Using Stocks and Flows Diagrams to Understand Business Process Behavior
Bibliographic record
Abstract
Business Process Modeling has traditionally focused on the activities and logic of how work is carried out. This is depicted through modeling notations like BPMN, which illustrate the sequence of activities, performers, and possible paths for each process instance. However, when measuring the performance of an organization and its processes, the aggregation of results from individual instances is often necessary. Unfortunately, these flows are not always smooth, as they may encounter variations, delays, accumulations, and other issues that can hinder expected performance levels. Therefore, understanding the behavior of business processes over time is crucial for improvement efforts. This paper demonstrates the use of stocks and flows diagrams for modeling business processes and simulating their behavior over time. Simulations aid in identifying critical points, removing bottlenecks, and enhancing overall process performance. We begin with a brief introduction to modeling business processes using stocks and flows diagrams, followed by a real-life case study in the healthcare sector, where stocks and flows models and simulations were employed to identify and resolve a problem.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".