Bad Actors Data
In this project, I was tasked with identifying high-impact equipment issues—referred to as "bad actors"—across J.M. Huber Corporation's Georgia plants using Oracle OEED data. Bad actors are machines or machine components that contribute significantly to unplanned plant downtime.
To develop a comprehensive list of bad actors, I analyzed Oracle OEED work order data, filtering machines by total downtime relative to each plant. I excluded equipment with inherent or unavoidable downtime, such as rolling mills. For the most problematic machines, I manually reviewed and categorized work order descriptions, as a newly implemented work order system led to inconsistent data entry due to limited familiarity among plant staff.
After compiling the list of bad actors, I presented my findings to each respective plant. This enabled the teams to better understand recurring issues and proactively prepare for them, ultimately aiming to reduce equipment-related downtime.
Key Achievements
- Was able to create a comprehensive list of all bad actors in J.M Huber Corporation's plants.
- Successfully was able to organize and interpret data
- Successfully able to present my findings to each respective plant
Challenges
- Inconsistent formatting and terminology across work orders, making automated categorization unfeasible.
Solutions
- Manually reviewed and classified each work order to identify its general category, ensuring accuracy despite variations in reporting.
Results
- Delivered a comprehensive, plant-specific list of problematic components, enabling targeted corrective actions and improved maintenance prioritization.