AI-Powered Blast Cell Operations Dashboard – Scaling Human-in-the-Loop Cold Storage Management
Organization:
Lineage Logistics – World’s Largest AI-Led Food Storage & Logistics Company
Overview:
Led a cross-functional initiative to design and deploy an AI-integrated dashboard for monitoring blast cells in cold storage facilities. The solution replaced manual whiteboard tracking with a real-time, data-governed interface, reducing operational delays, minimizing errors, and improving throughput by 90% across pilot sites.
The Challenge:
Blast cell operations were tracked manually via whiteboards, creating bottlenecks in loading/unloading and increasing susceptibility to human error. AI sensors were already in place to monitor temperature and capacity, but the data was not being leveraged in an actionable or auditable way.
- Product Goal: Enable real-time, trustworthy decision-making for blast cell operations by transforming existing AI sensor data into an auditable, supervisor-approved operational interface.
- Primary Users: Cold storage supervisors and operations managers responsible for throughput, safety, and compliance.
- Business Constraints: High-volume, time-sensitive operations; regulatory and food safety compliance; variable site-level workflows; limited tolerance for automation errors.
- Success Metrics: Operational throughput, error reduction, supervisor adoption rate, and auditability of AI-driven decisions.
Product Discovery & Delivery Approach
Stakeholder Engagement
Partnered with AI engineers, data scientists, and operations managers to align product functionality with algorithmic capabilities and compliance requirements.
Research & Insights
Conducted field studies, user interviews, and multiple rounds of usability testing to understand workflows, identify inefficiencies, and gather site-specific requirements.
Key Product Decisions & Tradeoffs
Human-in-the-Loop & Automation Tradeoff
Integrated live AI data feeds into the dashboard with built-in override functionality, enabling supervisors to validate, correct, and annotate AI recommendations.
Chose a human-in-the-loop design over fully automated decisioning to ensure safety, trust, and regulatory defensibility in a high-risk operational environment.
Governance & Compliance
Defined and designed workflows ensuring all AI-driven inputs were timestamped, auditable, and compliant with operational governance standards.
Change Management
Developed SWI (Standard Work Instructions) and led onboarding/training sessions to drive adoption and ensure long-term scalability.
Explainability Over Optimization: Prioritized transparent AI outputs and supervisor annotations over more aggressive optimization models to support accountability and adoption.
Scalability vs. Site Customization: Designed a modular dashboard architecture that allowed site-level configuration while maintaining standardized governance controls.
Build vs. Buy Considerations: Leveraged existing AI sensor infrastructure rather than introducing new tooling to reduce integration risk and accelerate deployment.
Outcome:
Drove a 90% improvement in blast operation throughput, measured through reduced loading delays and improved supervisor response time.
Reduced errors via automated anomaly alerts for unexpected temperature changes or capacity mismatches.
Defined and operationalized a repeatable AI governance process enabling audit-ready validation of AI recommendations across sites.
Created a scalable dashboard architecture adaptable to other AI-powered warehouse operations.
Key Skills Applied
AI Product Management & Human-in-the-Loop Workflow Design
UX Research & Usability Testing
AI Data Governance & Compliance Alignment
Cross-Functional Stakeholder Management
Change Management & Operational Scaling
