Author manuscript, published in "International Symposium on Requirements Engineering, Canada (2001)" Matching ERP System Functionality to Customer Requirements
Bibliographic record
Abstract
Although procuring Enterprise Resource Planning systems from commercial suppliers is becoming increasingly popular in our industry, fitting those systems to customer requirements remains problematic. In this paper, we propose an approach for matching ERP system functionality to customer requirements. The assumption made is that the ERP system postulates a set of requirements that are worth eliciting from the ERP documentation as abstractions of the ERP system functionality. Then, the requirements engineering process is a process that matches the ERP set of requirements against organisational ones. Those requirements that match, perhaps after adaptation identify the ERP system features and their adaptations, that must be included in the ERP installation. To facilitate the matching process, the ERP requirements and the organisational ones are both expressed using the same representation system, that of a Map. The paper presents the Map representation system and the matching process. The process is illustrated by considering the Treasury module of SAP and its installation in the financial management of a cultural exchanges unit. 1.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.267 | 0.093 |
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".