Discipline breakdown structure - bridging project management and systems engineering to form an integrated management system in multidisciplinary rail projects
Bibliographic record
Abstract
The complexity of multidisciplinary projects requires that many specialities and disciplines work together. In rail infrastructure projects, the term ‘systems engineering (SE)’ is being widely used, yet it is still loosely defined. This PhD thesis proposes the use of a Disciplinary Breakdown Structure (DBS), an approach that better integrates SE as it is currently understood with traditional project management (PM) to make PM more efficient. A review of PM, SE and their relationship, particularly in the rail sector, identified gaps in performance, the most significant of which is a lack of integration between the SE and PM activities. Case study material was examined and a survey was conducted. The results highlighted the lack of consensus and consistency of the definition of SE and its application by project practitioners at various levels. Interface management (IM) was identified as a key factor contributing in project failure or success. IM was reviewed in the context of SE and PM, and existing methods and solutions were examined. The DBS as a new solution, was developed and introduced to improve the IM life cycle from definition to closure. This solution is based on industry discipline sectors (in this case, the rail sector) and therefore it is independent from project specific requirement. Exploring more detail of the DBS revealed its capability in integrating SE and PM more generally. The DBS is a modular solution (with a potential to become an industry standard) that provides a basis for the rapid development of project-bespoke management systems, improving PM efficiency by saving time and resources. The approach has been tested in two major rail project case studies in the UK and one in Canada and the results, benefits, constraints and the areas of improvements are discussed in more detail.
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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 | 0.012 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".