This article is part of the How Do YOU CM2? blog series in collaboration with the Institute for Process Excellence (IpX). Although I receive compensation for writing this series, I stand behind its content. I will continue to create and publish high-quality articles that I can fully endorse. Enjoy this new series, and please share your thoughts!
Engineering designed it one way. Manufacturing built it another. Field service maintains something entirely different. And nobody knows until a customer finds out the hard way.
This is the baseline gap problem. In practice, 40 to 80% of BOMs arrive at an EMS for manufacturing with problems before anyone even gets to the as-built stage. The discrepancies only compound from there.
Manually reconciling these baselines across thousands of items, serial numbers, and modification histories is not realistic. It is not even attempted at most organizations.
The use case: AI continuously compares the as-designed, as-built, and as-maintained baselines, surfacing discrepancies that humans cannot detect at scale. A component was redesigned in the as-designed baseline to address a safety issue, but it did not fit; a fix was applied in manufacturing, which was not reflected in the as-designed baseline. While in the field, the as-maintained baseline has not been updated for the systems that received the solution for the safety issue. AI identifies these gaps systematically, across every product, every serial number, every site.
Machine learning pipelines for multi-level BOM anomaly detection are already being developed in research. But anomaly detection without baseline structures is just noise.
What CM2 provides: Because all three baselines share the same as-planned/as-released structure, AI can compare them directly. Same format, same change traceability. As-built records are provided with evidence of conformance, waivers, and deviations. The as-maintained baseline provides visibility of planned changes and retrievable modification history.
The human role: AI surfaces the discrepancies. Humans determine the disposition. A gap between the as-designed and as-built baseline might be an accepted deviation, a pending change not yet effectuated, or an actual nonconformance. That judgment requires understanding the history, the intent, and the risk. AI cannot determine whether a field modification that was never formalized is a documentation gap or a safety concern. A configuration manager can.
The CM2 role: CM2 ensures that each baseline has a defined source of truth. Every baseline derives its content from change notices and their impact matrices. As-built records trace to work authorizations. As-maintained baselines include a modification history that is retrievable at any time. Because all three share the as-planned/as-released structure, AI can pinpoint not just that a discrepancy exists, but exactly where in the lifecycle the baselines diverged.
Without governed baselines, AI compares opinions. With CM2, AI compares records built on the same structure.
Do you actually know where your as-designed, as-built, and as-maintained baselines diverge? Or are you waiting for the field to tell you?