Business Process Points: a proposal to measure BPM projects
Bibliographic record
Abstract
Organizations need more standardization and efficiency in business process execution, which is leading them to show an increasing interest in the business process management (BPM) approach. Stakeholders and workers from different departments are collaborating to obtain better products/services as a result of the BPM projects, but do not always achieve satisfactory outcomes. BPM projects must be well managed and measured for successful implementation. Software Engineering (SE) projects often use a Functional Size Measurement (FSM) technique to estimate the size of the project. This paper proposes an FSM technique for BPM, based on the Function Points metric from SE. We chose the Function Point Analysis, (an FSM technique), as the basis for the proposed Business Process Point Analysis (BPPA) technique. BPPA is a Process Size Measurement technique, developed for business processes modeled through the Business Process Management Model and Notation. BPPA will enable project managers to measure Business Process Points of BPM projects by allowing them to estimate important variables for better managing projects, such as required resources, human effort, cost and time. This paper provides an overview of the proposed BPPA technique and the results of an empirical analysis based on observations from a BPM specialist and an FPA specialist.
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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 | 0.020 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".