Bibliographic record
Abstract
[ABSTRACT] Today, tracking the global economy will show that being good is not enough, therefore each organization must strive for excellence if it wants to stay in the market. In order to be a leader, most companies are realizing that traditional management and other historic approaches are not enough and more effective methods are needed. Lean Six Sigma gives teams a comprehensive tool set to increase the speed and effectiveness of any process within the organization, resulting in increased revenue, reduced costs and improved collaboration. Moreover, service support processes, such as incident management, are among the first ITIL (Information Technology Infrastructure Library) processes that organizations start to implement, however, several challenges may exist in the process implementation. The process involved in this study is related with the incidents management of the UCA (Uso Compartido de Aena, Shared Use of Aena) environment in Barcelona Airport and the research question of this study is: which improvements are important in establishing the UCA's incident management process with the aim of building an ideal future-state of the service? The main contribution of this report is threefold: i) to define the basic concepts of Lean Six Sigma methodology and incident management, ii) to describe how the Lean Six Sigma methodology was applied to the UCA incident management process in order to determine its potential of improvement and iii) to present a proposal for the ideal future-state of the service.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".