Strategic selection of computerized maintenance management systems for institutional built assets using quality function deployment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose The selection of a computerized maintenance management system (CMMS) is a strategic asset management (AM) decision for any organization that owns and operates built assets. In the context of institutional built assets (IBA), these decisions are becoming increasingly critical due to the complex nature of these systems, the significant budget allocation to maintenance and the importance of maximizing the service life of these assets. This study aims to propose a structured approach to guide built asset owners in selecting a system that aligns with their strategic plans for operations and maintenance of their built assets. Design/methodology/approach A mixed-methods approach is used that combines participant observations with qualitative methods, including interviews, workshops, document analysis and focus groups for data validation. A case study of an educational IBA in Canada is carried out, and the quality function deployment (QFD) method is used to identify the most suitable CMMS tool. Findings The results show that the QFD method enables the identification of an overall set of requirements for IBAs by harmonizing AM and facility management (FM) needs with available CMMS capabilities in the market, providing a comprehensive roadmap for future system selection processes in IBA management. Originality The originality of this study lies in the development of systematic method that aids IBA owners in making informed system selection decisions while guiding CMMS vendors in aligning their products with AM and FM requirements.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it