An integrated framework for ERP system implementation
Bibliographic record
Abstract
Purpose The purpose of this paper is to propose an alternative integrated approach based on the stage-gate method to implement enterprise resource planning (ERP) systems which will enhance the effectiveness of ERP projects. Design/methodology/approach A literature review was conducted on ERP system implementation and its effectiveness. The need for improving implementation approaches and methodologies was examined. Based on the insights gained, a conceptual framework for ERP system implementation is presented by combining the state-gate approach with the pre-implementation roadmap. Findings The proposed framework aims to enhance the overall ERP implementation outcomes, ensuring critical success factors and eliminating common causes of failures. A pre-implementation roadmap is identified as a key element for eliminating many causes of failure including lack of organisations’ readiness for ERP. The post-implementation stage can be used for further improvements to the system through internal research and development. Research limitations/implications The development of the framework is an attempt to contribute to improving ERP implementation. This research is expected to motivate researchers to work in this area, and it will be beneficial to practicing managers in the identification of opportunities for improvements in ERP systems. Case studies will be valuable to refine and validate the proposed model. Originality/value This paper explores research in a needy area and offers a framework to help researchers and practitioners in improving ERP implementation. This framework is expected to reduce the implementation project duration, strengthen critical success factors and minimise common problems of ERP implementation projects.
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 | 0.018 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".