AI-Driven Quantitative Models for Improving Bill-of-Materials Accuracy in Medical Device Product Lifecycle Management

Abstract

Medical device manufacturers continue to lose engineering hours to a bill-of-materials (BOM) that behaves as a static ledger rather than a governed source of truth, and the consequence is measurable: 87% of engineering leaders in one industry-wide survey report needing hours or days to retrieve the data required to justify a single design decision [1]. This article argues that the fix is not another integration layer but a change in what the BOM mathematically is — from a tabular record to a directed graph that machine learning models can traverse, compare, and reconcile. Drawing on graph-theoretic representations, phylogenetic tree reconciliation, spectral and hierarchical graph neural networks, Temporal Production Graphs, and the Orthogonal Procrustes alignment framework, the analysis works through the mathematical formulations that let a PLM platform detect labeling mismatches, topological discrepancies, and combinatorial drift between an engineering BOM and its manufacturing counterpart before those errors reach the production floor. The discussion extends the model to unsupervised anomaly detection, intelligent document processing for legacy drawing sheets, and the semantic infrastructure needed to synchronize CAD, PLM, ERP, and MES systems along a seven-level maturity spectrum. Because medical devices carry regulatory exposure that most manufacturing sectors do not, the article also positions these quantitative models against the FDA's shift from Computer Software Validation to Computer Software Assurance, the GAMP 5 Appendix D11 lifecycle for machine-learning validation, the Predetermined Change Control Plan framework, and the EU's draft Annex 22 restrictions on adaptive algorithms in GxP manufacturing. The contribution is a unified quantitative architecture — graph representation, learned reconciliation, and regulatory-aware validation — that reframes BOM accuracy as a computable, auditable property of the digital thread rather than a downstream quality metric.