Big Data Analytics-Driven Supply Chain Traceability in the Coffee Industry: A Study in Vietnam

Linh T. K. Tran, Tu H. C. Nguyen, Khoi V. Ma and Anh H. G. Nguyen ()
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Linh T. K. Tran: Ho Chi Minh International University, Vietnam National University
Tu H. C. Nguyen: Ho Chi Minh International University, Vietnam National University
Khoi V. Ma: Ho Chi Minh International University, Vietnam National University
Anh H. G. Nguyen: Ho Chi Minh International University, Vietnam National University

A chapter in New Challenges of the Global Economy for Business Management, 2025, pp 943-966 from Springer

Abstract: Abstract The Vietnamese coffee industry has experienced significant growth, but its supply chain has struggled to achieve sustainability. Although some companies recognize the importance of sustainability, profitability remains their priority in business practices. Meanwhile, customers now focus on coffee selection for health reasons, which creates high demand for product traceability. This has led to a dilemma in the coffee industry since the current supply chain management system faces severe issues such as product counterfeiting, inadequate traceability, delays, and poor real-time information sharing. However, new technologies like Big Data Analytics (BDA) can help address these challenges by providing crucial features such as decentralization and transparency to share mass data for all supply chain stakeholders and trace the sustainable coffee supply. The study explores the drivers of e-traceability adoption through environmental, economic, social, and technological dimensions (Volume, Velocity, Variety, and Veracity). A multi-criteria decision-making framework was proposed, utilizing the best–worst method to calculate the weights of these criteria and the fuzzy TOPSIS method to rank the significant technology. The study identified social and technological factors as the most significant in adopting e-traceability supply chain. Sensitivity analysis was used to verify the research framework's validity and remove bias effects.

Keywords: Coffee supply chain; Big data; Multi-criteria decision-making; Best–Worst Method; Fuzzy TOPSIS (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/978-981-96-4116-1_60

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