Factors Potentially Influencing Productivity in Performance-Based Maintenance Contracts (PBMC)-- An International Study of Roads from Sweden
Bibliographic record
Abstract
Measuring productivity of the maintenance contractors is extremely difficult and not practical under existing scenarios. It would be more advantageous to determine what factors potentially influence productivity of the maintenance contractors, especially with Performance-Based Maintenance Contracts (PBMC). Sweden commissioned this study to determine practices in progressive countries involved in PBMC and how productivity can be influenced. The objective was to determine what factors potentially influence the productivity of the maintenance contractors and what actions can clients (agencies) consider. Additionally, it was intended to find potential solutions and investigate better practices that affect or influence productivity. The study approach consisted of a literature review and semi-structured interviews in six different countries consisting of Sweden, Finland, The Netherlands, England, Ontario, Canada, and the Virginia Departments of Transportation, in the USA. Each country responded to questionnaires concerning productivity factors. The results show that no clients included in the study measure the contractor’s productivity in PBMC. Competition for maintenance services is the primary factor to influence productivity and by using a performance based approach in a hybrid-type PBMC. Other factors identified include a more balanced risk approach, longer term agreements, bundling services, optimized service area, and using as much performance requirements as possible. The study showed that productivity of the maintenance contractor’s is complex and difficult to assess, but can be influenced indirectly by various factors. Also, there are options that practitioners can possibly adapt to help improve the productivity and efficiency by seeking solutions elsewhere.
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How this classification was reachedexpand
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 | 0.013 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".