A production bounce-back approach in the Cloud manufacturing network: case study of COVID-19 pandemic
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Industry 4.0 paradigm has enabled manufacturing systems with reformations for Cloud-based manufacturing business models. This reformation can create resilient structures as an inevitable opportunity for manufacturing supply networks. This is achieved by using service composition capabilities in Cloud manufacturing network which significantly enhances supply network performance when encountering disruptions. Focusing on redundancy as one of the most effective approaches to resiliency, a new model for manufacturing service composition is proposed. The model considers a minimum level of subentropy when responding to the demands at the process level while controlling the entropy overall at supply network level. This creates a balanced policy for entropy at the network level, and subentropies at the process level to both fulfill an optimal redundancy for disruption fulfillment and controling the complexity throughout the network. A case study is considered for manufacturing ventilator production COVID-19 pandemic. The capabilities of the proposed model for optimal application of unused firm capacities from other supply networks like military and university research groups have been discussed. The proposed model is also investigated for fulfillment of disruptions like COVID-19 equipment supply network with mentioned capabilities. These capabilities fulfill the transition of manufacturing business models to a service-oriented paradigm with resilient structures.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it