Introduction
Artificial intelligence (AI) has been widely adopted over the past several years, and this has resulted in a sea change in the supply chain industry. The transportation sector is one area where AI has had a significant impact. Supply chain managers have been able to optimize transportation networks, lower costs, and increase delivery times thanks to AI’s ability to evaluate massive volumes of data, learn from it, and provide actionable insights (Eling, Nuessle, and Staubli, 2021). AI has completely altered the way businesses handle transportation in the supply chain, from route optimization to predictive maintenance. Because of this, not only has operational efficiency increased, but so have customer satisfaction, sustainability, and the negative effects on the environment. Despite these advantages, however, the use of AI in transportation is not without its fair share of difficulties and possibilities (Chen, and Cao, 2022). Therefore, studies are required to investigate the myriad ways in which AI is changing transportation along the supply chain, the difficulties inherent in putting it into practice, and the opportunities for further innovation and improvement.
Self-driving Vehicles
By increasing effectiveness and lowering costs, self-driving cars have the potential to transform transportation in the supply chain. For instance, businesses may improve delivery schedules, cut fuel costs, and lower the risk of accidents brought on by human mistakes with autonomous trucks. Faster delivery times are achieved by self-driving cars’ ability to run for extended periods without stopping (Dash et al., 2019). Self-driving vehicle adoption in the supply chain is still in its early stages, and there are worries about potential job losses and cybersecurity issues. However, autonomous cars are positioned to revolutionize the supply chain sector in the next few years.
Intelligent vehicles run completely independently without any interaction from the passenger thanks to a variety of sensors, cameras, and radar. Many businesses, including BMW, Audi, and Tesla, have conducted research on and tested this automation. It is highly difficult and complex to get to this advanced stage of AI in the transportation sector (Helo and Hao, 2022). Complex neural networks have been created that are capable of multitasking and pattern recognition similar to the human brain.
Traffic Management
The transportation sector, particularly the supply chain, has seen a revolution in traffic management thanks to artificial intelligence (AI). To provide improved routing, scheduling, and load planning, AI-based traffic management systems analyze a tremendous amount of data, including traffic patterns, weather conditions, road conditions, and real-time traffic information. The ability of AI traffic management to lower transportation costs while increasing delivery times is one of the most important effects on supply chain transportation. AI assists supply chain managers in making informed decisions that save transportation costs and increase efficiency by offering real-time information on traffic conditions and suggesting alternate routes (Eling, Nuessle, and Staubli, 2021). By enhancing vehicle routes and decreasing idle time, AI-based traffic management systems also assist in lowering fuel consumption and carbon emissions. This reduces the transportation portion of the supply chain’s overall environmental impact.
Additionally, AI-enabled predictive maintenance of transport vehicles lowers the likelihood of delivery delays, minimizes downtime, and helps prevent unanticipated breakdowns. AI can use machine learning algorithms to find patterns in car problems and provide data-driven advice on how to avoid them in the future. The transportation sector has undergone a revolution thanks to AI-based traffic management, which offers real-time insights into traffic patterns, optimizes routing and scheduling, lowers transportation costs, speeds up delivery times, uses less fuel and emits fewer greenhouse gases, and improves vehicle maintenance (Chen and Cao, 2022). For many years to come, the supply chain industry will continue to change as a result of the integration of AI in traffic management.
The optimization of transportation routes is one of the key ways AI technologies have changed transportation in the supply chain. The most effective routes for transportation can be found by analyzing historical data on route usage. This contributes to a decrease in the time and expense associated with transportation, improving operational efficiency (Baryannis et al., 2019). AI in the transportation sector can potentially help to lessen traffic congestion. Real-time route data is collected, uploaded to the cloud, and examined to enable artificial intelligence-based traffic pattern prediction. Users will receive notifications about the fastest way to get to their destination (Dash et al., 2017). In this case, implementing AI in the transportation industry will reduce wait times while simultaneously improving route safety.
Drone Control
The supply chain transportation sector now has new opportunities thanks to drone technology, which provides quicker and more affordable delivery options. Small packages and other items can be easily transported by drones to far-off sites, cutting down on delivery times and expenses. Additionally, drones can run around the clock, and because of their small size, they can avoid traffic jams for faster delivery times (Spanaki, 2022). However, the use of drones in supply chain transportation is still in its infancy, and there are still issues with regulation and safety that need to be resolved. Despite these difficulties, supply chain transportation in the future is anticipated to be significantly impacted by drones.
Beautiful aerial footage has been feasible to record thanks to drones. But drones can give even more data when AI is applied. Use AI drones to scan construction sites rather than taking more than a week to do it by hand. It has been suggested that drones, which can observe a larger area than humans, be used for traffic management and monitoring. In Rwanda, finding blood banks now takes only 15 minutes instead of an hour thanks to drones (Garg, Gupta, and Agarwal, 2023). AI-enabled drones may also be used to help stop traffic accidents or deal with crises. This little device is performing at its best thanks to the application of AI.
Autonomous Trucks
In recent years, cutting-edge AI startups have focused on autonomous vehicles. Longer trips require more effort and are not simply more predictable. This is the ideal time to give artificial intelligence control of these rigs. On the long, straight highways of the region, a driver’s propensity to fall asleep behind the wheel and cause accidents can be reduced with the aid of AI. To carry butter across the nation, a California company developed an AI-based delivery vehicle (Sahni, Srivastava, and Khan, 2021). This was the first cross-country trip made by a commercial freight truck. The application of AI can lower the costs associated with truck drivers’ needs.
By lowering costs, boosting productivity, and enhancing safety, autonomous vehicles have the potential to revolutionize the transportation sector in the supply chain. Companies can optimize their delivery schedules, save money on fuel, and lower the risk of accidents brought on by human mistakes using self-driving trucks. Delivery times can be shortened by autonomous trucks’ ability to drive for extended periods without stopping (Espahbod, 2020). There are worries about job displacement and cybersecurity dangers, but the use of autonomous trucks in the supply chain is still in its infancy. Nevertheless, it is anticipated that autonomous vehicles will change the supply chain sector in the next few years.
Autonomous vehicles can now be used for transportation thanks to AI technology. The ability of autonomous cars to function without human oversight lowers the possibility of accidents and boosts the effectiveness of transportation operations (Katreddi, Kasani, and Thiruvengadam, 2022). This technology is especially helpful in sectors like manufacturing and retail where the supply chain heavily depends on the movement of items.
Intelligent automation
The railway industry may continue to be the most inventive in the transportation industry with the use of AI to improve management and operational processes. In 2018, several companies started creating driverless train prototypes for self-driving passenger and freight trains. By combining sensors, cameras, and radars, artificial intelligence transportation software can provide the “train’s eyes” capacity (Xia). The company aspires to develop a fully autonomous train by 2025.
It is also essential to anticipate potential railway infrastructure breakdowns. Operational intelligence allows for precise forecasting and maintenance recommendations using the data from the train’s sensors. Additionally, AI helps the railroad sector evaluate long-term performance and spot growth prospects. For instance, Laing O’Rourke’s logistics planning now only needs 19 seconds to complete (Dash et al., 2019). Thanks to artificial intelligence (AI) and transportation software solutions, they have had 23 days to prepare. Humans, on the other hand, can only schedule maintenance tasks three or a day in advance.
Route optimization
Even while the majority of logistics companies currently use technology to determine the best routes for their shipments, AI is expediting this procedure by automatically taking into account both recent and historical data. Data used by AI-powered route optimization software to find the best routes include capacity information, traffic reports, weather forecasts, and real-time position monitoring (Diran et al., 2019). Some technology can even advise you on the ideal times to start your journey, refuel, and stop for lunch.
Route optimization has had a big impact on transportation in the supply chain since it has made it possible for businesses to streamline their delivery procedures, use less fuel, and operate more effectively. Supply chain managers can reduce transportation costs and delivery times by identifying the most effective routes, delivery schedules, and vehicle types with the use of route optimization, which makes use of complex algorithms and real-time data. By lowering fuel consumption and carbon emissions, route optimization also lessens the environmental impact of transportation (Modgil, Singh, and Hannibal, 2022). For supply chain managers looking to boost sustainability, save costs, and improve delivery performance, route optimization has become a crucial tool.
The following are a few benefits of employing AI to optimize routes: For the customer experience to remain positive, improved transit times are crucial. We can guarantee quick delivery and improved levels of customer satisfaction by choosing the best route in real-time. On some routes, less fuel is lost than on others (Tirkolaee et al., 2021). Drivers spend less money on gas when routes are optimized because they don’t need to fill up as frequently. Less time spent driving – With the recent modifications to the HOS requirements, it is more crucial than ever to reduce unnecessary driving time. AI-powered software can recalculate routes in real-time to save travel times (Diran et al., 2019). Route optimization employs AI in transport logistics more than any other application due to these benefits.
Conclusion
In conclusion, supply chain managers now have the resources they need to optimize their transportation networks, cut costs, and speed up delivery times thanks to the incorporation of artificial intelligence technologies. Companies have been able to improve operational efficiency, lessen their environmental impact, and raise customer happiness by using AI-based route planning, predictive maintenance, and real-time data analysis. AI in transportation has many advantages, but many opportunities and issues need to be solved. These include concerns about data security, privacy, and ethical difficulties, as well as the opportunity for additional innovation and advancement in the industry. Therefore, to improve the efficacy, efficiency, and sustainability of transportation in the supply chain, future research should concentrate on exploring these potentials and difficulties.
References
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