Unlocking Blockchain’s Potential in Supply Chain Management: A Review of Challenges, Applications, and Emerging Solutions
Bibliographic record
Abstract
Blockchain’s decentralized, immutable, and transparent nature offers a promising solution to enhance security, trust, and efficiency in supply chains. While integrating blockchain into the SCM process poses significant challenges, including technical, operational, and regulatory issues, this review analyzes blockchain’s potential in SCM with a focus on the key challenges encountered when applying blockchain in this domain—such as scalability limitations, interoperability barriers, high implementation costs, and privacy as well as data security concerns. The key contributions are as follows: (1) applications of blockchain across major SCM domains—including pharmaceuticals, healthcare, logistics, and agri-food; (2) SCM functions that benefit from blockchain integration; (3) how blockchain’s properties is reshaping modern SCM processes; (4) the challenges faced by businesses while integrating blockchain into supply chains; (5) a critical evaluation of existing solutions and their limitations, categorized into three main domains; (6) unresolved issues highlighted in dedicated “Critical Issues to Consider” sections; (7) synergies with big data, IoT, and AI for secure and intelligent supply chains, along with challenges of emerging solutions; and (8) unexplored domains for blockchain in SCM. By synthesizing current research and industry insights, this study offers practical guidance and outlines future directions for building scalable and resilient global trade networks.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".