Manufacturing Systems for Unexpected Events: An Exploratory Review for Operational and Disruption Risks
Bibliographic record
Abstract
Manufacturing systems are the core of manufacturing industries that have evolved to adapt to the ever-changing landscape. Nowadays, manufacturing systems are more frequently exposed to disturbances and risks that affect their everyday operations. Hence, it is imperative to analyze and design proactive and reactive strategies to respond to those disruptive events. While there has been an increasing interest in tackling risks from the supply chain perspective, an analysis of risks from the manufacturing system view has been neglected. Risks are broadly classified into operational risks and disruption risks. This study presents a literature review of different manufacturing paradigms and what operational risk considerations have been discussed for each stage of the manufacturing life cycle. The review further analyzes how manufacturing systems are trying to handle disruption risks. Additionally, the article maps the different manufacturing systems and the strategies implemented to the different adaptability facets. Finally, the article discusses managerial insights and possible research avenues.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".