IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i2p858-d723308.html
   My bibliography  Save this article

A Data-Driven Packaging Efficiency Optimization Method for a Low Carbon System in Agri-Products Cold Chain

Author

Listed:
  • Jingjie Wang

    (Institute of Agricultural Economics and Information, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China)

  • Xiaoshuan Zhang

    (College of Engineering, China Agricultural University, Beijing 100083, China
    Beijing Laboratory of Food Quality and Safety, Beijing 100083, China)

  • Xiang Wang

    (College of Engineering, China Agricultural University, Beijing 100083, China
    Beijing Laboratory of Food Quality and Safety, Beijing 100083, China)

  • Hongxing Huang

    (Institute of Agricultural Economics and Information, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China)

  • Jinyou Hu

    (College of Engineering, China Agricultural University, Beijing 100083, China
    Beijing Laboratory of Food Quality and Safety, Beijing 100083, China)

  • Weijun Lin

    (Institute of Agricultural Economics and Information, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China
    Key Laboratory of Urban Agriculture in South China, Ministry of Agriculture and Rural Affairs, Guangzhou 510640, China)

Abstract

The of monitoring the Internet of Things (IoT) in the cold chain allows process data, including packaging data, to be more easily accessible. Proper optimization modelling is the core driving force towards the green and low-carbon operation of cold chain logistics, laying the necessary foundation for the development of a data-driven modelling system. Since efficient packaging is necessary for loss control in the cold chain, its final efficiency during circulation is important for realizing continuous loss prevention and efficient supply. Thus, it is urgent to determine how to utilize these continuously acquired data and how to formulate a more accurate packaging efficiency control methodology in the agri-products cold chain. Through continuous monitoring, we examined the feasibility of this topic by focusing on the concept of data-driven evaluation modelling and the dynamic formation mechanism of comprehensive packaging efficiency in cold chain logistics. The packaging efficiency in the table grape cold chain was used as an example to evaluate the comprehensive efficiency evaluation index system and data-driven evaluation framework proposed in this paper. Our results indicate that the established methodology can adapt to the continuity of comprehensive packaging efficiency, also reflecting the comprehensive efficiency evaluation of the packaging for different times and distances. Through the evaluation of our results, the differences and the dynamic processes between different final packaging efficiencies at different moments are effectively displayed. Thus, the continuous improvement of a low-carbon system in cold chain logistics could be realized.

Suggested Citation

  • Jingjie Wang & Xiaoshuan Zhang & Xiang Wang & Hongxing Huang & Jinyou Hu & Weijun Lin, 2022. "A Data-Driven Packaging Efficiency Optimization Method for a Low Carbon System in Agri-Products Cold Chain," Sustainability, MDPI, vol. 14(2), pages 1-17, January.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:2:p:858-:d:723308
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/2/858/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/2/858/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sundarakani, Balan & Ajaykumar, Aneesh & Gunasekaran, Angappa, 2021. "Big data driven supply chain design and applications for blockchain: An action research using case study approach," Omega, Elsevier, vol. 102(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Liu, Zheng & Huang, Yu-Qing & Shang, Wen-Long & Zhao, Yuan-Jun & Yang, Zao-Li & Zhao, Zhao, 2022. "Precooling energy and carbon emission reduction technology investment model in a fresh food cold chain based on a differential game," Applied Energy, Elsevier, vol. 326(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Su, Dan & Zhang, Lijun & Peng, Hua & Saeidi, Parvaneh & Tirkolaee, Erfan Babaee, 2023. "Technical challenges of blockchain technology for sustainable manufacturing paradigm in Industry 4.0 era using a fuzzy decision support system," Technological Forecasting and Social Change, Elsevier, vol. 188(C).
    2. Jinxuan Song & Xu Yan, 2023. "Impact of Government Subsidies, Competition, and Blockchain on Green Supply Chain Decisions," Sustainability, MDPI, vol. 15(4), pages 1-27, February.
    3. Liu, Weihua & Long, Shangsong & Wei, Shuang, 2022. "Correlation mechanism between smart technology and smart supply chain innovation performance: A multi-case study from China's companies with Physical Internet," International Journal of Production Economics, Elsevier, vol. 245(C).
    4. Balan Sundarakani & Okey Peter Onyia, 2021. "Fast, furious and focused approach to Covid-19 response: an examination of the financial and business resilience of the UAE logistics industry," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 26(4), pages 237-258, December.
    5. Li, Qiu-xiang & Ji, Hui-min & Huang, Yi-min, 2022. "The information leakage strategies of the supply chain under the block chain technology introduction," Omega, Elsevier, vol. 110(C).
    6. Ahmad A. A. Khanfar & Mohammad Iranmanesh & Morteza Ghobakhloo & Madugoda Gunaratnege Senali & Masood Fathi, 2021. "Applications of Blockchain Technology in Sustainable Manufacturing and Supply Chain Management: A Systematic Review," Sustainability, MDPI, vol. 13(14), pages 1-20, July.
    7. Khalid A. Eldrandaly & Nissreen El Saber & Mona Mohamed & Mohamed Abdel-Basset, 2022. "Sustainable Manufacturing Evaluation Based on Enterprise Industry 4.0 Technologies," Sustainability, MDPI, vol. 14(12), pages 1-22, June.
    8. Hongbo Tu & Mo Pang & Lin Chen, 2023. "Freshness-Keeping Strategy of Logistics Service Providers: The Role of the Interaction between Blockchain and Overconfidence," Mathematics, MDPI, vol. 11(17), pages 1-35, August.
    9. Ahmed Zainul Abideen & Jaafar Pyeman & Veera Pandiyan Kaliani Sundram & Ming-Lang Tseng & Shahryar Sorooshian, 2021. "Leveraging Capabilities of Technology into a Circular Supply Chain to Build Circular Business Models: A State-of-the-Art Systematic Review," Sustainability, MDPI, vol. 13(16), pages 1-26, August.
    10. Gehrlein, Jonas & Miebs, Grzegorz & Brunelli, Matteo & Kadziński, Miłosz, 2023. "An active preference learning approach to aid the selection of validators in blockchain environments," Omega, Elsevier, vol. 118(C).
    11. Rakshit, Sandip & Islam, Nazrul & Mondal, Sandeep & Paul, Tripti, 2022. "Influence of blockchain technology in SME internationalization: Evidence from high-tech SMEs in India," Technovation, Elsevier, vol. 115(C).
    12. Peng Xing & Junzhu Yao, 2022. "Power Battery Echelon Utilization and Recycling Strategy for New Energy Vehicles Based on Blockchain Technology," Sustainability, MDPI, vol. 14(19), pages 1-21, September.
    13. Ulpan Tokkozhina & Ana Lucia Martins & Joao C. Ferreira, 2023. "Uncovering dimensions of the impact of blockchain technology in supply chain management," Operations Management Research, Springer, vol. 16(1), pages 99-125, March.
    14. Ashish Dwivedi & Dindayal Agrawal & Sanjoy Kumar Paul & Saurabh Pratap, 2023. "Modeling the blockchain readiness challenges for product recovery system," Annals of Operations Research, Springer, vol. 327(1), pages 493-537, August.
    15. Anubhav Mishra & Anuja Shukla, 2023. "Gyan Fresh: Digital Transformation of Dairy Business with Resilience and Technology Innovation," FIIB Business Review, , vol. 12(1), pages 20-30, March.
    16. Jacob Lohmer & Elias Ribeiro da Silva & Rainer Lasch, 2022. "Blockchain Technology in Operations & Supply Chain Management: A Content Analysis," Sustainability, MDPI, vol. 14(10), pages 1-88, May.
    17. Yunfei Yang & Guifei Qu & Lianlian Hua & Lifeng Wu, 2022. "Knowledge Mapping Visualization Analysis of Research on Blockchain in Management and Economics," Sustainability, MDPI, vol. 14(22), pages 1-24, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:2:p:858-:d:723308. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.