Digital Replication of Product Flow in a Serial Production Line
Bibliographic record
Abstract
In the wake of the Covid-19 pandemic, North American Electronics Manufacturing Service (EMS) providers hope to “re-shore” potential business from companies currently reliant on offshore manufacturing. To capitalize on this opportunity, EMS providers must maximize efficiency to offer competitive rates. The Surface Mount Technology (SMT) assembly line is one area where increased efficiency can significantly impact plant capacity. SMT machines generate data describing their operating conditions, which can be leveraged to improve the efficiency of the production process. Discrete Event Simulation (DES) has been used across many industries to model complex systems and determine optimal operating parameters. In this research, a framework is proposed to evaluate the impact of “what-if” scenarios by digitally replicating the flow of product on an SMT assembly line at a Canadian EMS provider. The framework includes a flexible data management system for collecting, processing, and storing production data from multiple sources, a technique for addressing gaps in machine data using work in process (WIP) information, and a novel approach to DES where historical data is input directly to the model. The framework was applied to two industrial applications where the first saw potential cost savings of $270,000, and the second saw a projected throughput gain of 7%.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".