Probabilistic Demand Forecasting & Inventory Optimization (Supply Chain)

Data Ingestion & Stochastic Pipeline Engineering

• Transaction Processing: Aggregating historical Point of Sale (POS) or ERP data at the SKU-location level, tracking order dates, fulfillment timelines, returns, and lost sales due to historical stockouts.
• Intermittent Demand Classification: Identifying slow-moving or erratic items using frameworks like Syntetos-Boylan Classification to segment goods into Smooth, Erratic, Lumpy (high variance, intermittent), or Croston-type categories.
• Feature Engineering: Extracting temporal features (seasonality, holiday spikes, promotional periods, economic indicators) to serve as external regressors for predictive models.

Probabilistic Demand Forecasting

• Traditional Time-Series Baseline: Implementing foundational statistical methods (like Croston’s Method for intermittent demand or seasonal ARIMA).
• Probabilistic Machine Learning: Building models that output an entire conditional distribution or specific quantiles (e.g., DeepAR, LightGBM with Quantile Loss, or Prophet) using Python packages like GluonTS or scikit-learn.
• Quantile Generation: Extracting specific demand prediction intervals (e.g., P10 for conservative floors, P50 for median expectations, and P90/P95 for extreme high-demand scenarios) to feed downstream inventory models.

Stochastic Inventory Optimization

• Dynamic Lead Time Modeling: Treating supplier lead times as a probability distribution (e.g., fitting historical delay data to a Gamma or Log-Normal distribution) rather than assuming a static "14-day turnaround."
• Probabilistic Safety Stock Calculation: Replacing standard textbook formulas with complex statistical models that convolute the demand distribution and lead-time distribution to determine safety stock levels for non-normal distributions.
• Reorder Point (ROP) & Order-Up-To Level (S) Optimization: Setting dynamic, time-varying ROPs that automatically adjust based on the changing variance of upcoming demand.

Financial Simulation & Risk Analysis

• Newsvendor Model & Cost Optimization: Applying the Newsvendor framework for seasonal/perishable goods to balance the Cost of Underage (lost margin, customer churn) against the Cost of Overage (holding, liquidation, spoilage costs).
• Monte Carlo Simulation Engine: Writing simulation scripts to model thousands of supply chain paths over a 90-day horizon to project metrics like Service Level (Type 1: Cycle Service Level; Type 2: Fill Rate) against total capital tied up in inventory.

Executive Visualization & Deployment

• Risk-Profile Dashboards: Creating custom visual interfaces in Streamlit, Plotly, or Power BI that display the fan charts (confidence bands) of future demand alongside automated alerts for SKUs with a high structural probability of stockout.
• Strategic Reporting: Writing clear summaries that translate statistical probabilities into business metrics, helping procurement teams decide exactly how much capital to deploy to achieve a target 98% fill rate.