Real-World: Specialty Pharma Distributor Improves Lot Traceability with Databricks Agents

Real-World: Specialty Pharma Distributor Improves Lot Traceability with Databricks Agents

1. Problem / Context

A specialty pharmaceutical distributor (~$180M revenue) needed to strengthen lot-level traceability and cold-chain monitoring to meet DSCSA obligations while running with a lean IT team. Data lived in silos across WMS and ERP systems, while temperature and location signals streamed in from IoT devices riding along refrigerated shipments. When a mock recall or temperature excursion happened, analysts stitched together spreadsheets, EDI/ASN files, and warehouse movements by hand—slow, error-prone, and stressful under audit pressure.

The mandate: deliver real-time traceability and recall readiness across partners, including multiple 3PLs, without exploding integration costs or creating a fragile, one-off pile of scripts. The team chose Databricks agents and a governed, agentic approach to orchestrate data, detect risk, and generate FDA-compliant trace documentation end to end.

2. Key Definitions & Concepts

• Lot traceability: The ability to follow each lot’s chain-of-custody from receipt through storage, picking, packing, and shipment—including which customers received which lots and when.
• Cold-chain monitoring: Continuous temperature and handling oversight for temperature-sensitive products; “excursions” occur when readings breach thresholds for too long.
• Agentic AI: A pattern where AI agents coordinate tasks across systems—streaming data, reconciling events, classifying exceptions, and triggering human-in-the-loop reviews.
• Databricks agents: Autonomous workflows running on the Databricks platform that ingest streams (IoT, EDI/ASN), reconcile WMS/ERP events, detect excursions, and assemble trace reports.
• Delta Sharing: A secure, open protocol for sharing live data tables externally so partners (e.g., 3PLs) can consume governed data products without brittle point integrations.
• ASN/EDI: Advance Ship Notices and other EDI events that document chain-of-custody and shipment context.

3. Why This Matters for Mid-Market Regulated Firms

Mid-market distributors carry the same compliance obligations as much larger peers, but with tighter budgets and lean teams. DSCSA readiness, auditability, and partner collaboration all impose a heavy documentation and data-integration burden. A governed agentic approach lowers manual effort, reduces compliance risk, and shortens cycle times without requiring a large platform team. It also avoids the “pilot graveyard” where proof-of-concepts never reach production.

Kriv AI, a governed AI and agentic automation partner focused on mid-market companies in regulated industries, helps align the operational reality (WMS/ERP/3PL diversity) with a clear governance model so agentic workflows can run safely and predictably.

4. Practical Implementation Steps / Roadmap

1) Establish streaming data foundations

• Land IoT temperature and location telemetry in a scalable table format.
• Ingest ASN/EDI messages and normalize key events (ship, receive, putaway, pick, ship confirm).
• Curate WMS/ERP movements into standardized entities (lot, location, handling unit, order, customer).

2) Reconcile chain-of-custody

• Agents correlate ASN/EDI events with WMS receipts and movements by lot and handling unit.
• Discrepancies (e.g., partial receipts, split lots, late ASNs) are routed to an exception queue with human-in-the-loop review.

3) Monitor cold-chain in near real time

• Agents watch IoT readings against product-specific thresholds and dwell times.
• Detected excursions open an incident record linked to impacted lots, locations, and orders.

4) Generate FDA-compliant trace packets on demand

• Agents assemble lineage, custody events, temperature histories, and partner handoffs into a standardized trace dossier for audits or recalls.

5) Share the right data with the right partners

• Use Delta Sharing to publish governed data products (e.g., “Cold-Chain Exceptions,” “Lot Lineage”) to each 3PL and manufacturer with masks for sensitive fields.

6) Unify visibility for Operations, Quality, Compliance, and IT

• Shared dashboards show excursion trends, unresolved incidents, and recall readiness status by partner and product family.

7) Start small, scale deliberately

• Begin with two 3PLs to prove stability, then expand to all partners once SLAs and masking policies are validated.

5. Governance, Compliance & Risk Controls Needed

• Data contracts and SLAs: Define schemas, refresh cadences, and uptime expectations with each 3PL and manufacturer to reduce data-sharing fragility.
• Masking and entitlements: Apply masking policies to partner views so each party only sees what they are entitled to—especially important for customer and shipment-level details.
• Auditability: Preserve an immutable log of agent decisions, source events, and human approvals to support DSCSA documentation and internal QA.
• Exception workflows: Require human sign-off for unresolved excursions, custody gaps, or late/missing ASNs before releasing trace reports.
• Operational resilience: Use Delta Sharing instead of custom SFTP/CSV swaps to limit brittle, one-off integrations and enable versioned data products.

In this program, Kriv AI defined the external data contracts, masking policies, and SLAs underpinning Delta Sharing, ensuring the agentic workflows stayed reliable as more partners came online.

6. ROI & Metrics

The distributor instrumented the initiative with clear, operations-first metrics:

• Unresolved temperature excursions: down 50%, driven by faster detection, correlation, and triage.
• Recall mock drill duration: down 60%, because trace packets and chain-of-custody are generated on demand.
• Manual hours across Ops/Quality/Compliance: down 35%, as agents reconcile events and prepare documentation automatically.
• Cycle time to prepare a trace dossier: from days to hours, with fewer back-and-forth requests to partners.

A conservative benefits model based on these metrics showed the program paying back in two to three quarters, before counting soft benefits like improved partner confidence and audit readiness.

7. Common Pitfalls & How to Avoid Them

• Fragile partner integrations: Avoid ad hoc file drops. Define data products and SLAs, and use Delta Sharing so partners consume governed views that won’t break with schema drift.
• One-off automation scripts: Replace brittle scripts with agentic workflows that embed exception handling and human approvals.
• Overfitting to one 3PL: Validate patterns with two partners first, then generalize. Document differences explicitly in data contracts.
• Ignoring exception taxonomy: Name and route exceptions (late ASN, custody gap, sensor dropout, split lot) to the right owners with clear SLAs.
• Lack of shared dashboards: Ensure Operations, Quality, Compliance, and IT see the same metrics to reduce handoffs and delays.

30/60/90-Day Start Plan

First 30 Days

• Discovery: Inventory WMS, ERP, and IoT data sources; map lot and handling unit identifiers to a common model.
• Data checks: Validate temperature device coverage, thresholds, and latency; assess ASN/EDI completeness.
• Governance boundaries: Define partner-level access policies, masking needs, and initial SLAs for two pilot 3PLs.
• Environment setup: Stand up streaming ingestion and a basic agent framework in Databricks; create a lightweight exception queue.

Days 31–60

• Pilot workflows: Enable agents to reconcile ASN/EDI with WMS receipts and to monitor IoT feeds for excursions.
• Agentic orchestration: Automate creation of incident records and draft trace packets; introduce human-in-the-loop steps.
• Security controls: Apply masking and entitlements to shared tables; implement audit logging for agent actions and approvals.
• Evaluation: Run a recall mock drill with the two 3PLs, measure time-to-dossier and exception resolution rates.

Days 61–90

• Scaling: Expand Delta Sharing to additional partners using the validated contracts and SLAs; onboard more products/lots.
• Monitoring: Establish dashboards for unresolved excursions, exception aging, and partner SLA performance.
• Metrics: Track the target reductions (50% unresolved excursions, 60% recall drill time, 35% manual hours) and adjust workflows.
• Stakeholder alignment: Formalize operating rhythms across Operations, Quality, Compliance, and IT; plan quarterly resilience tests.

9. Industry-Specific Considerations

• Cold-chain nuance: Different molecules have different hold times and thresholds; agents should use product-specific rules rather than one-size-fits-all limits.
• Chain-of-custody complexity: Split lots, partial receipts, and relabeled handling units are common—ensure the data model tracks these transformations.
• 3PL diversity: Expect varied data quality and timeliness across partners; contracts, masks, and SLAs are essential for stability.
• Documentation rigor: DSCSA audits reward consistency. Standardize trace packet templates and keep immutable logs of all events and approvals.

10. Conclusion / Next Steps

A governed, agentic approach on Databricks helped this specialty pharma distributor turn fragmented signals into real-time lot traceability, faster excursions triage, and smoother recall readiness—without growing the IT team. By starting with two 3PLs and scaling through Delta Sharing, the program avoided the pilot graveyard and delivered measurable gains quickly.

If you’re exploring governed Agentic AI for your mid-market organization, Kriv AI can serve as your operational and governance backbone. As a mid-market-focused partner, Kriv AI helps with data readiness, MLOps, workflow orchestration, and compliance controls so your teams can adopt AI with confidence and sustained ROI.