Risk assessment in ERP projects using an integrated method
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.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".