Members: Gjarred Baker, Ryan Anderson, Stephen Dennis, Draper Sturdivant, Nicole Halper
Problem: Warehouse shift changes are high-risk moments for miscommunication. Critical updates—such as machinery malfunctions, task progress, staffing changes, and safety incidents—are often relayed verbally or in unstructured notes. This leads to lost tribal knowledge, repeated mistakes, and unsafe working conditions.
Our project, ShiftCast, solves this by using Microsoft Azure Foundry to orchestrate a multi-agent system that captures, processes, and delivers key operational knowledge at shift handover. It converts spoken updates into structured data, flags safety risks, and automatically generates a 2-minute audio “shift podcast” for the incoming team. This ensures continuity, accountability, and safety—without relying on memory or manual reporting.
Solution: Our solution, ShiftCast, is a multi-agent AI system built with Microsoft Azure Foundry that automates and standardizes shift handovers in warehouse operations. It addresses the communication gap between outgoing and incoming teams by capturing unstructured verbal updates and transforming them into structured, actionable summaries.
The system tracks and processes updates across four key operational domains:
Inventory Management – Tracks order volume spikes, low stock alerts, and restocking priorities.
Equipment Repair Status – Monitors machine faults, repair progress, and outstanding maintenance tickets.
Employee Attendance & Task Delegation – Logs call-outs (e.g., illness, PTO) and dynamically reassigns critical tasks.
Shift Checklist Completion – Captures daily operational checklists (e.g., zone cleanups, order packing status) as structured progress indicators.
The system is orchestrated through four specialized agents:
Outgoing-Shift-Agent: Captures voice notes using Azure Speech-to-Text, parses them into structured data (inventory, repair, HR, checklist items).
Machine-Agent: Cross-references equipment-related data to validate and enrich repair updates (via simulated or live CMMS/WMS integration).
Safety-Auditor-Agent: Performs NLP-based safety audits by scanning notes for high-risk keywords (e.g., “jam,” “leak,” “injury”) and flags near-miss events.
Incoming-Shift-Agent: Aggregates all data, generates a summarized task handover, and creates a scripted 2-minute audio podcast using Azure Text-to-Speech.
This agent collaboration ensures accurate, timely communication across operational systems—reducing human error, improving traceability, and boosting shift readiness.
