Digital Supply Chain Management: A Post-COVID-19 Perspective
Bibliographic record
Abstract
Abstract This chapter provides an in-depth look at how digital supply chain management (DSCM) can revolutionize supply chains in the post-COVID world. The COVID-19 pandemic exposed the vulnerabilities of traditional supply chains, highlighting the need for resilience and adaptability. The chapter begins by examining these COVID-induced disruptions, setting the foundation for the discussion on DSCM. DSCM, leveraging advanced technologies and data insights, offers a solution to these challenges, promoting agility, transparency, and sustainability in supply chain operations. This represents a significant shift from traditional practices, equipping organizations to cope with the dynamic postpandemic environment. Key capabilities of DSCM, such as resilience, integration, agility, and risk management, are discussed, supported by real-world examples from leading companies. These examples showcase the successful implementation of DSCM and its benefits in navigating the complexities of modern supply chains. However, the adoption of DSCM is not without challenges, including cybersecurity risks and integration difficulties. The chapter suggests strategies to overcome these challenges, emphasizing the importance of technology, collaboration, sustainability, and data-driven decision-making. By embracing these strategies, organizations can effectively manage their supply chains in the evolving global market, leveraging DSCM to withstand future uncertainties.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".