The code for the SRDQN algorithm to train an agent for the beer game problem

This is an Analysis to optimise inventory managemnt for FitCapacity company by analyzing sales and inventory data using SQL and PowerBI

This project provides an inventory optimization tool that uses synthetic data and applies demand forecasting, inventory classification, safety stock calculation, and other inventory management techniques using AI/ML!

Constrained optimization problems, including linear,network, dynamic,integer, and nonlinear programming, decision trees, queueing theory and Markov decision processes.

A production-ready, end-to-end system for spare parts inventory management. This project combines advanced forecasting (TSB, Prophet, SARIMAX), mathematical optimization (CPLEX), and supply chain simulation (anyLogistix) to achieve 98.9% forecast accuracy and a 39% ROI improvement.

🌌 ChaosChain-AI: Next-Gen Supply Chain AI Simulator Advanced AI control tower combining chaotic demand modeling, Monte Carlo simulations, and multi-factor risk scoring. Features real-time monitoring, predictive analytics, and automated mitigation across global supply chains. 🔬 Research | 🏭 Supply Chain AI | 🤖 Machine Learning |📊 Simulation

Strategic market validation study of a South Korean bakery using 2,400+ transaction records. Leveraged Excel and Google Sheets to perform time-series analysis and product-mix modeling, successfully challenging stakeholder bias with data-backed insights.

End-to-end demand forecasting and inventory optimization with Databricks - multi-modal ML (time series, images, tabular), LightGBM, MAPIE conformal prediction and MLOps for retail and fashion.

Workshop management software for tracking parts, fuel usage, reporting and more. Designed for auto repair shops and service workshops with minimal setup and training. With enough creativity, this can be used in many more fields. Includes 14-day free trial.

An inventory optimization system using Python and SQLite, integrating yearly demand simulation, safety stock modeling, and reorder strategies to reduce the risks of stockouts and frequently replacing purchasisng orders

Inventory optimization & warehouse consolidation analysis using MySQL

End-to-end retail analytics system for sales forecasting and inventory optimization using Machine Learning, FastAPI APIs, and Streamlit dashboard.

Predicting daily sales using store, promotions, competition, and time-series data

Description An AI-powered retail analytics platform that combines XGBoost demand forecasting with operations research to optimize inventory. It automates Reorder Point (ROP) and EOQ calculations via an interactive Streamlit dashboard to prevent stockouts and overstocking.

An AI-based inventory optimization system that leverages machine learning to predict demand, recommend menu items, and streamline stock management for restaurants and food service businesses.. — all deployed through a real-time Stream lit web app.

End-to-end inventory optimization project for an e-commerce warehouse, combining demand forecasting, SKU segmentation, safety stock, reorder point calculation, and scenario-based stress testing with automated testing and CI.

This project is a comprehensive Business Intelligence solution designed to identify inefficiencies, optimize capital allocation, and model operational improvements across three core supply chain pillars.

Demand forecasting, inventory optimization, and fulfillment routing in Go. Built from patterns shipping supply chain products at Amazon and Walmart.

An end-to-end SQL data analysis project using retail datasets to extract business insights.

Supply chain & sales demand forecasting with ML and deep learning (XGBoost, CNN, LSTM, CNN-LSTM, GRU, Transformer). Inventory and replenishment optimization.