The Problem
The company operates 20-plus distribution centers, 200-plus depots, and the Consumer Health Logistics Center managing 12,000-plus consumer health products — serving 100,000-plus healthcare locations worldwide with 99.98% order accuracy. Its Transportation Management System was Manhattan — a platform that had been running on extended support without updates, creating efficiency bottlenecks and functionality limitations that required constant manual workarounds. Freight planning and execution teams consumed productive hours every day compensating for what the system could not do on its own.
The procurement process had the same structural inadequacy. RFQ creation was cumbersome, lacking pre-bid intelligence on lane strategies and scenario planning for optimal carrier allocation. Contract and rate management was disconnected across transportation modes without historical tracking or audit capabilities. The company managed $400M-plus in annual freight spend through a procurement process that could not benchmark against current market conditions or model carrier allocation scenarios during active bid events.
The shipment visibility gap was the operational problem with the most direct patient impact. Critical visibility gaps existed for shipments moving from the National Logistics Center to Forward Distribution Centers — preventing effective labor planning and downstream logistics coordination. For a company delivering temperature-sensitive pharmaceuticals and controlled substances to hospitals, clinics, and pharmacies, real-time shipment status was not a reporting metric. It was a patient care requirement. The system was delivering it inconsistently.
Route optimization ran on static rules. Planners manually evaluated weight, volume, and shipment values to decide truck allocation reactively, without the ability to optimize for changing conditions or unexpected demand. The cold chain complexity — controlled ambient, refrigerated, frozen, ultra-low, and cryogenic storage — required precision that manual planning processes could not sustain at volume.
What Freehand Did
Freehand replaced Manhattan TMS with an AI-powered platform that eliminated the manual workarounds by design rather than by adding headcount to manage them. The AI Procurement Analyst automated RFQ creation with intelligent carrier recommendations, achieving 40% improved compliance. Procurement events that previously required days of manual preparation now configure automatically from the company's own lane and volume data, with pre-bid intelligence on market benchmarks and carrier performance built in before the first bid arrives.
Real-time SKU-level visibility now tracks shipments from pickup through final delivery with accurate ETAs — the visibility gap between the National Logistics Center and Forward Distribution Centers is closed. DC teams receive advance notice of inbound load timing. The cold chain movement data that had lived in disconnected systems is now unified in a single platform that integrates procurement, execution, and audit for cross-functional visibility with real-time synchronization. Labor planning at the FDC level is based on actual, current shipment data rather than estimates.
AI-powered route optimization analyzes patterns continuously, generating optimal routes based on weight, volume, urgency, temperature requirements, and changing conditions — not static rules applied reactively. The 12% improvement in customer service levels through intelligent route optimization translates directly to better outcomes for the healthcare locations depending on the company's pharmaceutical and medical supply delivery. The Audit Agent closes the billing side: every invoice across FTL, LTL, parcel, and courier services validated before payment, the $600K-per-month in billing discrepancies caught and resolved before they reach AP, and the 1,881 error shipments per month that previously required manual handling now processed autonomously through the exception workflow. The platform the company had been working around is the platform it now works through.
