Hi there 👋
I'm a Facilities Manager with a background in mechanical engineering and a passion for integrating machine learning into day-to-day operations. With over a decade of experience across manufacturing, automotive, and BPO environments, I now focus on leveraging data and predictive models to optimize facilities performance, reduce downtime, and support scalable, intelligent operations.

As part of my role in facilities management for a high-density BPO site, I’m leading a series of machine learning initiatives that integrate predictive analytics directly into our operations. These projects are organized across four key operational areas:

🔹 Supply Forecasting
Building demand prediction models for consumables such as tissue, drinking water, and clinic medicine to support just-in-time procurement and minimize stockouts or overstocking.

🔹 Resource Optimization
Creating tools to forecast usage trends based on employee headcount, shift patterns, and event schedules—ensuring better planning for utilities, cleaning schedules, and pantry resupply.

🔹 Predictive Maintenance
Using environmental sensor data (RoomAlert 32S) from MDF and UPS rooms to identify abnormal trends that may indicate HVAC issues, helping prevent downtime or asset damage.

🔹 Operational Dashboards & Automation
Deploying models and data workflows using tools like Django and Streamlit to provide facilities staff and admin teams with real-time predictions, alerts, and actionable insights.

These systems are designed to make BPO facilities smarter, more responsive, and data-driven—bridging traditional operations with modern machine learning practices.