Restructuring Global Supply Chains: Navigating Challenges of the COVID-19 Pandemic and Beyond
Bibliographic record
Abstract
Problem definition: The COVID-19 pandemic imposed unprecedented stresses on global supply chains (GSCs), compelling companies to reassess their supply chain structures and strategies. This crisis has also heightened awareness among businesses, consumers, and policymakers about the critical importance and far-reaching implications of GSC design and management. This unique moment presents a generational opportunity for Operations Management (OM) researchers to document and understand the ongoing restructuring of GSCs. Methodology/results: By analyzing microlevel data on U.S. customs import shipments (2019–2021), we uncover shifts in GSC strategies during the COVID-19 pandemic. Firms diversified suppliers within existing sourcing locations and reallocated volumes among them. Whereas dependence on China decreased, imports from other Asian nations like India and Vietnam, as well as North American countries like Canada and Mexico, increased. Industry-specific differences were pronounced, and a notable shift toward lower-frequency, higher-quantity shipments was also observed. Managerial implications: Beyond the challenges of COVID-19, recent years have witnessed other major supply chain disruptions, due to causes such as geopolitical tensions, natural disasters, and port worker strikes. We offer actionable insights for executives designing supply chain strategies to prepare for similar disruptions as they increase in frequency and severity. We identify future research avenues aimed at enhancing the resilience and adaptability of GSCs in a continuously evolving environment. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2024.0879 .
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.
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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.002 |
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".