Blockchain technology in supply chain management: insights from machine learning algorithms. Issue 2 (14th December 2020)

• Record Type: Journal Article
• Authors: Hirata, Enna; Lambrou, Maria; Watanabe, Daisuke
• Is Part Of: Maritime business review. Volume 6:Issue 2(2021)
• Page Start: 114
• Page End: 128
• Publication Date: 2020-12-14
• Subjects: LDA -- Supply chain -- Machine learning -- Blockchain -- Word2vec -- Natural language processing (NLP); Shipping -- Periodicals; 387.5
• DOI: 10.1108/MABR-07-2020-0043
• Languages: English
• ISSNs: 2397-3757

Abstract

Abstract : Purpose: This paper aims to retrieve key components of blockchain applications in supply chain areas. It applies natural language processing methods to generate useful insights from academic literature. Design/methodology/approach: It first applies a text mining method to retrieve information from scientific journal papers on the related topics. The text information is then analyzed through machine learning (ML) models to identify the important implications from the existing literature. Findings: The research findings are three-fold. While challenges are of concern, the focus should be given to the design and implementation of blockchain in the supply chain field. Integration with internet of things is considered to be of higher importance. Blockchain plays a crucial role in food sustainability. Research limitations/implications: The research findings offer insights for both policymakers and business managers on blockchain implementation in the supply chain. Practical implications: This paper exemplifies the model as situated in the interface of human-based and machine-learned analysis, potentially offering an interesting and relevant avenue for blockchain and supply chain management researchers. Originality/value: To the best of the knowledge, the research is the very first attempt to apply ML algorithms to analyzing the full contents of blockchain-related research, in the supply chain sector, thereby providing new insights and complementing existing literature.