Business process automation: A case study on quality management systems implementation
Bibliographic record
Abstract
Business Process Automation (BPA) is widely recognized for its potential in enhancing process efficiency, reducing costs, and boosting customer satisfaction, ultimately driving organizational success. This study makes a significant contribution to the existing knowledge on implementing Quality Management Systems (QMS) through BPA by offering a detailed analysis of their integration. By examining the motivations, strategies, and impacts, this research provides valuable insights for practitioners, academics, and decision-makers who aim to optimize quality management practices through automation. The findings demonstrate remarkable improvements in performance efficiency, error reduction, and flexibility in upgrading enterprise management systems. However, challenges related to technology integration and change management require careful planning and strategic alignment. This study offers critical insights for service-oriented organizations, highlighting the transformative potential of BPA in revolutionizing quality management practices and providing a comprehensive roadmap for organizations seeking operational excellence. Future research should focus on cross-industry comparisons and longitudinal studies to assess the sustainable impact of BPA. This research significantly enhances the literature on BPA and QMS, presenting both practical and theoretical implications.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".