Blockchain-Augmented Digital Supply Chain Management: A Way to Sustainable Business
Bibliographic record
Abstract
The objective of this article is to assist the reader in understanding the journey from traditional Supply Chain Management to Digital Supply Chain Management. It aims to augment the concept of Digital Supply Chain Management with blockchain technology and create an extensive literature review to assist in formulating the gaps and discovering the variables that contribute towards the efficiency of a Blockchain-Based Digital Supply Chain. Moreover, this article aims to validate the impact of specified parameters resulting in customer retention and market leadership for an organization. Digital technologies such as the Internet of Things, blockchain, etc., are disrupting the traditional ways of doing business and creating value propositions for customers. Supply Chain Management is a key business process for an organization that helps them compete in the market. Organizations have seized competition not as individual brands but as supply chains. Digital Supply Chain Management is the implementation of digital technologies to capture customer data at every interaction to create customer engagement strategies. This article provides an empirical analysis of parameters influencing a Blockchain-Augmented Digital Supply Chain resulting in customer retention and market leadership and shows how, through a Blockchain-Based Digital Supply Chain, the business objective of being a customer-centric organization is assisted with the customer data generated at each interaction that is enabled.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".