Deterministic and Stochastic Dynamic Programs for optimization of Supply Chain

Forecast-driven inventory optimization project for retail demand planning, combining SARIMAX, ML model comparison, feasibility auditing, Monte Carlo simulation, and inventory policy optimization.

demand planning engine that combines probabilistic forecasting, conformal prediction, and ordering policies into a single backtestable pipeline

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.

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

Warehouse demand forecasting — Prophet, ARIMA & ensemble models. Stock alerts, What-if simulator, PDF purchase orders. 45 French SKUs, 11-tab dashboard.

Demand shaping through pricing and promotion optimization

Product demand cannibalization model for new product launch impact

Time series forecasting for warehouse capacity planning, modeling outbound customer orders and inbound brand deliveries. Holt-Winters exponential smoothing, weekly aggregation, ratio-based shipment adjustment.

GenAI demand planner v2 with multimodal data fusion

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.

AI demand orchestrator for unified demand planning across channels

Aggregate production planning linear programming

Demand regime change detection

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

Demand forecasting models for supply chain and inventory planning

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

Near-term demand sensing ML external signals