Unnamed OEM (client identity not disclosed)

For electronics OEMs, returned goods represent one of the highest-risk inventory categories in the supply chain. Without intelligent orchestration, returned units sit in limbo — awaiting manual grading, routing decisions, and disposition approvals that slow recovery and erode asset value. This OEM's existing Warehouse Management System lacked any native returns intelligence layer, requiring operations staff to manually classify returned goods by condition and determine repair, refurbish, resell, or scrap pathways on a unit-by-unit basis. In a sector where product lifecycles are short and component value depreciates rapidly, delays in returns processing directly translate to lost recovery opportunity. The absence of real-time routing logic and workflow automation left significant recoverable value unrealized with every returns cycle.

G2RL deployed its Returns Management System (RMS) — built on a machine learning and predictive analytics engine — directly on top of the client's existing WMS infrastructure, completing integration within six months. Rather than replacing the incumbent platform, G2RL's approach layered intelligent orchestration over existing systems via API integration, preserving prior IT investments while adding capabilities the WMS could not provide natively. The core of the deployment was G2RL's DecisionAI engine, which performs real-time disposition routing by evaluating each returned unit against condition grade, SKU classification, and return type to determine the optimal recovery pathway. Operational teams gained access to drag-and-drop rule creation for no-code workflow configuration, enabling continuous refinement without engineering involvement. A Control Tower dashboard provided visibility into returns trends, exception handling, and performance metrics — giving management a single pane of glass across the returns lifecycle.

Within six months of go-live, the OEM recovered $500K+ in returns value — a direct result of faster, more accurate disposition decisions that maximized resale and recovery rates across returned inventory. The no-code workflow environment enabled operations staff to adapt routing rules independently, reducing reliance on IT for process changes and accelerating continuous improvement cycles. Qualitative outcomes included measurable gains in staff adoption, driven by guided step-by-step workflows for receiving, grading, and dispositioning that reduced training burden and decision variability on the warehouse floor.

• $500K+ in returns recovery value within the first six months
• Six-month deployment timeline from integration kickoff to full production
• Zero code required for ongoing workflow adaptation by operations teams

• Overlaying an intelligent returns orchestration layer on an existing WMS — rather than replacing it — can unlock substantial recovery value with lower implementation risk and shorter deployment timelines.
• AI-powered dispositioning that evaluates condition, SKU, and return type in real time is a primary lever for closing the gap between actual and potential recovery value in electronics reverse logistics.
• No-code workflow tooling is not just a convenience feature — it determines whether operations teams can sustain and improve the system without ongoing engineering support.
• Six-month deployments are achievable when the solution integrates via existing WMS APIs rather than requiring a platform migration.
• Control Tower visibility is essential for identifying returns patterns and exceptions at scale, enabling data-driven process improvements over time.