Deterministic and Stochastic Dynamic Programs for optimization of Supply Chain

Creating Supply & Demand during tough times of lockdown caused by COVID-19

A spare engine placement generator based on a Finite-Horizon Markov Decision Process

Multi-model time-series forecasting with Bayesian Optimisation (Optuna TPE): SARIMA, Random Forest, XGBoost, LightGBM, Prophet, LSTM, and QuantileML probabilistic forecasts behind a unified ModelSpec protocol. Walk-forward validated; supports monthly, weekly, daily, and hourly data.

GenAI demand planner v2 with multimodal data fusion

Demand shaping through pricing and promotion optimization

Product demand cannibalization model for new product launch impact

Stochastic model to predict event probability and forecast store demand using Python

Top-down demand disaggregation from aggregate forecast to SKU-location level

Demand forecasting models for supply chain and inventory planning

The goal of this project was to explore demand forecasting using R, analyze trends, seasonality, and create ARIMA models to predict future demand. This README provides an overview of the project and its structure.

Zero-dependency demand forecasting for seasonal businesses. Pure statistics, no ML, no cloud costs.

AI demand orchestrator for unified demand planning across channels

End-to-end supply chain analysis using SQL and Excel — uncovering delivery performance, fulfilment efficiency, product profitability and order priority insights across 2,500 orders.

XYZ demand variability classification

Demand regime change detection

Aggregate production planning linear programming

Forecasting weekly product demand using ARIMA, SARIMAX, and regression models. Built with Microsoft Fabric Lakehouse architecture to improve stock management and minimize stockouts.

Supply chain scenario planning tool with what-if analysis capabilities

Warehouse wave planning order release