Supply chain emerging aspects and future directions in the age of COVID-19: A systematic review
Bibliographic record
Abstract
Not only has the COVID-19 outbreak brought about public safety challenges, but there has also been a major disruption in the business world that impacts one and all from small to large businesses. During This pandemic, supply chains (SCs) have witnessed disruptions, and this has inspired the interest of this paper. Therefore, the objective of the paper is to address two research questions pertaining to exploring the emerging SC aspects in the age of COVID-19 and future directions of SCs. To achieve this objective, a methodology is developed entailing three steps as follows. First, data is collected and included documents are identified through PRISMA strategy. Second, document analytics is performed using the web-interface of bibliometrix package in R software,the shiny app. Third, the research questions are addressed accordingly. The results showed that the most prominent terms related to SCs include supply chain disruptions, supply chain management,supply chain resilience, viability, and flexibility. Consequently, the first research question is approached in which the aspects of SC disruptions, resilient SC, viable SC,Sustainable SC, and SC management, are addressed. With more focus on building resilient SC in the short-term to recover from disruptions, viable SC can be created in the long-term perspective, which eventually build sustainable SC accordingly. Subsequently, considering these aspects enable successful SC management. Additionally, the future directions are explored including the transformation from globalization to regionalization perspective, focus on digitalization, need for holding more inventory, managing SCs for high resilience, more dependence on operations research and business analytics, and reconsideration of food SCs. This paper contributes to the body of knowledge by providing insightful research agenda to scholars and practitioners concerned in exploring more of the influences of the current pandemic on SCs.
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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 | 0.010 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.014 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".