The contribution of global sourcing to the economic performance of organizations: Analysis of the points of view of the supply chain participants
Bibliographic record
Abstract
Purpose: This paper aims at investigating the reasons for the complex and long lead time from global suppliers that causes inventory shortages. It focuses on supply chain risk management in global sourcing. This study has revealed all the specific risks of a global sourcing project and has provided some solutions for risk management: three-step risk management, safety stock, data sharing and driving supplier performance.Design/methodology/approach: A qualitative study is conducted to propose concrete recommendations on three topics: risk management, safety stock and information sharing. A semi-structured survey-guided interview was used to collect related data, and the answers were assessed using syntactic, lexical, thematical and NVivo software analysis.Findings: This study has revealed all the specific risks of a global sourcing project and has provided some solutions for risk management: three-step risk management, safety stock, data sharing and driving supplier performance.Originality/value: Through research work, we have noticed that the world of the Supply Chain is constantly evolving and that it is becoming more and more complex. Through these interviews, we have noticed that the role of purchasing is changing differently in each sector.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".