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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.

In the cold supply chain (SC), effective risk management is regarded as an essential component to address the risky and uncertain SC environment in handling time- and temperature-sensitive products. However, existing multi-criteria decision-making (MCDM) approaches greatly rely on expert opinions for pairwise comparisons. Despite the fact that machine learning models can be customised to conduct pairwise comparisons, it is difficult for small and medium enterprises (SMEs) to intelligently measure the ratings between risk criteria without sufficiently large datasets. Therefore, this paper aims at developing an enterprise-wide solution to identify and assess cold chain risks.

A novel federated learning (FL)-enabled multi-criteria risk evaluation system (FMRES) is proposed, which integrates FL and the best–worst method (BWM) to measure firm-level cold chain risks under the suggested risk hierarchical structure. The factors of technologies and equipment, operations, external environment, and personnel and organisation are considered. Furthermore, a case analysis of an e-grocery SC in Australia is conducted to examine the feasibility of the proposed approach.

Throughout this study, it is found that embedding the FL mechanism into the MCDM process is effective in acquiring knowledge of pairwise comparisons from experts. A trusted federation in a cold chain network is therefore formulated to identify and assess cold SC risks in a systematic manner.

A novel hybridisation between horizontal FL and MCDM process is explored, which enhances the autonomy of the MCDM approaches to evaluate cold chain risks under the structured hierarchy.

The purpose of this paper is to analyze how far technology and information enable, facilitate or support the planning and implementation decisions in humanitarian vaccine cold chains for vaccination campaigns. The authors specifically focus on three emerging technologies that have the potential to create more flexible conditions in the field, and identify the need to further explore the link between uncertainty, information and irreversibility.

The authors present a basic structure for the analysis of cold chain disruptions in terms of three distinct yet connected layers of deficient infrastructure and capacity, information gaps and failures in decision making. The authors then review three humanitarian technologies and their impact on vaccine campaigns along these layers. From there, a research agenda is developed to address research gaps this review brought forward.

Three critical research gaps in the areas of technology innovation for humanitarian vaccine cold chain management are presented. The authors argue that technology to improve capacity, information and decisions need to be aligned, and that the areas of uncertainty, information and irreversibility require further investigation to achieve this alignment. In this way, the paper contributes to setting the research agenda on vaccine cold chains and connects humanitarian logistics to technology, information management and decision making.

This paper presents the humanitarian vaccine cold chain problem from an original angle by illuminating the implications of technology and information on the decisions made during the planning and implementation phases of a vaccine campaign. The authors develop an agenda to provide researchers and humanitarians with a perspective to improve cold chain planning and implementation at the intersection of technology, information and decisions.

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.

The cold chain has become an integral part of the supply chain of perishable items. Recent studies have shown a critical absence of a strong and dependable cold chain in developing economies. The purpose of this paper is to set out to identify and inter‐relate the inhibitors that significantly influence the efficiency of a cold chain in developing economies like India.

The synthesis and prioritization of inhibitors are done on the basis of an extensive literature review as well as consultation with academicians and industrial professionals. Using semi‐structured interviews and Fuzzy Interpretive Structure Modeling (FISM) approach, the research presents a hierarchy‐based model.

The end result is a model that establishes the relationships among the identified inhibitors with their respective dominance. The research shows that there exists a group of inhibitors having a high driving power and low dependence with strategic importance and requiring maximum attention and another group includes inhibitors that have high dependence and the consequential actions.

At the time when cold chain is the key domain for the food sector, these findings will be immensely helpful for industry professionals, Government, non‐government, academia and the community in developing strategies and impounding the root causes responsible for the inefficient and weak cold chain in India. The Indian situation echoes to the situation in most of the developing economies and similar solutions can apply there also. These findings will be truly useful for organizations that are planning to operate food chains in developing nations.

Presentation of inhibitors in hierarchy and their classification into driver and dependent categories with their respective dominance on the system is a unique effort in the area of cold chain management. This would help decision makers to better utilize the limited resources.

This study proposes the use of cumulative prospect theory (CPT) to predict over- and under-estimation of risks and the counteractive adjustment in a cold chain context. In particular, the purpose of this paper is to address the importance of the socio-demographic characteristics of an individual in influencing risk attitude and the analysis of measurable risk probability.

This study uses CPT as the basis to develop a decision analysis model in which the two functions of value editing and probability weighting are nonlinear to adequately determine the flexible risk attitudes of individuals, as well as their prospects with numerous outcomes and different probabilities. An experiment was conducted to obtain empirical predictions, and an efficient Markov Chain Monte Carlo algorithm was applied to overcome the nonlinearity and dimensionality in the process of parameter estimation.

The respondents overweigh the minor cold chain risks with small probabilities and behave in a risk-averse manner, while underweighting major events with larger ones, thereby leading to risk-seeking behavior. Judgment distortion regarding probability was observed under risk decision with a low probability and a high impact. Moreover, the findings indicate that factors, such as gender, job familiarity and confidentiality significantly influence the risk attitudes and subjective probability weighting of the respondents.

The findings fit the framework of CPT and extend this theory to deal with human risk attitudes and subjective bias in cold chains. In particular, this study enhances the literature by providing an analysis of cold chain risk from both the human decision-making and managerial perspectives. Moreover, this research determined the importance of the socio-demographic characteristics of an individual to explain the variability in risk attitudes and responses.

Managers must consider the issues of flexible risk attitude and subjective judgment when making choices for risk mitigation strategies. Given the focus on counteractive adjustment for over- and under-estimated risk, firms could evaluate cold chain risk more accurately, and thereby enhance their resilience to risky events while reducing the variability of their performance.

The current study is the first to materialize the phenomena of over- and under-estimation of cold chain risks, as well as to emphasize the different characteristics for loss aversion and judgment distortion at the individual level.

The purpose of this paper is to develop a quantitative risk assessment method for agricultural products cold chain logistics to assess the condition of the fresh agricultural products cold chain process objectively and accurately.

A risk assessment index system of agricultural products cold chain logistics is designed on the basis of the risk identification for the process of agricultural products cold chain logistics. This paper first uses catastrophe progression method and a new maximum deviation method to build an improved catastrophe progression assessment model for agricultural products cold chain logistics. In order to verify the reliability and validity of the model, two representative enterprises are selected as the case in the study.

The results in the empirical research indicate strong support for the assessment model and coincide with the reality. The risk assessment index system can also reflect the key risk factors from agricultural products cold chain logistics scientifically. In addition, the improved catastrophe progression assessment method proposed in this paper can be scientific and reasonable to predict risk.

This paper contributes to provide a new risk assessment model for agricultural products cold chain logistics. The new model overcomes the limitation of subjective empowerment and it increases the objectivity and scientificity in the process of cold chain logistics risk assessment. This paper also shows that practitioners involved in the field of products cold chain logistics can manage the potential risk by a set of scientific methods for assessing the risk before the accident.

The paper provides a practical guideline to practitioners, especially for cold chain logistics managers, relevant management departments, and cold chain logistics management consultants. It is proved that the new risk assessment method and the risk assessment index system of agricultural products cold chain logistics can help them assess the risk scientifically and reasonably.

Although the calculation is simple, the new model can overcome the limitation of subjective empowerment scientifically and reasonably, and thus has important practical value.

The objective of the present study is to identify and analyze a set of critical success factors (CSFs) for ice-cream industry [cold chain management (CCM)] that helps in increasing the efficacy, quality, performance and growth of the supply chain organization.

A questionnaire survey with companies in ice-cream sector and a panel study with experts were conducted to identify and validate CSFs and their associated sub-factors. Eight CSFs identified from the cold chain domain vetted for the ice-cream industry and then prioritized by using one of the most well-known decision-making frameworks, Decision-Making Trial and Evaluation Laboratory. The general verdicts of the modelling and its application to the real-world case have been tested through an ice-cream company supply chain.

The result shows that the significant CSFs accountable for the growth of the ice-cream industry are the infrastructure and capacity building, consistent product improvement and operational efficiencies of the value chain. Subsequently, it was identified that the use of IT and related technologies and improved processes for operations also play a considerable role in the performance of ice-cream industry.

The study successfully outlines the effective CCM practices for critical issues. The proposed methodology and factor modelling case demonstration might be useful in analyzing the logistic chains of products such as fruits, drugs and meat.

The meritorious identification of critical areas and executing mitigation plans bring notable benefits to the firms such as improved operational efficiencies, improved time to market performance and product innovation, which bring additional benefits to the producers.

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.

Generic business process models for the supply chain do not cover the specific requirements of the cold chain catering to the needs of temperature-sensitive, perishable goods. The purpose of this paper is to draft a reference model specific to the cold chain.

Following an object-oriented modeling approach, conceptual elements that have been synthesized from a literature review are transferred into the static view (object model) of the reference model. In addition, the reference model’s dynamic properties representing the business process view are outlined.

While a few atomic process steps are sufficient to model the cold chain’s dynamic properties, the complexity of the cold chain lies in the object model. The classes of the object model are highly interrelated and cover four domains: perishable product, information technology, infrastructure/equipment and regulatory framework. This technical approach is more adequate to the complex nature of cold chains than typical business process models.

In the present draft status, the reference model is limited by the pure conceptual approach of this paper. As it is in the nature of things for a draft of a reference model, case studies to challenge the draft and a discourse of experts are required before detailed specifications can be added or any software implementation can be started. It is expected that the reference model is able to substantially support further research on cold chain design and optimization.

The cold chain reference model is intended to be a standard toolbox for planning and evaluating cold chains. By integrating the technical, information technology and regulatory objects behind the business processes, it allows to design and analyze cold chains from a more holistic perspective.

To the best knowledge of the author, this paper is the first to outline a reference model for the cold chain that goes beyond the business processes.