Columbia University’s M.S. in Industrial Engineering program blends systems thinking, quantitative analysis, and practical modeling to prepare students for challenges in logistics, operations, and manufacturing. These project ideas offer applied problem-solving in dynamic and large-scale systems.
Simulation-Based Optimization of Assembly Line Productivity
Human Factors Analysis in Ergonomic Workstation Design
Real-Time Production Monitoring Using IoT and Power BI
Optimization of Resource Allocation in Service Systems
Data-Driven Scheduling for Batch Manufacturing Operations
Lean Six Sigma Framework for Reducing Production Defects
Design of Smart Warehousing Systems with RFID Integration
Sustainable Supply Chain Design Using Multi-Objective Optimization
Bottleneck Analysis and Throughput Improvement in FMCG Plants
Predictive Maintenance Modeling for Industrial Equipment
Warehouse Space Utilization Optimization Using Heuristics
Inventory Classification and Forecasting Using ABC + ML
Process Mining for Operational Workflow Improvement
AI-Based Quality Control in Manufacturing Using Image Recognition
Dynamic Simulation of Healthcare Service Queues
Design of Decision Support System for Industrial Procurement
Multi-Criteria Vendor Evaluation Using AHP and TOPSIS
Human-Centered Design in Industrial System Interfaces
Carbon Footprint Reduction Modeling in Factory Systems
Integration of Digital Twin for Real-Time Operations Control
Collexa helps students in industrial engineering with simulation (Arena, Simul8), optimization (Gurobi, Excel Solver), value stream mapping, and technical documentation.
Whether it's Machine Learning, Data Science, or Web Development, Collexa is here to support your academic journey.
"Collexa transformed my academic experience with their expert support and guidance."
Computer Science Student
Reach out to us for personalized academic assistance and take the next step towards success.
