Framework for Optimizing Team Performance and Project NPV: Enhancing the Probability of Success by Team Alignment
Bibliographic record
Abstract
This study addresses a gap in enterprise risk management related to project team performance. Poorly functioning teams may severely erode project net present value (NPV). The erosion of project NPV can be quantified in terms of probability of success (POS). In the oil business POS is based on success criteria for likelihood that exploration efforts for oil & gas prospects will realize the EMV for those assets. Similarly, POS in team work and negotiations is based on success criteria for the likelihood that cooperation between team individuals will be able to deliver the maximum value for the project. Practical rules are formulated to support teams and team leaders in their efforts to optimize the alignment of team members in order to enhance the team’s effectiveness. The probability of success (POS) is split into three fundamental factors of alignment: PCulture , PSkills and PGoals. The dynamic effect of team learning on team alignment is graphed as the Cumulative POS. The cost of failure is graphed for a range of POS values, and visualizes the impact on the EMV of extra Team OPEX, each normalized by the project NPV. Applications are possible in all kinds of functional teams, including change management teams that need to build coalitions to effectuate lasting change. The interaction between members of engineering and other professional teams has been studied intensively, but the expression of team performance in numbers as quantified here is a new direction.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".