Scope of Business Process Reengineering in Public Sector Undertakings
Bibliographic record
Abstract
Business Process Reengineering (BPR) is a managerial tool used for bringing in drastic performance improvements in organizations. Towards this advanced techniques like Business Process Management (BPM) and Knowledge Management are employed the world over. In India, many Public Sector Undertakings (PSUs) were recently closed down and still more are on the verge of closure due to various reasons. Despite the utility of BPR in improving performance, Indian PSUs are yet to effectively use this tool. Data pertaining to 41 State PSUs (SPSUs) and two Central PSUs (CPSUs) in the State of Kerala were considered for the present study. The performance for the last 12 years and the factors responsible for poor performance were analyzed. The performance of most of the PSUs analyzed was found to be below satisfactory levels. This suggests the need for employing scientific tools like BPR to bring in drastic performance improvement. The study identified 12 factors that could contribute towards drastic performance improvement. An average improvement of 57.5 per cent was found to be possible in each of the 12 factors identified. The findings of the study have significant bearing on poorly performing PSUs in a developing country like India. The study also contributes substantially towards theory building, since it has identified certain additional factors of performance improvement.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".