Digitalization strategies and evaluation of maritime container supply chains
Bibliographic record
Abstract
Purpose This study proposes practical digitalization strategies and well-grounded evaluation criteria for maritime container supply chains. Design/methodology/approach The authors identified the status of supply chain digitalization of the Port of Busan in South Korea and developed three digitalization strategies based on industry requirements and consultations with port experts. The authors proposed 11 evaluation criteria for examining the main digitalization strategies in the supply chain operations reference model, based on a survey among 46 experts and used multi-criteria decision-making approaches to prioritize the strategies and evaluation criteria. Findings The results delineate the status of the digitalization of a real-world port-focal supply chain. The model can be successfully customized to include well-grounded evaluation criteria for digitalization strategies, and presents a practical way to advance the supply chain digitalization strategies. Based on the survey and evaluation, the authors find that increasing data accessibility and improving quality are preferred to adopting a data and information sharing platform. Research limitations/implications As the study is limited to the Port of Busan, future case studies could be undertaken to container supply chains driven by different regional ports. Practical implications Stakeholders, such as truckers, terminal operators, and shipping liners, might consider the proposed strategies and evaluation criteria when digitalizing their supply chains. Originality/value By identifying the needs and specifications of maritime container supply chain digitalization strategies, developing evaluation criteria, and conducting a case study for proof of concept, the study proposes an operational management process with practical, real-world benefits for port-focal supply chains.
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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 | 0.020 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".