Yes, AOG risk mapping can be applied to both OEM and MRO operations, but it does not look the same in each environment and it does not eliminate AOG events. In OEM contexts it is mainly a design, initial provisioning, and global supply-chain tool, while in MRO it is more tightly coupled to shop scheduling, parts availability, and turnaround-time commitments. The underlying concepts transfer, but the data structures, time horizons, and decision points differ enough that a single, generic template usually fails in practice. Both uses also depend heavily on data quality, integration with existing systems, and disciplined change control. In regulated environments, AOG risk mapping is decision support, not a guarantee of service levels, compliance, or audit outcomes.
For OEMs, AOG risk mapping is typically anchored in design, reliability, and spares provisioning rather than day‑to‑day maintenance events. The focus is on which part families and configurations are most likely to create AOG exposure once the fleet is in service, based on criticality, lead times, repair capacity, and obsolescence risk. This usually requires integrating engineering data, reliability predictions, approved supplier lists, and global stocking strategies across multiple ERPs and PLM systems. The useful output is not just a “high‑risk part list,” but design and provisioning decisions: alternate part options, dual‑sourcing, recommended initial provisioning, and repair network strategy. Because OEM product lifecycles are long, the mapping must be maintained under change control; new revisions, service bulletins, and supplier changes all alter the AOG risk profile and must be traceable.
In MRO environments, AOG risk mapping is operationally closer to the point of impact: which components and workscopes most often lead to AOG situations or extended ground times. The emphasis is on turnaround time, shop capacity, parts availability, and the variability of findings during disassembly and inspection. MROs typically combine historical work package data, unplanned findings, vendor repair lead times, and local inventory performance to identify steps where AOG exposure spikes. This mapping often needs to reflect customer‑specific contracts, different aircraft configurations, and regulatory approvals for repair alternatives or DER solutions. The actionable outcome is usually targeted: pre‑positioning specific parts, adding alternate repair vendors, adjusting work instructions, or re‑sequencing work to protect AOG‑sensitive path steps. As with OEMs, the mapping is only as credible as the underlying data and the rigor of how new findings and changes are incorporated.
While the method can be shared, the risk drivers and time horizons differ enough that one model rarely serves both OEM and MRO without tailoring. OEMs typically work with longer lead times, global demand uncertainty, and configuration diversity, so models are more strategic and aggregated. MROs work on much shorter horizons, constrained by shop schedules, specific tail numbers, and committed delivery dates, so they need more granular, real‑time‑capable views. OEMs usually have better control over design and approved suppliers, while MROs have to work within customer‑specified configurations and certificates, limiting some mitigation options. These differences mean that data sources, integration points, and governance structures are not interchangeable, even if both parties call it “AOG risk mapping.” Trying to force a single, shared template or tool across OEM and MRO operations often leads to oversimplification that nobody trusts.
In both OEM and MRO settings, AOG risk mapping depends heavily on pulling consistent data out of legacy systems that were not designed for this purpose. Typical sources include ERP, MRP, MES, maintenance records, reliability databases, and supplier performance logs, many of which exist in separate instances or on-premise systems with limited APIs. In aerospace‑grade environments, replacing these systems just to improve AOG analytics is rarely realistic due to validation burden, downtime risk, and integration complexity with certified equipment and processes. Instead, most organizations layer AOG risk analytics on top, using data warehouses, reporting layers, or point‑to‑point integrations, accepting that some data will remain incomplete or delayed. These integration compromises must be made explicit in the risk maps themselves (e.g., flags for low‑confidence data) so that operators and planners understand the limits of what they are seeing. Without this transparency, decision‑makers will either overtrust the maps or ignore them entirely.
AOG risk mapping improves visibility and prioritization; it does not prevent all AOG events or guarantee on‑time performance. Models can be biased by historical data that reflect past contracts, fleets, or suppliers, and may not adapt quickly when the business mix or supply base changes. Any algorithmic or scoring logic used for AOG risk must go through appropriate validation, configuration control, and documentation, especially if it influences planning, stocking, or work sequencing in regulated environments. Over‑focusing on high‑scored AOG risks can pull attention and inventory away from lower‑scored areas that still have significant operational or safety impact, so mitigation strategies need periodic review. OEM and MRO organizations should treat AOG risk mapping as a living, documented tool within the broader quality and operations management system, with clear ownership, review cycles, and traceable change history.
In many programs, OEMs and MROs both attempt AOG risk mapping but from different angles and with different data, leading to conflicting conclusions. A more realistic approach is to keep separate OEM and MRO models, then define a limited set of shared indicators or part families where alignment really matters. For example, both parties can agree on a critical component list, shared lead time assumptions, and a standard way of flagging AOG‑relevant events, even if their internal models differ. This respects brownfield realities—different systems, contracts, and regulatory approvals—while still allowing meaningful dialogue about AOG risk across the value chain. Attempting to impose a unified, end‑to‑end system across both OEM and MRO environments often stalls on integration and validation costs; a federated but aligned approach tends to be more achievable. Over time, this coordination can be expanded, but only as systems, data pipelines, and governance mature enough to support it reliably.
Whether you're managing 1 site or 100, Connect 981 adapts to your environment and scales with your needs—without the complexity of traditional systems.
Whether you're managing 1 site or 100, C-981 adapts to your environment and scales with your needs—without the complexity of traditional systems.
