Implementing Construction Planning and Control Software: A Specialized Contractor Perspective
Bibliographic record
Abstract
Many companies in the construction industry have recently expressed a particular interest in undertaking digital transformation by adopting new technologies in an attempt to increase productivity and quality. In responding to this new reality, these companies, particularly small and medium-sized enterprises (SMEs), face multiple challenges. Software vendors have recently been developing a large range of digital solutions, such as project planning and control software. Nevertheless, target companies are not generally guided in their choices and cannot find the support they need. The delays seen in these companies’ adoption of new technologies can be attributed squarely to a lack of references allowing them to understand their real needs and manage the main challenges they face. The project reported in this article was carried out with an industry partner specializing in construction mechanics. The first step was to understand and characterize the partner’s current project planning and control processes. Then, different information technology (IT) solutions were compared through a number of criteria defined with the company in order to choose the most appropriate software solution. Finally, new processes were proposed, and a prototype was developed and validated with the company in order to ensure the effectiveness of proposed changes. Using research action methods, the research brings a new perspective on the needs of specialized contractors, highlighting the challenges related to the characterization of the needs due to the difference of perspective of the different practitioners in the company. It also showed how it is important to define new processes to map the complementarity between the selected tools and the existing ones. The research finally highlighted the importance of an active involvement of the practitioners in order to validate the proposals and avoid resistance to change.
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 | 0.022 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| 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".