ERP Systems in Humanitarian and Private Sectors’ Supply Chains: Challenges and Success Factors
Bibliographic record
Abstract
Purpose: This paper investigates and compares challenges and success factors within different supply chain ERPs used globally across humanitarian and private organizations in Africa, Asia, Canada, Australia, Europe, and the Americas. Eighteen challenges and 27 success factors were selected from literature published between 2015 and 2020 to determine whether they are equally relevant globally in the private and humanitarian sectors. Design/methodology/approach: The research utilized an anonymous online questionnaire advertised on different social media websites and completed by 50 humanitarian supply chain professionals and 53 private sector professionals worldwide. The collected data was analyzed using a descriptive statistic–crosstabulation analysis to show the differences or similarities in supply chain professionals’ opinions from humanitarian and private organizations. Additionally, the hypotheses were tested by using the Mann–Whitney Test. Findings: Findings revealed that all the examined success factors were supported except one, which was similar in both sectors. However, the challenges during the implementation of ERPs differ in these two sectors — with four success factors not supported in the humanitarian sector and nine not supported challenges in the private sector. Originality: This study’s significance is that, as per the researchers’ knowledge, such a comparative study was never done before, and it will allow both sectors’ professionals to understand all the elements mentioned above better and integrate them while implementing supply chain ERPs.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.000 | 0.003 |
| 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".