Probabilistic modeling of business interruption and reputational losses for process facilities
Bibliographic record
Abstract
This article presents probabilistic models to estimate business losses due to abnormal situations in process facilities. The main elements of business loss are identified as business interruption loss and reputational loss. The business interruption insurance approach is used to model business interruption loss. The subelements of business interruption loss are modeled based on expert knowledge using Program Evaluation Review Technique, which are then integrated using the Monte Carlo simulation approach. The reputational loss is considered as Weibull distributed, and the parameters are estimated by applying a scenario‐based approach. Copula functions are then used to develop the distribution of the aggregate loss, considering the correlation between business interruption and reputational losses. The application of the loss models is demonstrated using a distillation column case study. The models presented here provide a mechanism to monitor process facility's business performance, with associated uncertainties, and to make swift operational and safety decisions. This will help to improve process facilities safety performance and optimal allocation of resources where they are needed the most. © 2015 American Institute of Chemical Engineers Process Saf Prog 34: 373–382, 2015
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".