Towards a Process Analysis Approach to Adopt Robotic Process Automation
Why this work is in the frame
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Bibliographic record
Abstract
Robotic Process Automation (RPA) is an emerging approach that automates repetitive human tasks using robots. For business processes, RPA refers to configuring software-based robots to do the work previously done by actors in the organizations. RPA offers many benefits including improved business efficiency, increased productivity, data security, reduced cycle time, and improved accuracy while allowing organizations to relieve their employees from repetitive and tedious tasks. However, implementing RPA represents a challenge and organizations must learn to manage RPA adoption to achieve maximum results. This paper aims to help organizations to effectively adopt RPA for automating their business processes. More precisely, it proposes a new method to guide organizations in analyzing their business processes in order to identify the most suitable for RPA. We present the principles underlying our method and the results obtained in the context of key processes from the banking domain.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it