Bibliographic record
Abstract
Abstract The ever-increasing fast paced economy brings the need for faster project development and construction. This has led to a term known as "fast-tracked projects" in which the schedule is reduced by shortening and overlapping the design, procurement and construction phases. The objective of this paper will be to provide a practical guideline to project managers and engineers who have been tasked with executing a fast-track project, with particular focus on brownfield projects in the process industry. The scope will cover project management and engineering for fast-track projects, during the concept to detailed design phases. The approach to the paper starts with a literature review of past journal research published on fast-tracked projects and review of the experiences that Hatch has had with fast-tracked projects. Case studies have been prepared, to highlight project management and engineering decisions which succeeded in accelerating the schedule. Pitfalls and recommendations will be identified and future tools to aid fast-track projects will be discussed. The results of analysis of historical vendor quotations and procurement show typical lead times for equipment and materials, allowing engineers to identify long lead equipment and items which need early focus. Observations from the case studies have shown that success in fast-track projects involves setting a fixed basis of design early in the project and making key project scoping decisions as early as possible in the project to minimise changes. Observations have been made on historical projects and how many of the key fast-track practices (from the literature review) were implemented as part of successful fast-tracking; lessons learned and recommendations have been shared in the paper. Observations have also been made about projects which attempted to fast-track by removing of project stage decision gates and/or jumping design stages, which then led to increased re-work/change scope. The cost-quality-schedule of a project can be represented as the three corners of a triangle, which are key drivers on a project. The main conclusion is that the Project Manager and Engineering Teams must plan carefully and have buy-in from all key stakeholders in the project from an early stage on all the key decisions, otherwise one portion of the cost-quality-schedule triangle will always fall behind the others, with the most concerning one being quality, as it is often linked to safety. Clients may have to accept a feasible, fit for purpose design, rather than an optimised design in order to meet a fast-track schedule. There are currently limited engineering guidelines for how to execute the design of a fast-track project. The paper provides additive information in the form of case study examples, practical engineering guidance for the process industry and identifying new technologies, which may aid fast-track projects in the future.
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 | 0.032 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.026 | 0.018 |
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".