Resilient Digital Supply Chain Twins Modelling: Simulation-based Analysis on the COVID-19 Pandemic Outbreak
Bibliographic record
Abstract
Over the past few years, Supply Chains (SC) have expanded rapidly in terms of dimensions and complexity (e.g., globalization, outsourcing, etc.). Besides, numerous practitioners and researchers proposed models mainly focused on minimizing SC’s total cost. Consequently, the potential financial advantages of reduced stock levels and inventory buffers have made SCs more vulnerable to local and global Low-Frequency High-Impact disruption risks which have long-term destructive effects. For instance, the COVID-19 pandemic outbreak has severely disturbed SCs, especially for essential products, by a sharp increase in demand and raw material supply failure. During this challenging situation, the focus should be shifted from cost minimization to SC’s survival, maximizing demand satisfaction, and minimizing delivery time. Consequently, these emerging issues have put forth the need for greater emphasis to develop resilient supply chains. \nThis study presents a methodological SC simulation modelling framework that enables visualizing the SC and making quick decisions by SC managers in near real-time to ensure resiliency during the disruption. The solution approach is applied as a case study in Luxxeen Co., a Canadian manufacturer of green disposable products, i.e. Toilet Tissues, which is considered an essential product. \nFirst, we develop SC’s structural and behavioral conceptual model by customizing the SCOR reference model. Afterwards, we translate it to Discrete Event Simulation formalist and implement it using the “Arena simulation software” platform. Next, we design three COVID-19 pandemic outbreak disruption scenarios in suppliers, transportation networks, and retailers. Finally, three risk mitigation strategies (i.e., Multiple Sourcing, Changing Inventory Control Policy and Buffering) are suggested to ensure SC resiliency in terms of reliability and responsiveness performance metrics. Moreover, by conducting a comparison analysis using “Process Analyzer” and “Optquest” between these scenarios, the best set of actions are proposed for each disruption scenario.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".