Introduction
Contemporarily, the world is only possible with logistics as it is one of the foundational elements of trade and economy. In their article, The Necessity of Introducing Autonomous Trucks in Logistics 4.0, Eunbin Kim, Youngrim Kim, and Jieun Park discuss the transition to Logistics 4.0 as a modern business model, its advantages, and the challenges associated with it. Although employing autonomous driving can be an effective solution in economic, social, and environmental terms, it gives rise to numerous barriers in legal and ethical terms. Nevertheless, the main claim of the article is that the current logistics system should move toward changing its nature instead of focusing on expanding the workforce. Therefore, there is a need to critically analyze the information provided in the piece to gain a deeper understanding of the subject to be discussed and find possible implications for further research.
The Facts
One should begin with the evaluation of facts that support the transition of businesses to automated truck-driving solutions. The first premise is the inevitable aging of the trucker workforce, which causes the overall shortage of drivers in logistics. For instance, the U.S., the UK, Japan, and Australia currently encounter issues with insufficient staffing, which is critical for these countries as more than 70% of freight is carried by road (Kim et al., 2022). It means that organizations have to handle delays in the delivery of goods due to the lack of human resource capacities and prevent additional costs caused by the problem.
The second fact is the urgent need to reduce the number of truck accidents. Trucks represent large vehicles that are difficult to maneuver or rapidly stop, which increases the risk of fatal accidents (Kim et al., 2022). Some factors, such as unfavorable weather conditions or poor vision, can be key reasons for disastrous occurrences, but the most critical one is human error, accounting for 90% of mischances on the road (Kim et al., 2022). As the major contributor, excessive daytime sleepiness (EDS) compromises attention, alertness, and reaction, which can lead to severe injuries and long-term disabilities (Kim et al., 2022). From the organizational perspective, carriers also have to face additional expenses not only in operational matters but also in legal ones, as the companies are often responsible for accidents with victims.
The third fact concerns a hotly debated topic of employment within the context of robots substituting humans. Although a shift to technology-based solutions creates downsizing, which frequently intimidates truck drivers with unemployment, automated-driving trucks are not supposed to be without humans. Kim et al. (2022) state that human drivers should constantly be on board in cases of emergency, though they will not be involved in the driving process to the extent that drivers do today. Finally, such a technology can help companies to reduce operational costs, significantly minimize the risk of human error, and decrease delivery time.
In addition, one needs to evaluate the quality of sources the authors utilize in support of their claims. The major part of the literature was published within ten years and amounted to sixteen items. The rest, eight sources, are published earlier and examine various topics around truck driving. However, some of them investigate the connection between sleepiness and road accidents, which suggests that some findings in the analyzed article may be obsolete.
Discussion of Strengths and Weaknesses
The information presented in the article should find support in other recently published pieces of literature to be reliable. One can assert that the problem of aging among truck drivers exists, and this finds reflection in other studies. For instance, Gittleman and Monaco (2020) explained the difference between the specialization of drivers and found that 61% of heavy truck driver jobs require prior experience. It suggests that carrier companies would choose a skillful driver, though he or she can be relatively older than other applicants. However, one can admit particular biases in terms of safety that autonomous driving supposedly brings. Kim et al. (2022) attempt to convince that self-driven trucks will warrant travel without road misadventures. On the one hand, the authors seem to contradict themselves because they cannot know whether safety is provided due to an insignificant number of tests performed with automated driving cars. Moreover, the tests were conducted under acute control with a driver and IT expert onboard, so it is unknown what challenges may occur in real-life circumstances. On the other hand, Wang et al. (2021) claim that driving is associated with contextual decision-making that the system may not recognize. Furthermore, some unpredicted breakdowns can appear within the system, which also increases the risk of more serious accidents.
Conclusion
Finally, the critical analysis of information provided in the article The Necessity of Introducing Autonomous Trucks in Logistics 4.0 has been performed. The facts regarding the aging of truck drivers, truck accidents, and employment have been investigated. The claims made by the authors emphasize the urgent need to shift toward driverless technologies as an advantageous option in many stances. It has been found that the problem of the constantly declining driving workforce is caused by aging and requirements of prior experience. However, the declared absolute safety of driverless trucks is questionable due to the contextual nature of decision-making in driving and unpredicted errors in the system.
References
Gittleman, M., & Monaco, K. (2020). Truck-driving jobs: Are they headed for rapid elimination?ILR Review, 73(1), 3-24. Web.
Kim, E., Kim, Y., & Park, J. (2022). The necessity of introducing autonomous trucks in Logistics 4.0. Sustainability, 14(7). Web.
Wang, C., Weisswange, T.H., Krueger, M., & Wiebel-Herboth, C.B. (2021). Human-vehicle cooperation on prediction-level: Enhancing automated driving with human foresight. 2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops). IEEE. Web.