A leading global steel manufacturer with a relatively modern mill was plagued by frequent small equipment failures, causing substantial production halts and profit loss. To tackle this, we implemented a hybrid strategy, combining traditional maintenance improvement with advanced digital analytics and side-by-side implementation to ensure lasting results. 

Underinvestment during a past industry downturn and high turnover among operations and maintenance staff resulted in a cycle of constant repairs and neglected preventive maintenance. To break this cycle, we developed a unique solution and improvement program for the plant. This program integrated deep root-cause analysis with AI and machine learning that utilized extensive plant process data, including temperature, pressure, and vibration readings. 

Working with reliability engineers and data scientists, along with operators and trade craft during implementation, we helped create predictive analytics algorithms that were incorporated into shift operating parameters and decision making, including operator and millwright response procedures. To ensure the sustainability of this initiative, our teams supported capability building, KPI development, and a governance refresh to ensure tracking and visibility.

60%

reduction in conveyor failures resulting in ~6% pt. improvement in OEE.