Improving the Management of Innovation Risks - R&D Risk Assessment for Large Technology Projects
Bibliographic record
Abstract
Global network structures of products and services are important value creators for many companies. Complex business models include a variety of relationships and interrelationships within and across different systems particularly in the case of innovation processes. This increases innovation risks. Risk management is becoming more and more important and is crucial for the German Machinery and Plant Engineering Industry (MPEI). Many companies are medium-sized and are using standard static risk management methods. Use of these methods often means that critical situations are detected late, they do not help in the understanding of problem characteristics and their interdependencies and, consequently, lead to erroneous decisions.Therefore, the modelling of cause-and-effect structures of innovation risks in the German MPEI facilitates the exploration and understanding of the behavioral dynamic of risk clusters. In a comparison of standard risk assessment with the Causal Loop Diagram and the System Dynamics Model of Innovation Risks, the potential of System Dynamics for systemic and multi-dimensional risk management is demonstrated. In this paper, particular emphasis is given to the risk of shortages of skilled workers from a common and System Dynamics perspective. This is relevant as these shortages are the main risk associated with innovation, impacting on project timings, output and performance amongst others. The research concludes that the development of specific System Dynamic models can help to overcome certain problems and incorporate multi-causal interconnections and multidimensional views on risk.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".