Extracting business process decomposition
Bibliographic record
Abstract
Purpose This paper seeks to offer a step‐by‐step methodology to extract and model business processes with a focus on business process decomposition. The main motivation is the lack of repeatable measurable methods and techniques for extracting business process decomposition. Design/methodology/approach A step‐by‐step methodology for extracting business process models is provided. This methodology is called “the V methodology” because of its approach to break down the enterprise through a functional hierarchy and then collecting it up again via its process decomposition. Porter's value chain model is noticed as a powerful tool to complete the roadmap. The paper is designed using sections: benefits and necessities of having such a methodology, terminology, the V methodology, learned lessons and further work. Findings Success stories using the V methodology expressing its impact on quality business process models are mentioned. Experts find it useful to gain unified process models as well as helping them to discover gaps, bottlenecks and redundancies amongst business processes. Research limitations/implications It is too hard to receive reliable information about previous experiences from the working companies in the area. Practical implications The main implications found when using the V methodology are: practiced analysts feel it dangerous to their position, younger analysts use it as the only source of modeling and people find it difficult to apply Porter's model in practice. Originality/value Almost all of the previous papers refer to modeling notations when using the term “methodology”. This paper instead describes the process of business process modeling. Another original value of the paper would be using Porter's model to classify the processes within their right process groups.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".