Artificial Intelligence in trucking industry

Artificial Intelligence in trucking industry. The fact that technology is proliferating in all areas is evident in all industries since technology has taken root in easing processes and making achievement of outcomes easier. The trucking industry is one such area openly embracing technological adoption with regards to the use of artificial intelligence (AI) in the management of logistics and facilitating movement through autonomous cars. As the freight industry continues to grow in the US, there is increasing demand for tricking services and this has brought the need to deal with challenges such as delays and accidents, low labor supply in the industry, and cost reductions. AI has been argued and proven to be the solution to such challenges. Even with the efficiency that AI purports to bring to the industry, there are certain drawbacks that include job losses, high insurance premiums, and high capital costs in the industry.

The trucking industry

             The freight industry is one of the most important and this is because it supports all other industries through the movement of goods and or raw materials. With the world enjoying technological and economical improvements, industries are growing and so is the freight industry. According to the American Trucking Association, forecasts in the US indicate that freight will increase to 20.73 billion tons by 2028 which will be an increase of over 36 percent from 2017 tonnage (Amanaji, 2018). In light of the increasing demand, the trucking industry is already facing challenges that are in line with the demand for efficient, safe, and reliable transportation while also minimizing environmental impacts. Such has proven to be a hard task as the high demand for trucking services is marred by driver shortages, increasing fuel prices, and reducing truck capacity. A 2017 report by the American Trucking Research Institute (ATRI) identifies driver shortage as the most critical issue while others include hours of service, drier distraction, and driver health and wellness (ATRI, 2017). The ability of the trucking industry to support the future projected demand is dependent on the successful addressing of these challenges to ensure efficiency.

            Artificial intelligence in the industry is seen as a fit-all solution since it addresses several issues that the industry is currently facing. AI has the ability to extend predictive maintenance services that can help avoid machine failures and increase productivity. Further, vehicle to vehicle communication and the advanced driver assistance systems (ADAS) portend to increase safety by reducing accidents by about 40 percent (Amanaji, 2018). As such, AI will be an essential part of the trucking industry as demand and need for efficiency increases.

Artificial Intelligence in trucking industry

The pros and cons of AI in the industry

            The advanced driver assistance system (ADAS) is one of the most progressive areas in the application of AI in the trucking industry. ADAS advances the essence of automation in trucking and an example is the level 2 automation that was adopted in 2014, where the driver has the added advantages of automated steering, acceleration, and braking, thus implying that the driver can keep his hands off the steering wheel. In the level 3 automation expected to be rolled out in 2019 to 2020, there will be the ability to shift all safety functions to the vehicle. The figure below shows these different levels of automation.

            The current application of AI in the industry shows semi-autonomous functioning, where future changes will oversee the complete adoption of self-driving trucks in the industry. As noted, the technology will address certain challenges with the main one being driver shortages. The industry is currently facing a shortage of drivers and this is culminated by the fact that the industry is characterized by an aging demographic in its workforce who are retiring yet there is a challenge of attracting new and younger drivers (ATRI, 2017). With AI in the industry, fewer drivers will be needed and hence this addresses the challenge of driver shortages and increases efficiency.

Artificial Intelligence in trucking industry

             Beyond reducing the need for drivers, ADAS technology seeks to improve safety for the drivers in the semi-autonomous phase due to advanced functionalities provided by AI. As an example, current technology has the ability to monitor the driver’s vital status, while also taking some functionalities such as automated braking, acceleration, warnings for inappropriate driving, and automatic accident report (Deloitte, 2016). AI is also being applied in the administrative areas to minimize mistakes and improve coordination. In light of the above factors, AI in the industry seeks to improve safety by minimizing driver linked errors and improving efficiency thus bringing about a reduction in costs. In 2016, Uber made the first successful autonomous truck delivery from Fort Collins to Colorado Springs and more companies are taking the approach that is set to lower costs, minimize emissions, and improve safety on the road (Bolwell, 2018). As such, AI is becoming an effective solution to some of the pertinent problems that the industry has been facing.

            As much as AI portends great advantages, there are certain drawbacks that come with a reliance on technology and these are such as job losses. Trucking is one of the semi-skilled profession in the US and this implies that replacement of drivers with AI will take away jobs from some of the people who need it the most. Most of the drivers in the US are immigrants who occupy the low tier jobs and are also lacking in education and skill development. Such implies that most of them lack the skills for their adoption in other industries and thus will have to deal with a loss of income (Neuweiler and Riedel, 2017). Further, the technology adoption means that the industry will be subject to high insurance premiums due to the likelihood of technological malfunction while initial capital costs are expected to rise as a result of high costs of autonomous trucks and the support services. Even with such shortcomings, the adoption of AI in the trucking industry is unavoidable and this implies that the industry has to find ways to address such drawbacks.

Conclusion

            Technological proliferation in the trucking industry through AI is seen as a solution to some of the challenges that the industry has been facing. Driver shortages, fluctuating fuel prices, and human errors leading to accidents are some of the issues that the industry has had to deal with. AI in the trucking industry has enabled the facilitation of autonomous and semi-autonomous vehicles that increase driver safety, reduce the need for drivers, lower costs, and increase efficiency in the industry. The technology has some drawbacks that include job losses, high insurance premiums, and high initial capital cost requirements. Advantages and efficiencies derived from AI supersede the drawbacks and this means that adoption of AI in the industry will be the golden goose that drives continued growth.