Inbound Damages Process Modernization

Role: UX & AI Research Lead | Client: Global Logistics Network

Brief:
Developed and deployed an AI-powered damage detection and reporting workflow for inbound shipments, replacing manual paper-based and delayed-reporting methods. The system uses computer vision and anomaly detection algorithms to identify, log, and escalate potential damages in real-time—improving operational speed, accuracy, and compliance.

Key Objectives

• Reduce reporting latency and human error in damage documentation.

• Leverage computer vision to automate damage recognition and severity classification.

• Ensure compliance with internal quality assurance standards and customer SLAs.

• Integrate governance protocols for data labeling accuracy, auditability, and bias mitigation.

Process & Responsibilities

• Conducted multi-site workflow analysis to document current-state damage reporting methods.

• Partnered with data scientists to train and validate AI damage detection models using historical image datasets.

• Developed a role-based digital reporting tool that integrates with the AI model for real-time damage alerts.

• Applied AI governance practices by implementing confidence scoring thresholds, bias reviews, and audit trails for all model decisions.

• Led usability testing to optimize dashboard interface for dock workers, supervisors, and quality managers.

• Facilitated change management and training for operational staff.

Outcome

• Reduced reporting time from hours to under 2 minutes per incident.

• Increased accuracy of initial damage classification by 88% compared to manual reports.

• Delivered a governance-compliant AI pipeline with explainability features and audit logs.

• Improved SLA compliance and customer trust through consistent, verifiable reporting.