Developing in-House ERP System for the Construction Industry in a Developing Country: A Case Study
Bibliographic record
Abstract
Benefits reaped from implementing Enterprise Resource Planning (ERP) systems have made them a critical part of organisations. These systems, which are developed on best business practices, are sometimes unable to satisfy unique organisational needs, such as those specific to the construction industry which present a unique set of challenges different from those of manufacturing and service industries. This paper aims to study the development of in-house ERP system in an organisation in a developing country, and seek to explore and understand the development of ERP system designed exclusively around the needs of an organisation. This study adopts a case study based qualitative research methodology. Primary data is collected through a series of interviews, discussions with the project manager, development staff and end users. The outcome of the study shows that through proper planning coupled with detailed needs analysis, suitable change management strategy, an experienced project team and selecting the appropriate software development process, any organisation can design and develop ERP system that caters for the organisation specific needs. Therefore, eliminating the need of complex software customisation or altering business processes. Further, by developing an in-house system, the probability of a failed implementation is greatly reduced thus allowing the organisation to focus on its core business while benefitting from the new system.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".