kjoyini/mining-maintenance-operations-analytics

SQL and operational analytics project evaluating mining-equipment reliability, downtime exposure, and maintenance optimisation across African mining operations. # Mining Reliability & Maintenance Optimisation This project analyses operational reliability, downtime exposure, and maintenance effectiveness across mining operations using SQL, relational database design, and operational analytics. The analysis focused on how maintenance strategy, equipment reliability, and sensor-event monitoring influence operational performance, repair costs, and production disruption within mining environments. ## Key Insights - Identified haul trucks as the largest driver of repair expenditure and operational downtime across mining assets - Demonstrated that predictive and preventive maintenance strategies materially reduced downtime exposure relative to reactive maintenance approaches - Found that sensor-alert coverage and maintenance response times significantly influenced operational reliability outcomes - Showed that maintenance-event concentration across a small number of asset categories created disproportionate operational disruption and repair costs ## Objectives - Analyse maintenance and downtime patterns across mining equipment - Evaluate operational drivers of repair costs and production disruption - Design a structured relational database for reliability analysis - Generate operational insights supporting maintenance optimisation ## Tools & Technologies - MySQL - SQL - DBeaver - Python - Matplotlib ## Analytical Techniques - Relational database design - Third Normal Form (3NF) normalisation - SQL joins and aggregations - Downtime analysis - Reliability analytics - Operational KPI analysis ## Project Outcome Project completed at Hult International Business School.