Overview
Developed a data-driven supply chain planning methodology for a multi-stage network connecting plants, mother hubs, Clearing and Forwarding Agents (CFAs), and customers. Combined inventory planning with Mixed-Integer Linear Programming (MILP) to optimize production and distribution decisions, evaluate capacity constraints, and analyze cost and demand-fulfillment trade-offs, earning National Runner-Up recognition in the case competition.
About this project
Developed a data-driven supply chain planning methodology for a multi-stage network connecting plants, mother hubs, Clearing and Forwarding Agents (CFAs), and customers. Combined inventory planning with Mixed-Integer Linear Programming (MILP) to optimize production and distribution decisions, evaluate capacity constraints, and analyze cost and demand-fulfillment trade-offs.
Key Highlights
- Supply Chain Network Modeling: Structured production, inventory, and distribution decisions across plants, mother hubs, CFAs, and customer demand.
- Inventory Planning: Developed an inventory norm methodology using average demand, forecast variability, lead times, safety stock, reorder points, and days of cover.
- MILP Formulation: Formulated a mathematical optimization model to minimize modeled production, transportation, inventory, and unmet-demand penalty costs while satisfying operational constraints.
- Production & Capacity Planning: Incorporated fixed 25 kL production batch sizes and plant- and category-level capacity constraints into the planning model.
- Scenario Analysis: Evaluated a 70% Overall Equipment Effectiveness (OEE) scenario to assess its impact on effective production capacity, modeled costs, and unmet demand.
- Bottleneck Analysis: Interpreted optimization outputs to identify structural capacity bottlenecks and evaluate production planning and resource allocation decisions.
Optimization Tool Development
- Excel-Driven Workflow: Implemented spreadsheet data import to populate the optimization model.
- Configurable Constraint Engine: Enabled users to add custom constraints and modify or remove default constraints for flexible scenario evaluation.
- Optimization Backend: Developed the backend using Python and FastAPI, with PuLP for MILP formulation and the CBC solver for optimization.
Recognition
- Competition result
National Runner-Up
Castrol PowerUp 4.0 Supply Chain Track Case Competition.
Technology stack
Python · Pandas · PuLP · CBC Solver · FastAPI · Excel · Mixed-Integer Linear Programming
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