Data platforms and analytics for warehousing, transportation, and fulfillment — integrating WMS, TMS, ERP, and telematics data to improve inventory health, logistics cost, and OTIF performance.
End-to-end ETL pipeline integrating WMS, ERP, and POS data into curated inventory models on Snowflake / Databricks. Computes on-hand, in-transit, reserved, and available-to-promise quantities with reorder point, safety stock, and days-of-cover metrics by SKU–location.
Ingests shipment, carrier, and GPS telematics data to build lane-level KPIs such as cost-per-mile, cost-per-drop, transit reliability, and utilization. Powers dashboards to benchmark carriers, lanes, and modes for continuous freight optimization.
Consolidates order, shipment, ASN, and delivery events across plants, DCs, and customer sites to compute OTIF, fill rate, backlog, and lead time variance. Includes exception views for chronic offenders by customer, lane, and product family.
Modeling dataset and Python engine for DC location & network design scenarios, using demand, freight cost, and service-time assumptions. Produces total landed cost and service-coverage trade-offs per scenario for strategic footprint decisions.
Aggregates PO, receipt, and quality data to build supplier scorecards with on-time delivery, lead-time variability, defect rate, and cost variance. Highlights at-risk suppliers and items for safety-stock and sourcing decisions.