In modern logistics and distribution, invisible inventory is a direct drain on profitability. When supply chain data disappears between physical scans, warehouse handoffs, or carrier transitions, businesses are forced to overstock materials, rely on expensive emergency freight, and absorb massive holding costs.

Leveraging deep domain expertise in enterprise logistics and complex systems like Manhattan Active WM, I build high-frequency supply chain intelligence systems that illuminate operational blind spots. This service physically connects raw WMS database feeds, telematics, visual recognition tracking data, and external vendor APIs into a unified, real-time reconciliation engine. I deploy predictive machine learning models to isolate SKU defect patterns and utilize math-based heuristics to automatically detect data-entry errors or missing inventory scans before they compound. The output is a suite of dynamic, high-refresh-cycle dashboards that provide executives and warehouse floor managers with absolute visibility across the entire network, significantly reducing financial waste and optimizing fulfillment pathways.

Core Deliverables:
- Direct API and raw database extraction from legacy WMS and ERP platforms.
- Deployment of high-frequency data reconciliation pipelines.
- Predictive cluster modeling for defect tracking and inventory optimization.
- Real-time logistics visibility dashboards deployed via Power BI or custom portals.