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

Sales forecasting dashboard using Power BI, LSTM models, and SHAP for explainable insights

Monthly demand forecasting using SARIMA + Bayesian Optimisation (Optuna TPE). Walk-forward validated against seasonal naïve, ETS, and AutoARIMA baselines.

Product demand cannibalization model for new product launch impact

Demand shaping through pricing and promotion optimization

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

GenAI demand planner v2 with multimodal data fusion

Demand regime change detection

Aggregate production planning linear programming

Demand orchestration platform unifying S&OP with execution

AI demand sensing tower with multi-horizon planning

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

Near-term demand sensing ML external signals

Multi-method demand forecasting toolkit using ARIMA, Prophet, LSTM, and XGBoost for supply chain planning with automated model selection and accuracy benchmarking

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.