Utilizing Value Management to Increase Project Competitiveness
Bibliographic record
Abstract
With the current economic pressures being faced by the oil and gas sector, organizations are increasingly required to become more competitive on their capital projects. Enbridge has implemented the practice of Value Management (VM) to help achieve the needs and expectations of stakeholders with the least possible resources. VM is a systematic approach that is used by a multidisciplinary team to improve the value of a project (or aspects of a project) through the analysis of its functions, and is most effective when applied at the planning and development stages. A value study enables the expected performance (i.e. the desired functions) of a project to be clearly identified at the onset, and assesses a range of possible solutions/alternatives against the functions required by the owner. While VM is commonly used in the manufacturing industry, as well as on transportation and municipal projects, few examples of its application in the oil and gas sector were found. Enbridge researched a variety of VM best practices and created a framework that compliments existing company practices. This paper also highlights how the value methodology was recently applied to a capacity expansion project at the Front End Engineering and Design (FEED) stage. Our approach to the various elements of a value study will be discussed, including pre-workshop activities, the VM workshop, and post-workshop activities. Enbridge has seen significant benefits from the VM studies completed on projects to-date. Given the broad applicability of the value methodology, it is believed that our approach can also be successfully applied in other areas (e.g. improving business processes).
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 | 0.026 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.002 | 0.012 |
| 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".