Logistics Nodes Research Articles

Supply Chain Management in Smart City Manufacturing Clusters: An Alternative Approach to Urban Freight Mobility with Electric Vehicles

The development of green production types such as personalized production and shared manufacturing, which use additive technologies in city multifloor manufacturing clusters (CMFMCs), has led to an increase in last-mile parcel delivery (LMPD) activity. This study investigates the integration of electric vehicles and crowdshipping systems into smart CMMCs to improve urban logistics operations related to the distribution of products to consumers. The aim of this study is to improve the LMPD performance of these integrated systems and to provide alternative solutions for sustainable city logistics using the potential of crowdshipping and vehicle sharing fleets (VSFs) in the city logistics nodes (CLNs) of CMFMCs. The issues presented by the loading–unloading operations and sustainable crowdshipping scenarios for LMPD in CMFMCs are considered. This paper presents a new performance evaluation model for crowdshipping LMPD in CMFMCs using VSFs. The case study shows that the proposed model enables the analysis of LMPD performance in CMFMCs, taking into account their finite production capacity, and that it facilitates the planning of cargo turnover and the structure of VSFs consisting of e-bicycles, e-cars, and e-light commercial vehicles (e-LCVs). The model is verified based on a case study for sustainable LMPD scenarios using VSFs. The proposed model enables the planning of both short- and long-term logistics operations with the specified performance indicator of VSF usage in CMFMCs. The validity of using the integrated potential of crowdshipping and vehicle sharing services for LMPD under demand uncertainty in CMFMCs is discussed. This study should prove useful for decision-making and planning processes related to LMPD in CMFMCs and large cities.

A Decision Support Model for Lean Supply Chain Management in City Multifloor Manufacturing Clusters

City manufacturing has once again become one of the priority areas for the sustainable development of smart cities thanks to the use of a wide range of green technologies and, first of all, additive technologies. Shortening the supply chain between producers and consumers has significant effects on economic, social, and environmental dimensions. Zoning of city multifloor manufacturing (CMFM) in areas with a compact population in large cities in the form of clusters with their own city logistics nodes (CLNs) creates favorable conditions for promptly meeting the needs of citizens for goods of everyday demand and for passenger and freight transportation. City multifloor manufacturing clusters (CMFMCs) have been already studied quite a lot for their possible uses; nevertheless, an identified research gap is related to supply chain design efficiency concerning urban CMFMCs. Thus, the main objective of this study was to explore the possibilities of lean supply chain management (LSCM) as the integrated application of lean manufacturing (LM) approaches and I4.0 technologies for customer-centric value stream management based on eliminating all types of waste, reducing the use of natural and energy resources, and continuous improvement of processes related to logistics activities. This paper presents a decision support model for LSCM in CMFMCs, which is a mathematical deterministic model. This model justifies the minimization of the number of road transport transfers within the urban area and the amount of stock that is stored in CMFMC buildings and in CLNs, and also regulating supplier lead time. The model was verified and validated using appropriately selected test data based on the case study, which was designed as a typical CMFM manufacturing system with various parameters of CMFMCs and urban freight transport frameworks. The feasibility of using the proposed model for value stream mapping (VSM) and managing logistics processes and inventories in clusters is discussed. The findings can help decisionmakers and researchers improve the planning and management of logistics processes and inventory in clusters, even in the face of unexpected disruptions.

Demand and supply shocks derived from COVID-19 on the freight transportation system in Mexico

PurposeThis paper's purpose is twofold. First, based on a case study, it aims to comprehend the consequences of COVID-19 on the demand and supply shocks of the freight transportation system in Mexico. Second, it seeks to provide an integrated perspective of four transportation modes, which would help prepare public policies for future global pandemics.Design/methodology/approachAnalyzing the impact of the COVID-19 pandemic on the freight transportation system, which affects national and global economies, is essential to drawing valuable insights for the future. To facilitate international comparative analysis, conducting case studies at a country level was deemed necessary. As a result, a case study was conducted in Mexico using an integrated approach involving four transportation modes.FindingsTo manage disruptions in freight flow during uncertain conditions, a comprehensive perspective on the four modes of transportation and data-driven decision-making is crucial. Under this context, three initiatives can be identified: 1) establishing a National Center for Intelligence in Logistics to improve data-driven governance; 2) appointing the “Integrated Transportation Corridor Management Manager” (ITCMM) function to coordinate multiple authorities with different acting in critical freight transport corridors, and 3) creation of a digital tool based on millions of GPS data to monitor freight flows, allowing for collective intelligence among logistics actors.Research limitations/implicationsThis research's limitations are related to using non-standardized databases to gather information on four transportation modes. However, this limitation is also an interesting discovery. Mexico is becoming a strategic logistics hub between North America and Latin America, especially under the “Nearshoring” trend. Unfortunately, the lack of an integrated public policy in logistics and transportation reduces Mexico's capacity to deal with disruptions and its economic competitiveness.Practical implicationsThis research has identified practices that could be crucial in improving public policies to optimize shipping routes and reduce wait times while minimizing disruptions caused by unforeseen events. A concrete example is the digital platform called “eraclitux,” a computer tool similar to an Enterprise Resource Planning (ERP) system companies use. This tool can enable a “Control Tower” that monitors freight flow in transportation corridors under the supervision of “Integrated Transportation Corridor Management Managers.” The tool can make reactive and predictive decisions that help to enhance the logistics value provided by transportation infrastructure.Social implicationsThe importance of a well-coordinated and integrated public policy for freight transportation was identified to ensure better performance during disruptions. Delays in the flow of goods can significantly impact the supply of essential items such as food and medicine, ultimately affecting the population's quality of life.Originality/valueNumerous studies have been conducted to determine the extent of vulnerability and the impact of COVID-19 on freight transportation. However, most of these studies assume a developed market context or a single-mode transportation approach, which only applies to some situations. To gain a comprehensive understanding of how pandemics-induced demand and supply shocks affected freight transportation in developing countries such as Mexico, this paper offers insights from a four-transportation mode perspective. Mexico is facing a challenging Nearshoring trend in manufacturing, making it a significant logistics node between North and South America.

Military Spacesteading: Space-based Logistics Mediums for Future Beachheads

This article explores the concept of spacesteading as it pertains to military operations. Specifically, it expands on both potential and emerging technologies that could enable logistics nodes to be positioned in geosynchronous and geostationary orbit above the Earth to enable to rapid deployment of equipment. This article proposes that space domain domination would allow for a superior alternative to existing expeditionary logistics caches such as maritime prepositioning force ships and the Marine Corps Prepositioning Program–Norway. A pair of vignettes help to illustrate the value in the military enabling logistics capabilities within the space domain. While this article largely focuses on space-based logistics applications for the U.S. Marine Corps, these efforts would have far-reaching impacts to the whole of the U.S. military and beyond.

Bayesian Algorithm for The Construction of Logistics Node Delay Model and Its Impact on Subsequent Nodes in Supply Chain

The logistics nodes delay of supply chain has become a focus of customer complaints at the State Post Bureau of China in recent years. Nodes delay usually also brings uncertain business loss. The purpose of this paper is to explore propagation of nodes delay and make it precisely controllable. To address this, Netica software is used and a hybrid strategy of genetic algorithm (GA) and tabu search algorithm (TS) validated by datasets is utilized to optimize the Bayesian algorithm model. Bayesian algorithm model of logistics nodes delay of supply chain is constructed through parameter learning. The empirical analysis is conducted on the basis of three types of nodes delay with 2041 sets of data for cargo departures and arrivals, upstream nodes delay, and nodes delay of supply chain. The results show that the nodes delay of supply chain gradually decreases from upstream to downstream; there are strong correlations between propagation probability and transfer time of nodes delay; the effects of initial node delay are the maximum, after 3 transitions, still having 30% probability to affect the succeeding nodes. Therefore, some suggestions, such as controlling the transfer points and transfer time and strengthening the management of initial node delay, are given.

Städte als Knotenpunkte von Konflikten. Die Rolle transnationaler Konzerne in städtischen Konflikten um Lieferketten und Logistik

AbstractMuch conflict studies scholarship on cities has focused on post-war urban reconstruction. However, high rates of violence and perceptions of urban insecurity, particularly in Latin America, have diluted the clear distinction between armed conflict and peace. From a different perspective, logistics studies have pointed out the conflictive impacts of goods movement on urban communities where efficient supply chains are the ultimate priority. This contribution offers an explorative reading of the 2021 protests in Colombia (‘paro urbano’) as the manifestation of a conflict in supply chain urbanism, and foregrounds the role of TNCs (transnational corporations) in escalating the conflict. Particularly in the city of Cali, protesters blocked roads and access for businesses for weeks. While contesting the national government and highlighting the extreme urban inequality exacerbated by policies primarily catered to TNCs and direct investments, they employed the role of the city as an essential logistics node as leverage in the conflict.

Logistics Center Selection and Logistics Network Construction from the Perspective of Urban Geographic Information Fusion.

The last-mile logistics in cities have become an indispensable part of the urban logistics system. This study aims to explore the effective selection of last-mile logistics nodes to enhance the efficiency of logistics distribution, strengthen the image of corporate distribution, further reduce corporate operating costs, and alleviate urban traffic congestion. This paper proposes a clustering-based approach to identify urban logistics nodes from the perspective of geographic information fusion. This method comprehensively considers several key indicators, including the coverage, balance, and urban traffic conditions of logistics distribution. Additionally, we employed a greedy algorithm to identify secondary nodes around primary nodes, thus constructing an effective nodal network. To verify the practicality of this model, we conducted an empirical simulation study using the logistics demand and traffic conditions in the Xianlin District of Nanjing. This research not only identifies the locations of primary and secondary logistics nodes but also provides a new perspective for constructing urban last-mile logistics systems, enriching the academic research related to the construction of logistics nodes. The results of this study are of significant theoretical and practical importance for optimizing urban logistics networks, enhancing logistics efficiency, and promoting the improvement of urban traffic conditions.

Smart logistics nodes: concept and classification

ABSTRACT This paper presents the Smart Logistics Node concept, which combines the physical infrastructure of logistics nodes with digital systems to enhance collaboration. The Smart Logistics Node benefits from data sharing, supporting infrastructure, and Connected and Automated Transport (CAT) technologies. Based on a literature review on logistics nodes and CAT, we propose a general classification of Smart Logistics Nodes distinguishing upon the node function, degree of organisational (de-)centralisation, digital integration, and infrastructure support for automated driving. Then, we classify sixteen logistics nodes and find that high digital integration is common while automation is lacking. Further automation entails mixed traffic on public roads and requires organisational changes that do not always align with current business models. Our work supports the adoption of emerging technology at logistics nodes and the comparability of business cases. Ultimately, node authorities can use our concept and classification to draw a roadmap to develop CAT capabilities.

A study on the evaluation of competitiveness in the aviation logistics industry cluster in Zhengzhou

As the global economy continues to evolve, air transportation is increasingly seen as a crucial factor in enhancing regional competitiveness. In particular, aviation logistics industry clusters have emerged as a new driving force for regional economic development. In this context, the current study aims to evaluate the competitiveness of the aviation logistics industry cluster in Zhengzhou, China. To achieve this goal, the study employs the “GEM model” and constructs a GKA evaluation model using evaluation index data from 21 logistics node cities across China in 2021. The entropy-weighted TOPSIS method is used for empirical analysis of the data. The results of the study reveal that the competitiveness of Zhengzhou’s aviation logistics industry cluster is moderately low. This is primarily due to the weak competitiveness of its foundational and regulatory subsystems. Specifically, the study finds that Zhengzhou’s resources, facilities, markets, government, and industry aspects are all less competitive when compared to other cities in China. In order to enhance the competitiveness of Zhengzhou’s aviation logistics industry cluster, the study recommends that efforts be made to improve the competitiveness of key elements such as resources, facilities, markets, and government. In particular, the focus should be on elevating industry competitiveness, followed by the development of appropriate regulatory strategies. By doing so, the aviation logistics industry cluster in Zhengzhou would be better positioned to compete with other clusters within China and globally.

Research on Supply Chain Optimization Model Construction and Efficiency Improvement Strategy in Automobile Industry

Abstract This thesis combines the ABC classification method and hierarchical analysis method to propose the evaluation method of logistics spatial layout and applies it to actual automobile manufacturing enterprises. The spatial layout scheme of logistics nodes in the park has been adjusted and optimized to achieve dynamic inventory classification and improve inventory management. When the demand of the decision-making body decreases, the optimal production and transportation scheduling plan for the automobile industry supply chain is formulated using the dynamic scheduling decision-making model. The results show that the spatial layout optimization scheme of supply chain logistics nodes in the automobile industrial park is RDC-B3, VDC-B5, and PDC-B1. The total cost of supply chain logistics after optimization is reduced by 3.8 × 108 yuan. The inventory items in the storage center are divided into 3 categories: A category for inventory items numbered 1-6, B category for 7-9, and C category for 10-11, to improve the turnover rate of the whole inventory and reduce the impact of inventory risk. When the demand side of the supply chain is reduced, the total cost of scheduling the optimal solution is 138.01 million. The research results of this paper have some reference significance.

Transition to synchronization-driven smart inbound logistics: an action research-oriented study

The context evolution has highlighted the need of rapidity and reactiveness for supply chains to face uncertainties, which are amplified by today’s shortage of labor and transportation capacity and increased complexity of operations. The synchronization of logistics flows, coupled with physical and digital automation technologies, is an answer to such challenges. Using an action research-oriented approach, this paper develops and documents the deployment of a reference architecture for synchronization-driven smart inbound logistics processes that could guide the synchronization of the material flows at the intersection between transportation and logistics nodes. Reflections deriving from the architecture deployment are discussed.

Research on the Spatial Pattern of the Logistics Industry Based on POI Data: A Case Study of Zhengzhou City

How to optimize the spatial layout of the logistics industry is a challenge faced by every city under the rapid development of e-commerce nowadays. Enterprises and nodes as the core of logistics and their locational formation mechanisms are the key content of the study. Based on the API platform of Gaode Map, the POI data of Zhengzhou City with representative years during 2012–2021 were collected. Using the methods of industry concentration evaluation and nuclear density analysis, the spatial pattern changes of the logistics industry in Zhengzhou in recent years are demonstrated, the differences in location selection of the logistics industry are analyzed, and the analysis of its formation mechanism is made. The results of the study show that: (1) the logistics industry in Zhengzhou has developed rapidly in recent years, forming a “double core” spatial pattern of agglomeration near the four urban areas in the center of the metropolitan area and Xinzheng Airport, with a high degree of coordination in the agglomeration of industries and practitioners; and (2) the layout of logistics nodes is influenced by government planning, and logistics enterprises have strong autonomy in the layout. In the process of industrial agglomeration, the government guides the industry to gradually achieve agglomeration by laying out the transportation environment and providing low-rent land for industrial parks, while logistics enterprises provide services for different types of groups through segmenting specialized markets for different types of groups to provide services.

Fast Corridors: innovative customs processes and technology to increase supply chain competitiveness. The case of IKEA Italy

Customs play a crucial role in facilitating and speeding up the international flow of goods, especially in logistic nodes such as seaports or dry ports. Burdensome customs procedures can significantly affect the competitiveness of supply chains by increasing times and costs. This paper describes the features and benefits of the so called “Fast Corridor”, an innovative customs process and technological procedure introduced in Italy in 2015. Compared to standard import processes, the Fast Corridor streamlines the flow of import containers from ports to their inland destinations, thanks to a transparent, more economical, secure and IT-integrated procedure, making supply chains more competitive. The Fast Corridor is based on the concept of the virtual long quay, according to which the hinterland becomes an extension of the port areas. Once unloaded from ships, goods are virtually considered to be in the inland node of final destination. Route assurance from port to the destination is provided by geofencing technology, a location-based service that uses truck-mounted GPS to trigger pre-programmed actions. IKEA was the first to adopt this new procedure. Company benefits amounted to −30% in terms of customs costs per TEU, and up to −35% in terms of port-inland node transit time and dwell time for all containers, including those not using the Fast Corridor. Based on the successful implementation by IKEA, other Italian Fast Corridors were activated. This paper provides a deeper understanding of new business models and technologies in freight transport and can outline useful best practices for practitioners in the field.

Evaluation of the Environmental Cost of Integrated Inbound Logistics: A Case Study of a Gigafactory of a Chinese Logistics Firm

In recent years, sustainable development has become an emerging trend in the logistics industry. Smart manufacturing factories pursue green logistics processes with lower energy consumption and reduced carbon emission. The environmental sustainability of the logistics process is widely acknowledged as an important issue. However, a standardized methodology for assessing the environmental cost of logistics-process-aided smart manufacturing is lacking. This paper presents a concept for determining the inbound logistics environmental cost (ILEC) of a gigafactory. Additionally, a novel structured methodology for ILEC assessment is proposed to uniformly describe the gigafactory’s logistics environmental cost, regarding the “double carbon” goal (peak carbon dioxide emissions and carbon neutrality). First, eight types of basic logistics activities and six logistics phases associated with the gigafactory’s inbound logistics are defined. The mapping relationships between the logistics phases and the basic logistics activities are constructed. Then, the novel concepts of environmental price cost (EPC) and environmental impact cost (EIC) are defined and presented. Finally, the ILEC of the gigafactory, including EPC and EIC, is assessed based on mapping relationships and an environmental cost model. We validate this model using the advanced Geely Automobile factory in China in order to analyze the actual inbound logistics environmental costs and how to assess its environmental price and environmental impact. Results from the data model show the environmental costs throughout the whole process and the detailed composition ratio of EPC and EIC in the inbound logistics. Based on the implementation of the ILEC model, our study helps gigafactories to identify critical logistics nodes through energy consumption and to measure the environmental performance of the inbound logistics process. Furthermore, our study helps gigafactories to develop practical environmental strategies.

HUs Fleet Management in an Automated Container Port: Assessment by a Simulation Approach

Freight fleet management (FM) can be defined as an optimization process of freight vehicles scheduling and routing, aimed at reducing time, costs, energy, and environmental impacts. In the specialized literature, there are many FM studies. The focus of this paper is on the FM in the context of a container port to increase the productivity and pursue the sustainability of the logistics node improving the performance by using freight advanced handling units (HUs). The use of automated HUs reduces the time and costs of each port activity, eliminating timewasters and increasing safety; however, it requires advanced intelligent management. Moreover, the automation is in line with energy and environmental sustainability. The paper aims to assess the impacts due to the automation of HUs by using a simulation approach. After a framework of traditional and automated HUs, allowing to highlight their main characteristics, the work considers the organizational problems of a container port and introduces a methodological approach to manage the FM of HUs. Finally, the application to a real context is presented to compare the present configuration of a container port with some project scenarios, considering different levels of automation, as the partial and total replacements of traditional HUs with advanced/automated vehicles.

Efficient Energy Management for the Smart Sustainable City Multifloor Manufacturing Clusters: A Formalization of the Water Supply System Operation Conditions Based on Monitoring Water Consumption Profiles

This study is devoted to improving the energy efficiency of urban infrastructure systems (UISs), in particular, the centralized water supply of a city multifloor manufacturing cluster (CMFMC), by developing the principles of effective energy consumption management. The CMFMCs are located in the residential area of a megapolis and include manufacturing and service enterprises, residential and non-residential buildings, and a city logistics node. Demand monitoring and identification of the influence of seasonal and social environmental factors on its fluctuations is considered as a tool for identifying changes in the operating conditions of the water supply system (WSS) for the CMFMC facilities. To identify the typical operating conditions of water supply facilities, an approach is proposed that involves the analysis of daily water consumption profiles (WCPs). The formation of a database, the formation of groups of the same type of daily WCPs, and the construction of typical daily WCPs for typical groups and their description are the main stages of the proposed approach. The database contains a set of classification characteristics that describe the daily water consumption and its unevenness, as well as the shape of the daily WCP. The principal component analysis was applied to determine the dominant components of daily water consumption. A set of morphometric parameters was used to describe the shape of the daily WCPs. The methods of cluster and discriminant analysis were used to identify the influence of seasonality and social factors on water consumption and to form groups of the same type of daily WCPs. The analysis of sets of similar type of daily WCPs for typical days of typical seasons was carried out for a formalized description of the typical operating conditions of water supply facilities. The results of the analysis are the clarification of the equations of the dominant components of daily water consumption, the determination of the average values of the characteristics of daily water consumption, and the construction and description of typical daily WCPs for typical operating conditions of water supply facilities. The research results were obtained on the basis of the data of the monitoring systems for water supply enterprises in Ukraine and Poland in 2021–2022. The obtained results are the basis for planning the water supply process and adjusting the operation modes of WSS pumping stations for the CMFMC, as well as planning power consumption for typical operating conditions, which will contribute to increasing the efficiency of water and electricity use.

Deep Learning Algorithm Aided E-Commerce Logistics Node Layout Optimization Based on Internet of Things Network

INTRODUCTION: In recent years, e-commerce has shown a booming trend. Influenced by e-commerce, people's logistics needs have also increased sharply in recent years. OBJECTIVES: Research on the node layout and optimization of e-commerce logistics is conducive to improving the scientificity and rationality of logistics node layout, improving logistics distribution efficiency, reducing logistics distribution costs, and better meeting consumers' logistics needs. However, due to the unreasonable layout of logistics nodes in some areas, it has brought huge logistics cost investment to e-commerce companies, and also laid hidden dangers for the long-term development of e-commerce companies. METHODS: Based on this, this paper studied the node layout and optimization of e-commerce logistics by using IoT big data and deep learning algorithms, and proposed an improved logistics node layout scheme based on IoT big data and deep learning algorithms. The experimental research was carried out from five aspects: the transportation cost of logistics, the efficiency of logistics distribution, the accuracy of logistics information transmission, the location and traffic conditions of logistics nodes, and the evaluation of the plan by e-commerce enterprises. RESULTS: The research results showed that the improved logistics node layout scheme can improve the efficiency of logistics distribution by 3.69% and the accuracy of logistics information transmission by 4.34%, and can reduce the logistics transportation cost of e-commerce enterprises. CONCLUSION: The node locations selected by the improved logistics node layout scheme are more reasonable, and e-commerce companies have higher evaluations of the improved logistics node layout scheme.

Influential nodes identification method based on adaptive adjustment of voting ability

Influential nodes identification technology is one of the important topics which has been widely applied to logistics node location, social information dissemination, transportation network carrying, biological virus dissemination, power network anti-destruction, etc. At present, a large number of influential nodes identification methods have been studied, but the algorithms that are simple to execute, have high accuracy and can be better applied to real networks are still the focus of research. Therefore, due to the advantages of simple to execute in voting mechanism, a novel algorithm based on adaptive adjustment of voting ability (AAVA) to identify the influential nodes is presented by considering the local attributes of node and the voting contribution of its neighbor nodes, to solve the problem of low accuracy and discrimination of the existing algorithms. This proposed algorithm uses the similarity between the voting node and the voted node to dynamically adjust its voting ability without setting any parameters, so that a node can contribute different voting abilities to different neighbor nodes. To verify the performance of AAVA algorithm, the running results of 13 algorithms are analyzed and compared on 10 different networks with the SIR model as a reference. The experimental results show that the influential nodes identified by AAVA have high consistency with SIR model in Top-10 nodes and Kendall correlation, and have better infection effect of the network. Therefore, it is proved that AAV algorithm has high accuracy and effectiveness, and can be applied to real complex networks of different types and sizes.

Data Fusion Approach to Identify Distribution Chain Segments in Freight Shipment Databases

Understanding the logistic determinants of freight trips is an important goal in freight transport modeling. Freight shipments move between nodes in the supply chain for different logistic purposes, including production, storage, transshipment, and consumption. A key problem with data availability is that databases generally do not identify these purposes, given the commercial sensitivity of the data. In addition, including information on senders and receivers of the shipments is often prohibitively costly. Therefore, one of the challenges of transport data analysis is to identify freight trip purposes using data fusion, linking information about the main function of logistics nodes to trips in existing databases. This paper proposes a data fusion approach to enrich big truck shipment databases with firm registry data. We use the national freight shipment micro-database from the Netherlands which includes shipment, vehicle, and tour information. Although our presentation here uses formats and methods of accounting for freight data used in the Netherlands, it can be readily replicated for conditions in other countries, as long as similar data sets on shipment data and firm registry are available. The enriched, new database contains transport and firm data for more than 2 million observed trips with information on the vehicle used, shipments carried, and sender/receiver firm. An initial descriptive analysis provides unique empirical insights into the logistic determinants of freight trips. These include the share of national trips that use intermediate nodes, typical changes in shipment sizes, and the role of distribution centers for (de)consolidation of shipments.

Quantum Secure Undeniable Signature for Blockchain-Enabled Cold-Chain Logistics System

Data security and user privacy are two main security concerns in the cold-chain logistics system (CCLS). Many security issues exist in traditional CCLS, destroying data security and user privacy. The digital signature can provide data verification and identity authentication based on the mathematical difficulty problem for logistics data sharing in CCLS. This paper first established a blockchain-enabled cold-chain logistics system (BCCLS) based on union blockchain technology, which can provide secure data sharing among different logistics nodes and guarantee logistics data security with the untampered blockchain ledger. Meanwhile, a lattice-based undeniable signature scheme is designed to strengthen the security of logistics data and user privacy against quantum attacks. This scheme is based on the lattice assumption, which can provide anti-quantum security for BCCLS in the future quantum computer age. It also establishes the undeniable mechanism that guarantees that the actual signer cannot deny a valid signature. Then, security proof shows that this undeniable signature scheme is correct and safe, and an efficiency comparison shows that it is more efficient than other schemes in similar literature. The performance evaluations of transaction throughput and latency show that the proposed BCCLS is efficient and practical. This work can improve the security and efficiency of logistics data management and promote the reform and development of the logistics industry.