Supply Chain Engineer · Industrial Systems
Turning complex operations into clear decisions.
I build analytics, optimization models, and practical operating systems that strengthen inventory availability, procurement performance, and material flow.
About
Engineering the link between operations and analytics.
I am a supply chain and manufacturing professional with more than three years of experience across inventory control, MRO procurement, warehouse optimization, supplier coordination, and production support.
My approach combines industrial engineering fundamentals with Python, SQL, Power BI, and enterprise systems to move from scattered operational data to decisions teams can execute.
01
Supply chain
Inventory, procurement, logistics, supplier performance
02
Analytics
Python, SQL, Power BI, forecasting, optimization
03
Operations
Lean, material flow, warehouse design, continuous improvement
04
Systems
Oracle ERP, Trapeze EAM, ECMS, RFID integration
Experience
Work grounded in measurable operational value.
Oct 2025 — Present
Supply Chain Engineer
Rutgers University — Center for Advanced Infrastructure and Transportation (CAIT)
Supporting New Jersey Transit rail operations · Newark, NJ
- Built a probability-based warehouse optimization model and decision-support dashboard for inventory placement and risk.
- Connected fragmented Oracle ERP, Trapeze EAM, and ECMS information into a shared analytical layer.
- Applied classification, forecasting, and replenishment methods to improve availability and procurement planning.
May 2022 — Dec 2024
Industrial Engineer — Supply Chain & Operations Analytics
Carborundum Universal Ltd · Murugappa Group
Chennai, India
- Integrated RFID-enabled shop-floor data with Oracle ERP and improved inventory accuracy.
- Developed Python, SQL, and Power BI tools for stock visibility, supplier performance, and procurement analysis.
- Improved warehouse movement, material flow, processing throughput, and equipment performance through industrial engineering methods.
Featured project
Rail operations decision support.
A warehouse and inventory analytics program designed to turn fragmented operational records into actionable placement, replenishment, and cycle-count decisions.
Rutgers-CAIT · New Jersey Transit
Space Optimization Dashboard
Combined warehouse spatial data, open purchase orders, usage history, min-max policy, and procurement signals into one decision-support experience for rail-parts inventory.
13% fewer stock-outs15% better on-time delivery$400K inventory value recovered
Operational decision layer
PlacementSpatial
ReplenishmentRisk-led
Cycle countPriority
Decision flow
ERP + EAMAnalyticsAction
Python & analytics
Technical depth, presented without exposing proprietary code.
Private implementations are demonstrated through model architecture, validated outputs, libraries used, and business outcomes. Source code is available only for controlled review.
analytics_workflow.py · private
# Architecture summary — source withheld
data = integrate(erp, eam, usage_history)
signals = engineer_features(data)
forecast = evaluate_models(
demand, price, lead_time
)
policy = optimize_inventory(
service_level, risk, capacity
)
→ validated decisions for operationsDemand & lead-time forecasting
Time-series and regression models supporting procurement and maintenance planning.
Inventory policy simulation
Scenario testing for service level, stock exposure, replenishment frequency, and demand variability.
Cycle-count prioritization
Multi-factor scoring and coverage optimization to direct effort toward the highest operational exposure.
Education
Industrial engineering foundation.
Rutgers University
Master of Science, Industrial & Systems Engineering
GPA 3.875 / 4.0SASTRA University
Bachelor of Technology, Mechanical Engineering
GPA 3.56 / 4.0Contact
Let’s build better operating decisions.
Open to supply chain, industrial engineering, procurement analytics, and operations opportunities.