The concept of equipment maintenance is as old, if not older, as industrial revolution. However, the mode, medium and timing of maintenance during equipment life cycle have evolved from reactive maintenance to prescriptive maintenance. Reactive maintenance was the main approach practiced until 1980s. The proactive maintenance was introduced in 1990s to insulate customers from equipment failures by using equipment data and fixing problems remotely. Prescriptive maintenance, which incorporates the Internet of Things, digitization and artificial intelligence, especially machine learning, has gained importance as in intelligent approach to equipment maintenance and has the potential to greatly improve upon proactive maintenance. However, existing prescriptive maintenance solutions are piece-meal and lack complete solutions to keep equipment in working conditions most of the time at optimal cost. We propose a Holistic End to End Prescriptive Maintenance (HeePMF) framework resulting in much reduced unplanned equipment downtime at optimal cost. This framework provides solution by integrating maintenance needs analysis, equipment and operational data, predictive technologies with feedback, personnel scheduling, supply chain improvement, part management, process improvement and knowledge management. The working of framework is demonstrated by providing a case study.
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