Data-driven robust strategic sourcing considering supply-side competition: Insights into blockchain adoption for coordination
Bibliographic record
Abstract
• Exploring blockchain’s role in establishing long-term partnerships among stakeholders. • Incorporating competitive dynamics into the buyer-vendor coordination problem. • Integrating data-driven robust optimization to model multidimensional uncertainties. • Enhancing supply chain resilience through strategic sourcing and information sharing. Given the increasing vulnerability of global supply chains (SCs) to disruptions, improving resilience through strategic sourcing is crucial for maintaining continuity and adaptability in dynamic markets. Integrating blockchain technology (BT) can further support these efforts by ensuring data integrity, transparency, and real-time visibility across SCs. This study develops a data-driven robust multi-objective programming (DDRMOP) model to explore the role of BT in designing sourcing strategies and creating effective partnerships in the face of uncertainties. The DDRMOP employs a data-driven robust optimization approach utilizing principal component analysis and robust kernel density estimation to formulate uncertainty sets for market demand. The model aims to minimize SC coordination costs, defective rates, and delivery delays while enhancing sourcing efficiency by selecting the most sustainable and BT-friendly suppliers. A Nash game-enabled data envelopment analysis is incorporated into this model to investigate sourcing efficiency under competitive dynamics and demand uncertainty simultaneously. This integration provides insights into how these dynamics influence the trade-off between cost efficiency and SC resilience. As the DDRMOP model includes three conflicting objectives, the augmented ε-constraint method is adopted to analyze the impact of each function on strategic sourcing across multiple products. The findings highlight the importance of BT and sustainability in forming reliable partnerships between the buyer and suppliers to enhance dynamic SC capabilities during disruptions. BT-friendly suppliers are preferred for their alignment with sustainability and information coordination goals. Although supply-side competition may increase coordination costs and operational complexities, it ultimately improves overall sourcing efficiency.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".