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Industry 4.0 concepts and technologies ensure the ongoing development of micro- and macro-economic entities by focusing on the principles of interconnectivity, digitalization, and automation. In this context, artificial intelligence is seen as one of the major enablers for Smart Logistics and Smart Production initiatives. This paper systematically analyzes the scientific literature on artificial intelligence, machine learning, and deep learning in the context of Smart Logistics management in industrial enterprises. Furthermore, based on the results of the systematic literature review, the authors present a conceptual framework, which provides fruitful implications based on recent research findings and insights to be used for directing and starting future research initiatives in the field of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in Smart Logistics.

Given the importance of human centricity, resilience, and sustainability, the emerging concept of Industry 5.0 has pushed forward the research frontier of the technology-focused Industry 4.0 to a smart and harmonious socio-economic transition driven by both humans and technologies, where the role of the human in the technological transformation is predominantly focused on. Several studies discuss the impacts of disruptive technologies on smart logistics operations in Industry 4.0. However, since Industry 5.0 is a new concept and still in its infancy, its implications for smart logistics have not been discussed. To fill this gap, this paper presents a comparative bibliometric analysis to show the connection and differences between Industry 4.0 and Industry 5.0 and their implications for smart logistics. A thorough content analysis is then given to illustrate the features of smart logistics in Industry 5.0 concerning four areas, namely intelligent automation, intelligent devices, intelligent systems, and intelligent materials. The results show that, compared with Industry 4.0, the research of smart logistics in Industry 5.0 puts more focus on the interaction between humans and technology in the digital transition, with the increasing adoption of collaborative technologies, e.g., human-machine systems, collaborative robots, and human-robot collaboration. Finally, a research agenda is proposed for identifying future research directions of smart logistics in Industry 5.0.

PurposeThe purpose of this study is to gain a better understanding of the impacts of Logistics 4.0 initiatives (focusing on automated warehousing systems) on the economic, environmental and social dimensions of firms' sustainability performance. To achieve this objective, a new framework for the assessment of sustainable warehousing in the 4.0 era is developed.Design/methodology/approachThe framework, developed via the item-objective congruence index, Q-sort method and interviews with experts, is employed to assess performance changes through management interviews in two warehousing companies after the implementation of automation technologies.FindingsMost aspects of both companies' sustainability performance are considerably improved (e.g. productivity, accuracy, air emission, worker safety and supply chain visibility); however, the outcome for some criteria might be worsened or improved depending on each company's solutions and strategies (e.g. increasing electricity bills, maintenance costs and job losses).Practical implicationsThe findings provide insight into the effective implementation of warehousing technologies. The proposed framework is also a valid and reliable instrument for sustainability assessment for warehousing operators, which companies can utilise for self-assessment.Originality/valueThis paper contributes to establishing a body of literature that explores the previously unclarified effects of Logistics 4.0 on firms' sustainability performance. The proposed framework, which captures critical concerns of corporate sustainability and technological adaptation, is also the first of its kind for warehouse performance assessment.

PurposeTechnologies continue to disrupt logistics and freight transport (known as smart logistics), but their impacts on smart city sustainability is underinvestigated. Drawing on technology, organisation and environment (TOE) perspective, the objective of this study is to empirically investigate the hierarchical effects of smart logistics on smart city sustainable dimensions (i.e. environmental, social and economic).Design/methodology/approachThe study used cross-sectional survey to collect data from urban transporters, warehouse managers, retailers and information technology (IT) managers in Australia. Data were analysed using structural equation modeling (SEM) to test the hypothesised relationship between constructs of smart logistics and smart city sustainable performance.FindingsThe findings reveal that information and communications technologies (ICTs) use and IT capability (ITC) have positive and significant effects on smart logistics. Technology-enabled smart logistics have an immediate positive effect on smart city environment, which in turn has positive impacts on social and economic performance.Practical implicationsThe study informs managers that smart logistics equipped with freight transport telematics can improve smart city environment through enhanced tracking and tracing of goods movement. The improved environmental stewardship is likely to support social and economic performance.Originality/valueSmart city research remains primarily theoretical and focussed on concerns surrounding sustainable growth amid urbanisation and digitalisation. City logistics and urban freights play key role in smart city economic growth, but vehicular pollution pose social and environmental challenges. Technology-assisted smart logistics are likely to improve smart city sustainable performance but yet to find how they affect each other.

The ever increasing digital transformation in the logistics sector contributes to sustainable development goals. This study investigated the awareness levels of Logistics 4.0 among companies operating in national and international markets, members of the International Association of Transport and Logistics Service Providers (UTIKAD). Data were collected using a survey developed in 2020 (Çiçekli, 2020). The survey was distributed to the firms via email, and statistical analyses were performed using the responses received from 110 companies. All statistical procedures were carried out using the SPSS 25.0 software package. After confirming that the dataset was suitable for factor analysis, a Principal Component Analysis (PCA) was conducted. Four factors were identified: economic benefit, firm structure, digital technology usage, and awareness. To determine the variables that influence firms’ awareness of Logistics 4.0, a multiple linear regression analysis was performed. The results revealed that the most significant predictors of Logistics 4.0 awareness were the economic benefit and digital technology usage variables.

The development history of smart logistics and smart manufacturing in China demonstrates a high degree of synergy. This study aims to analyze the impact, mechanisms, and heterogeneous performance of smart logistics on the manufacturing industry chain resilience. The analysis is based on panel data collected from 30 provinces in China, covering the period from 2012 to 2023. The empirical findings suggest a significant positive impact of smart logistics on the manufacturing industry chain resilience, and the research findings are relatively robust. This impact can be primarily attributed to the reduction of transaction costs and improvements in logistics efficiency. In a comparison of the impact relationships between the three different economic regions of the East, Center and West, it was found that the Eastern region had a stronger facilitating effect, the Central region had a less pronounced facilitating effect, and the Western region had a weaker facilitating effect relative to the Eastern region. Moreover, Intelligent logistics has a greater role in promoting the resistance and renewal of the manufacturing industry chain, and a relatively weaker role in promoting the recovery of the manufacturing industry chain. Futhermore, threshold test shows that, in terms of long-term dynamics, there is a threshold effect of smart logistics development on the enhancement of manufacturing industry chain toughness; at the same time, higher level of advanced industrial structure and innovation environment, smart logistics is conducive to the enhancement of manufacturing industry chain toughness level.

The main objective of this article is to identify the direction of change in logistical activities and their critical segments as a result of the COVID-19 pandemic in a country that is an important logistical hub of Europe. The specific objectives are to identify changes in logistical activities, especially during the COVID-19 pandemic, to determine the dynamics of changes in business revenues and in quantitative parameters for total logistics services and their segments during the pandemic, to establish the relationship between the economic situation and parameters related to logistics services, including during the COVID-19 pandemic. Using the method of purposive selection, Poland, which is well developed in logistics and aspires to be a crucial logistical hub of Europe, was selected for the study. The analysed period covered the years 2015–2021. The material sources were the literature on the subject and data from reports on individual logistics segments. Dynamic indicators with a fixed and variable base, coefficient of variation and Kendall’s tau correlation coefficient were used for analysis and presentation. It was found that the COVID-19 pandemic accelerated changes in logistics activities. These changes included digitalisation, the development of the e-commerce market, multi-channel sales and the development of these services, and the introduction of automation and artificial intelligence. In all activities, 2020 was the most challenging year, but there was generally a reduction in revenue growth and, less often, stagnation. Logistics companies gained in the second year of the pandemic (2021) when implemented solutions generated record revenues. Among the winning segments were logistics services in general, especially sea freight forwarding, warehousing services, courier services related to e-commerce, and a lesser extent, freight transport. Losses were incurred in the segment related to passenger transport. COVID-19 became a positive catalyst for change. The logistics industry ultimately benefited from the pandemic. Additionally, due to the pandemic, logistics operations have had greater sustainability, contributing to resource conservation and environmental protection.

In a two-tier supply chain consisting of a smart logistics platform and a smart logistics provider, for value-added service innovation in logistics, there are usually two innovation modes: collaborative innovation (CI) in the supply chain and independent innovation by the provider. In this study, we research the strategic choice of the platform about whether to co-innovate with the provider when the platform requires the provider to innovate new value-added services. By building a two-tier supply chain game model, this study focuses on the influence of leadership and smart level on the platform's CI strategy. This study obtains many interesting findings. Firstly, we give the cost thresholds for the platform to choose CI under different relationships, with higher thresholds under the provider-led relationship compared to Nash and platform-led relationships, the platform is most likely to choose CI under the provider-led relationship, while most difficult to choose under Nash relationship. Secondly, we find that under Nash and platform-led relationships, the platform's participation in the CI will lead to an increase in both parties' profits, so that the CI can be achieved, but under the provider-led relationship, there may be a situation where the platform's profits increase and the provider’s profits decrease when the two parties co-innovate, and since under this relationship it is easiest for the platform to choose to participate in the CI, the platform can choose a cost-sharing contract to solve this problem and achieve CI. Thirdly, with the same leadership, the higher the smart level of the provider is, the more incentive the platform is to participate in CI. If the smart level of the provider is very low or the smart level of the platform is very high, the platform will never participate in CI, regardless of the leadership.

Automation of logistics enhances efficiency, reduces costs, and minimizes human error. Image processing—particularly vision-based AI—enables real-time tracking, object recognition, and intelligent decision-making, thereby improving supply chain resilience. This study addresses the challenge of deploying deep learning-based object detection on resource-constrained embedded platforms, such as NVIDIA Jetson devices on UAVs and ground robots, for real-time logistics applications. Specifically, we provide a comprehensive comparative analysis of YOLOv5 and YOLOv8, evaluating their performance in terms of inference speed, accuracy, and dataset-specific metrics using both the Common Objects in Context (COCO) dataset and a novel, custom logistics dataset tailored for aerial and ground-based logistics scenarios. A key contribution is the development of a user-friendly graphical user interface (GUI) for selective object visualization, enabling dynamic interaction and real-time filtering of detection results—significantly enhancing practical usability. Furthermore, we investigate and compare deployment strategies in both Python 3.9 and C# (ML. NET v3 and .NET Framework 7) environments, highlighting their respective impacts on performance and scalability. This research offers valuable insights and practical guidelines for optimizing real-time object detection deployment on embedded platforms in UAV- and ground robot-based logistics, with a focus on efficient resource utilization and enhanced operational effectiveness.

The complex integration of Industry 4.0 technologies into SMEs necessitates robust frameworks to address adoption barriers and enhance sustainability. The present study investigates the impact of artificial intelligence (AI), the Internet of Things (IoT), and blockchain on smart manufacturing, logistics, and sustainability in SMEs. Using a cross-sectional design, data were collected from 300 SMEs across manufacturing, logistics, and retail sectors through purposive sampling, focusing on technology adoption, and sustainability performance from 2018 to 2022. Data were analyzed using advanced machine learning models, including XG Boost and Random Forest, alongside Recursive Feature Elimination (RFE) for dimensionality reduction and quantile regression for an inferential analysis. Findings revealed that IoT adoption improved resource utilization efficiency, while blockchain enhanced ethical sourcing—furthermore, AI-driven predictive maintenance reduced operational downtimes. XG Boost achieved a Mean Squared Error (MSE), highlighting its superior predictive capability, while Random Forest achieved perfect fitness but risked overfitting. However, adoption varied significantly across firms due to financial and technical constraints, with SMEs reporting limited access to capital and skilled labor. This study underscores the need for policy interventions and targeted support for SMEs to bridge adoption gaps. The study advances the existing body of knowledge by highlighting the synergistic benefits of integrating Industry 4.0 technologies to enhance SME sustainability. Practical implications include policy recommendations for financial incentives, technical support, and capacity-building programs, empowering SMEs with actionable insights to overcome adoption barriers and achieve sustainable growth. These findings offer industry leaders and policymakers’ actionable insights to drive SME transformation in Industry 4.0, empowering them to make a difference.