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Enhancing Coffee Supply Chain towards Sustainable Growth with Big Data and Modern Agricultural Technologies

Author

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  • Yotsaphat Kittichotsatsawat

    (Graduate Program in Industrial Engineering, Department of Industrial Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, Thailand)

  • Varattaya Jangkrajarng

    (Excellence Center in Logistics and Supply Chain Management, Chiang Mai University, Chiang Mai 50200, Thailand
    Department of Management and Entrepreneurship, Faculty of Business Administration, Chiang Mai University, Chiang Mai 50200, Thailand)

  • Korrakot Yaibuathet Tippayawong

    (Excellence Center in Logistics and Supply Chain Management, Chiang Mai University, Chiang Mai 50200, Thailand
    Department of Industrial Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, Thailand)

Abstract

Modern agricultural technology management is nowadays crucial in terms of the economy and the global market, while food safety, quality control, and environmentally friendly practices should not be neglected. This review aims to give perspectives on applying big data analytic and modern technologies to increase the efficacy and effectiveness of the coffee supply chain throughout the process. It was revealed that several tools such as wireless sensor networks, cloud computing, Internet of Things (IoT), image processing, convolutional neural networks (CNN), and remote sensing could be implemented in and used to improve the coffee supply chain. Those tools could help in reducing cost as well as time for entrepreneurs and create a reliable service for the customer. It can be summarized that in the long term, these modern technologies will be able to assist coffee business management and ensure the sustainable growth for the coffee industry.

Suggested Citation

  • Yotsaphat Kittichotsatsawat & Varattaya Jangkrajarng & Korrakot Yaibuathet Tippayawong, 2021. "Enhancing Coffee Supply Chain towards Sustainable Growth with Big Data and Modern Agricultural Technologies," Sustainability, MDPI, vol. 13(8), pages 1-20, April.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:8:p:4593-:d:539890
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    Cited by:

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    2. Alireza Abdollahi & Karim Rejeb & Abderahman Rejeb & Mohamed M. Mostafa & Suhaiza Zailani, 2021. "Wireless Sensor Networks in Agriculture: Insights from Bibliometric Analysis," Sustainability, MDPI, vol. 13(21), pages 1-22, October.
    3. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    4. Tadesse Kenea Amentae & Girma Gebresenbet, 2021. "Digitalization and Future Agro-Food Supply Chain Management: A Literature-Based Implications," Sustainability, MDPI, vol. 13(21), pages 1-24, November.
    5. Sule Birim & Ipek Kazancoglu & Sachin Kumar Mangla & Aysun Kahraman & Yigit Kazancoglu, 2024. "The derived demand for advertising expenses and implications on sustainability: a comparative study using deep learning and traditional machine learning methods," Annals of Operations Research, Springer, vol. 339(1), pages 131-161, August.
    6. Ioannis Margaritis & Michael Madas & Maro Vlachopoulou, 2022. "Big Data Applications in Food Supply Chain Management: A Conceptual Framework," Sustainability, MDPI, vol. 14(7), pages 1-21, March.

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