A High-Level Risk Management Framework as Part of an Overall Asset Management Process for the Assessment of Industry 4.0 and Its Corollary Industry 5.0 Related New Emerging Technological Risks in Socio-Technical Systems
Bibliographic record
Abstract
Suffice to say that long-established businesses have their own challenges. Furthermore, accurate systematic methods and tools for managing risks in the context of industry 4.0 are lacking or less efficient, spreading unrealistic awareness of risk (or situational awareness) in various domains where risk management is needed. Conventional methods have their own limits and might not identify all aspects that influence system safety. Once traditional industry challenges are combined with emerging risks along with new systemic and organizational risks as well as cognitive and motivational biases in human logic, there will be the necessity of building thorough Asset Management and Decision Support approaches accounting both for conventional and emerging risk safety management. Hence, innovative, and efficient approaches that can investigate issues from a broad systemic perspective to support asset management practitioners to deal with those threats associated with the complexity of socio-technical systems are of interest. On these grounds, this paper focuses on identifying and analyzing components of risk management approaches especially for new emerging safety risks within industry 4.0 (emerging technology-related risks), as well as the rising of extreme, rare, and disruptive events, at a time of continued uncertainty in the global economy, in conjunction with the highly insecure political situation caused by recent armed conflicts (for e.g., Russia vs Ukraine), and the coronavirus disease pandemic (COVID-19) that might create fatal disturbance of the performance of organizations. We opt for the relatively new methods that have been developed based on system theories, viz. the Functional Resonance Analysis Method (FRAM), the System-Theoretic Accident Model and Processes (STAMP, System Theoretic Process Analysis (STPA)) and the global risk-informed decision-making approach (RIDM) in asset management as the best suited approach for this research. We first discuss the benefits of these methods then outline the possibility of combining them to conduct high-level risk management and decision-making framework. Further research would validate their efficiency and practicality. Therefore, future research initiatives will be devoted to conducting case studies in order to obtain more accurate data.
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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 | 0.015 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".