Applying Supply Chain Logistics Technology to Improve International Border Crossing
Bibliographic record
Abstract
Government agencies in North America are in the process to increase efficiency through the facilitation of international trade. North America Freight Trade Agreement (NAFTA) is a step in the right direction by the elimination of tariffs on goods originating in member countries. The current cross border freight movement has increased drastically that create significantly congestion around and near the North America Border area. There is an eminent need to increase the efficiency of the flows of these goods between the member nations. The major objectives are to eliminate barriers to trade in, and facilitate the cross-border movement of, goods and services between the territories of the interested parties, promote fair competition in the free trade area, and Increase substantially investment opportunities. Supply Chains Management (SCM) has become the businesses lifeline in the global market. The major components include (1) integrated information network, (2) logistic network that connecting all origin of supply/demand, and all trading partners, and (3) integrated inventory control and material flow that spans multiple organizations and operations. The system must work together to avoid waste, carry minimum amounts of inventory, and provide customer competitive satisfaction in the shortest cycle time. The impacts of well-organized supply chains can be quite dramatic. For example, in US, the logistics costs as a percentage of GDP declined from approximately 17% in 1980 to just over 10% in 1993 that mirrored the inflation decline over the same period. In a similar time frame (1983 to 1997), the value of inventory as a percentage of GDP fell from 24% to less than 17%. This paper describes the Supply Chain Management (SCM) analysis being made to improve cross border freight movements among Canada, Mexico, and United States. The process can save time, reduce distribution cost of freight, and enhance customer services. This work will result in a win-win situation for Customs Service, customs brokers manufacturers, customers, and shippers alike. Increased efficiency in the supply chain and information flows will result in reduced processing time and waiting time for freight movements, foster seamless border crossing freight movements, reduce distribution costs, and lower product costs for end customer at the global scale. For the covering abstract see ITRD E134653.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".