A Systematic Review on the Intersection of the Cold Chain and Digital Transformation

Abstract

Digital transformation (DT) is reshaping cold chain operations through technologies such as the Internet of Things (IoT), artificial intelligence (AI), blockchain, and digital twins. However, evidence remains fragmented, and a systematic synthesis focused on how these technologies affect cold chain performance, sustainability, and cost-efficiency is limited. This PRISMA-based systematic literature review analyzes 107 studies published between 2009 and 2025 to examine enabling technologies and application areas, operational and sustainability impacts, and the main adoption challenges. The reviewed evidence suggests that digitalization can improve real-time visibility, temperature control, traceability, and energy management, supporting waste reduction and improved quality assurance. Key challenges include high implementation costs and uncertain returns on investment, interoperability constraints, data governance and cybersecurity concerns, and organizational readiness gaps. The paper concludes with implications for managers and policymakers and a future research agenda emphasizing integrated multi-technology solutions, standardized sustainability assessment, and rigorous validation through pilots, testbeds, and real-world deployments to enable scalable and resilient cold chain digitalization.

1. Introduction

The supply chain connects manufacturers, suppliers, and consumers and must cope with fluctuating demand, disruptions, and resource constraints [1,2,3,4,5,6]. Within the broader scope of supply chain operations, the cold chain emerges as a specialized and indispensable segment which is dedicated to preserving the quality and safety of perishable goods [7]. Critical stages encompass storage, transportation, and last-mile delivery, all requiring stringent temperature control. However, the cold chain is characterized by significant energy demands, with energy-intensive cooling and transportation processes contributing substantially to both operational expenses and environmental concerns [8].

The arrival of Industry 4.0 is forcing enterprises to adopt digital transformation (DT) strategies and rethink traditional operations [9]. DT, the integration of digital technologies into all areas of business, offers opportunities to enhance efficiency, agility, and sustainability [10]. The cold chain is at the forefront of this shift, leveraging technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), blockchain, and digital twins [11,12,13]. These transformations promise enhanced visibility, streamlined operations, improved quality control, and the development of resilient cold chain networks.

Existing studies show growing interest in applying digital technologies to cold chain systems, highlighting their potential to address contemporary operational demands and sustainability requirements. Therefore, the purpose of this study is to conduct a systematic literature review on the intersection of DT and the cold chain, focusing on research developed from 2009 to 2025. This review aims to uncover insights guided by the following research questions:

• RQ1. How is DT impacting the cold chain industry?
• RQ2. How can digital twins enhance cold chain operations, and what are the key implementation considerations?
• RQ3. How does DT affect cold chain sustainability?
• RQ4. What are the key benefits and challenges of integrating digital technologies in cold chain operations?
• RQ5. What are the future trends and opportunities for digitalization in the cold chain?

Despite this growing body of work, there is still no comprehensive systematic review focused specifically on DT in cold chains. Managers also lack evidence-based guidance on how to adopt and operate these technologies, what returns to expect, and how to align digitalization with sustainability and cost-efficiency goals [14]. This review addresses these gaps by synthesizing recent studies on digital technologies, applications, benefits, and challenges in cold chains and by translating the findings into implications for managers and policymakers.

The subsequent sections of this paper are structured as follows: Section 2 outlines the research methodology and presents initial data statistics. Section 3 provides an in-depth discussion of the results derived from the literature review. Section 4 focuses on the research gaps found in the existing literature and offers recommendations for future studies. Finally, Section 5 presents the conclusion of this study and summarizes the key findings pertaining to the role of DT within the cold chain.

2. Materials and Methods

This section outlines the methodology used for the systematic literature review on DT in cold chains (2009–2025). It describes the search strategy, screening and eligibility criteria, and the approach to data extraction and synthesis, including analyses of publishing trends, key sources, and citation impact.

2.1. PRISMA Framework

This study adopts a systematic literature review of DT in cold chain management, focusing on enabling technologies, key challenges, and sustainability implications. The review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework [15], ensuring a transparent and rigorous process for identifying, screening, and analyzing relevant studies. The methodological approach is informed by prior systematic reviews of Zhang and Mohammad [16] and Rolf et al. [17] and uses a broad inclusion strategy to capture both qualitative and quantitative studies on digital technologies, applications, benefits, and challenges across cold chain stages. A broad inclusion strategy was applied to capture both qualitative and quantitative evidence relevant to DT applications, benefits, and challenges across cold-chain stages.

The literature search was conducted in Scopus (2009–2025) due to its broad multidisciplinary coverage of peer-reviewed literature. Scopus served as the primary database due to its comprehensive and widely recognized multidisciplinary coverage of peer-reviewed literature across science, technology, engineering, social sciences, and management disciplines [18]. Compared to other databases, it offers broader journal coverage and includes nearly all journals indexed in Web of Science, in addition to many more from emerging and regional sources, which enhances the inclusivity of literature from diverse research contexts. The search was limited to English-language documents to ensure consistency and facilitate analysis. As depicted in Figure 1, the systematic selection process encompassed four distinct phases including identification, screening, eligibility assessment, and inclusion. The initial search yielded 787 articles. Following the removal of duplicates and the application of predefined inclusion and exclusion criteria, 272 articles were assessed for eligibility. The final review included 91 articles that met the selection criteria, supplemented by extra articles leading to the inclusion of 107 articles included in the study. To further strengthen the robustness of the review, a quality assessment process was integrated with the PRISMA framework (see Supplementary Materials). It was a single reviewer who carried out all screening and quality appraisals to ensure methodological consistency and minimize bias by assessing design, data reporting, and relevance to the research objectives.

Although no formal, standardized risk-of-bias instrument was employed, all included studies were manually and critically appraised during screening and selection, with attention to methodological rigor, clarity of reporting, and alignment with the research objectives. To support transparency and comparability, key characteristics and findings from each article were extracted into structured evidence tables, which organized the literature by publication year, primary research focus, methodological approach, and main outcomes. Where appropriate, these tables were complemented by figures that consolidated thematic insights and enhanced the interpretability of the synthesis.

Because of the diverse designs, measures, and contexts represented in the included studies, statistical pooling and meta-analysis were not feasible. Consequently, the results were synthesized descriptively, focusing on abstracting and summarizing key data related to cold chain and DT and then aggregating emergent patterns, trends, and themes to provide a holistic understanding of the outcomes, consistent with the exploratory aims of this review. Formal evaluation of risk of bias from missing results and quantitative sensitivity analyses of pooled effects were not undertaken. Instead, potential reporting bias and heterogeneity were addressed by including a broad range of relevant studies, giving preference to those with clear methods and complete reporting, and qualitatively examining thematic differences, recurring patterns, and cross-study trends. This approach emphasizes the coherence and dependability of the evidence base, preserves the contextual nuance of individual studies, and transparently acknowledges the diversity of designs and methodologies.

2.2. Detailed Document Search Process

During the identification phase, the literature search in Scopus utilized the following designed keywords: (“cold chain” OR “cold supply chain” OR “cold chain logistic”) + “manag*” + (“enhanc*” OR “improv*” OR “optim*”) + (“logistic*” OR “digital” OR “technolog*” OR “digital transformation” OR “transport” OR “reefer” OR “fleet” OR “warehous*” OR “4.0” OR “5.0” OR “temperature” OR “energy consum*” OR “Sustainab*” OR “digital technolog*” OR “internet of things” OR “iot” OR “blockchain” OR “artificial intelligence” OR “ai” OR “machine learning” OR “big data” OR “cloud computing” OR “robot*” OR “automat*” OR “digital twin*” OR “big data” OR “smart” OR “intelligent” OR “augmented reality”). An additional search string, (“cold chain” OR “cold supply chain” OR “cold chain logistic”) + “Digital twin”, was used to identify extra papers. The review included related studies to ensure comprehensive analysis.

Preliminary scoping without time restrictions indicated that most research on digital technologies in cold chains has been published since 2009. The review therefore focused on the period 2009–2025 to capture the main developments associated with Industry 4.0 and the increasing use of IoT, AI, blockchain, and related technologies in cold chain logistics. Using this time window and the search strings described above, the initial search identified 787 English-language records.

During the screening phase, duplicate records identified through the keyword search were removed. Titles and abstracts were then reviewed to exclude studies unrelated to the main focus of this paper. Articles containing irrelevant keywords were also omitted, such as “female,” “male,” “immunization,” “immunization programs,” “adult,” “child,” and “animals” [19,20,21]. Consequently, the screening step resulted in reducing the number of articles to 272. In the subsequent eligibility phase, a more detailed review of the remaining papers was carried out by focusing on the methodologies described within the main text to ensure relevance. For example, some articles were excluded from this review as their primary focus diverged from the central theme of DT’s intersection with the cold chain. While each addresses important aspects of cold chain management, their emphasis lies instead on specific areas such as routing and supply chain optimization, network design and modelling, material and technology-specific applications, regional or industry-focused analyses, logistics and distribution planning, or sustainability-driven investigations, rather than on the overarching role and impact of DT within cold chain operations [22,23,24,25,26,27]. This meticulous approach led to the selection of 91 articles deemed suitable for inclusion in the review. A subsequent, updated search and citation tracking within Scopus identified 16 additional relevant studies that yielded a total of 107 articles included in the study. After the selection process, the retained articles were thoroughly analyzed to address the research questions outlined in this study.

The review adopted a broad search strategy to support a comprehensive and inclusive synthesis of literature. Rather than constraining eligibility to predefined variables, it sought to capture the full range of evidence at the intersection DT and cold chain. Flexible inclusion criteria across study designs, data types, and contexts enabled the incorporation of heterogeneous perspectives, ensuring wide representativeness and analytic depth.

All relevant information was available at the time of data extraction. The inclusion criteria were applied comprehensively and, because they did not impose narrow constraints on variables, no assumptions or imputations for missing data were required. This broad approach supported representativeness and ensured alignment with the study’s objectives.

Given the review’s descriptive orientation, no formal assessment of certainty in the evidence was undertaken. The synthesis emphasized identifying patterns and themes rather than quantitatively grading confidence in outcomes. Reliability was supported by prioritizing studies with rigorous methods and transparent reporting at selection, and by applying inclusion criteria calibrated to admit high-quality, relevant literature, thereby strengthening the credibility of the conclusions.

2.3. Chronological Growth of Publications

According to Figure 2, the analysis of publication trends from 2009 to 2025 reveals a notable evolution in research related to DT and the cold chain. The period from 2009 to 2019 exhibits a low level of activity, with annual publications generally remaining below five, suggesting an early stage of exploration in this interdisciplinary area. A shift occurs around 2020, marked by a gradual increase in publications, indicating growing recognition of DT’s relevance to the cold chain. This upward trend culminates in a peak in 2023, with approximately 40 publications, potentially driven by technological advancements and the need for more resilient cold chain solutions highlighted by global events. While the chart suggests a sharp decline in publications in 2024 and 2025, this should be interpreted cautiously. This apparent decline is likely due to a combination of factors, including potential data lags in indexing recent publications and, importantly, the fact that data collection for this review concluded in the first few days of January 2025, thus not capturing the full extent of 2025 publications. Nonetheless, it warrants further investigation into evolving research within this dynamic field.

2.4. Top Sources of Publication

The 107 papers investigated, along with the 94 journal and conference proceedings, were distributed across a range of sources. Figure 3 showcases the top 10 most frequent publication sources, accounting for 22 of the analyzed papers, thus representing approximately 21% of the total. Notably, Journal of Food Engineering and Scientific Reports each contributed 3 papers, highlighting their prominence in this field. Similarly, 2 papers each appeared in other top journals such as Resources, Conservation and Recycling, Journal of Industrial Information Integration, International Journal of Production Research (with two separate publications), Lecture Notes in Electrical Engineering, Global Journal of Flexible Systems Management, Expert Systems with Applications, and Computers, Materials and Continua. The majority of these papers are published in leading journals within disciplines such as Food Science, General Science, Sustainability, Industrial Information Systems, Production Research, Electrical Engineering, Flexible Systems Management, Expert Systems, and Materials Science.

2.5. Citation Impact of the Reviewed Articles

The ten most cited works (Figure 4) reveal the key contributions shaping research at DT and cold chain intersection. The effect of a paper is determined by calculating how frequently it appears in the writings of other authors [28]. The work by Sunny et al. [29] lead with 361 citations, exploring blockchain-based traceability systems that enhance supply chain transparency through IoT integration and smart contracts. Following closely, Defraeye et al. [30] received 135 citations for developing biophysical twins and digital avatars that simulate temperature and biodegradation processes in food logistics. Other influential contributions include Martínez-Sala et al. [31], who pioneered the integration of Radio-Frequency Identification (RFID) tags into packaging and transport units for enhanced tracking. Additionally, Onwude et al. [32] reviewed advanced digital tools such as imaging and IoT for reducing postharvest losses; and Ivanov [33] proposed the intelligent digital twin (iDT) as a framework for resilience analysis and disruption prediction. Additional notable works include Tsang et al. [34], Ivanov [35], Shoji et al. [14], Bhattacharyya et al. [36], and Wang et al. [37] further advanced cold chain monitoring through low-cost sensors, multi-gas detection, and digital twins for shelf-life management. Collectively, these studies represent the foundation of current research on the digitalization of cold chain systems, illustrating both the technological diversity and the rapid evolution of the field.

3. Discussion of Key Findings

This section synthesizes the main findings on how DT is reshaping cold chain operations. It first links key challenges to corresponding technological solutions (Table 1), then examines digital twin applications and their benefits (Table 2), and reviews sustainability initiatives enabled by digital tools (Figure 5). It concludes by summarizing the broader applications and benefits of these technologies (Table 3) and the technological, economic, organizational, and regulatory barriers that shape their adoption (Figure 6).

3.1. Overview of Cold Chain Issues and Technological Solution

Across cold chain stages, the literature consistently identifies temperature control and product quality as dominant operational concerns (Table 1). Temperature excursions and weak monitoring contribute to spoilage, economic loss, and, in health-related chains, potentially severe safety consequences. DT addresses these issues through real-time sensing, data-driven analytics, and automated or semi-automated control, which improve visibility and responsiveness across storage, transport, and last-mile activities.

A recurring theme is the move from periodic checks to continuous condition monitoring using RFID, IoT sensors, and data loggers. These tools create granular visibility into product state (i.e., temperature and humidity) and enable faster exception handling. Studies highlight the use of RFID for real-time temperature tracking and improved traceability [38], while IoT-based systems integrate sensing and data logging for end-to-end oversight of cold chain activities [39]. Data-management tools including artificial intelligence, big data analytics, and cloud computing convert these streams into actionable insight. AI models optimize temperature settings using live data [32], and big data techniques detect anomalies and forecast disruptions [40]. In parallel, automation technologies (i.e., smart reefers and digital twin simulations) support proactive operational adjustments and “test-before-deploy” process improvement, shifting the cold chain from reactive handling toward predictive management [30]. Together, these systems create an integrated network that reduces waste and enhances efficiency across the cold chain [41].

The review also shows that energy consumption is both a practical cost driver and a sustainability hotspot in cold chains due to refrigeration-intensive storage and transport. Studies describe energy reduction pathways enabled by digitalization, including tighter temperature control (i.e., avoiding overcooling), predictive routing and scheduling, and complementary interventions such as alternative energy integration and energy-efficient materials [42,43,44,45,46]. However, research attention is uneven, as many contributions concentrate on storage and transport, while certain last-mile contexts, including localized energy optimization and some high-stakes integrity scenarios, which are comparatively underexplored [47,48]. Therefore, future work should integrate efficiency, sustainability, and visibility into unified frameworks that balance performance with environmental responsibility [49].

Finally, the literature emphasizes that performance gains depend on end-to-end traceability and secure information flow. As networks become more connected, data integrity and cybersecurity concerns rise [50]. Blockchain applications offer secure, transparent recordkeeping that enhances trust among partners [51]. As cold chain networks become increasingly interconnected, collaboration and data sharing are vital for operational optimization and timely responses to disruptions [13,45]. These technical advances must be supported by organizational practices and regulatory frameworks that encourage transparency, accountability, and cooperative decision making across all stakeholders.

Table 1. Cold chain issue and technological solutions.

Cold Chain Issue/Problem	Cold Chain Stage	Consequences	Technological Solution	Reference
Quality, safety, and temperature control and monitoring	Storage, Warehousing, Transportation, Last-Mile Delivery, Outbound Logistics, Entire Cold Chain.	Spoilage, quality degradation, vaccine inefficacy, food loss, economic and operational losses, emissions, public health and safety risks, illness outbreaks, product recalls, contamination, reduced efficiency, and environmental impact.	3G, 5G, Acoustic Impulse Sensors, Artificial Intelligence (AI), Internet of Things (IoT), Artificial Neural Network, Anticipatory Shipping, Advanced Telematics Systems, Barcode, Battery-free Sensing, Big Data Analytics, Bluetooth Low Energy, Blockchain, Controlled Atmosphere Systems, Cloud Computing, Data Collection Systems, Data Loggers, Database Systems, Data Mining, Digital Twins, Distributed Ledger Technology, Electronic Data Interchange, Electronic Nose, General Packet Radio Service (GPRS), Grid Computing, Imaging Systems, Intelligent Packaging, Internet of Everything (IoE), Machine Learning (ML), Mobile Apps, Multi-Gas Sensors, Multi-Sensors, Near Field Communication Sensors, Optimization Algorithms, Printed Sensors, Real-Time Systems, Radio-Frequency Identification (RFID), Stacked Auto-Encoder,, Smart Reefers, Solar Harvesting, Time Temperature Integrators, Wireless Sensor Networks (WSN), ZigBee.	[29,30,32,39,46,47,48,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90]
Efficiency and Operational	Post-harvest, Storage, Transportation, Entire Cold Chain.	Product quality deterioration and spoilage, inventory obsolescence and waste, high energy use and associated environmental impact, increased operational costs and reduced profitability, customer dissatisfaction and reduced food security, safety incidents, and worker hazards, and data integrity, information-sharing, and cybersecurity and blockchain vulnerabilities.	IoT, RFID, WSN, Neural Networks, GPS, Temperature Sensors, Phase Change Materials (PCMs), Predictive Algorithms, Internet of Vehicles, Sensors, Big Data Analytics, Genetic Algorithm, Digital Twins, AI, ML, Real-time Sensors, Edge Computing, Cloud, Blockchain,, IoE, BLE, Real-time Detection, T&H Monitoring, Mobile Apps, Motion Detection, Positioning.	[34,45,91,92,93,94,95,96,97,98]
Energy consumption and Environmental Impact	Storage, Transportation, Entire Cold Chain.	High and inefficient energy use leading to higher costs and financial losses, increased emissions and pollution with associated quality damage, food loss and waste operational disruptions and unmet demand, data and cybersecurity risks, safety and worker hazards, and reduced investment, implementation difficulties, and loss of stakeholder trust.	AI, IoT, Improved Fireworks–Artificial Bee Colony, Blockchain, Photovoltaic Panels, Kinetic Energy Recovery System, PCMs, Sensors, Ant Colony Algorithm, Cloud, Big Data Analytics, Digital Twin Service (DTS), IoE.	[43,44,49,98,99,100,101,102,103,104,105,106]
Lack of end-to-end visibility and traceability	Storage, Transportation, Last-Mile Delivery, Entire Cold Chain.	Energy inefficiency, resource waste, and inadequate infrastructure increase costs and cause economic losses, maintenance, capacity, and coordination problems create bottlenecks, and poor forecasting, limited traceability, weak data integration, and handling errors lead to spoilage, safety risks, and reduced transparency across the cold chain.	ZigBee Technology, Sensor Technology, Integrated Development, ML, Real-Time Data, Intelligent Technology, Stochastic Process, WSN, AI, IoT, Big Data, RFID, GPRS, Kalman Filter, Cloud Computing, Digital Twins, Blockchain, Temperature Devices, Technological Infrastructure, Deep Neural Networks, Logistic Regression, IoE, Adaptive Data Smoothing and Compression, Smart Contracts, 5G, GIS, GPS, Stackelberg Models, Sensors.	[13,30,40,41,45,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125]

Table 1. Cold chain issue and technological solutions.

Cold Chain Issue/Problem	Cold Chain Stage	Consequences	Technological Solution	Reference
Quality, safety, and temperature control and monitoring	Storage, Warehousing, Transportation, Last-Mile Delivery, Outbound Logistics, Entire Cold Chain.	Spoilage, quality degradation, vaccine inefficacy, food loss, economic and operational losses, emissions, public health and safety risks, illness outbreaks, product recalls, contamination, reduced efficiency, and environmental impact.	3G, 5G, Acoustic Impulse Sensors, Artificial Intelligence (AI), Internet of Things (IoT), Artificial Neural Network, Anticipatory Shipping, Advanced Telematics Systems, Barcode, Battery-free Sensing, Big Data Analytics, Bluetooth Low Energy, Blockchain, Controlled Atmosphere Systems, Cloud Computing, Data Collection Systems, Data Loggers, Database Systems, Data Mining, Digital Twins, Distributed Ledger Technology, Electronic Data Interchange, Electronic Nose, General Packet Radio Service (GPRS), Grid Computing, Imaging Systems, Intelligent Packaging, Internet of Everything (IoE), Machine Learning (ML), Mobile Apps, Multi-Gas Sensors, Multi-Sensors, Near Field Communication Sensors, Optimization Algorithms, Printed Sensors, Real-Time Systems, Radio-Frequency Identification (RFID), Stacked Auto-Encoder,, Smart Reefers, Solar Harvesting, Time Temperature Integrators, Wireless Sensor Networks (WSN), ZigBee.	[29,30,32,39,46,47,48,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90]
Efficiency and Operational	Post-harvest, Storage, Transportation, Entire Cold Chain.	Product quality deterioration and spoilage, inventory obsolescence and waste, high energy use and associated environmental impact, increased operational costs and reduced profitability, customer dissatisfaction and reduced food security, safety incidents, and worker hazards, and data integrity, information-sharing, and cybersecurity and blockchain vulnerabilities.	IoT, RFID, WSN, Neural Networks, GPS, Temperature Sensors, Phase Change Materials (PCMs), Predictive Algorithms, Internet of Vehicles, Sensors, Big Data Analytics, Genetic Algorithm, Digital Twins, AI, ML, Real-time Sensors, Edge Computing, Cloud, Blockchain,, IoE, BLE, Real-time Detection, T&H Monitoring, Mobile Apps, Motion Detection, Positioning.	[34,45,91,92,93,94,95,96,97,98]
Energy consumption and Environmental Impact	Storage, Transportation, Entire Cold Chain.	High and inefficient energy use leading to higher costs and financial losses, increased emissions and pollution with associated quality damage, food loss and waste operational disruptions and unmet demand, data and cybersecurity risks, safety and worker hazards, and reduced investment, implementation difficulties, and loss of stakeholder trust.	AI, IoT, Improved Fireworks–Artificial Bee Colony, Blockchain, Photovoltaic Panels, Kinetic Energy Recovery System, PCMs, Sensors, Ant Colony Algorithm, Cloud, Big Data Analytics, Digital Twin Service (DTS), IoE.	[43,44,49,98,99,100,101,102,103,104,105,106]
Lack of end-to-end visibility and traceability	Storage, Transportation, Last-Mile Delivery, Entire Cold Chain.	Energy inefficiency, resource waste, and inadequate infrastructure increase costs and cause economic losses, maintenance, capacity, and coordination problems create bottlenecks, and poor forecasting, limited traceability, weak data integration, and handling errors lead to spoilage, safety risks, and reduced transparency across the cold chain.	ZigBee Technology, Sensor Technology, Integrated Development, ML, Real-Time Data, Intelligent Technology, Stochastic Process, WSN, AI, IoT, Big Data, RFID, GPRS, Kalman Filter, Cloud Computing, Digital Twins, Blockchain, Temperature Devices, Technological Infrastructure, Deep Neural Networks, Logistic Regression, IoE, Adaptive Data Smoothing and Compression, Smart Contracts, 5G, GIS, GPS, Stackelberg Models, Sensors.	[13,30,40,41,45,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125]

3.2. Digital Twins in Cold Chain

Table 2 highlights the role of digital twin technology in the cold chain and reveals several trends and gaps requiring attention. Digital twins create virtual replicas of physical assets to optimize performance across operations and act as important enablers of DT [126]. While the table categorizes applications, benefits, and challenges, a deeper reading underscores their strategic implications and the complexity of real-world deployment.

The literature shows that digital twins have broad applicability across nearly all cold chain stages, from postharvest to storage, transportation, and retail. Their widespread use demonstrates an ability to deliver end to end visibility and process optimization, vital for preserving the integrity of perishable goods [32]. This capability directly addresses the limitations of traditional systems, where fragmented data and delayed feedback often restrict informed decision-making [64].

However, the applications of digital twins are not uniform across all stages. While quality control and traceability are emphasized across the entire cold chain, specific applications such as predictive maintenance tend to be more focused on certain stages (i.e., packaging, pre-cooling, transportation, storage, and postharvest) [32,45,46,89]. This suggests that the value proposition of digital twins varies depending on the specific operational needs and priorities of each stage.

The benefits of digital twins outlined in Table 2 extend beyond mere operational efficiency. Besides improving logistics and resource use, digital twins contribute to quality assurance, risk mitigation, and sustainability. Simulation and prediction of potential quality loss before implementation enable proactive interventions that reduce spoilage and waste [89]. This shift from reactive to preventive management yields measurable economic and environmental advantages.

Translating these benefits into practice remains challenging because implementation must overcome technical, organizational, and economic barriers. Key issues include model accuracy and validation, data integration and interoperability, and limited technical expertise, all of which constrain large-scale adoption [13,32,46,53,76,89,118,124]. Furthermore, high investment and resource demands underscore the need for clear ROI evaluation and phased strategic planning.

Consequently, a critical area for future research lies in addressing the ethical considerations associated with digital twin technology. While Table 2 acknowledges this challenge, the literature needs to further investigate the ethical implications of data use, privacy, and potential biases in model predictions. As digital twins become more integrated into decision-making processes, it is essential to ensure fairness, transparency, and accountability. In addition, existing studies offer valuable frameworks for understanding digital twin adoption, yet the field must progress beyond descriptive overviews. The literature therefore points to a need for robust implementation strategies, standardized validation protocols, and clearer frameworks for responsible deployment.

Table 2. Digital twin in cold chain.

Digital Twin Application	References	Cold Chain Stage	Applicability of Digital Twin in Real-World (Future Application)	Benefits of Applying Digital Twin	Challenges of Applying Digital Twin
Quality Control and Traceability	[30,32,46,64,76,88,89,90,98,117]	Storage, transportation, and the entire cold chain	Real-time monitored, quality-controlled logistics with food-quality prediction; optimized energy use; greater resilience and efficiency; less food waste; and improved cybersecurity and sustainability via IoT, AI, and blockchain integration.	Digital twin applications enable simulation of operational impacts, real-time monitoring of environmental parameters, and integration of live data for informed decision-making. They help predict quality degradation, optimize refrigeration and logistics, enhance cybersecurity, and improve overall efficiency while reducing food and energy losses.	Model accuracy, verification and validation processes, ethical considerations, integration challenges, data utilization, interoperability, standardization, cost and resource constraints, lack of technical expertise, simplification of models, generalizability of results, need for further research on rollout. Simplification of models, low generalizability, need for further research on rollout. Cost and resource constraints, interoperability, standardization, lack of technical expertise.
Real-time Monitoring and Optimization	[12,30,32,33,45,46,53,64,72,76,88,89,98,117,118]	Postharvest, storage, transportation, and retail.	Improvement in operational efficiency, resilience, and sustainability in food supply chains. Improvement of product quality and economic profitability.	Real-time monitoring and optimization of cold chain operations strengthen supply chain resilience, enable automatic control of refrigeration systems, and enhance product quality and economic performance.
Decision Support	[12,30,33,35,46,53,64,76,88,89,118]	Postharvest, logistics, storage, transportation, and retail.	Enhancement in decision-making and performance across sectors, enhancement of growing practices, postharvest logistics, retail marketing, and cold chain strategies.	Digital twins provide real-time insights and predictive analytics that enhance visibility, transparency, collaboration, and traceability, supporting informed decision-making and optimizing overall supply chain operations.
Risk Assessment and Mitigation	[46,76,88,127]	Storage and transportation	Improvement in risk prediction and management, automation of processes, enhanced resilience, assessment of supplier risks, monitoring of compliance, development of contingency plans.	Enhanced resilience through automation and real-time data acquisition, enabling faster decision-making, simplified process control, and early risk detection during disruptions.
Predictive Maintenance	[32,45,46,89]	Packaging, pre-cooling, transportation, storage, postharvest.	Anticipation of maintenance needs, optimization of processes, improved efficiency.	Real-time monitoring and data-driven insights enhance decision-making, improve efficiency, and enable continuous operational improvement.
Real-time Occupational Safety Monitoring	[54]	Storage	Heightening of occupational safety monitoring, real-time safety tracking, incident handling improvement.	Real-time monitoring with dynamic tracking maps integrates worker status and environmental conditions, enabling rapid response to anomalies.
ESG Evaluation	[98]	Storage and transportation	Enhancement of ESG-driven transformation in energy enterprises by supporting long-term digital twin deployment through coordinated government incentives and private equity investments.	Predictive analytics and real-time monitoring enhance decision-making, strengthen traceability in sustainable logistics, and improve data management and security.
Others	[33,35]	Entire cold chain	Enhancement of decision-making, visualization, analysis, simulation, and optimization of supply chains.	Enhanced management capabilities, real-time data-driven decision-making, descriptive, predictive, and prescriptive analytics, recognition of physical supply chain structure.

Table 2. Digital twin in cold chain.

Digital Twin Application	References	Cold Chain Stage	Applicability of Digital Twin in Real-World (Future Application)	Benefits of Applying Digital Twin	Challenges of Applying Digital Twin
Quality Control and Traceability	[30,32,46,64,76,88,89,90,98,117]	Storage, transportation, and the entire cold chain	Real-time monitored, quality-controlled logistics with food-quality prediction; optimized energy use; greater resilience and efficiency; less food waste; and improved cybersecurity and sustainability via IoT, AI, and blockchain integration.	Digital twin applications enable simulation of operational impacts, real-time monitoring of environmental parameters, and integration of live data for informed decision-making. They help predict quality degradation, optimize refrigeration and logistics, enhance cybersecurity, and improve overall efficiency while reducing food and energy losses.	Model accuracy, verification and validation processes, ethical considerations, integration challenges, data utilization, interoperability, standardization, cost and resource constraints, lack of technical expertise, simplification of models, generalizability of results, need for further research on rollout. Simplification of models, low generalizability, need for further research on rollout. Cost and resource constraints, interoperability, standardization, lack of technical expertise.
Real-time Monitoring and Optimization	[12,30,32,33,45,46,53,64,72,76,88,89,98,117,118]	Postharvest, storage, transportation, and retail.	Improvement in operational efficiency, resilience, and sustainability in food supply chains. Improvement of product quality and economic profitability.	Real-time monitoring and optimization of cold chain operations strengthen supply chain resilience, enable automatic control of refrigeration systems, and enhance product quality and economic performance.
Decision Support	[12,30,33,35,46,53,64,76,88,89,118]	Postharvest, logistics, storage, transportation, and retail.	Enhancement in decision-making and performance across sectors, enhancement of growing practices, postharvest logistics, retail marketing, and cold chain strategies.	Digital twins provide real-time insights and predictive analytics that enhance visibility, transparency, collaboration, and traceability, supporting informed decision-making and optimizing overall supply chain operations.
Risk Assessment and Mitigation	[46,76,88,127]	Storage and transportation	Improvement in risk prediction and management, automation of processes, enhanced resilience, assessment of supplier risks, monitoring of compliance, development of contingency plans.	Enhanced resilience through automation and real-time data acquisition, enabling faster decision-making, simplified process control, and early risk detection during disruptions.
Predictive Maintenance	[32,45,46,89]	Packaging, pre-cooling, transportation, storage, postharvest.	Anticipation of maintenance needs, optimization of processes, improved efficiency.	Real-time monitoring and data-driven insights enhance decision-making, improve efficiency, and enable continuous operational improvement.
Real-time Occupational Safety Monitoring	[54]	Storage	Heightening of occupational safety monitoring, real-time safety tracking, incident handling improvement.	Real-time monitoring with dynamic tracking maps integrates worker status and environmental conditions, enabling rapid response to anomalies.
ESG Evaluation	[98]	Storage and transportation	Enhancement of ESG-driven transformation in energy enterprises by supporting long-term digital twin deployment through coordinated government incentives and private equity investments.	Predictive analytics and real-time monitoring enhance decision-making, strengthen traceability in sustainable logistics, and improve data management and security.
Others	[33,35]	Entire cold chain	Enhancement of decision-making, visualization, analysis, simulation, and optimization of supply chains.	Enhanced management capabilities, real-time data-driven decision-making, descriptive, predictive, and prescriptive analytics, recognition of physical supply chain structure.

3.3. Sustainability Initiatives Through Digital Transformation in Cold Chain

Sustainability has become a central driver of DT research in cold chains, with three focus areas repeatedly emphasized: energy optimization, emission reduction, and waste minimization (Figure 5). This shift reflects a growing acknowledgment of the sector’s substantial ecological footprint and of the capacity of digital technologies to enable environmentally responsible operations.

The review indicates that energy consumption is the most frequently examined sustainability theme, cited in 36 studies. The dominance of this theme reveals the energy-intensive character of refrigeration, transport, and storage processes within the cold chain [42]. DT introduces tools that allow continuous monitoring and intelligent control, resulting in measurable energy savings. The inclusion of technologies such as IoT sensors, AI-based optimization algorithms, and digital twin models support real-time temperature regulation and adaptive energy use. For instance, dynamic temperature control allows precise adjustment of cooling levels according to product maturity and retailer demand, which prevents unnecessary energy expenditure [99]. Complementary research on renewable energy integration, particularly solar power and Phase Change Materials (PCMs), demonstrates viable routes for reducing dependence on conventional energy and limiting greenhouse gas emissions [100].

Waste reduction is also prominent, with 24 studies, targeting both packaging waste and product loss caused by temperature fluctuations and spoilage. Technology convergence, including IoT sensing, blockchain traceability, and digital twins, supports condition tracking and earlier intervention, while AI/ML optimization models are used to improve capacity planning and allocation to reduce waste and emissions simultaneously [12,31]. Overall, the review highlights a continuing need for standardized sustainability metrics, broader assessment frameworks (including social and economic dimensions), and more consistent evaluation practices to compare impacts across contexts and technologies.

Emissions reduction is another major focus, with 20 papers addressing strategies for mitigation. Regulatory pressures and environmentally conscious consumers continue to drive the decarbonization of logistics [46]. Emerging digital systems, such as blockchain-based traceability platforms and AI-enabled route optimization, enhance transparency and operational efficiency, thereby lowering fuel use and emissions. Studies highlighting the deployment of electric vehicles and optimized delivery scheduling exemplify how digital innovation can separate economic performance from environmental degradation [49,101]. Additional models, including green cost calculation frameworks and the use of unmanned aerial vehicles (UAVs) for lightweight deliveries, offer innovative approaches for balancing cost efficiency, infection control, and emission reduction [109].

Figure 5. Sustainability initiatives through DT in cold chain. (Source: the authors’ analysis of Scopus records for the period 2009–2025).

Figure 5. Sustainability initiatives through DT in cold chain. (Source: the authors’ analysis of Scopus records for the period 2009–2025).

3.4. Applications and Benefits of Digital Transformation in Cold Supply Chains

Table 3 summarizes the wide-ranging benefits of integrating digital technologies into cold supply chains. These outcomes function not as isolated improvements but as interconnected drivers of broader transformation in cold chain management, which signals a shift from fragmented initiatives to a more cohesive, data-driven operational ecosystem.

Efficiency and productivity enhancements emerge as dominant themes across the reviewed literature. Numerous studies report notable reductions in costs, waste, and delivery times, alongside better vehicle utilization and resource management. These outcomes demonstrate how digital technologies streamline workflows, optimize asset use, and minimize inefficiencies throughout the cold chain. Quantitative evidence reinforces this impact, with results such as a 34.76% reduction in costs [108] and a 35% increase in deliveries [67] revealing the measurable contribution of DT to performance improvement.

On the other hand, efficiency does not exist in isolation. The reduction in discarded reefer containers and associated greenhouse gas emissions illustrates how operational efficiency directly supports sustainability goals [105]. Similarly, warehouse digitization contributes simultaneously to cost control and improved resource utilization [36,92]. These linkages highlight the holistic character of DT, where advancements in one domain generate reinforcing benefits across others.

Another recurrent benefit involves greater visibility and traceability across the supply chain. Blockchain platforms and RFID systems are widely recognized as central enablers for transparent product tracking, quality assurance, and reliable logistics information [128]. Enhanced traceability is particularly vital in temperature-sensitive logistics, where product safety and authenticity depend on real-time monitoring and the ability to verify origin and handling [123]. These capabilities build trust among stakeholders while reducing the incidence of counterfeit or compromised goods.

DT also strengthens decision-making capacity. The availability of real-time data and predictive analytics allows managers to anticipate risks, adjust temperature parameters, and improve inventory and procurement planning. For instance, some studies report measurable reductions in food waste and more accurate forecasts of purchase volumes, both of which enhance efficiency and resource allocation [65,118,121]. Beyond internal improvements, enhanced service quality and delivery performance have also elevated customer satisfaction [39,123]. Collectively, these findings demonstrate that DT not only optimizes internal operations but also creates tangible value for end consumers through reliability, transparency, and service excellence.

Table 3. Applications and benefits of DT in cold chains.

Benefits of Digital Transformation in Cold Chain	Reference	Description
Improved Efficiency and Productivity	[34]	Improves operational effectiveness and enhanced customer satisfaction, with the obsolescence rate for handling environmentally sensitive products decreasing from 13% to 8%.
	[49]	Reduces the total delivery cost by 3.30% and 4.93% compared to other algorithms.
	[67]	Results in a 35% increase in deliveries and a 3.23% reduction in forklift travel distances, leading to lower operational costs and enhanced logistics efficiency.
	[68]	Improves transparency, traceability, and efficiency in logistics operations, enhances service quality, and strengthens data security and management.
	[99]	Achieves a minimum comprehensive loss value of 3.374, demonstrating superior sustainability compared with constant low-temperature storage.
	[105]	Reduces discarded reefer containers by 42.1% and associated GHG emissions by 21.8%, demonstrating the technology’s effectiveness in improving food safety and lowering costs.
	[108]	Results in 34.76% reduction in costs and a 15.6% reduction in resource wastage, which improves overall resource utilization and minimizes unnecessary expenses.
	[111]	Cuts vehicle requirements from 12 to 6, revealing higher logistics efficiency.
	[77,85,94,96]	Improves efficiency through smart logistics platforms that provide real-time visualization, automated alerts, and intelligent decision support.
	[74,76,93,103,129]	Optimizes cold chain logistics operations through improved scheduling, distribution, and warehouse management.
Enhanced Visibility and Traceability	[29]	Provides blockchain-based traceability solutions tackle the shortcomings of centralized traceability solutions.
	[40]	Enhances data processing and supervision, supports evidence-based decisions, improves logistics flow, and safeguards food safety.
	[13,53]	Strengthens traceability and quality control, improves efficiency, reduces medication risks, and enhances transparency, trust, and secure data management.
	[41,59]	Improves transparency, traceability, and accountability, strengthens quality assurance, and increases efficiency across the food supply chain.
	[84]	Improves the efficiency of food-cold chain logistics traceability through Bar code technology and RFID.
Enhanced Sustainability	[73]	Reduces average energy consumption by 87.04% compared to battery powered systems and decreases costs by 15.3% when compared to traditional battery powered wireless sensing systems.
	[100]	Enables prolonged operation without direct sunlight through PCMs.
	[99]	Reduces emissions and costs while optimizing energy use and logistics efficiency.
	[101]	Improves efficiency and traceability using blockchain and sensors to monitor environmental conditions in fresh food distribution.
Cost Optimization	[55]	Improves recognition accuracy, achieving 97.4% with three feature values and 98.6% with six feature values, while maintaining low-cost increases for temperature management in cold chain logistics
	[67]	Delivers a 35% rise in deliveries, a 3.23% reduction in forklift travel, and 10–15% lower labor and resource costs, yielding annual savings of EUR 26,880–40,320.
	[68]	Shows an increase of 32% in total income and improves environmental criteria in a real-world case study on the poultry supply chain. Additionally, a 4% reduction in total profit can lead to a 12% increase in total greenness and human health.
	[92,116]	Optimizes costs through greater efficiency, better resource use, streamlined warehouse operations, and cost-effective system design.
	[108]	Achieves a 34.76% reduction in costs and a 15.6% reduction in resource wastage, which improves overall resource utilization and minimizes unnecessary expenses.
	[110]	Improves monitoring accuracy, achieving prediction errors as low as 0.98 °F surpassing manufacturer-reported temperature precision.
	[41,63,97,123]	Strengthens quality management and data integrity, enhances trust and transparency, improves tracking and tracing efficiency, and reinforces quality and safety assurance in frozen and chilled product distribution.
Improved Quality and Safety	[47]	Enables continuous temperature monitoring, identification of critical breach points, and data-driven temperature control, ensuring stronger vaccine preservation.
	[65]	Enables real-time collection of temperature and humidity data, improving quality control and reducing food waste.
	[33,114,123,127]	Enhances quality management and data integrity, strengthens transparency and trust, supports informed decision-making and demand forecasting, optimizes transportation, reduces food waste, and improves visibility, risk assessment, and scenario simulation for proactive management.
Enhanced Decision-Making	[45]	Reduces food wastage about 30% globally each year due to poor supply chain management.
	[89]	Provides detailed insight into fruit quality degradation during transport, showing that fruits lose 43–85% of their quality before reaching retail outlets.
	[112]	Reduces spoilage rates of 25–30% in fruits, preventing nearly 12 million tons of annual losses.
	[39,111,113,118]	Improves visibility, traceability, and efficiency, optimizes resource use, reduces product loss, boosts sustainability and customer satisfaction through reliable data and logistics tracking.
Improved Customer Satisfaction	[67]	Results in a 35% increase in deliveries and a 3.23% reduction in forklift travel, lowering operational costs and enhancing logistics efficiency during peak demand periods.
	[121]	Achieves 96.35% accuracy, 97% precision, and 94.89% recall in predicting commodity purchase volume, showing the effectiveness of technology in enhancing supply chain management.

Table 3. Applications and benefits of DT in cold chains.

Benefits of Digital Transformation in Cold Chain	Reference	Description
Improved Efficiency and Productivity	[34]	Improves operational effectiveness and enhanced customer satisfaction, with the obsolescence rate for handling environmentally sensitive products decreasing from 13% to 8%.
	[49]	Reduces the total delivery cost by 3.30% and 4.93% compared to other algorithms.
	[67]	Results in a 35% increase in deliveries and a 3.23% reduction in forklift travel distances, leading to lower operational costs and enhanced logistics efficiency.
	[68]	Improves transparency, traceability, and efficiency in logistics operations, enhances service quality, and strengthens data security and management.
	[99]	Achieves a minimum comprehensive loss value of 3.374, demonstrating superior sustainability compared with constant low-temperature storage.
	[105]	Reduces discarded reefer containers by 42.1% and associated GHG emissions by 21.8%, demonstrating the technology’s effectiveness in improving food safety and lowering costs.
	[108]	Results in 34.76% reduction in costs and a 15.6% reduction in resource wastage, which improves overall resource utilization and minimizes unnecessary expenses.
	[111]	Cuts vehicle requirements from 12 to 6, revealing higher logistics efficiency.
	[77,85,94,96]	Improves efficiency through smart logistics platforms that provide real-time visualization, automated alerts, and intelligent decision support.
	[74,76,93,103,129]	Optimizes cold chain logistics operations through improved scheduling, distribution, and warehouse management.
Enhanced Visibility and Traceability	[29]	Provides blockchain-based traceability solutions tackle the shortcomings of centralized traceability solutions.
	[40]	Enhances data processing and supervision, supports evidence-based decisions, improves logistics flow, and safeguards food safety.
	[13,53]	Strengthens traceability and quality control, improves efficiency, reduces medication risks, and enhances transparency, trust, and secure data management.
	[41,59]	Improves transparency, traceability, and accountability, strengthens quality assurance, and increases efficiency across the food supply chain.
	[84]	Improves the efficiency of food-cold chain logistics traceability through Bar code technology and RFID.
Enhanced Sustainability	[73]	Reduces average energy consumption by 87.04% compared to battery powered systems and decreases costs by 15.3% when compared to traditional battery powered wireless sensing systems.
	[100]	Enables prolonged operation without direct sunlight through PCMs.
	[99]	Reduces emissions and costs while optimizing energy use and logistics efficiency.
	[101]	Improves efficiency and traceability using blockchain and sensors to monitor environmental conditions in fresh food distribution.
Cost Optimization	[55]	Improves recognition accuracy, achieving 97.4% with three feature values and 98.6% with six feature values, while maintaining low-cost increases for temperature management in cold chain logistics
	[67]	Delivers a 35% rise in deliveries, a 3.23% reduction in forklift travel, and 10–15% lower labor and resource costs, yielding annual savings of EUR 26,880–40,320.
	[68]	Shows an increase of 32% in total income and improves environmental criteria in a real-world case study on the poultry supply chain. Additionally, a 4% reduction in total profit can lead to a 12% increase in total greenness and human health.
	[92,116]	Optimizes costs through greater efficiency, better resource use, streamlined warehouse operations, and cost-effective system design.
	[108]	Achieves a 34.76% reduction in costs and a 15.6% reduction in resource wastage, which improves overall resource utilization and minimizes unnecessary expenses.
	[110]	Improves monitoring accuracy, achieving prediction errors as low as 0.98 °F surpassing manufacturer-reported temperature precision.
	[41,63,97,123]	Strengthens quality management and data integrity, enhances trust and transparency, improves tracking and tracing efficiency, and reinforces quality and safety assurance in frozen and chilled product distribution.
Improved Quality and Safety	[47]	Enables continuous temperature monitoring, identification of critical breach points, and data-driven temperature control, ensuring stronger vaccine preservation.
	[65]	Enables real-time collection of temperature and humidity data, improving quality control and reducing food waste.
	[33,114,123,127]	Enhances quality management and data integrity, strengthens transparency and trust, supports informed decision-making and demand forecasting, optimizes transportation, reduces food waste, and improves visibility, risk assessment, and scenario simulation for proactive management.
Enhanced Decision-Making	[45]	Reduces food wastage about 30% globally each year due to poor supply chain management.
	[89]	Provides detailed insight into fruit quality degradation during transport, showing that fruits lose 43–85% of their quality before reaching retail outlets.
	[112]	Reduces spoilage rates of 25–30% in fruits, preventing nearly 12 million tons of annual losses.
	[39,111,113,118]	Improves visibility, traceability, and efficiency, optimizes resource use, reduces product loss, boosts sustainability and customer satisfaction through reliable data and logistics tracking.
Improved Customer Satisfaction	[67]	Results in a 35% increase in deliveries and a 3.23% reduction in forklift travel, lowering operational costs and enhancing logistics efficiency during peak demand periods.
	[121]	Achieves 96.35% accuracy, 97% precision, and 94.89% recall in predicting commodity purchase volume, showing the effectiveness of technology in enhancing supply chain management.

3.5. Challenges of Digital Transformation in Cold Supply Chains

Although DT delivers substantial benefits to cold supply chains, it also introduces complex challenges that must be strategically managed. Literature identifies barriers across technological, economic, data-related, organizational, operational, and regulatory domains (see Figure 6). If unaddressed, these constraints can limit adoption, reduce effectiveness, and ultimately weaken the transformative potential of digital initiatives.

Technological challenges remain among the most significant. The cold chain’s diverse stakeholders and legacy infrastructure complicate the integration of modern solutions. Hence, deploying and connecting systems such as IoT devices, AI platforms, and blockchain networks within existing frameworks require considerable coordination [119]. Another persistent obstacle is achieving interoperability and reliable data exchange across systems, as is scalability to accommodate fluctuating demand and long-term growth [45,79]. Therefore, robust architecture is needed to address these issues, in addition to standardized protocols and system designs that support flexibility and sustained performance.

Besides, the economic constraints shape the pace of adoption. The high capital requirements for advanced technologies remain a major deterrent, specifically for small and medium-sized enterprises (SMEs) [43,46]. As a result, pressure to demonstrate short-term ROI, combined with limited financial support, restricts spending on infrastructure, training, and maintenance. These realities highlight the importance of innovative financing models, scalable solutions, and clearer articulation of long-term value to justify continued investment.

The digitalization of cold chains also generates vast volumes of data that require secure and reliable management. It is also critical to safeguard sensitive information from cyber threats, yet connectivity across multiple partners increases exposure to security breaches [130]. Hence, maintaining data integrity, ensuring accuracy, consistency, and reliability throughout the data life cycle is equally vital [117,130]. Conversely, weak governance can result in flawed insights and operational inefficiencies. Therefore, strong data governance frameworks, quality standards, and compliance mechanisms are essential to preserve trust and accountability.

Beyond the technological and economic considerations, organizational and human factors play a critical role in the success of DT. Insufficient preparedness within organizations, resistance to change, and a lack of digital readiness can significantly hinder implementation efforts [43,120,131]. Fostering a culture of technological acceptance among employees and providing comprehensive training programs are crucial but often challenging. The limited availability of personnel with the necessary expertise to implement, manage, and maintain advanced technologies further compounds these difficulties. Effective stakeholder collaboration and change management strategies are vital to ensure that employees are equipped and motivated to embrace DT.

Operational and logistical challenges compound these challenges. The integration of diverse technologies into unified systems requires detailed planning, while unpredictable events and disruptions further strain coordination [132]. As such, maintaining real-time visibility and control across extended networks and ensuring efficient information sharing remain ongoing difficulties [109]. To overcome them, it depends on adaptable workflows, agile management, and resilient system design.

Figure 6. Summarized challenges of DT in cold chain. (Source: the authors’ analysis of Scopus records for the period 2009–2025).

Figure 6. Summarized challenges of DT in cold chain. (Source: the authors’ analysis of Scopus records for the period 2009–2025).

Finally, regulatory and standardization barriers also impede progress, as compliance with evolving legal frameworks and data protection regulations is essential for responsible technology use [133]. Yet, the absence of universally accepted standards for technology deployment and data exchange constrains interoperability and scalability [29,45]. Therefore, establishing clearer industry standards and harmonized regulatory guidelines is central to enabling consistency, accountability, and sustainable innovation in the digital cold chain.

4. Research Gaps and Further Research Recommendations

This section consolidates the review’s contributions and identifies research gaps that require targeted investigation. The synthesis of 107 studies (2009–2025) shows rising interest in DT of the cold chain, yet several areas remain underexplored.

4.1. Limitations and Future Research Recommendations

The synthesis of 107 studies confirms that digital technologies are beginning to address long-standing problems in cold supply chains—especially temperature integrity, energy intensity, and limited end-to-end visibility across production, storage, transport, and retail. Nevertheless, the evidence shows that DT remains fragmented, and several gaps persist. These relate to the integration of technologies across stages, the systematic assessment of sustainability and economic performance, the scarcity of robust empirical validation, and the underexplored organizational and policy conditions needed to scale these innovations.

First, the literature consistently determines challenges related to quality and safety, energy consumption, and end-to-end traceability as the main constraints on cold-chain performance. These issues cut across production, storage, transport, and retail, which affect both cost and public health [49,65]. To address these multifaceted challenges, a range of technological solutions has been proposed, including sensing and IoT systems for real-time monitoring, analytics and AI for prediction and control, automation for process optimization, and blockchain for data security and traceability. While such solutions show promise, most studies remain isolated by stage or technology. Future work should test integrated, cross-stage frameworks that combine multiple tools, assess sustainability through standardized metrics and life-cycle assessments (LCAs), and evaluate financial viability, particularly for SMEs.

Empirical validation can be strengthened through structured assessment and experimentation. As such, maturity-assessment models can benchmark an organization’s digital competence, which reveals progression from manual control to predictive, IoT-based monitoring [134]. On top of this, applying AI Application Programming Interface (API) to enhance functionality is another future research area. API is defined as the connective nodes of digital components and an instrumental enabler of the integration of different systems and actors [135]. Furthermore, employing physical testbeds (i.e., PhyNetLab) offer a real-scale environment with interconnected, computationally and communicatively enabled physical objects, is crucial for realistic testing and validation in this domain [136]. Future research should use these instruments to develop standardized evaluation frameworks that link technological sophistication with measurable gains in efficiency, reliability, and energy savings.

Second, digital twin technology represents a pivotal enabler of transformation, which provides virtual replicas that simulate and optimize cold-chain operations [67]. Evidence shows value across all stages, from storage to distribution, through enhanced visibility, predictive control, and proactive quality management [89]. Yet implementation remains limited by model accuracy, data integration, interoperability, and cost [32]. Given these insights, future studies should establish standardized design and validation protocols, explore adaptive and learning twins that update in real-time, and investigate their coupling with AI and blockchain to balance transparency, privacy, and economic feasibility.

Third, sustainability now anchors DT research in cold chains, with studies emphasizing energy efficiency, emission reduction, and waste minimization [99]. IoT-enabled monitoring and adaptive control optimize energy use, while renewable integration and traceability systems reduce losses. Building upon this progress, future research should establish consistent sustainability metrics, extend LCAs to digital technology footprints, and examine economic and social trade-offs of green innovation. Equally vital is designing incentive mechanisms, including financial, regulatory, or collaborative, that accelerate adoption of sustainable digital practices.

Fourth, digital technologies collectively enhance operational efficiency, visibility, and customer satisfaction. Empirical studies document tangible benefits, including lower costs, reduced resource waste, optimized delivery times, and improved quality assurance [67,118]. In addition, real-time analytics support proactive decisions on temperature control and risk management, while transparent tracking builds consumer trust. The next research step is to quantify how these operational, environmental, and service gains interact through evaluating long-term effects on competitiveness and resilience and codifying best practices for technology deployment across sectors.

Finally, persistent challenges span technological, economic, and regulatory domains. As such, the integration of heterogeneous systems, interoperability, scalability, and cybersecurity remain pressing concerns [79,117]. Addressing these issues requires coordinated advances in data governance, financing models, and managerial capabilities. Building on these challenges, future research should concentrate on several aspects. First, it should support a shift from reactive monitoring to proactive management through predictive analytics and AI models capable of anticipating temperature deviations, equipment failures, and logistical disruptions before they occur [137]. Second, as discussed by Zemanek et al. [138], integrating intelligent and autonomous systems, such as drones and self-driving vehicles, can enhance delivery efficiency, safety, and sustainability within regulated environments. Third, the concept of personalized cold-chain solutions where AI-driven analytics tailor storage and transportation parameters to product, consumer, or regional characteristics could enable adaptive temperature and routing strategies that balance quality preservation, cost efficiency, and sustainability [9]. Finally, building adaptive and resilient networks that withstand and recover from global supply disruptions by combining AI-based risk forecasting with blockchain-enabled transparency and diversification represents another promising direction [139].

4.2. Managerial and Policy Implications

The findings of the review carry actionable implications for practitioners, managers, and policymakers. For practitioners, the results provide a roadmap to identify and adopt digital tools that directly address operational challenges while advancing sustainability goals [4]. For managers, phased digital adoption strategies should evolve from basic IoT and RFID monitoring to advanced analytics, automation, and digital twin integration [33,45,46]. Additionally, effective implementation demands cross-functional coordination between operations, IT, and sustainability teams to prevent data silos and enhance visibility [43,98]. Another vital aspect is financial justification through total cost-of-ownership and ROI analysis given the high capital requirements of advanced systems [49,99]. Managers must also strengthen data-governance frameworks to mitigate cybersecurity and interoperability risks [50,51], while cultivating a digitally skilled workforce through continuous learning and AI-assisted decision-making [120,131]. From a policy standpoint, supportive regulatory ecosystems are essential for scaling DT. Hence, governments should incentivize adoption through targeted subsidies, public–private R&D programs, and standardized interoperability protocols, while addressing data privacy and ethical concerns [140]. Harmonized global standards can ensure equitable technology diffusion, especially among SMEs. As such, the managerial, practitioner, and policy implications highlight that cold chain DT is a systemic shift demanding strategic alignment, institutional support, and human readiness not merely a technical upgrade.

5. Conclusions

This study highlights the transformative impact of digital technologies on the evolution of cold chain operations. Through a systematic analysis of 107 studies published between 2009 and 2025, conducted under the PRISMA framework, the research revealed how DT is redefining the management of temperature-sensitive logistics. Technologies such as IoT, AI, blockchain, and digital twins are collectively modernizing the sector through enabling new levels of efficiency, transparency, and sustainability. These innovations address persistent challenges related to product quality, safety assurance, and the long-standing need for end-to-end visibility and traceability across the supply chain.

The findings confirm that DT extends far beyond the adoption of isolated tools and instead represents a strategic shift toward intelligent, data-driven, and sustainable cold chains. The integration of real-time monitoring, predictive analytics, and automation has enhanced temperature control, optimized energy consumption, and supported proactive decision-making throughout the supply chain. Additionally, sustainability has emerged as a central theme, as digital technologies facilitate energy optimization, waste reduction, and lower carbon emissions. Nevertheless, the realization of these benefits continues to be challenged by the high cost of implementation, interoperability limitations, data governance concerns, and organizational resistance to change. These constraints highlight the importance of developing adaptable and inclusive strategies that align technological innovation with economic feasibility and institutional readiness.

Nonetheless, this review has limitations that should be considered when interpreting its findings. The search was restricted to English-language publications indexed in Scopus, which may have excluded relevant studies captured in other outlets. In addition, given the heterogeneity of study designs, sectors, and reported outcomes, the evidence was synthesized descriptively rather than through a quantitative meta-analysis; therefore, the conclusions should be understood as thematic insights rather than pooled effect estimates, particularly in a rapidly evolving DT landscape.

Looking ahead, the review identifies several gaps and areas for future exploration. It highlighted the need to go beyond descriptive accounts and focus on developing integrative frameworks that unify the technical, economic, and environmental dimensions of DT. Also, there is a pressing need to design models that combine the strengths of multiple technologies to achieve holistic visibility and operational resilience across cold supply chains. Moreover, establishing standardized sustainability metrics and conducting comprehensive life cycle assessments would also provide a clearer understanding of the environmental implications of digital adoption. Furthermore, developing maturity assessment models could help organizations evaluate their progress and identify areas requiring improvement. The emergence of predictive and autonomous systems, supported by AI and digital twins, offers additional opportunities for proactive and adaptive cold chain management. Future research should also emphasize experimental validation through physical testbeds and API-based integrations that link digital tools across platforms. Ethical considerations, particularly concerning data privacy, transparency, and accountability, should also remain central to these technological developments to ensure trust and fairness in digital ecosystems.