[ AUTOMOTIVE ]

MENA Dealership Service-Centre Scheduler

After-sales service centres run the same play every day. Receive job cards. Decompose them into tasks. Assign tasks to bays and technicians under capacity, parts, and skill constraints. Push status updates to customers. We replaced the manual dispatch board with an agentic orchestration layer that reads each job card, decomposes it into tasks, runs a proprietary scheduling optimisation engine for the day's timesheet, auto-assigns work, and broadcasts customer-facing status updates as the day unfolds.

Client

A global automotive OEM operating dealership service centres across MENA

Timeline

PoC, multi-month

Role

Agentic orchestration over an operational workflow

Team

Pod: agent + optimisation

Year

2024

Industry

Automotive after-sales

01. The Challenge

Manual dispatch boards in busy service centres produce cascading delays. Bays sit idle while job cards queue. Technicians switch context. Customers chase updates because nobody owns the inflight view.

A scheduler that survives the floor needs to respect a long tail of constraints: bay capacity, technician skill, parts availability, and the realities of a working day. Off-the-shelf optimisers typically need handholding to encode these well.

02. The Solution

Built an orchestrator agent that ingests each job card, decomposes it into tasks, and routes the request to a proprietary scheduling optimisation engine. The optimiser returns the day's timesheet under the full constraint set; the agent assigns tasks back to the operational system and triggers customer notifications.

Packaged the optimiser as a containerised service on Google Cloud Run. The agent runtime calls it as a tool, keeping the orchestration layer thin and the optimisation logic isolated and independently versionable.