Reducing Cost and Increasing Efficiency through Predictive Maintenance
Fierce competition, thin product margins, aging fleets of factory assets and fast-changing customer choices are putting manufacturers under tremendous pressure to re-think their operations strategy. Senior operations executives must think differently and consider innovative approaches to predictive maintenance.
The main challenges that affect profitability in the manufacturing industry are unplanned downtime and asset failure. These challenges stem from the lack of transparency into machine performance required to predict and prevent failures in the factory or across plants.
The goals of predictive maintenance are first to predict when a specific asset failure might occur, and secondly, to prevent that failure by performing maintenance on the asset with proactive planning. Ideally, predictive maintenance allows the maintenance frequency to be as low as possible to prevent unplanned, reactive maintenance

Dr. Haidar Almohri received his B.Sc. degree in Electronic Engineering Technology and M.Sc. degree in Electrical Engineering from the University of Hartford, CT. He worked with Siemens Kuwait as an electrical engineer for five years before starting his second M.Sc. degree in Industrial and Operations Engineering at the University of Michigan, Ann Arbor, MI. After successfully completing his second M.Sc. degree, he started his PhD in Industrial Engineering at Wayne State University and graduated in December 2017. He is currently an employee in Siemens Kuwait, working on digitalization and IoT. His research interests include Big Data analytics, High-Dimensional Data Analysis, Business Analytics, Predictive Analytics, Mixture Models, Causal Analysis, and Multi-objective Optimization.