Cloud-Integrated Enterprise Resource Planning for Sustainable Business Growth
Bibliographic record
Abstract
The cloud-based ERP system is becoming a driving force for effective business transformation. The public information data of the three sectors manufacturing, retail and service sector has been used in the study. The data is available from 2021 to 2024. The study adopts a comparative experimental study, where performance measures before and after adoption produced significant IT cost savings, efficiency, energy, paperless, employee and CSR compliance benefits. Results show that the organizations making use of cloud ERP systems are getting commercial benefits as well as environmental benefits that are simultaneously improving the sustainability dimensions. Further, manufacturing organizations gained a significant economic and environmental benefit; retail organizations received almost equal benefit across the dimensions; service organizations scored high on enabling remote work and social performance. Studies show that implementing cloud-based enterprise resource programs enables the company’s effectiveness significantly. Further, it helps the organization quicken its pace to achieve the environment-friendly international goal. This study contributes to theory and practice by conceptualising cloud ERP as a strategic enabler of digital resilience and sustainability across industry contexts.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".