Leveraging the Principles of Lean Six Sigma in Creating Value for the User Community
Bibliographic record
Abstract
This is a case study on how Singapore Management University (SMU) Libraries used an evidence based approach to decision making, based on the principles of Lean Six Sigma.Academic Libraries and the services offered by them have been evolving over the years along with the changing landscape of higher education.By using a data-driven methodology, SMU Libraries, was able to provide a service that was relevant and tailored to the needs of its community. BackgroundProvisioning of personal computers for use is one of the services provided by SMU Libraries to support the learning and research needs of the user community.A total of 48 computers are provided spread over 2 levels of Li Ka Shing Library, one of the two libraries under the umbrella of SMU Libraries.Of these, about 8 are dedicated for access to specialized financial databases with the remaining being used for general purposes.The computers are commonly used by students to access electronic databases or for initiating print jobs.In recent years, the library had seen a sharp increase in the number of laptops owned and operated by its patrons.In addition, the library had also undergone a master planning exercise to better utilize its space.As result of the evolving external environment, the Library decided to study if the current model of providing common PC's was effective in supporting the learning needs of the SMU community.Anecdotal and casual observation gave strength to the opinion that students did not require such PC's anymore, and their needs would be better served by removing the PC's and turning the whole area into a student study space.
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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 | 0.049 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.026 | 0.015 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.004 | 0.005 |
| 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".