Automated Refurbishment Work Order Creation and Routing Workflow

Manual work order creation and technician assignment are major bottlenecks, often taking 24-48 hours. A custom workflow ingests inspection data (e.g., from a Computer Vision agent) and uses an LLM orchestrator (like LangGraph) to instantly generate a structured work order with required parts, tools, and SOP references. This eliminates administrative lag, allowing repair operations to begin immediately upon receiving a unit.

Inefficient routing based on manual dispatch or simple queues leads to skill mismatches and repeat visits. The workflow integrates with your CMMS (like SAP PM, Fiix, or UpKeep) and HR systems to match fault types against technician certifications, location, and current workload. This intelligent assignment ensures the right person gets the job, improving fix quality and reducing rework.

Manual parts sourcing leads to over-ordering, stockouts, and costly expedited shipping. The workflow's Parts Sourcing Agent checks real-time availability across internal warehouses, approved vendor catalogs (via API), and marketplaces. It automatically reserves or orders the optimal parts based on cost and lead time, updating the work order and inventory systems (e.g., Oracle, NetSuite) in a single transaction.

Inconsistent repair costing and poor visibility into refurbishment economics erode margins. The workflow embeds a Costing Engine that calculates estimated labor (based on standard times) and parts cost for each work order. This real-time cost data, combined with dynamic pricing for the refurbished item, provides immediate visibility into unit profitability and supports better channel decisions (resell vs. liquidate).

Ad-hoc processes and paper trails create compliance and scaling risks. The custom workflow enforces a structured, digital process where every decision—from fault diagnosis to part selection to assignment—is logged with rationale. This creates a defensible audit trail for quality control, warranty claims, and partner billing. The orchestration layer (e.g., using CrewAI for multi-agent coordination) allows you to scale operations across multiple facilities or external repair partners without process degradation.

The architecture is designed for rapid, low-risk implementation. A pilot can target a single product category or facility. The workflow uses API-first design to integrate with your existing CMMS and inventory systems, with human-in-the-loop approval gates for the first 100 work orders to validate logic. This staged rollout de-risks the build and delivers measurable ROI within one quarter.

This agent ingests data from computer vision inspection systems, IoT diagnostic tools (for electronics), and historical repair logs. It uses multimodal reasoning to classify the defect, determine the root cause, and generate a preliminary bill of materials (BOM) for required parts. It's the critical first step that automates the manual technician assessment, turning raw sensor data into a structured repair plan.

Once a BOM is generated, this component queries integrated inventory systems (ERP, WMS), supplier APIs, and internal stock levels in real-time. It evaluates lead times, costs, and supplier ratings to source parts optimally. For unavailable items, it can trigger automated purchase orders or suggest suitable substitutes, eliminating the manual calls and spreadsheet checks that delay repair cycles.

This engine integrates with workforce management or CMMS systems to maintain a dynamic registry of technician certifications, current workload, location, and historical performance on similar repairs. It uses constraint optimization to match the work order's complexity and required parts availability with the best-suited, soonest-available technician, maximizing throughput and first-time fix rates.

This is the system-of-record bridge. It takes the fully defined repair plan (diagnosis, BOM, assigned tech, estimated duration) and formats it into a structured work order, pushing it directly into the CMMS (e.g., ServiceNow, IBM Maximo, Fiix). It also establishes two-way sync for status updates, parts consumption, and completion notes, ensuring the CMMS remains the single source of truth without manual data entry.

Not every case can be fully automated. This component monitors for low-confidence diagnoses, parts sourcing failures, or scheduling conflicts. It routes these exceptions to a human-in-the-loop queue in a tool like Jira or a custom dashboard, attaching all relevant context (images, diagnostic logs, sourcing results). This ensures the workflow degrades gracefully while maintaining SLA adherence and a clear audit trail.

This observability layer tracks key metrics: work order creation time, repair cycle time, first-time fix rate, and parts cost variance. It correlates outcomes with initial diagnoses and technician assignments, generating insights to retrain the diagnosis agent or adjust assignment logic. This closed-loop system is what allows the workflow to improve continuously and demonstrate measurable ROI.