Risk assessment in ERP projects using an integrated method
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Enterprise resource planning (ERP) projects are very complex tasks, expensive, time-consuming, and risky investments. One of the main reasons for the high ERP project failure rate is that managers don't assess and manage the risks involved in these projects. In addition, they don't know the importance degree for each of these risks. On the other hand, to the best of our knowledge papers proposing specific Risk Management approaches, methodologies and techniques for ERP projects are very limited. Therefore, the aim of this paper is to present a new framework for risk analyze in ERP projects. At first we introduce the main risks retrieved from literature review, affecting the performance of ERP projects. Then, we propose a new integrated framework for evaluating potential risks using Fuzzy Failure Mode and Effect Analysis (FFMEA) and Grey Relational Analysis (GRA) tools. The results indicate which risks are most important and critical in ERP projects. This framework can guide managers during risk quantification and mitigation in order to manage ERP projects better and within the systematic framework.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it