1 Introduction

This section narrates the IR4.0, supply chain 4.0, logistics 4.0 and transportation 4.0 with each succeeding item as a sub-system of the predecessor adapted from the reductionist approach in a wider system theory (Babbie 2010). The transportation sector is transitioning from a traditional to an efficient, interconnected, and environment-friendly system due to recent technological advancements of the IR4.0 (Esmaeilian et al. 2020). The transition is led by automation of transportation jobs in smart factories, supplier-to-producer and producer-to-consumer shipments. The automation is further categorized into two main types: supervised automation, that requires human administration or pre-programming, and unsupervised automation, which is completely autonomous. The administration and execution of automated operations can be rendered through cutting-edge technologies, including artificial intelligence, robotics, automated guided vehicles, internet-of-things (IOT), big data, cloud computing and other hyper-connected technologies (Bălan 2020; Martínez-Gutiérrez et al. 2021; Popkova et al. 2021; Saoud and Bellabdaoui 2023). Consequently, transportation 4.0 would allow companies to enhance competitiveness and resilience in order to cope with intricate pressures of business existence, market dynamics, operational excellence, serviceability and environmental concerns (Bányai 2018; Barreto et al. 2017; Du et al. 2023; Gružauskas et al. 2018; Hoffmann and Prause 2018; Liu et al. 2019; Ordieres-Meré et al. 2020; Frazzon et al. 2019).

By definition, IR4.0 is a transformational archetype that embodies the digitization, intelligent systems, and informatization of industrial systems through the application of cyber-physical systems (CPS) and IOT technologies (Nikitas et al. 2020). Gilchrist and Gilchrist (2016) elaborates CPS as a set of technologies and systems to monitor the physical operations and sets up a virtual replica, and to enable decentralized decisions in smart factories. The CPS further connects and collaborates with each other and people in real-time through the IOT, while the IOS (internet of services) allows players across the value chain to access resources inside and outside the smart factories.

Initially, IR4.0 focused on enhancing mass production in manufacturing systems with modularity and flexibility (Zhong et al. 2017). Following this, scholars believed IR4.0 is a diverse concept addressing sustainability, information systems, and social innovation (Piccarozzi et al. 2018). With scholarly developments, IR4.0 evolved and extended into business value streams, including supply chains (Frederico 2021a). Industry 4.0 integration in supply chain areas, including project management (Frederico 2021b), quality management (Bui et al. 2022), big data utilization (Narwane et al. 2021), and supply chain integration enhancement (Tiwari 2021) were examined. Supply Chain 4.0 encompasses the application of IR4.0 across material sourcing, manufacturing, warehousing, and customer dispatch (Govindan et al. 2022). Transportation is crucial in supply chains, linking manufacturers, buyers, and sellers through goods’ physical movement; disruptions can adversely affect the entire supply chain operation (Crainic and Laporte 2016; Wilson 2007; Zhu et al. 2020). Previous studies often blend transportation into the logistics spectrum, generating numerous logistics 4.0 papers (Bag et al. 2020; Efthymiou and Ponis 2021; Facchini et al. 2019; Kucukaltan et al. 2022; Moldabekova et al. 2021; Winkelhaus and Grosse 2020). Logistics 4.0, stemming from supply chain 4.0, targets synchronization among functions (warehousing, inventory handling, packaging, and distribution) to meet performance indicators. This study omits the logistics-transportation distinction. It focuses on transportation 4.0 within IR4.0, supply chain 4.0, and logistics 4.0 orbits (Fig.  1). Aligned with a reductionist approach (Babbie 2010), this study intentionally selects elements in each circle to represent inter-relationships within its sub-system.

Fig. 1
figure 1

Reductionist approach of concentric circle diagram of Industry 4.0 and its selected sub-system. Source: Authors’ compilation

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With increasing technological sophistication, individual deliveries, and extensive order customization, contactless deliveries and recent Covid-19 impact have put immense pressure on transportation job performance (Goulias 2021; Sun and Yin 2017). This is because transportation jobs account for a bigger portion of costs, efforts, resources, and emissions (Carter and Ferrin 1995; Daniels and von der Ruhr 2014; Mendoza and Ventura 2013; Wang et al. 2020). In addition, industrial (inbound) and freight (outbound) transportation have evolved drastically with the absorption of IR4.0 technologies and hyper-connected environments (Steyn 2020). These have broadened the scope of transportation research to a more interdisciplinary topic. They involve prevailing productivity and societal issues like routing, resource allocation, traffic congestion, sustainability, and environmental justice (Sun and Yin 2017). For example, big data analytics support transportation businesses in the ecosystem to strengthen route planning, risk prediction, smart transportation, systems maintenance and decision-making to optimize environmental and operational performance. These involve factors of cost, time, emissions and distance traveled in transportation (Bányai et al. 2018; Gružauskas et al. 2018; Honda and Tsujio 2020; Lyapina et al. 2020; Rahman et al. 2020; Turner et al. 2019).

In contrast, prior to IR4.0, the futuristic technological paradigms in transportation were limited to hi-tech research and defense institutions. For instance, the use of digital-twin simulation models for testing and trials of space-age NASA and the US air force vehicles to overcome the complexities of physical tests and operations (Glaessgen and Stargel 2012).

Furthermore, according to PricewaterhouseCoopers (Hawksworth et al. 2018), the automation rate in the transportation sector is proliferating and it is anticipated that by 2030 almost 52 percent of transportation jobs would be carried out through supervised and unsupervised automation. These call for the attentiveness of organizations to leverage transportation 4.0 for a sustainable future. While the capabilities of the Industry 4.0 paradigm to establish advanced automated transportation systems within supply chain management are evident, there exists an ongoing debate regarding the role of humans and societies within the former Supply Chain 4.0 and IR4.0 paradigms. Scholars contend that these revolutions were predominantly driven by the impetus to optimize IR4.0 technologies rather than guided by human and societal objectives. This realization has paved the way for the emergence of IR5.0 (Frederico 2021b; Sołtysik-Piorunkiewicz and Zdonek 2021; Xu et al. 2021; Zekhnini et al. 2020). Expanding on this notion, the influence of the 4.0 paradigm encompasses both commercial and public spheres. Whether individuals are integral to the IR4.0 transformation within commercial activities like production, logistics, transportation, and warehousing, or they act as consumers, their interaction with IR4.0 technologies are becoming increasingly inevitable. The pivotal challenge lies in enhancing the skills and knowledge of workers to effectively engage with machines and robots, a pressing matter for the transition to IR5.0. Furthermore, as technology proliferates in both commercial and public realms, its expansion must be harmonized with societal and environmental considerations (European Comission 2020).

Upon reflection, though developing and advanced economies have displayed remarkable development in transportation 4.0, literature is unclear about its global progress. Any disparity of global progress in transportation 4.0 could likely be attributed to the poor understanding and inadequate capabilities of the fundamental IR4.0 principles in some parts of the world. Further, the critical technology and applications of transportation 4.0 in the advanced economies should be positively identified to remedy the disparity. To aid in this effort, the main and unequivocal important features of transportation 4.0 systems to attain competitiveness and contribute to an efficient and responsible ecosystem should also be identified for remediation. For the longer term and intergenerational benefit, the research gaps, challenges and prospects that could probably form future research directions for transportation 5.0 ought to be discovered for sustainability.

Hence, the following research questions are addressed in this paper.

  • RQ1: What is the current global research progress on transportation 4.0?

  • RQ2: What are the principles of IR4.0?

  • RQ3: What are the desired system capabilities, enablers, IR4.0 critical technologies and applications in transportation 4.0?

  • RQ4: What are the main features of transportation 4.0 systems to attain competitiveness and contribute to an efficient and responsible ecosystem?

  • RQ5: What research gaps, challenges and prospects could probably form the future research directions for Transportation 5.0?

This study intends to conduct an SLR (systematic literature review) to shed light on how transportation 4.0 can tackle the rising pressures on transportation function. Thus, it is imperative to clearly understand the themes under transportation 4.0 systems such as capabilities, enablers and characteristics. Therefore, the study will contribute to literature in many ways. Firstly, it conducts extensive systematic review of literature pertaining to the integration of IR4.0 in transportation sector. Secondly, it uncovers the geographical contributions for transportation 4.0. Thirdly, it develops a novel framework for transportation 4.0. And finally identify research gaps, challenges and prospects for successive transition to transportation 5.0.

The following sections of this paper comprise methods to explain the procedure of data collection and analysis. Following this, we disseminate findings by spatial analysis of global progress on transportation 4.0, the development of transportation 4.0 framework and holistic view of transportation to illuminate the domain’s current and possible future state. Finally, we present the discussion, implications, conclusion and limitations of the study.

2 Methods

In terms of our research methods, we employed a systematic literature review (SLR) as the initial step. This was subsequently complemented by content analysis and thematic synthesis of information extracted from previous studies pertaining to transportation 4.0 within the domain of supply chain management. As a research-oriented approach, literature reviews help researchers to uncover research trends, gaps, themes and difficulties that need to be addressed and contextualize future research directions (Dutta et al. 2020). It is widely accepted that literature reviews are valuable intellectual contributions that support locating, collecting, summarizing, and formulating dispersed scholarly knowledge on a certain topic (Khirfan et al. 2020). Particularly, SLR is a well-known procedure for systematically analyzing and overviewing the existing literature (Büyüközkan and Göçer 2018). The approach has been practiced in research to evade scholars’ partiality and mitigate the chances of missing relevant papers in selection criteria. McLean et al. (2017) extended SLR procedure to a more robust form by considering the work of preceding researchers such as Bakker (2010), Rashman et al. (2009) and Tranfield et al. (2003). We adapted SLR procedure from McLean et al. (2017) which comprises three phases: planning the review, conducting the review, and reporting and dissemination. The authors modified the SLR procedure and incorporated elements in phases 2 and 3 as shown in Fig. 2 with additional steps to scrutinize the paper’s screening criteria for synthesis and presentation. As a result, a careful filtration of irrelevant subject papers and the chances of errors of unauthentic and predatory publications can be minimized during the step-wise screening of papers in the SLR procedure.

Fig. 2
figure 2

Systematic literature review (SLR) process. Source: McLean et al. (2017) and four additional steps by the authors

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2.1 Planning the review

For this study, we selected Scopus because it has extensive coverage compared to the Web of Science (WoS). According to Mongeon and Paul-Hus (2016), only 5 percent of the WoS journals do not appear in the Scopus database. In contrast, 50 percent of Scopus journals do not appear in WoS. The range of journal articles was selected from 2011 until 2021 (10th March 2021) since the IR4.0 was first formally conceptualized in 2011 by German scholars and industrialists.

2.2 Conducting the review

The basis of search for inclusion criteria on the Scopus database is as follows with exclusion criteria thereafter:

  • Duration - journal articles ranging from 2011 to 2021 (as of 10th March 2021)

  • Keywords - Fourth Industrial Revolution, 4IR, Industry 4.0, IR4, Transportation and Supply Chain

  • Search Command - ("Fourth Industrial Revolution" OR "4IR" OR "Industry 4.0" OR "4.0" OR "IR4") AND ("Transport*" OR "Supply chain*")

  • Type - Article and review

  • Access Type - all

A total of 4558 items were identified in the Scopus database upon entering the assigned criteria. In addition, we performed additional steps to refine the search string to exclude non-English documents (180) and irrelevant subject area documents (3268), e.g., biochemistry, genetics and molecular biology, medicine, physics and astronomy, chemistry, earth and planetary sciences. The refined string is:


TITLE-ABS-KEY ( ( "Fourth Industrial Revolution" OR "4IR" OR "Industry 4.0" OR "4.0" OR "IR4" ) AND ( "Transport*" OR "Supply chain*" ) ) AND PUBYEAR> 2010 AND ( LIMIT-TO ( DOCTYPE, "ar" ) OR LIMIT-TO ( DOCTYPE, "re" ) ) AND ( LIMIT-TO ( LANGUAGE, "English" ) ) AND ( EXCLUDE ( LANGUAGE, "German" ) OR EXCLUDE ( LANGUAGE, "Portuguese" ) OR EXCLUDE ( LANGUAGE, "Ukrainian" ) ) AND ( EXCLUDE ( SUBJAREA, "BIOC" ) OR EXCLUDE ( SUBJAREA, "MEDI" ) OR EXCLUDE ( SUBJAREA, "PHYS" ) OR EXCLUDE ( SUBJAREA, "CHEM" ) OR EXCLUDE ( SUBJAREA, "EART" ) OR EXCLUDE ( SUBJAREA, "AGRI" ) OR EXCLUDE ( SUBJAREA, "MATE" ) OR EXCLUDE ( SUBJAREA, "IMMU" ) OR EXCLUDE ( SUBJAREA, "CENG" ) OR EXCLUDE ( SUBJAREA, "PHAR" ) OR EXCLUDE ( SUBJAREA, "NEUR" ) OR EXCLUDE ( SUBJAREA, "HEAL" ) OR EXCLUDE ( SUBJAREA, "ARTS" ) OR EXCLUDE ( SUBJAREA, "NURS" ) OR EXCLUDE ( SUBJAREA, "PSYC" ) OR EXCLUDE ( SUBJAREA, "VETE" ) OR EXCLUDE ( SUBJAREA, "DENT" ) )

Following that, the 1110 documents were further refined based on title, abstract and duplication with 171 documents retained. Finally, the documents were reviewed thoroughly and 84 papers were analyzed and presented in the SLR results.

3 Results

From the SLR paper qualification and extraction procedure, we found 84 papers (documents) on IR4.0 in the transportation function of supply chains. Here, the quantum of papers specifically reflects transportation from the lens of IR4.0. Next, we employed SLR methods to find themes on RQs in the literature.

3.1 Analysis of global research progress

The geo-spatial analysis is performed to determine the current level of global progress at regional and country levels through the number of research papers published. From the SLR, we identified 84 publications by 37 countries globally as shown in Table 1.

Table 1 Country and continent-wise summary of publications
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At the continent level, Europe leads with 52 papers (62 percent), Asia stands in second place with 15 papers (18 percent) and the remaining were published in North America (7 papers, 8 percent), Africa (4 papers, 5 percent), South America (3 papers, 4 percent) and Oceania (3 papers, 4 percent). At the country level, the top 5 contributors by the number of publications are Germany (7), Italy (6), Spain (5), Sweden (5) and Turkey (5). Surprisingly, all these are situated in Europe. The results determined that Europe is the center of transportation IR4 research and Germany remains the pioneer. Figures 6 and 7 Appendix shows the geo-spatial visualization of published works and regional contribution knowledge.

The analysis reveals global disparities in Transportation 4.0 progress due to regional differences in infrastructure and connectivity, economic and regulatory environments, and digital literacy with skilled workforces. Developed regions like Western Europe drive Transportation 4.0 initiatives, integrating technologies like smart sensors, autonomous vehicles and real-time data analytics (Cepa et al. 2023; Paprocki 2017; Tripathi and Gupta 2021). Conversely, underdeveloped regions like parts of Africa face obstacles, resulting in marked disparities (Awinia 2023; Kuteyi and Winkler 2022). Economically advanced regions, including Europe, Asia, and North America, heavily invest in IR4.0 technology RnD (Gkoumas et al. 2022; Milakis et al. 2017; Yang and Gu 2021). In contrast, resource-limited regions, noted by Malhotra et al. (2021), may struggle with funding, leading to slower integration and a widening technology gap. Supportive regulatory environments, exemplified by the United States and China with self-driving car testing frameworks (Aoyama and Leon 2021; McAslan et al. 2021; Wang et al. 2023), are crucial for deploying IR4.0 transportation innovations. Sindi and Woodman (2021) emphasized that legislation remains a barrier beyond upfront costs. Regions with established regulations will foster Transportation 4.0 progress, while those lacking in regulation will hinder its advancement (Othman 2022). Lastly, regions with a highly skilled and digitally literate workforce, seen in countries like Germany, Italy, China, and the United States, swiftly adapt to and benefit from Transportation 4.0 technologies (Lloyd and Payne 2019; Pedota et al. 2023; Teixeira and Tavares-Lehmann 2022). Conversely, regions lacking such a workforce face challenges in maximizing the potential of IR 4.0 (Li 2022).

The analysis of global research progress serves as a foundation for the exploration of the principles of the IR4.0 paradigm in the next section. By understanding the current global research landscape and technological advancements, we can now delve into the principles that underpin the IR4.0 paradigm, providing valuable context and insight into the evolving landscape of Industry 4.0.

3.2 Principles of IR4.0 paradigm

There are six principles of IR4.0 paradigm (Fig.  3) that set it apart from previous industrial revolutions. The explanation is as follows (Michael and Emine 2019):

  • Interoperability - refers to the aptitude of humans, products, equipment, machines or devices to interact with each other through the internet or IOT.

  • Virtualization - means the control of physical processes or plants virtually.

  • Decentralization - is the decentralization of cyber-physical systems to take decisions independently. The technology shifts from automated to autonomous.

  • Data Integration and Analytics - is the competence to share and analyze real-time data to enhance understanding and decision-making.

  • System Utilities - are linked to the services level of humans and cyber-physical infrastructures.

  • Modularity/ Flexibility - refers to the modularity or flexibility in smart systems or factories to accommodate changes in current systems/environment and future expansions.

Fig. 3
figure 3

Principles of IR4.0. Source: Michael and Emine  (2019)

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The next section will explore how these principles manifest in the real world within the context of transportation 4.0. This aligns to RQ3 where we will investigate the desired system capabilities, enablers, IR4.0 critical technologies, and practical applications, bridging the gap between theory and application in the field of transportation.

3.3 Development of transportation 4.0 framework

The synthesis of 84 relevant papers has enabled the identification of essential components within transportation 4.0, aimed at addressing the various challenges that hinder the transportation function in achieving business competitiveness and fostering an efficient and responsible ecosystem. These key components included desired system capabilities, enablers, IR4.0 critical and baseline technologies, and decisive features. The findings of SLR enabled us to develop a framework for transportation 4.0, as shown in Fig. 4. The explanations for each component are presented in the following sub-sections.

Fig. 4
figure 4

Proposed framework of transportation 4.0

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3.3.1 Desired transportation system capabilities

Transportation is a critical resource of various economic activities. It connects upstream business partners (e.g., raw material suppliers with the manufacturers) and downstream partners (manufacturers with distributors, retailers until consumers) in the economic system Bureau of Transportation Statistics (2021). Based on the SLR synthesis of literature, the following are the desired transportation system capabilities:

  • Enhanced physical goods monitoring

  • Intelligent and sustainable utilization of resources (carriers and vehicles)

  • Interconnected and smart transport infrastructure

  • Collaborative digital platforms

  • Knowledge transfer and information sharing

  • Customer and business partner relationships

  • Unlocking business risk prediction and forecast models via big-data technologies

  • Predictive maintenance to mitigate system breakdown costs

  • Facilitating environmental sustainability

3.3.2 Human readiness as enabler

Human readiness is the fundamental enabler of transportation 4.0. This is because the drastic automation, computerization and complexity of operational processes entail a constant upskilling of workers, professionals and business partners. It covers the use of data-driven technologies, supervised automation processes, cloud computing, human–machine interaction and development of systems and infrastructure. One practical explanation is that in the service industry, managers are likely to perform data-driven analysis to discover and predict new market dynamics and consumer behavior patterns to satisfy customers (Lyapina et al. 2020). In addition, it allows them to capitalize on the benefits of digitalized and technological resources in a networked transport business environment (Schroeder et al. 2019).

3.3.3 IR4.0 technologies in transportation 4.0

Transportation 4.0 is recognized as the driver of enhancing productivity and service levels for businesses and governments (Gružauskas et al. 2018; Priyanka et al. 2021; Schroeder et al. 2019). Hence, it is adopted for internal transportation (smart factories and warehouses) and external transportation (supplier to manufacturer and manufacturer to consumer) (Klumpp 2018; Klumpp et al. 2019; Zhang et al. 2021). Concerning technology, the use of autonomous vehicles (AVs) is more prominent in terms of the potential impact on transportation. The internet-of-things (IOT) has the highest rate of adoption, and big-data analytics is a promising technology where practitioners and organizations in the transport industry are more willing to spend (Hopkins 2021). The summary of IR4.0 technologies in transportation is presented in Table 2.

Table 2 Technologies of IR4.0 in transportation
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3.3.4 The decisive features of transportation 4.0

The SLR of 84 papers identified seven decisive features of transportation 4.0. Further explanations of each decisive feature are presented as follows:

Digitization of transportation

Digitization, vital in today’s competitive post-COVID-19 environment, integrates cutting-edge IR4.0 technologies, fosters innovation, and involves stakeholders in transport operations (Vural et al. 2020; Hopkins 2021). Noteworthy are digitization-enabling technologies, including Internet of Things (IoT), Cloud of Things (CoT), and Edge of Things (EoT), connecting physical objects to digital interfaces and intelligent systems (Steyn 2020). IoT organizes physical devices through the internet, reducing complexity in multi-actor transactions within the cold supply chain (Gupta et al. 2019; Molka-Danielsen et al. 2018). Teucke et al. (2018) find that sensors coupled with IoT in automotive supply chains offer real-time monitoring, mitigating quality issues. IoT also fosters a network of shared resources and capabilities among transporters (Schroeder et al. 2019). Cloud-based IoT (CoT) revolutionizes logistics and transportation by leveraging the powerful, efficient, and customizable cloud computing environment (Javed et al. 2021; Kyriazis 2018; Okumuş et al. 2020; Priyanka et al. 2021). Liu et al. (2019) highlight CoT’s interactions in a smart logistics system, speeding up data flow and reducing costs and transportation time. Vural et al. (2020) study reports that CoT-enabled service architecture can efficiently plan and schedule inter-modal transport, influencing flexibility, costs, and communications. Edge of Things (EoT) strengthens the connection between IoT and CoT, supporting data placements based on need analysis (Buzachis et al. 2020). Singh et al. (2019) emphasize the effectiveness of smart technologies in processing data swiftly, mitigating the potential risks of data clogging in IoT-CoT interaction.

Contactless first-mile/last-mile (FMLM) delivery systems

Before COVID-19, the first-mile/last-mile delivery (FMLM) systems companies were concerned about efficient use of resources, operational costs and timelines (Frontoni et al. 2020a, b) because of skyrocketing demands and needs (e.g., customized order and express deliveries) for individual shipment deliveries. Bányai (2018) suggests a mathematical model (black-hole optimization heuristic) to reduce fuel consumption with the efficient scheduling of delivery orders. Bányai (2018) further enhance the FMLM algorithm for instant customer deliveries by considering resource availability, operational costs, capacity and timeframes. In addition, the delivery robots were also the focal point for most developed countries to improve FMLM delivery performances (Hoffmann and Prause 2018).

In early 2019, the COVID-19 pandemic hit society and reframed the whole scenario of FMLM to contactless delivery systems owing to health concerns. Many small-to-large scale local and multi-national transportation companies transformed to contactless delivery solutions for the survival of business operations. Moreover, consulting and IT solution provider companies (Accenture, Chaione, Vector, GSM tasks and others) have also joined the cause. Yet, the development and future of FMLM in post-COVID situations is a hot topic in the present and future transportation research because of increasing human–machine interactions.

Transport optimization models

The transport operations are major cost centers for businesses in the industrial and service sectors, whether they receive incoming supplies for manufacturing/production or movement of raw materials and work-in-process inventories within smart factories or delivery of finished products to the customers. The optimization models for transport routing and resource allocation extend operational, cost and service performance (Abderrahim et al. 2022; Fathi et al. 2020; Frontoni et al. 2020a, b; Wu et al. 2021). In the context of smart factories, the scheduling of transport activities (delivery of parts to the manufacturing processes and within factory material handling processes) favorably affect production rate, job balancing and downtime reduction. Abderrahim et al. (2022) propose the interactions of transport constraints with production scheduling using VNS (variable neighborhood search) algorithm. Similarly, Rahman et al. (2020) suggest a two-layer approach, first applied heuristic algorithm to schedule transport vehicle and then PSO (particle swarm optimization) algorithm for line balancing and controlling of material feeding from warehouse to the manufacturing process. He et al. (2022) applied branch-and-price algorithm to integrate just-in-time systems with 3D printing production facility to optimize delivery cycle times and reduce 16.27% transportation costs.

Furthermore, due to dynamic demands, capacity constraints and customized orders, equating transportation cost-efficiency and customer satisfaction are difficult for service organizations. In this regard, various scholars have suggested solutions. Abdirad et al. (2021) leverage data agility feature of IR4.0 paradigm to overcome capacity underutilization in moving vehicles by accommodating continuous customer orders. This was accomplished with metaheuristic algorithms applied in two steps. First, plans the initial route by construction algorithm (path-cheapest-arc, savings algorithm and global-cheapest-arc), and then accommodate continuous orders and revised the transport route using improvement algorithm (simulated annealing, tabu search and GLS). Bocewicz et al. (2019) optimize transport and inventory costs by applying the milk-run approach on the nexus of the size of the transport fleet and the capacity of storage locations. Rahimi et al. (2020) propose an AnyLogic environment based on artificial intelligence and knowledge systems for end-to-end integration of logistics supply chains.

Operational aptitude of transportation systems

In transport operations, the basic needs of transportation managers, customers, and collaborating partners are transparency, vigilance, visibility, tracking and traceability. These needs are applicable to shipments, vehicles, infrastructure, transactions, records, traffic, weather, and city conditions (Gupta et al. 2019; Ma et al. 2020; Molka-Danielsen et al. 2018; Sołtysik-Piorunkiewicz and Zdonek 2021; Frazzon et al. 2019; Yörükoğlu and Aydın 2020). Under IR4.0, service providers are now able to meet the operational competencies of modern transportation systems. The various competencies below are also explored by Jachimczyk et al. (2021), Moldabekova et al. (2021), Priyanka et al. (2021), Bui et al. (2020), Mistry et al. (2020), Moussa et al. (2012), Barreto et al. (2017) and Schleipen et al. (2015).

  • Transparency - is sharing accurate information or events and a sense of accountability in transactions or operations.

  • Vigilance - is the ability to monitor transactions, operations or other defined conditions through supervised and unsupervised methods.

  • Visibility - is enabled through end-to-end integration of processes where managers can see desired operations or transaction details.

  • Tracking - is the progress or status of any transaction, operation, shipment or vehicle.

  • Traceability - is the ability of systems to see impressions (trails) of transactions, operations and resources.

Real-time risks prediction and decision making

Big-data generated by advanced computing and scientific technologies may help predict the likelihood of suggested responses and their impact of event risk occurring in an ecosystem. Companies face many operational, business opportunities, uncertainty and hazards or environmental risks daily to survive and succeed. In this regard, data-analytics have been widely used for real-time predicting events and decision-making (Bányai 2018; Honda and Tsujio 2020; Nitti et al. 2020; Sahal et al. 2020; Wally et al. 2020). Therefore, transportation as a cornerstone of the ecosystem should always be equipped with risk prediction abilities to cope effectively with such events.

In compliance with operational performance, Johansson et al. (2019) and Karakose and Yaman (2020) suggest a predictive maintenance model to enhance serviceability and real-time diagnosis of trains (railways); they gather real-time data from sensors to monitor the impact of external environmental conditions on train performance. Georgescu et al. (2012) study traffic flow predictions to alert drivers on unusual traffic conditions such as road accidents and traffic congestion. The prediction model envisages future traffic events of up to 30 min. The estimations from data technologies further mitigate shipment delays and costs (Liu et al. 2019).

Online service platforms emerged as a business opportunity for transportation and logistics companies. ShipBob, Flexport, UPS, FedEx, DHL and many others have implemented such platforms to lead the market. However, infrastructure’s role is critical to accomplishing performance indicators in service platforms. In order to increase the serviceability performance, Kyriazis (2018) proposed a failure detection mechanism to protect online service platforms from potential breakdowns caused by cyberattacks, hardware malfunctions, and applications crash. The failure detection mechanism would oversee the performance of data centers through voting and ping mechanism.

In addition, data analytics facilitates transport service providers in assuring the value of physical goods by identifying substantial risks and responsibilities (Teucke et al. 2018; Uygun and Jafri 2020). Also, enable service providers to make market predictions and assess expected changes in customer orientation (Correa et al. 2020). As a result, companies either adjust, improve business strategies or may decide to diversify product lines upon acquiring technological capabilities (Bilbao-Ubillos et al. 2021).

Robotization of transport operations

Numerous advantages have led researchers to urge robotic transport operations:

  1. (i)

    Robotization has promising potential to increase productivity, flexibility and innovation while lowering expenditures (Dharmasiri et al. 2020; Rakyta et al. 2016; Zhang et al. 2021).

  2. (ii)

    The attainment of sustainability by reducing emissions and operational costs (Gružauskas et al. 2018).

  3. (iii)

    Robotization would help companies experiencing manpower shortages, and mitigate the process-specific unwilling behavior of labor (Klumpp et al. 2019). According to The World Bank (2018), the shortage of human resources exists globally. There is a scarcity of blue-collar workers in developed nations, whereas demand for managerial staff is high in developing nations.

  4. (iv)

    The detachment of humans from mundane and repetitive jobs and capitalization on technical or knowledge-specific roles D’Souza et al. (2020).

The term ‘robotization’ in the transportation industry primarily refers to the automated and autonomous nature of operations. The IR4.0 journey began by replacing manned vehicles with unmanned (AGV) to perform tasks automatically (Sierra-García and Santos 2020). These automated vehicles or transport resources have received recognition for years. After the technological sophistication of IR4.0 and the amalgamation of AI, operations have evolved from automated to autonomous. The specificity of AI lets transportation resources (vehicles, drones, robots, and conveyors) operate autonomously without being stringent to pre-programmed tasks (Davis et al. 2021; Ellefsen et al. 2019). These are supported with contemporary topics in autonomous transport operations which are the optimization of routes and jobs, regulation compliance, preventive collision mechanisms, human–machine/robot interaction location tracking and control of transportation resources (Hoffmann and Prause 2018; Klumpp et al. 2019; Moussa et al. 2012; Okumuş et al. 2020; Park et al. 2018; Schleipen et al. 2015).

Enabler of sustainable consumption and production

Rebalancing economic development while considering the degradation of the environment and natural resources has stressed economies to adopt sustainable production and consumption practices. The United Nations Environment Program (UNEP 2021) describes sustainable production and consumption as the utilization of products or services as per human needs or improving the quality of life, and to reduce natural resources and harmful waste in the entire lifespan of products. In line with this, re-distribution and re-manufacturing initiatives are taking place using additive manufacturing or 3D printing. Where, companies and economies focus on reducing carbon emissions and natural resource depletion by recycling or reusing materials (Dev et al. 2020; Tozanlı et al. 2020). In succession, transportation system is responsible to develop the loop between customers and manufactures. It starts with collection of damaged or defective products from customers, delivery for recycling, re-manufacturing to manufacturers, and then re-distribution to customers. However, minimizing carbon emissions via transportation time and operational efficiency are crucial trade-off criterion for re-distribution and re-manufacturing models (Turner et al. 2019). Researcher believed that transportation 4.0 can augment the initiatives towards sustainable consumption and production (Tang and Veelenturf 2019). For instance, cloud-based technology platform aids recycling facilities to make the best decisions for transport routes for waste pick-up based on location and waste classification (Nascimento et al. 2019).

3.4 Future research directions for transportation 4.0 towards 5.0

Heading to IR5.0 would probably shed light on the engagement of humans and society to enhance productivity and efficiency (Carayannis and Morawska-Jancelewicz 2022; Coronado et al. 2022; Fraga-Lamas et al. 2021; Nahavandi 2019; Narvaez Rojas et al. 2021). The notion of IR5.0 is expected to pair two objectives which are the mass-level production and customized production and services (Bednar and Welch 2020). To enhance understanding, Fig. 5 presents a comprehensive overview of extended framework for transition from transportation 4.0 to 5.0.

Fig. 5
figure 5

Extended framework for transition of transportation 4.0 to 5.0

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Theme 1: Human development

The acceptance and utilization of transportation 4.0 among employees required the development of motivation, technology harmonization and change of behavior, as they failed to realize the significance of these initiatives on long-term health (ergonomic fatigue) and productivity (Klumpp et al. 2019). The current study has identified three pathways of human development for future studies on transportation 4.0 towards 5.0.

First, Kaczmarek (2019) proposes the game-based approaches to effectively motivate humans (transport staff) to learn, accept and utilize technology (IR4.0) and other organizational development programs such as training, workshops and seminars. Despite the potential, researchers have not given attention to expand research on gamification methods to develop and excel humans on using IR4.0 technologies in the transportation sector. Thus, require the attention of future researchers.

Second, the emphasis placed by IR4.0 transitioning companies on human factors mainly dependent on hiring of skilled staffs, consultants, and organizational development programs. Quite a few researchers have exhibited the competence models to train transportation professionals. Such as Lyapina et al. (2020) suggest trainer’s competence in four distinct fields which are ICT, mathematics, computer sciences and technologies in transportation to develop analysts and experts.

Third, developing a safe IR4.0 workplace and safety mindfulness of transport workers and professionals is another avenue to explore further. This is because the modular operational nature of IR4.0 requires a sophisticated system to assure workplace safety and safety mindful transport workers and professionals. However, the focus of researchers is primarily on improving system infrastructure. For example, Javed et al. (2021) employ HAZOP (a qualitative risk assessment approach of systems evaluation) and FTA (a problem-solving technique that tends to find the underlying causes of failure) methods on transport operations to develop safety contracts and incorporate them into the IR4.0 environment. Therefore, in order to reduce the complexities of human-computer and human-robot interactions (HCI and HRI) transport workers should have been trained on safety mindfulness and professionals.

Theme 2: Social orientation

Future researchers are perhaps recommended to investigate how transportation 4.0 possibly eases humans and capably integrates into communities and social systems. Such as Nuanmeesri (2023) proposes a mobile application (with experimental accuracy of 97 percent) for elderly farmers to transport their agricultural products to the markets. The solution follows a location-based strategy through algorithms (Dijkstra’s and Ant-colony) to suggest minor (non-primary) routes to keep older people away from risky and heavy traffic routes. The first future direction of social orientation is, easing PwD (persons with disabilities) to avail transportation services is an important segment of a social system (Chiscano 2021) though there is a gap in incorporating PwD in the topical modernization of transportation systems. Second future directions in social orientation calls for alignment of technological advancement of the transportation sector with localities (Ferrari et al. 2023) because the transportation function may not be isolated from passing through cities, districts, towns or other public places to pick or lease a load or shipment. To a large extent, all modes of transportation begin and end in ports (air, dry and sea) and in the middle distribution hubs also exist. These transport focal points are integrating advanced technologies to increase throughput and autonomous operations (Molka-Danielsen et al. 2018) and are expected to impact urban traffic (López-Bermúdez et al. 2020). As a result, urban planners and industry stakeholders must assess alternative solutions to reduce the strain of ports and connecting facilities on the human communities.

Theme 3: Enterprise size and regional disparity

Implementing IR4.0 in micro, small and medium scale industries is still an open challenge for researchers and policymakers. Vrchota et al. (2020) perform an empirical experiment in a European country and found that firm size is expected to impact IR4.0 in the transportation sector significantly. Similarly, according to Hopkins (2021) the readiness of large firms towards IR4.0 adoption is higher than smaller ones, particularly on futuristics transportation technologies, robotization and contactless delivery drones. Yet, unexpectedly, micro-level businesses still use the old-fashioned mode of transportation (handcarts) (Campbell 2020). There have also been a smaller number of research on small and medium-sized businesses, for instance, integrated CPS (Liu et al. 2019) and shared-cost business models (Dev et al. 2020). The gaps in IR4.0 that should have been filled are critical points for transitioning from transportation 4.0 to 5.0.

Another challenge is existence of regional disparity on IR4.0 performance and growth around the world, which is calculated through number of publications. According to Young Tae Kim (Secretary General) International Transport Forum (ITF), creating ease and expediting constraints in trade and transport is fundamental in the direction of economic development for any country (The World Bank 2018). He further stated that transport management is the prerequisite for national competitiveness. According to Christina Wiederer, an economist at The World Bank and co-author of ‘Connecting to Compete (Logistics Performance Index)’, even a minor disturbance across a supply chain of an economy has the propensity to affect the region and other countries. In other words, a technologically balanced transport structure across all regions is crucial for facilitating the movement of goods along the global supply chains. Hence, the proposed framework of transportation 4.0 would serve as a benchmark for countries to compare and align transportation sector. Similarly, the arrangement of key components in the framework would allow governments to correctly make policies, allocate funds, update information and communication infrastructure, and skilled human resources. Future researchers may also find low cost and collaborative alternatives for adoption of transportation 4.0 for micro, small, and medium scale organizations. Future researchers may work to assess the importance and performance of each decisive feature of transportation 4.0 on increasing economic, environmental and social performance.

Theme 4: Secondary/third-party geographic data sourcing

Using secondary or third-party data such as GPS and Maps has significant ramifications on transportation 4.0. However, the impact of selecting free or subscribed packages which come with different prices and services, of data sources on the performance of transport activities is yet to be explored (Liu et al. 2019). The reason for exploring this impact is to find an optimal balance between geographic data packages and different business conditions such as firm size, nature of commodity being transported, and service offerings. Consequently, it would allow transporters to excel in performance while reducing systems setup and operational costs.

Theme 5: Edge-of-things and edge computing

The enormous data volumes created by IOT-enabled systems put operational burden on cloud platforms regarding processing, storing, and retrieving in a real-time operational environment. To respond, novel edge technologies (edge-of-things and edge computing) have emerged to expedite the data processing between IOT and clouds. The edge-of-things offers a quick-witted local data storage facility near to the IOT systems (Buzachis et al. 2020). Moreover, edge computing enhances the serviceability of systems by analyzing data based on execution logics at the edge of clouds-IOT systems (Kyriazis 2018). This analysis enables systems to take multiple decisions on data redundancy, storage location (local or clouds), time-based actions, and much more. Future researchers may explore the applications and precisions of edge technologies in the transportation sector to maximize the performance of intelligent transport systems.

Theme 6: Systems Security and Privacy Issues

With the well-heeled benefits of IR4.0 in transportation, systems security and privacy issues are the pain points for companies, collaborative partners and potential technology adopters (Tang and Veelenturf 2019). Protecting current and future intelligent transportation systems against cyber-invasion threats necessitate well-structured risk assessment methods (de la Peña 2021). Gunes et al. (2021) develop an integrated approach for assessing cyber-attacks by forming attack-scenarios given the relative importance and susceptibilities of cyber-physical connected ICT and port assets, users and employees, and organizational cyber-security policies. They also recommended incorporating experimental approaches (vulnerability scanning and cyber-security and penetration testing) as forthcoming research topics to safeguard systems. Similarly, Arachchige et al. (2020) emphasize capitalizing on machine learning approaches to ensure systems security.

Systems are also susceptible to another type of risk because of the shift in technology from central to decentralized systems. Nevertheless, blockchain technology has emerged as the safest and most practical opportunity for decentralized networks (Ahmad et al. 2021). There exit issues of energy consumption and data capacity constraints of IOT devices and systems (Mistry et al. 2020), demands resolution by future researchers.

Finally, based on the literature synthesis in this study and the above discussions, this research proposed further research directions (see Table 3) for smooth transition of transportation 4.0 to 5.0.

Table 3 Technologies of IR4.0 in transportation
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4 Discussion

The discourse explores the role of transportation 4.0 in supply chain management and its potential progression towards transportation 5.0. This progression is influenced by the current global status of advancement. Furthermore, the establishment and development of the transportation 4.0 framework, as well as its future trajectories, are discussed.

To develop the Transportation 4.0 framework, we address various hindrances (Fig. 4) to the transportation function. Drawing from Herold et al. (2021) resilience framework, devised post-Covid-19, it includes strategies like revenue generation, resource optimization, flexible operations, and digitalization. Our study further extends competitiveness in the transportation system. Our synthesis and review identified essential system capabilities, enablers, technologies, and defining attributes of Transportation 4.0. Previous studies often overlooked IR4.0 principles, foundational to the paradigm. Adhering to these principles is crucial for companies to maximize IR4.0 benefits. Moreover, the critical technologies of IR4.0 facilitate their implementation in transportation systems, enhancing the value proposition of transportation roles.

The forthcoming research will explore unresolved issues, challenges, and prospective topics that researchers should concentrate on while considering the evolution towards transportation 5.0 (Fig. 5). The current study presented six themes as future directives. Among scholarly works, the term ’industrial revolution’ resonates as a prominent and intensively researched topic. Nevertheless, a fair number of publications still discuss IR4.0 systems, while relatively few focus on IR5.0. What would be the exact theme of transportation 5.0 is perhaps a complex and debatable topic. Currently, there are two distinct viewpoints that have been elucidated regarding the essence of the IR5.0 paradigm. One stems from the Japanese perspective, which seeks to further evolve IR4.0 into Society 5.0. Society 5.0 is defined as “a human-centered society that balances economic advancement with the resolution of social problems by a system that highly integrates cyberspace and physical space” (Government of Japan 2019). It implies that society is the center of future advancements. The Japanese initiative has also inspired global institution such as UNESCO (United Nations Educational, Scientific and Cultural Organization). As per UNESCO (2022), Japan is anticipated to unveil the blueprint for Society 5.0, aligning it with the Sustainable Development Goals (SDGs) during this year’s G20 summit and at the SDG Expo in 2025. The alternate perspective, originating from Europe, emphasizes the core tenets of IR5.0, namely human-centricity, sustainability, and industry resilience, as highlighted by the European Union in 2021. The European region has established a commission with dual objectives: the ’Green Deal’ and ’Fit for the Digital Era’. These initiatives are designed to promote human-centric adoption of disruptive technologies, align human development with desired skill sets, facilitate the shift towards circular economies, enhance global competitiveness, and create favorable conditions for investment to accelerate innovation rates (European Comission 2020). Drawing from the discourse, two pivotal future directives of transportation 5.0 emerge: the cultivation of human potential and the harmonization of the 5.0 revolution with social orientations.

The results report global disparity on transportation 4.0 is perhaps a roadblock to increasing competitiveness and sustainability dimensions of the transportation function. Economically, the competence of domestic transportation system increases the productivity and economic growth of a country encompassing its foreign direct investment and exports (Halaszovich and Kinra 2020; Tracey 2004). At a global level, the efficient and effective transportation systems would mitigate supply chain disruption and promote trade (Wilson 2007). Maintaining delivery of goods at times of COVID-19 is the recent real-life example of social contribution of transportation 4.0 where it bids social distancing through contactless systems and fulfills high volume individual deliveries for e-commerce platforms. Environmentally, it promotes conservation of resources and decarbonization through transport route and resource optimization models and simulation experiments. All these endeavors contribute to the development of sustainable and innovative business models (Chen et al. 2020; Martens and Carvalho 2017). Therefore, collaborative efforts should be pursued in academic research, industrial development, and policymaking to foster equilibrium within the global ecosystem.

Enhancing the effectiveness of current security systems and addressing privacy concerns pose additional challenges for organizations pursuing transportation 5.0. The escalating adoption of technologies and advancements could potentially give rise to vulnerabilities that compromise data privacy and the secure functionality of autonomous transport operations. In this context, organizations must diligently examine and compare security risk assessment methodologies, as well as explore novel avenues to bolster system security. Furthermore, the increasing prominence of blockchain in security management is a trending topic in scholarly research. Nevertheless, this approach faces limitations including energy consumption, data mining, and transaction completion time.

The study has illuminated two potential avenues that hold the key to unlocking numerous opportunities for enhancing the efficiency of future transportation systems. The first involves the judicious utilization of satellite services such as GPS and mapping applications. Striking the right balance between expenditure and utility, and strategically allocating resources based on demand, is crucial to preventing excessive satellite utility costs from impeding the efficiency of the transportation sector. The second prospect pertains to the integral role of cyber-physical systems in driving transportation 4.0 and 5.0 advancements. With a constant influx of vast amounts of data being stored, retrieved, and processed between clouds and physical devices, the risk of cloud overload, extended response times, and compromised storage capacity arises. Here, the application of edge-of-things (EOT) and edge computing emerges as a solution. By storing and processing critical data within local data layers, these technologies alleviate the burden on clouds, facilitating expedited operations.

Lastly, during the final stages of this research, a novel term ’metaverse’ surfaced in an opinion paper published in the International Journal of Information Management, signifying potential implications for futuristic transportation 5.0 systems (Dwivedi et al. 2022). According to the authors, the metaverse holds the potential to expand the cyber-physical experience across operations, supply chain management, business streams, healthcare, and social domains. Therefore, in addition to the six themes underscored in this study, future research endeavors should also contemplate the metaverse as an emerging transportation 4.0 technology.

5 Implications

The specificity of transportation in supply chain management grabs more attention as technology and business trends evolve. This SLR discovered that a number of publications continued to expand from 2019 onwards, where 70 papers have been published out of 84. Transportation 4.0 will play a significant role in shaping and revolutionizing the future of supply chains. It will reinforce transportation facilities management, routing and design of multi-actor supply chain models (Crainic and Laporte 2016). Furthermore, the researchers emphasize the need for enhanced research collaboration to address regional disparities and facilitate the effective integration of transportation 4.0 within SMEs. This is particularly crucial as SMEs often possess limited resources and expertise to readily embrace technological advancements. The transportation industry is expected to see a massive surge in automation over the next decade. Consequently, this underscores the necessity for managers and practitioners to reevaluate their approaches to adopting transportation technology, along with preparing their workforce for the evolving landscape of inbound and outbound transport operations. Concurrently, the systematic literature review (SLR) effort unveils insights that can guide managers in addressing the numerous challenges they encounter in their daily organizational responsibilities.

6 Conclusion

The business environment has experienced a rapid surge in technological advancements, with the expectation of achieving complete integration of IR4.0 within the transportation sector. However, the transportation domain also confronts various challenges, some of which pose a threat to businesses’ competitiveness and hinder the transition towards a streamlined and responsible ecosystem. In response, this study aims to develop a framework to operationalize ‘transportation 4.0’ and identify open concerns, difficulties and prospective trends for researchers, corporations and governments to prepare the technology for future systems.

Moreover, we have proposed the definition “Transportation 4.0 elucidates how innovative information systems and technologies (e.g., big data, AI, blockchain, augmented reality, cloud computing, etc.) networked with physical systems (e.g., smart factories and autonomous vehicles, robots, sensors) through Internet-of-things (IOT) to perform transportation jobs in order to increase productivity, profits, service levels and environmental sustainability”.

7 Limitations and future works

Like most other studies, the present study has certain limitations. The first limitation of the study was selecting Scopus database to locate scholarly and overlooking other subscribed (paid) databases. The second limitation is the super-fast publication trend on IR4.0 when we ran document e-search on March 2021 and that some developments might have occurred in the literature during the analysis and publication process. Still, to the best of our knowledge, no such efforts to operationalize transportation 4.0 are in the literature. Thirdly, the study has developed a transportation 4.0 model, and its empirical validation is left for future researchers. In addition, future works may use reviewing techniques like bibliometric analysis to explore IR4.0 in transportation and quantify influential authors and countries, explicating the impact of research collaborations, cold and hot topics, citation analysis and much more.