NAFTA supply chains: facilities location and logistics
Bibliographic record
Abstract
Abstract North American Free Trade Agreement (NAFTA), the free‐trade agreement between Canada, Mexico and the United States, has caused North American companies to consider inclusion of Mexico in their supply chain. The lower Mexican wages may offset the additional transportation costs; capital‐intensive operations are preferably still done in the United States or Canada. With a consumer base focused in the United States, can an organisation leverage the benefits of NAFTA to their individual advantage? This paper aims to show how, through a real‐world example, overall supply chain costs (total system costs of inventory, transportation and facilities) can be minimised under those circumstances. We formulate and solve a mixed‐integer programming model to find the optimal supply chain for Tectrol Inc., a manufacturer of power supplies. In the first case, components produced in Canada undergo final assembly in the United States, followed by distribution there. The second case is a ‘NAFTA’ supply chain: the Canadian components are converted to sub‐assemblies in Mexico, processed in finishing plants across the US border, then shipped through distribution centres to the final customer. Model solutions indicate in each instance where to locate finishing plants and distribution centres, and how many of each there should be. Results provide Tectrol (hence other manufacturers) some general guidelines on distribution and supply chain decisions in the NAFTA context.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".