A Review of Artificial Intelligence Applications in Cold Chain and Reverse Logistics

artificial intelligence cold chain reverse logistics predictive maintenance supply chain management systematic literature review

Authors

  • Josip HABAZIN
    josip.habazin@gmail.com
    University of Zagreb, Faculty of Transport and Traffic Sciences, Zagreb, Croatia
  • Ivona BAJOR University of Zagreb, Faculty of Transport and Traffic Sciences, Zagreb, Croatia
  • Patricija BAJEC University of Ljubljana, Faculty of Maritime Studies and Transport, Ljubljana, Slovenia
  • Tomislav ROŽIĆ University of Zagreb, Faculty of Transport and Traffic Sciences, Zagreb, Croatia

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The development of artificial intelligence (AI) is profoundly transforming modern supply chains, particularly cold chains in dairy, pharmaceutical and perishable goods industries. By leveraging technologies such as the Internet of Things (IoT), blockchain and predictive maintenance, logistics operations can achieve greater efficiency, quality and sustainability. The dairy sector, with its stringent temperature controls and rapid delivery requirements, is especially well-suited for AI integration. Simultaneously, growing environmental awareness is accelerating the adoption of AI in reverse logistics to support the transition to a circular economy. In this study, we examine the technological foundations and baseline capabilities of AI in logistics, explore how AI supports sustainability-driven reverse logistics and assess its potential to enable the integrated management of cold and general supply chains. To that end, we applied a systematic literature review methodology involving structured database searches, screening and analysis of 95 scientific articles published between 2010 and 2024. The articles were categorised into three domains: AI in logistics, AI in reverse logistics and the integrated management of cold and general supply chains. The findings highlight the need for further research to develop AI-based solutions for predictive maintenance, temperature monitoring, demand forecasting and return management. Such advancements aim to improve the resilience and efficiency of cold chain and reverse logistics systems while enabling integrated, sustainable supply chain management.

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