Deterministic and Stochastic Dynamic Programs for optimization of Supply Chain
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Updated
Dec 16, 2022 - Python
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
Stochastic model to predict event probability and forecast store demand using Python
GenAI demand planner v2 with multimodal data fusion
Demand shaping through pricing and promotion optimization
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
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
Forecasting weekly product demand using ARIMA, SARIMAX, and regression models. Built with Microsoft Fabric Lakehouse architecture to improve stock management and minimize stockouts.
End-to-end Amazon Sales Analytics Dashboard with PPC Optimization, Funnel Analysis, Forecasting & Stock Planning
Supply chain scenario planning tool with what-if analysis capabilities
Warehouse wave planning order release
Top-down demand disaggregation from aggregate forecast to SKU-location level
Demand forecasting models for supply chain and inventory planning
Sales forecasting model utilizing Armstrong Cycle Transformers to predict supermarket demand patterns.
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