How technology can advance port operations and address supply chain disruptions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Supply chain disruptions continue to be a significant challenge as the world economy recovers from the pandemic-related shutdowns that have strained global supply chains. Shocks challenge the adaptability and resilience of maritime ports. The reaction of automated container terminals to supply chain disruptions has renewed interest, given the dramatic scenes of ships anchored for weeks. In this dissertation, I provide a vision of how technology can enhance a port’s ability to anticipate and handle shocks by improving coordination, cooperation, and information exchange across port stakeholders. The vision will be helpful for academics and practitioners to perform research that advances theory and practice on the use of advanced technologies to improve port operations. I use complex adaptive systems theory to develop a qualitative cross-case study of the ports of Los Angeles, Vancouver, and Rotterdam. I examine the effect that automation and other technologies have had on the efficiency of these ports, both in daily operations and during the disruption caused by the COVID-19 pandemic. Using critical tenets of complexity and with a rigorous application of the case study method, I develop theoretical propositions and practical insights to ground the vision of the port of the future based on current practices. The findings from the cross-case study suggest that automated terminals were more efficient during the pandemic than non-automated terminals. I propose that transitioning to higher levels of automation, supported by emerging technologies like blockchain and the internet of things, will make ports more resilient to supply chain disruptions when those systems are coordinated through Port Community Systems.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it