George Amu
Planned Maintenance Pillar Lead @Olam
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WORK HISTORY
Planned Maintenance Pillar Lead @Olam
Accra, GH
Roles Include- Defining and leading maintenance team in achieving maintenance goals - Ensuring Machine reliability through promotion of preventive and predictive maintenance -Leading Root Cause Analysis and Failure Mode Analysis on breakdowns - Promotion of 5S at maintenance areas - Provision of understanding on the planned maintenance framework - Identify skill gaps in maintenance team and provision of requisite support
EDUCATION
Coventry University
Master of Science - MS, Engineering and Management
Kwame Nkrumah University of Science and Technology, Kumasi
Bachelor of Science (BSc), Petrochemical Engineering
St Peters' Senior High School
W.A.S.S.C.E, General Science
Florida State University
Doctor of Philosophy - PhD, Industrial Engineering
ABOUT GEORGE AMU
I support transportation agencies and infrastructure stakeholders turn complex geospatial and operational data into decision ready intelligence using AI- based models.I began my career leading operational excellence initiatives in large scale manufacturing systems, delivering a 50% reduction in setup time, improving plant availability from 80% to 85%, and driving a 30% reduction in utility cost inefficiencies. That experience shaped my focus on measurable impact, structured problem-solving, and system level optimization. Currently, as a PhD researcher in Industrial Engineering, I design computer vision models for automated roadway asset detection and build spatially informed machine learning models for traffic forecasting and performance prediction. My work sits at the intersection of Operations Research, Machine Learning, and GIS. A central pillar of my research is Explainable AI (XAI), ensuring that predictive and vision models remain transparent, interpretable, and actionable for engineers and policy decision makers.My goal is to partner with industry and research teams where AI-powered perception and predictive analytics create scalable, real-world impact across transportation and infrastructure systems.
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