This project delivers an AI-driven platform for optimizing complex supply chain decisions, focusing on the minimization of total logistics and tariff costs. It functions as a conversational “Shipment Assistant,” allowing non-technical users to simply describe their shipping needs, including product type, value, factory location, and customer destination in plain language. An integrated chatbot extracts these critical details, confirming all parameters are collected before proceeding. The system then acts as a sophisticated data engine, automatically integrating real-world data sources to determine the current ocean freight costs per TEU and precise bilateral tariff rates based on the product category and country pair, which are instantly used to calculate the user’s current estimated shipment cost.
Once the current costs are established, the platform runs a powerful Linear Programming (LP) optimization model, effectively acting as a “black box” that searches globally for the most cost-effective supply chain routes. This model identifies the absolute cheapest configuration by analyzing thousands of potential origin-destination pairs and incorporating data on major global container ports. The results provide users with both the calculated current cost and the optimal solution, including suggested production relocation countries that possess both high manufacturing capacity and advantageous tariff agreements. The final outputs are delivered via an interactive Streamlit dashboard and a downloadable CSV file containing the optimal variables, all packaged within a standalone executable file for seamless, local use.
Tags: Supply Chain Optimization, Logistics Planning, Linear Programming, AI/Chatbot Interface
Authors
Anish Jamedar | Srujan Chinta | Anand Manohar Agnihotram | Kushwanth Sai Bollepalli | Lohith Surampudi
Savan Reddy Poduturi | Gurusai Ravi Raja Reddy Ankireddy