The Implementation of Enterprise Resource Planning Systems for Roads and Infrastructure Construction Companies in Developing Countries
Bibliographic record
Abstract
Construction Enterprise Resource Planning (CERP) systems started to infiltrate the construction world after ERPs became crucial in modern enterprises. In simple terms, CERP integrate and keep track of the various processes within construction companies. As a minimum, these possess include management of general contractors, subcontractors, financial work, accounting, payroll, logistics, workflow processes ... data related to different processes are stored within one unique database. Despite this intuitive objective, developing and standardizing CERP systems to fit the needs of all construction companies is not a straightforward course. This research focuses on the development of a framework that integrates the minimum required modules to be included within a CERP, specifically for road and infrastructure construction companies. This objective is achieved based on literature review on CERP despite its shortage, in-depth interviews with construction professionals requiring CERP, and the results of a structured questionnaire filled by CERP users and developers. The developed framework identifies the procurement module linked to the on-site deliveries as the first stone that should be developed and implemented. Then, budgeting and work progress modules must be added. After that, timesheet and equipment follow-up modules need to be implemented. Limitations encountered highlighted the main considerations to be considered in future work such as the cost, the company's size, development and implementation period, and type of work.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".