A dual-layer BOM change control model for efficiency improvement in ETO manufacturing
Bibliographic record
Abstract
To address the frequent changes, dynamic evolution, and complex collaboration of BOM (Bill of Materials) under ETO (Engineer-to-Order) mode, this paper proposes a dual-layer BOM-based change control model. First, to enable model definition and change expression throughout the product lifecycle, a version control-based BOM model is defined by introducing material revision, material relationship links, and a multi-view mechanism, while also constructing a general BOM structure system. Then, to ensure traceability of product structural changes and cross-view consistency in the ETO mode, we design a dual-layer change traceability model. This model features vertical version chains and horizontal view collaboration traceability as its core components. Finally, an ETO-oriented BOM change operation model is constructed to standardize both in-view change operations and cross-view cooperative operations. This standardization enhances change control capability and lifecycle traceability efficiency of product structures in ETO manufacturing environments. The application of this model in a large equipment manufacturing enterprise shows that it significantly improves the change response efficiency and provides strong support for the digital transformation and supply chain collaboration of ETO enterprises.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".