The Machine learning for predictive maintenance in supply chain management

Abstract

It has recently come to light that one of the most important applications of machine learning in a variety of sectors, including supply chain management, is predictive maintenance. The purpose of this research is to investigate the use of machine learning strategies for predictive maintenance within the framework of supply chain management. Traditional procedures of maintenance often cause inefficiencies and interruptions in the supply chain as a result of unanticipated breakdowns of various pieces of equipment. It is possible to greatly improve both the reliability and performance of supply chain operations via the use of predictive maintenance approaches. This article starts out by giving an overview of predictive maintenance and the role that it plays in supply chain management. The issues that are presented by unanticipated equipment failures and the cascade consequences that these failures have on the supply chain are discussed. In the context of predictive maintenance, a number of different techniques to machine learning, including supervised learning, unsupervised learning, and deep learning, are analyzed and discussed. In addition to this, the study digs into data-gathering strategies, discussing topics such as sensor data, past maintenance records, and external influences that might influence the health of equipment. In addition, the article discusses the implementation issues that are associated with installing predictive maintenance systems in supply chain environments. Some of these challenges include data quality and integration, real-time decision-making, cost concerns, and others. This paper investigates the role that edge computing and industrial Internet of Things (IoT) devices play in making data gathering, analysis, and preventative maintenance more efficient.