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Real-Time Anomaly Detection in Cold Chain Transportation Using IoT Technology

Author

Listed:
  • James Gillespie

    (School of Computing, Engineering and Intelligent Systems, Ulster University, Londonderry BT48 7JL, Northern Ireland, UK)

  • Tamíris Pacheco da Costa

    (School of Biosystems & Food Engineering, University College Dublin, D04 V1W8 Dublin, Ireland)

  • Xavier Cama-Moncunill

    (School of Biosystems & Food Engineering, University College Dublin, D04 V1W8 Dublin, Ireland)

  • Trevor Cadden

    (Department of Management, Leadership & Marketing, Ulster University, Belfast BT15 1ED, Northern Ireland, UK)

  • Joan Condell

    (School of Computing, Engineering and Intelligent Systems, Ulster University, Londonderry BT48 7JL, Northern Ireland, UK)

  • Tom Cowderoy

    (Department of Management, Leadership & Marketing, Ulster University, Belfast BT15 1ED, Northern Ireland, UK)

  • Elaine Ramsey

    (Department of Global Business and Enterprise, Ulster University, Londonderry BT48 7JL, Northern Ireland, UK)

  • Fionnuala Murphy

    (School of Biosystems & Food Engineering, University College Dublin, D04 V1W8 Dublin, Ireland)

  • Marco Kull

    (Whysor B.V., 5944 ND Arcen, The Netherlands)

  • Robert Gallagher

    (Musgrave Northern Ireland, Belfast BT3 9HJ, Northern Ireland, UK)

  • Ramakrishnan Ramanathan

    (Essex Business School, University of Essex, Southend-on-Sea, Essex SS1 1LW, UK)

Abstract

There are approximately 88 million tonnes of food waste generated annually in the EU alone. Food spoilage during distribution accounts for some of this waste. To minimise this spoilage, it is of utmost importance to maintain the cold chain during the transportation of perishable foods such as meats, fruits, and vegetables. However, these products are often unfortunately wasted in large quantities when unpredictable failures occur in the refrigeration units of transport vehicles. This work proposes a real-time IoT anomaly detection system to detect equipment failures and provide decision support options to warehouse staff and delivery drivers, thus reducing potential food wastage. We developed a bespoke Internet of Things (IoT) solution for real-time product monitoring and alerting during cold chain transportation, which is based on the Digital Matter Eagle cellular data logger and two temperature probes. A visual dashboard was developed to allow logistics staff to perform monitoring, and business-defined temperature thresholds were used to develop a text and email decision support system, notifying relevant staff members if anomalies were detected. The IoT anomaly detection system was deployed with Musgrave Marketplace, Ireland’s largest grocery distributor, in three of their delivery vans operating in the greater Belfast area. Results show that the LTE-M cellular IoT system is power efficient and avoids sending false alerts due to the novel alerting system which was developed based on trip detection.

Suggested Citation

  • James Gillespie & Tamíris Pacheco da Costa & Xavier Cama-Moncunill & Trevor Cadden & Joan Condell & Tom Cowderoy & Elaine Ramsey & Fionnuala Murphy & Marco Kull & Robert Gallagher & Ramakrishnan Raman, 2023. "Real-Time Anomaly Detection in Cold Chain Transportation Using IoT Technology," Sustainability, MDPI, vol. 15(3), pages 1-24, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:3:p:2255-:d:1046806
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    References listed on IDEAS

    as
    1. Zhibo Pang & Qiang Chen & Weili Han & Lirong Zheng, 2015. "Value-centric design of the internet-of-things solution for food supply chain: Value creation, sensor portfolio and information fusion," Information Systems Frontiers, Springer, vol. 17(2), pages 289-319, April.
    2. Verena Brenner, 2015. "Causes of Supply Chain Disruptions," Springer Books, Springer, edition 127, number 978-3-658-08662-6, March.
    3. Cattaneo, Andrea & Federighi, Giovanni & Vaz, Sara, 2021. "The environmental impact of reducing food loss and waste: A critical assessment," Food Policy, Elsevier, vol. 98(C).
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    Cited by:

    1. Lohithaksha M. Maiyar & Ramakrishnan Ramanathan & Indira Roy & Usha Ramanathan, 2023. "A Decision Support Model for Cost-Effective Choice of Temperature-Controlled Transport of Fresh Food," Sustainability, MDPI, vol. 15(8), pages 1-22, April.

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