From arrival?only logger reads to Azure IoT + OTM/Veeva routing for in?transit actions

Overview

A biotech company shipping temperature-sensitive materials was discovering cold chain excursions only after delivery, when data loggers were downloaded at the destination. Intelligex integrated real-time IoT sensors with a monitoring platform, codified lane- and product-specific temperature rules, and routed exceptions to Logistics and Quality Assurance (QA) with clear triage paths. Interventions began happening during transit, and investigations were supported by consistent evidence packs rather than ad hoc spreadsheets or email threads—without replacing the client’s transportation or quality systems.

Client Profile

• Industry: Biotech/biopharmaceuticals
• Company size: Multi-site manufacturing and distribution with global cold chain lanes
• Stage: Mature supply chain with established quality systems and third-party logistics (3PL) partners
• Department owner: Procurement, Supply Chain & Logistics
• Other stakeholders: Quality Assurance (QA), Regulatory Affairs, Manufacturing, Warehousing, Clinical Operations (where applicable), 3PLs and carriers, IT/Enterprise Applications

The Challenge

Shipments relied on data loggers that were read only on arrival. When a box showed up out of range, the team learned too late to intervene. Corrective actions were reactive: product was quarantined, QA launched deviations, and Logistics retraced the route with the carrier to reconstruct what happened. Evidence lived in PDFs, emails, and shared drives, so investigations took time and often hinged on fragmented data.

Device diversity compounded the issue. Different 3PLs used different loggers and gateway solutions, which produced various file formats and dashboards with uneven access. Triage responsibilities were unclear: if a temperature alert appeared during a weekend or after hours, it was not obvious whether the carrier, the control tower, or QA should act first. Compliance requirements under Good Distribution Practice (GDP) and electronic records rules meant the team needed traceable actions and a reliable audit trail, not just faster texts or calls.

Replacing the transportation management system, quality system, or 3PL networks was not an option. The organization needed a way to unify telemetry from multiple sensor vendors, apply consistent rules tied to product stability, and route exceptions into the tools already used for shipments and quality events—so that action and documentation could proceed in lockstep.

Why It Was Happening

The cold chain depended on delayed, manual data handling. Most lanes used single-use or reusable loggers that produced data at the destination. Even where gateways existed, feeds were siloed by vendor. Without a normalized event model or lane-specific rules, signals were noisy and not actionable. Teams relied on generic alert thresholds that did not reflect the true stability budget or excursion criteria for each product and packout configuration.

Workflow ownership was also fragmented. Logistics monitored shipments; QA owned deviations and product release; 3PLs handled physical interventions. With no shared triage playbook and no integration to the transportation system or quality management system, alerts became email threads. This created delays, inconsistent handoffs, and investigation packages assembled after the fact rather than captured as events unfolded.

The Solution

Intelligex deployed a monitoring layer that ingests sensor data from multiple providers, normalizes it, and applies lane- and product-specific rules to create actionable exceptions. We integrated the layer with the client’s transportation management system (TMS) and quality management system (QMS), so exceptions opened tasks for Logistics and draft records for QA with the right context. A human-in-the-loop triage console guided responders through the steps: verify, intervene, document, and, if needed, escalate to a deviation. Nothing was ripped out; existing tools governed execution and records, now fed by consistent, real-time evidence.

• Device and gateway ingestion via Azure IoT Hub, supporting cellular and Bluetooth Low Energy (BLE) sensor streams from multiple vendors.
• Unified event model for temperature, location, dwell, door open, and battery state, mapped to shipment IDs and packages.
• Rule engine for lane- and product-specific thresholds based on stability data and packout design, including duration-above-threshold logic and cumulative exposure windows.
• Exception routing to Logistics via tasks in the TMS (e.g., Oracle Transportation Management), and to QA via draft quality events or deviations in the eQMS (e.g., Veeva Vault Quality), with pre-filled fields and attachments.
• Human-in-the-loop triage console with standardized checklists, carrier contact details, lane-specific intervention instructions, and reason codes for decisions.
• Evidence pack generation: time-stamped sensor plots, route traces, handling notes, and system actions bundled for QA review and release decisions.
• Dashboards for real-time lane health, exception aging, top recurring routes and carriers by incident type, and packout performance.
• Role-based permissions, electronic signatures, and immutable audit logs to support 21 CFR Part 11 expectations and GDP documentation needs (EU GDP Guidelines).

Implementation

• Discovery: Mapped end-to-end shipment flows across priority lanes; cataloged sensor and logger types by 3PL; examined historical excursions to identify patterns; reviewed stability budgets and packout SOPs; assessed TMS and QMS integration points and approval workflows.
• Design: Defined the canonical telemetry schema and shipment linkage; authored lane- and product-specific rules with QA and Logistics; designed triage playbooks and escalation paths; planned event payloads and field mappings to TMS tasks and QMS records; set access controls and e-sign checkpoints.
• Build: Implemented ingestion adapters to IoT feeds; built the rule engine and exception router; integrated with TMS and eQMS APIs; developed the triage console and evidence pack generator; configured dashboards and alert policies.
• Testing/QA: Ran in shadow mode on live lanes, comparing automated exceptions with manual reviews; tuned thresholds to align with stability criteria and reduce false positives; validated record creation and attachments in TMS and QMS; performed audit trail and e-sign tests with QA.
• Rollout: Phased by lane and 3PL, starting with high-risk routes; enabled read-only monitoring first, then activated exception routing and triage; maintained contingency procedures for logger-only shipments during cutovers.
• Training/hand-off: Delivered role-based sessions for Logistics coordinators, QA reviewers, and 3PL contacts; provided lane-specific quick guides; established a governance cadence to review rules, packout changes, and vendor device updates; included human-in-the-loop checkpoints at key decision gates.

Results

Temperature risks were surfaced in time to act. Logistics teams received clear, lane-specific alerts and could coordinate with carriers to add gel packs, swap equipment, or expedite delivery as appropriate. QA had immediate visibility into the context of each event, with sensor plots and route details attached, so release decisions were grounded in consistent evidence rather than reconstructed narratives.

Investigations became faster and more defendable. Exception histories, actions, and rationales were recorded as they happened, which improved readiness for audits aligned to GDP expectations and reduced back-and-forth with 3PLs over what occurred. Collaboration improved: Logistics and QA worked from the same facts, and carriers received unambiguous instructions drawn from the triage playbook instead of open-ended emails. Overall, cycle time from detection to decision shortened, product holds were more targeted, and repeat issues on specific lanes were identified and addressed.

What Changed for the Team

• Before: Logger data was downloaded at receipt, and excursions were discovered too late. After: Real-time telemetry flagged issues in transit with lane-specific thresholds.
• Before: Alerts lived in emails and vendor portals with unclear ownership. After: Exceptions opened tasks in TMS and draft records in QMS with assigned owners and due actions.
• Before: Investigations required stitching together PDFs and messages. After: Evidence packs were generated automatically with time-stamped plots, locations, and actions.
• Before: Carrier instructions varied by coordinator. After: Triage checklists and lane playbooks drove consistent interventions and documentation.
• Before: QA decisions were delayed waiting for context. After: QA reviewed standardized records with the data needed for timely, defensible release calls.

Key Takeaways

• Real-time monitoring only creates value when alerts route into existing TMS and QMS workflows with clear owners and next steps.
• Lane- and product-specific rules grounded in stability data reduce noise and focus teams on meaningful exceptions.
• A human-in-the-loop triage console ensures interventions are consistent, documented, and audit-ready.
• Unifying diverse sensor feeds behind a common event model avoids vendor lock-in and supports a multi-3PL network.
• Start with high-risk lanes, run in shadow mode, and tune thresholds with QA before enabling automated routing.

FAQ

What tools did this integrate with?
We ingested device data through Azure IoT Hub and integrated exceptions with the client’s transportation management system (e.g., Oracle Transportation Management) and electronic quality management system (e.g., Veeva Vault Quality). The approach also aligns with carrier and 3PL portals where needed via API.

How did you handle quality control and governance?
Rules were authored jointly by Logistics and QA, with approvals captured in the audit log. The triage console enforced checklists, reason codes, and electronic signatures at key decision points, supporting 21 CFR Part 11 practices. Evidence packs were versioned, and periodic reviews aligned rules with evolving stability data and packout SOPs. GDP expectations were used as a reference baseline (EU GDP Guidelines).

How did you roll this out without disruption?
We began in shadow mode on selected lanes, leaving existing logger workflows unchanged. Once thresholds and playbooks were tuned, we turned on exception routing with clear rollback paths. Each lane had a cutover plan, and 3PLs received concise instructions so interventions could be coordinated without changing contractual responsibilities.

How did you deal with different sensor vendors and calibration records?
Ingestion adapters normalized feeds to a common schema, so device diversity did not affect downstream workflows. Calibration certificates and device IDs were captured as metadata and attached to evidence packs. Where a vendor lacked an API, gateway exports were polled and transformed without manual handling.

What standards guided the process design?
We aligned exception handling and documentation with Good Distribution Practice expectations and electronic records requirements, and we considered lane certification goals such as IATA CEIV Pharma where relevant. Rules were derived from product stability data and packout validations provided by the client, ensuring scientific rationale behind thresholds and interventions.