Resilience and Responsiveness in Logistics Industry during Disruptive Events: A Case Study on the Impact of the Coronavirus Pandemic
Bibliographic record
Abstract
Understanding operational resilience during disruptive events is critical in the dynamic global logistics field. This qualitative study explores the challenges faced by a logistics company during the COVID-19 pandemic based on surveys and interviews with twelve logistics management experts. A thematic analysis was used to identify recurring themes regarding logistics disruptions and response strategies. The data revealed internal disruptions such as delays in pickup or delivery, inaccurate delivery information, and communication challenges with drivers. External disruptions include supply-demand imbalances, freight rate volatility, port congestion, and unexpected supplier shutdowns. Strategies to enhance logistics resilience are discussed, emphasizing strategic decision-making, robust leadership, digitalization for improved communication and supply chain visibility, and agility in adapting to change. These findings provide a thorough understanding of logistics disruptions and offer practical recommendations for professionals to navigate challenges and strengthen their logistics operations.
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How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".