A new framework for risk assessment in ERP maintenance
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Bibliographic record
Abstract
In recent decades, companies across the world have implemented enterprise recourse planning (ERP) systems. Proper ERP implementation has been a more explored issue. Specifically, numerous papers have presented the critical success factors in these projects. But even when the implementation finished satisfactorily, success in ERP adoption is not guaranteed. It also depends on the effectiveness process in the post-implementation ERP systems. The maintenance of the ERP is necessary to correct and prevent systems risks as well as to enhance its performance and adapt continuously to the system. Nevertheless, this is often managed intuitively and without taking into account the existing risks. In this sense, the maintenance managers need to know the importance of all risks identified. Given this gap existing in the literature and the professional needs, the aim of this research is to analyze the risk factors (RFs) that threaten ERP maintenance performance. With this in mind, at first we introduce the main risks retrieved from literature review, affecting the performance of ERP maintenance. Moreover, we propose a systematic approach for identifying and evaluating potential risks using a Fuzzy Failure Mode and Effect Analysis (FFMEA) and Grey Relational Analysis (GRA). The proposed approach consists of two stages: construction of FFMEA and application of GRA. The first stage, aims at incorporating the ERP Maintenance-specific characteristics to the new FFMEA model, providing different dimensions and sub-dimensions, encompassing the ERP Maintenance characteristics. At the second stage, GRA is applied to calculate the risk priority of each failure mode to deal with the necessities of a flexible evaluation framework under these interrelated multi-dimensions. Finally, all risks presented in the general risks taxonomy are ranked from more to less critical according to their risk importance. The results highlight which risks are most important in ERP maintenance. This framework helps managers, vendors, consultants, auditors, users and IT staff to manage ERP maintenance better and within the systematic framework.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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