This 2-hour backend workshop shows how to build a real-time, explainable inventory and fulfillment engine with MongoDB Atlas. The system combines geospatial proximity, semantic precedent via Vector Search, and reactive automation to drive reorder and substitution decisions with full traceability.
Key technical components:
Schema design: for warehouses, inventory_levels, shipments, past_incidents, substitute_products, reorder_requests, and audit_logs.
Geospatial fulfillment: using 2dsphere indexes and proximity queries to find nearest warehouses with available stock.
Operational KPIs via Aggregation Framework pipelines: (stock by region, aging inventory, shipment throughput, hotspot detection).
Reactive detection of inventory risk using Change Streams: to flag low-stock and initiate downstream logic.
Automated decisioning service: that generates reorder or substitution recommendations based on threshold rules enriched with historical context.
Semantic layer powered by Atlas Vector Search: matching current demand patterns or stockout situations to similar past incidents and viable substitute products to inform fallback strategies.
Hybrid ranking: that fuses geospatial score (proximity + availability) with vector similarity to rank fulfillment candidates.
Consistency & explainability through multi-document transactions, versioned audit logs capturing provenance and vector similarity scores, and “why” payloads attached to every recommendation.
