Types of ERP Systems and Their Impacts on the Supply Chains in the Humanitarian and Private Sectors
Bibliographic record
Abstract
(1) Background: This paper explores different ERP systems used in the supply chains of humanitarian and private sectors and their key impacts on supply chain performance. The study examined 19 potential impacts from the published literature from 2015 to 2020 and investigated whether they are equally relevant in the global private and humanitarian sectors. (2) Methods: An anonymous online questionnaire was used and advertised on different social media websites. Fifty humanitarian supply chain professionals and 53 private sector professionals completed the questionnaire. A descriptive statistic cross-tabulation analysis was used to show the differences or similarities in the collected responses, and a Mann–Whitney Test was used to test the research hypotheses. (3) Results: The findings highlighted the key impacts of ERP systems on supply chain performance and confirmed that these impacts are similar in both sectors. Additionally, the humanitarian sector prefers to implement custom-made ERPs, whereas the private sector purchases ready-to-use products. (4) Conclusions: The reviewed literature did not show studies conducting such a comparative study. The results provide a better understanding of the types of ERP systems and their impacts on supply chain operations within the two examined sectors.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".