Evolution of Resilience Strategies Synergies in Supply Chains: Leveraging Disruptive Technologies Integration
Bibliographic record
Abstract
Supply chains face numerous vulnerabilities, accentuated by pandemics and geopolitical tensions, necessitating robust resilience strategies to enhance adaptability and reactivity. This research explores the integration of emerging technologies, such as blockchain IoT-based traceability systems, to explore the dynamic interplay among three widely used resilience strategies: collaboration, flexibility, and redundancy. Grounded in Complex Adaptive Systems (CAS) and Synergy theories, the study employs a Design Science Research framework to develop an artefact based on a hybrid simulation methodology informed by real data from a seafood supply chain. This approach uncovers how blockchain integration enhances supply chain performance and reveals the rebalanced synergistic and antagonistic effects of strategy combinations. This work contributes to the theory by investigating the interaction of resilience strategies in technology-driven supply chains. Moreover, it provides practitioners with guidelines and decision-making support to align resilience strategies with blockchain capabilities, thereby enhancing operational adaptability.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".