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.

DuckDB extension for Git-like database branching. Create isolated scenarios for what-if analysis with copy-on-write storage, diff comparisons, and audit trails.

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.

Logility APIs.json profile

Executive-level B2B Sales Forecasting & Revenue Trend Analysis dashboard built with HTML, CSS, and JS, featuring a Pandas aggregation pipeline to analyze 100k+ transactional sales records.

GenAI demand planner v2 with multimodal data fusion

Python demand forecast for pipeline throughput using real CER data — 30/60-day projection with a transparent moving-average model, explicit assumptions, and honest uncertainty ranges.

Demand shaping through pricing and promotion optimization

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.

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

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

Demand forecasting models for supply chain and inventory planning

Aggregate production planning linear programming