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Cold supply chain technology is critical for extending the shelf life of perishable leafy green vegetables. This study aims to investigate the concept of managing leafy green products using cold supply chain technology and visualise the findings.

Using expert interviews and data visualisation approaches, this study examines how organisations deal with the complexity of cold supply chain processes and networks. Thematic data analysis was conducted. Two types of software were used to accomplish the research objectives. The first software used AntConc version 3.5.8 with word frequency (N-gram) analysis, whereas the second software, VOSViewer offered co-occurrence network visualisation and cluster analysis.

The findings show that the appropriate design of cold chain technology is critical in ensuring the freshness and quality of leafy green vegetables. The primary goal of managing the complexity of the cold supply chain is to achieve product freshness and energy efficiency. Regardless of the importance of energy efficiency, cold supply chains require warehouse management solutions for transportation and storage.

This study found that proper design and selection of appropriate technology in the cold supply chain have driven the companies to improve the firms’ competitive advantage while delivering the best quality of perishable leafy green food products. In addition, the freshness, quality, safety, and health of leafy green vegetables will be determined by the company’s capacity to handle long-distance transportation and select the appropriate distribution channels and storage. Warehouse management system technology was found to be secondary compared to cold chain technology, although distribution and warehousing practices are critical for supply chain performance.

This study has established the conceptual indicators based on best practices and outcomes for the cold supply chain. This study argued that cold supply chain management and performance should be monitored independently. Furthermore, the theory of technological adoption can be expanded to include product nature as a driver. Finally, this study has established cold chain best practices based on a perishable supply chain perspective. The findings of this study can promote healthy foods to solve zero hunger and achieve sustainable development goals. Although this study demonstrates that technology improves supply chain practises, cold storage and logistics benefit the most from technological advancements. In contrast, non-cold supply chains benefit from technology-driven improvements in performance.

This study aims to investigate the vulnerability of cold chain logistics through a comprehensive assessment and provide targeted control measures.

The index system of the cold chain vulnerability assessment was established with knowledge obtained from three different dimensions, namely, exposure, sensitivity and adaptability. The final index weight was determined through combination of the intuitionistic fuzzy (IF) entropy and compromise ratio approaches, followed by the comprehensive vulnerability assessment through the two-stage grey comprehensive measurement model. The feasibility and effectiveness of the proposed method were verified by evaluation with SF, HNA, China Merchants and COFCO as target examples.

The results revealed that the most influential factors in the cold chain vulnerability problem were the temperature reaching the standard, as well as the storage and preservation levels; through their analysis combined with the overall cold chain vulnerability assessment, the targeted control measures were obtained.

Based on the research perspective of cold chain vulnerability assessment, a novel assessment model of cold chain logistics vulnerability was proposed, which is based on IF entropy two-stage grey comprehensive measurement. It provides more powerful theoretical support to improve the quality management of cold chain products.

Cold chain has become an integral part of the supply chain domain. The purpose of this paper is to consider all the significant factors in a single study. This will result into a better model to study the effectiveness of a cold chain because there has been absence of such an integrated study.

The basis of the factors is justified by performing extensive literature review. Inter relations are drawn based on critical analysis of each factor and its implications on cold chain. Bayesian Network is used to develop the model.

The end result is an established model, depicting the interdependencies of the factors. The model ultimately provides effectiveness of a given cold chain when the corresponding values of factors are put in.

The findings will be helpful for government and non-government bodies to analyse the effectiveness of a cold chain. This can be used to increase the performance of different stages in the cold chain. From a business perspective, an investor can analyse the cold chains of various geographies in order to make an investment decision.

The value lies in developing and introducing new factors which were not considered in the related literature previously. To identify the inter relations among the factors in order to build a causal model is another contribution of the present paper. This would assist in decision-making process with respect to any given cold chain. It can be applied to any cold chain as proposed model is not specific to a particular country or material.

The purpose of the paper is to identify the multiple types of data that can be collected and analyzed by practitioners across the cold chain, the ICT infrastructure required to enable data capture and how to utilize the data for decision making in cold chain logistics.

Content analysis based literature review of 38 selected research articles, published between 2000 and 2016, was used to create an overview of data capture, technologies used for collection and sharing of data, and decision making that can be supported by the data, across the cold chain and for different types of perishable food products.

There is a need to understand how continuous monitoring of conditions such as temperature, humidity, and vibration can be translated to support real-time assessment of quality, determination of actual remaining shelf life of products and use of those for decision making in cold chains. Firms across the cold chain need to adopt appropriate technologies suited to the specific contexts to capture data across the cold chain. Analysis of such data over longer periods can also unearth patterns of product deterioration under different transportation conditions, which can lead to redesigning the transportation network to minimize quality loss or to take precautions to avoid the adverse transportation conditions.

The findings need to be validated through further empirical research and modeling. There are opportunities to identify all relevant parameters to capture product condition as well as transaction data across the cold chain processes for fish, meat and dairy products. Such data can then be used for supply chain (SC) planning and pricing products in the retail stores based on product conditions and traceability information. Addressing some of the above research gaps will call for multi-disciplinary research involving food science and engineering, information technologies, computer science and logistics and SC management scholars.

The findings of this research can be beneficial for multiple players involved in the cold chain like food processing companies, logistics service providers, ports and wholesalers and retailers to understand how data can be effectively used for better decision making in cold chain and to invest in the specific technologies, which will suit the purpose. To ensure adoption of data analytics across the cold chain, it is also important to identify the player in the cold chain, which will drive and coordinate the effort.

This paper is one of the earliest to recognize the need for a comprehensive assessment for adoption and application of data analytics in cold chain management and provides directions for future research.

Since the handling of environmentally sensitive products requires close monitoring under prescribed conditions throughout the supply chain, it is essential to manage specific supply chain risks, i.e. maintaining good environmental conditions, and ensuring occupational safety in the cold environment. The purpose of this paper is to propose an Internet of Things (IoT)-based risk monitoring system (IoTRMS) for controlling product quality and occupational safety risks in cold chains. Real-time product monitoring and risk assessment in personal occupational safety can be then effectively established throughout the entire cold chain.

In the design of IoTRMS, there are three major components for risk monitoring in cold chains, namely: wireless sensor network; cloud database services; and fuzzy logic approach. The wireless sensor network is deployed to collect ambient environmental conditions automatically, and the collected information is then managed and applied to a product quality degradation model in the cloud database. The fuzzy logic approach is applied in evaluating the cold-associated occupational safety risk of the different cold chain parties considering specific personal health status. To examine the performance of the proposed system, a cold chain service provider is selected for conducting a comparative analysis before and after applying the IoTRMS.

The real-time environmental monitoring ensures that the products handled within the desired conditions, namely temperature, humidity and lighting intensity so that any violation of the handling requirements is visible among all cold chain parties. In addition, for cold warehouses and rooms in different cold chain facilities, the personal occupational safety risk assessment is established by considering the surrounding environment and the operators’ personal health status. The frequency of occupational safety risks occurring, including cold-related accidents and injuries, can be greatly reduced. In addition, worker satisfaction and operational efficiency are improved. Therefore, it provides a solid foundation for assessing and identifying product quality and occupational safety risks in cold chain activities.

The cold chain is developed for managing environmentally sensitive products in the right conditions. Most studies found that the risks in cold chain are related to the fluctuation of environmental conditions, resulting in poor product quality and negative influences on consumer health. In addition, there is a lack of occupational safety risk consideration for those who work in cold environments. Therefore, this paper proposes IoTRMS to contribute the area of risk monitoring by means of the IoT application and artificial intelligence techniques. The risk assessment and identification can be effectively established, resulting in secure product quality and appropriate occupational safety management.

This article examines the business relationships in the cold chain used for exporting food to new markets in developing countries. The American Potato Trade Alliance, a cross‐network alliance that includes all levels of the value chain, is the subject of case study research involving participant observation and fieldwork in the Philippines and Thailand. Multinational restaurant companies manage technical challenges in target markets with tight specifications and exclusive supply chains, while smaller firms use extensive networks to supply imported frozen potatoes. Pricing strategies for cold chain services are closely related to quality and potentially affect the availability of outsourced cold chain services. Opportunistic behavior by buyers could reduce incentives for private investment in cold chain infrastructure, while long‐term commitment by chain partners would strengthen the potential for private markets to provide cold chain services in newly developing markets.

This study focuses on identifying potential challengers affecting cold chain performance in the pharmaceutical industry. Considering the history of humanity, every pandemic experienced could only be controlled with the vaccine found. While the world is fighting the unforgettable epidemic called COVID-19, scientists are also working to find the therapeutic vaccine. The vaccines studied have different temperature storage and transport properties. In the pharmaceutical industry, it is necessary to know and analyse every step of the cold supply chain to provide the most appropriate and safe cooling level. In addition, it is important to understand the relationship between all the facilities, equipment, tools and materials needed to avoid mistakes along the chain.

Hence, this study focuses on identifying potential challengers affecting sustainable cold supply chain performance in the pharmaceutical industry and proposing a conceptual framework that involves these main challengers. In this study, firstly, different main and sub-factors are defined from the literature, and fuzzy Decision Making Trial and Evaluation Laboratory method is applied to analyse the causal link between these factors for an effective application.

Results showed that packaging, transportation and shipping, storage specifications and handling practices, inventory management, technical issues and delivery delay are the most affected factors during the sustainable cold supply chain performance in the pharmaceutical industry. This study offers both managerial implications and a roadmap that are designed with a holistic view to guide manufacturer, logistics parties and even policymakers.

Some of the studies related to the pharmaceutical industry are monitoring and controlling the temperature in the cold supply chain steps; the other part is the studies where the chain steps are examined with a focus on production or transportation. While these issues are the focus, the requirements and conditions of each stage of the supply chain must be studied for a safe, effective and sustainable cold chain beyond the current global pandemic crisis. To the best of the authors’ knowledge, this is the first study that highlights identifying the potential challengers that affect cold supply chain performance for the pharmaceutical industry both theoretically and empirically, solving the causal relationships among these challengers and designing a holistic framework.

The purpose of this paper is to identify the obstacles that impede cold chain implementation in developing countries.

A grounded theory methodology was employed to execute this research. Eight semi-structured, in-depth interviews were conducted with senior managers in Vietnam. In addition, consistent with grounded theory guidelines, the authors utilized archival data to facilitate theory development.

The results of the analysis revealed a total of ten key obstacles: deficient professional skills, lack of quality and safety-control measures, high concentration of intermediaries, poor infrastructure, lack of information systems, high cost of installation and operation, inadequate education and training at farmer level, deficiency of standardization, lack of government support for local businesses and social norms.

Although many authors have recognized the enormity of food wastage and limited employment of cold chain in developing countries compared to developed countries, few efforts have been made to explore the reasons for the limited use of cold chains in developing countries. There are several noteworthy theoretical and managerial contributions that emerged from addressing this gap.

The cold supply chain industry is still emerging and digital transformation is in the nascent stage in this industry. This paper argues that there are various barriers to implementing blockchain technology in the cold supply chain and aims to develop and validate a model for overcoming key barriers to implementing blockchain technology in the cold supply chain.

The adoption of blockchain technology was proposed through interpretive structural modeling (ISM) and further it is validated using structural equation modeling (SEM).

In this study, ten key barriers to implementing blockchain technology in the cold supply chain were identified, modelled and analysed. Poor leadership style of top management was found to be the most important barriers to implementing blockchain technology in the cold supply chain. The results of SEM indicate that all the paths are supported. The findings showcase the barriers responsible for the lack of blockchain technology infrastructure that ultimately impacts the cold supply chains.

This study highlights the fact that the fate of blockchain technology infrastructure development depends on the leadership style of top management. Demonstrating good leadership style by top management can help overcome the barriers. A good leader pulls the entire team instead of pushing the team. A good leader can guide the entire team to improve IT governance, financial investment, digital footprint, digital readiness, skills and collaboration with service providers to implement blockchain technology. Not only that, a good leader provides mental strength to the team and helps overcome the fear of implementing blockchain in the cold supply chain. A good leader demonstrates good administrative skills and focus on security and privacy policies.

This is a novel contribution towards analysing the key barriers to implementing blockchain technology in the South African cold supply chain using the integrated ISM–MICMAC and SEM approach.

The demand for the cold chain logistics of agricultural products was investigated through demand forecasting; targeted suggestions and countermeasures are provided. This paper aims to discuss the aforementioned statement.

A Markov-optimised mean GM (1, 1) model is proposed to forecast the demand for the cold chain logistics of agricultural products. The mean GM (1, 1) model was used to forecast the demand trend, and the Markov chain model was used for optimisation. Considering Guangxi province as an example, the feasibility and effectiveness of the proposed method were verified, and relevant suggestions are made.

Compared with other models, the Markov-optimised mean GM (1, 1) model can more effectively forecast the demand for the cold chain logistics of agricultural products, is closer to the actual value and has better accuracy and minor error. It shows that the demand forecast can provide specific suggestions and theoretical support for the development of cold chain logistics.

This study evaluated the development trend of the cold chain logistics of agricultural products based on the research horizon of demand forecasting for cold chain logistics. A Markov-optimised mean GM (1, 1) model is proposed to overcome the problem of poor prediction for series with considerable fluctuation in the modelling process, and improve the prediction accuracy. It finds a breakthrough to promote the development of cold chain logistics through empirical analysis, and give relevant suggestions based on the obtained results.