ITIL-based IT service support process reengineering
Bibliographic record
Abstract
The Information Technology Infrastructure Library (ITIL) supports best practices, reengineering activities and IT service support processes. ITIL framework only provides recommendations, and companies need to utilize this framework to improve their IT service support processes and establish best pr actices. This study provides a methodology on how to apply the ITIL framework for evaluating the IT service support processes, its reengineering and alignment to best practices, and subsequent integration into a decision support system framework. A case study approach was used to identify a set of Key Performance Indicators (KPI) which were monitored by a decision support system (DSS) for triggering on-going reengineering of IT service support processes. This paper focuses on the implementation of the ITIL guidelines at the operational level, improvement of the service desk, and incident, problem, change, release, and configuration management. It also presents the implementation of the ITIL guidelines at the tactical level for the improvement of the service level, capacity, IT service continuity, service availability, and security management. We conclude by providing recommendations for future research.
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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 | 0.035 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".