Novel Integrated Framework for ERP Selection and Implementation
Bibliographic record
Abstract
As the economic pressure on businesses increases, organizations try to adopt innovative technology solutions to cope with this pressure and adapt to the rapid market changes.Particularly for small and medium enterprises (SMEs).They must integrate all resources and information levels to highly utilize their limited resources and survive the local and global competition.This could be achieved by adopting the best suitable Enterprise Resource Planning (ERP) system.On the other hand, properly selecting and implementing the right ERP system is challenging for many reasons.Hence, this paper provides a novel integrated framework for establishing and implementing ERP systems for SMEs.It is a four-phase theoretical framework that is verified through a case study in a manufacturing plant.The phases start with top-level managers' commitment, problem identification, and documenting expectations.Then, the overall system scanning, and data gathering is made for the next step of system selection.And finally, the implementation phase.The framework is agile for using qualitative or quantitative tools based on the company's nature, size, and requirements.
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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 | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".