Operational Framework for Managing Risk Interactions in Product Development Projects
Bibliographic record
Abstract
Risk management in the context of product development projects faces many difficulties: systemic risk is often omitted and interactions between risks are seldom present. To address these problems an operational framework is presented in this paper. This framework introduces the concept of standardized development task and process, risk event, risk factor and risk behavior. Standardized task and process enable propagation of risks from task to process level and they are modeled using system engineering approach (activity model). Risk interactions are modeled on task level and propagated to process level using inter-activity input-output relationships. Risk interaction model includes risk events, risk factor and risk behavior. Risk event defines the value of risk factors. This risk event - risk factor relation is modeled using Bayesian networks and expert opinions. Relationships between risk factors form risk behavior with included interactions. Mentioned relation is represented using Fuzzy Cognitive Maps. The main advantage of this approach is that this manner of addressing risk can be at the same time easier from the aspect of necessary data and more precise from the perspective of obtained results. Apart from the framework introduction, usage of the solution is illustrated with the academic example at the end of the paper.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".