheduling through a systematic review of literature. The review includes 165 research articles published in international journals indexed in the Scopus database. Press articles, conference papers, and non-English papers are not considered in this study. After carefully evaluating each study for its purpose and scope, 50 research articles are selected for this review, following the 2020 Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA) statement. Benchmarking of predictive maintenance methods was used to understand the parameters contributing to improved production scheduling. The results of our comparative analysis, which assessed various methods for prediction, underscore the promising potential of artificial intelligence in anticipating breakdowns. An additional insight from this study is that each equipment has its own parameters that must be collected, monitored, and analyzed.