Unlocking blockchain's potential for supply chain transformation: A robust system analysis for enhanced strategic performance
Bibliographic record
Abstract
Supply chains (SCs) are becoming more complex due to globalization, the increased risk of disruptions, and regulatory requirements, which mandate greater coordination among stakeholders. The integration of a blockchain-based information-sharing mechanism can improve compliance and streamline operations across SC networks. However, blockchain adoption in SCs remains relatively unexplored, leading to managers' unfamiliarity with its potential outcomes and increasing conservatism. Therefore, this study incorporates a strategic perspective into analyzing blockchain adoption enablers. We propose a robust system analysis-based framework to investigate the interplay between enablers and supply chain performance (SCP) improvement. First, the critical success factor theory and the balanced scorecard are employed to identify and categorize the enablers in establishing a blockchain-based sustainable SC network. The systems theory-enabled fuzzy cognitive map is developed to create a causal-based model of the identified 23 enablers, and then a hybrid learning algorithm is adopted to quantify the impact of these factors on the four strategic SCP metrics, including cost-effectiveness, improved quality, lead time minimization, and increased customer satisfaction. Afterward, the blockchain adoption enablers analysis problem is formulated using the fixed-input robust data envelopment analysis to identify the most effective factors on SCP across diverse scenarios. The findings imply that each enabler affects SCP metrics either directly through causal relationships or indirectly via other enablers. Considering the overall impact, smart contracts, traceability, and streamlined communication can be regarded as key drivers that shape mediators and consequently enhance SCP. The results further indicate that the proposed framework supports greater flexibility in designing adoption strategies.
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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 | 0.001 | 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".