Rehearsal: AI Factory Recovery Planner

A machine stops, but the orders still need to ship. A production planner has to decide what to move, which deliveries may slip, and whether overtime is worth considering. Rehearsal helps them see those choices before changing the shift. The planner enters a supervisor's message in everyday language. DeepSeek V4 Flash, served through AMD Token Factory, proposes planning changes linked to the original words. The application checks machine names, times and conflicts, and asks for human review when required information is unclear. OR-Tools generates recovery alternatives on CPU. A separate schedule checker verifies each returned plan. The planner can then rehearse a plan across 500 simulated scenarios, inspect exposed delivery promises, and open a specific failure to see where delays build up. Before exporting, the planner acknowledges the required tradeoffs. The server rechecks its stored plan and produces a dispatch CSV and a decision record containing the constraints, approvals, AMD trace and simulation evidence. Our intended users are production planners in machine shops. The working application is deployed on AWS, with public code and reproducible evaluation records. All factory data and uncertainty assumptions are synthetic; customer time savings and real-world reliability have not been established. AMD powers message interpretation; scheduling, checking and NumPy simulation run on CPU. Built by Muskan Verma and Abhishek Sharma, Team Forge Ahead, for the Intelligent Industry track.