Critical Analysis of Autonomous Vehicles and Transport Industry

Description of Autonomous Vehicles and the Transport Industry

Autonomous vehicles refer to self-driving cars which are capable of moving without necessarily requiring human input (Anderson et al 2014). These vehicles can sense elements in their environment thereby operating according to the perceived signals. The operation mechanism of autonomous cars includes an integration of sensors such as radar, GPS, Lidar, and advanced control systems (Pendleton et al 2017). These components are linked through a digital network which facilitates the exchange and interpretation of signals. Now, from this description, it can be affirmed that autonomous cars belong to the transportation industry. By definition, the transportation industry is a sector which comprises of various companies concerned with the movement of people and goods, as well the development of infrastructure which enhances these activities (Faisal et al 2019). In particular, the transportation industry entails air and freight logistics, airlines, road, and rail infrastructure. Therefore, autonomous cars can be classified under the subcategory of roads.

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Nature and Functions of Autonomous Vehicles and Transport Industry

According to Zhao, Liang and Chen, autonomous vehicles use a technology called artificial intelligence (AI) (2018). This technological innovation enhances the coordination of various sensors in the robot cars thereby facilitating movement without any input of a driver (Litman 2017). For instance, Google and Tesla have managed to design cars which are driverless. Through the processing power of artificial intelligence, the vehicles can initiate machine operations and move safely in the environment (Zhao, Liang and Chen 2018). Therefore, artificial intelligence can enable autonomous vehicles to transport goods from one place to another without requiring any human driver. The researchers also mentioned that artificial intelligence is being applied in the transportation industry to achieve safety of road users by installing this technology as a co-driver. This is because AI can monitor various sensors and notify the driver in case a dangerous situation is detected. Accordingly, this technology has the capability of coordinating emergency operations like braking, reading traffic detectors, and monitoring blind spots (Zhao, Liang and Chen 2018).

Another research conducted by Schmidt, confirmed that artificial intelligence is an emerging technology in the transportation industry (2018). The researcher mentioned that AI utilizes cloud services to achieve predictive maintenance for vehicles. That is, the technology is automated to monitor engine lights, battery indicators, and various mechanical processes which might be difficult to detect while a vehicle is in motion (Schmidt 2018). By performing these background checks, AI promotes the safety of vehicle operations by averting any accident which might have resulted from undetected technical hitches. Accordingly, the technology has gained applications in driver risk assessment and car manufacturing (Schmidt 2018). Therefore, it can be affirmed that autonomous vehicles can be used for safety transportation of passengers and commodities from one location to another since the cars have mechanisms for performing mechanical maintenance while on transit.

Critical Analysis

Theory

The major theoretical framework which can be used in explaining the position of autonomous cars in the transportation sector is called disruptive innovation. This is because the robot vehicles are made using a new technology which enables the cars to operate efficiently even without the input of a driver (Bagloee et al, 2016). The introduction of autonomous vehicles has affected the transportation industry because logistics firms are opting for these driverless cars when transporting commodities (Litman 2017). When disruptive innovation is introduced in the transportation industry, it highly benefits logistics firms adopt it immediately while works against entities which are reluctant to acquire the new services (Bagloee et al 2016). For instance, when autonomous cars were launched, only a few logistics firms were showing interest while other companies still depended on trucks operated by human drivers. However, this trend is changing gradually with most transportation companies acquiring autonomous vehicles because they are convenient, reliable, and less prone to accidents during transit.

Pestle Analysis

Pestle analysis entails the evaluation of political, economic, social, technological, legal, and environmental factors as discussed below. The main political factors which affect the use of autonomous vehicles for transportation are strict regulatory practices against the driverless cars on highways because the technology can fail and endanger other road users (Debye 2014). On the other hand, economic factors which influence the incorporation of autonomous cars in the transportation of goods involve high costs of acquisition and maintenance of the robotic vehicles while on transit. In terms of social factors, the use of autonomous vehicles in the transportation of commodities is highly opposed because it is seen as a destructive innovation which replaces drivers with robots (Faisal et al 2019). In terms of technology, autonomous vehicles are suitable for use in the transportation of goods because they are fitted with; artificial intelligence, cloud services, predictive maintenance, GPS, and sensors (Debye 2014). As such, the vehicles can operate with a higher level of accuracy and precision than human drivers. In terms of legal influence, the use autonomous vehicles in the transportation of goods is hindered by the regulations and market restrictions, especially in the global platforms. Lastly, the environmental concerns of autonomous cars entail emission of hazardous gases as well the efforts for promoting sustainability (Fleetwood 2017).

Porter Five Forces

Here the analysis entails the following; firstly, the competitive rivalry of firms in the transport industry (Faisal et al 2019). It was observed that many rival companies are adopting the use of autonomous vehicles in the transportation of goods as a way of intensifying direct competition. Secondly, the bargaining power of suppliers. The analysis revealed that suppliers of raw materials are advocating for the incorporation of autonomous vehicles in logistics processes. Therefore, firms are also considering the idea because of its technologically advanced operations. Thirdly, the bargaining power of buyers. Here, an analysis indicated that most customers prefer autonomous vehicles to be used during delivery of goods because the trucks are secure and easy to monitor while on transit (Baporikar 2015). The fourth concept entails the threat of substitution. Apparently, autonomous cars can be easily replaced with diesel engine vehicles. Therefore, these effects have hindered the adoption of autonomous vehicles in the transportation of goods (Faisal et al 2019). Lastly, the threat of new entry. It can be stated that the use of autonomous vehicles in the movement of goods has not been threatened by any available alternative. Thus, it implies that the vehicles will gradually gain prominence in the transport industry.

 

Porter and Millers Value Chain Analysis

Fig 1.0 Showing Value Chain

The main role of this framework is to help in the identification of activities which are highly valuable to the transportation industry as well as those ones which enhance competitive advantage of autonomous vehicles. The value chain classifies organization activities as either primary or support. The distinction of these concepts is that; primary activities add value directly to a company’s production processes (Barnes 2002). They include; inbound logistics, operations, outbound logistics, marketing and sales, and services. These components add value to the transportation industry in the following ways; inbound logistics entail the internal processes involved in the movement of commodities within the firm. For instance, using autonomous vehicles to transport raw materials from the assembly point to the warehouse. Operations are concerned with the management of processes which are necessary in converting inputs to outputs (Barnes 2002). For instance, whereby firms are adopting the use of autonomous vehicles in transport as opposed to diesel engine vehicles. Outbound logistics include the functions related to the storage and movement of final products from the company to the intended consumers. This is where autonomous vehicles are needed the most, especially where the commodities need to be secured (Litman 2017). Marketing and sales entail the procedures for communicating and delivering the finished products to customers. By using autonomous vehicles, it can be easy to achieve these objectives because the cars use artificial intelligence to connect suppliers and customers (Pendleton et al 2017). Lastly, services refer to any other input which is needed to improve the effectiveness of the finished product. For instance, innovation and artificial intelligence which are required in the operation of autonomous cars whenever the vehicles are transporting commodities.

On the other hand, support services improve the competitive advantage of autonomous vehicles. They include; firm infrastructure, human resource management, procurement, and technology. In the transport industry, infrastructural components of a logistics include; accounting, quality assurance, and strategic management. When these processes are effectively coordinated, it becomes easier for logistics operations to be conducted using autonomous vehicles (Litman 2017). Technology refers to the equipment, hardware, and software which are required to enhance effective operations of autonomous vehicles during transit. They include; artificial intelligence, GPS, sensors, and radar (Pendleton et al 2017). In the transport industry, human resource management entails all the activities concerned with planning and executing logistics operations. While using autonomous vehicles, these processes can be enhanced with the help of artificial intelligence. Lastly, procurement involves the acquisition of essential goods and services from outside sources. This is whereby autonomous vehicles are used in the transportation of commodities from different regions to the firm (Pendleton et al 2017).

McFarlan and McKenny Grid

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Fig 1.1 Showing McFarlan and McKenny Grid

This grid illustrates the strategic impacts of an existing operation procedure and those of planned application development portfolio. The factory section implies that a transport firm has resolved to reduce its operational costs in a bid to improve performance (Baporikar 2015). The strategic section indicates that a transport firms are utilizing information technology to enhance operations. The local quadrant has low impacts on transport firms because it entails basic improvements and incremental cost savings. Lastly, the turnaround section implies that transport firms are exploiting emerging opportunities in emerging technology (Baporikar 2015). Therefore, according to the evaluations from this grid, it can be stated that autonomous vehicles can be suitably used in the transportation of goods if logistics firm acquire necessary technology which is needed for operating the robotic cars.

Extended Balanced Score Card

Balanced Score Card Perspective

(Financial, Customer, Internal, Learning)

Perspectives Objectives Measure(s) Action (CSF) IS Needs
Financial To improve financial performance of transport firms Return on Investment (ROI)

Operating Margin

By acquiring autonomous vehicles

By setting a realistic budget

Quantity of goods transported

Cost of operation

Customer To promote customer satisfaction Service Ratings

Level of Returns

By engaging customers

By doing research

Use of autonomous vehicles

Market segmentation

Internal To improve business efficiency New markets reached

Lead Time

Unit Costs

By adopting new technology

By acquiring additional autonomous vehicles

Time taken in distribution

Efficiency and convenience of autonomous vehicles

Learning To Promote knowledge and innovation Employee Retention

Flow of Ideas

By encouraging innovation

By introducing new technology

Employees competency index

New production ideas

Fig 1.2 Showing Balanced Scorecard

Conclusion

From the critical analysis of this report, it can be concluded that autonomous can be suitably utilized in the transportation of goods. It was explained that autonomous vehicles have sensors which detect the cars’ environment while the transport industry was defined as an integration of companies which facilitate the movement of people and goods. The report discussed that autonomous vehicles are gaining prominence due to the inclusion of artificial intelligence in these cars. This technology has enhanced the production of driverless cars as well as robotic assistants which detect mechanical problems and alert the driver. Therefore, the vehicles are very effective in logistics operations, especially where safety and security are required.

The report also discussed that disruptive innovation is the major theoretical framework which guides the creation of technologically advanced autonomous cars. It was noted that disruptive innovation introduces new technology in the industry thereby improving the competitive advantage of autonomous cars as compared to diesel engine vehicles. In addition, this discussion entailed analysis of frameworks such as; Pestle, whereby it was confirmed that the implementation of autonomous cars in the transportation of commodities is affected by political regulations, economic performance, social beliefs, technological innovations, legal issues, and environmental concerns like hazardous emissions.

Porters Five Forces was also conducted in the transport industry and the results revealed that autonomous cars can be effectively used in logistics operations. However, this implementation is affected by factors like; competitive rivalry, bargaining power of buyers and sellers, and threats of substitutions or new entry. Accordingly, a value chain analysis was conducted whereby the findings indicated that performance of autonomous cars are influenced by the need for creating value in the transport industry and gaining competitive advantage for the respective logistics firm. On the other hand, balanced scorecard analysis revealed that autonomous cars can facilitate logistics operations and customers’ satisfaction if advanced technology is applied. Lastly, McFarlan and McKenny grid proved that the use of autonomous cars in the transportation of goods is majorly motivated by the strategic and turnaround operations.

 

References

Anderson, J.M., Nidhi, K., Stanley, K.D., Sorensen, P., Samaras, C. and Oluwatola, O.A., 2014. Autonomous vehicle technology: A guide for policymakers. Rand Corporation.

Bagloee, S.A., Tavana, M., Asadi, M. and Oliver, T., 2016. Autonomous vehicles: challenges, opportunities, and future implications for transportation policies. Journal of modern transportation24(4).

Baporikar, N., 2015. Holistic Framework for Evolving Effective Information Systems Strategy. International Journal of Strategic Information Technology and Applications (IJSITA), 6(4), pp.30-43.

Barnes, S.J., 2002. The mobile commerce value chain: analysis and future developments. International journal of information management, 22(2), pp.91-108.

Debye, Y., 2014. E-car industry analysis from the perspective of business model dynamics (Bachelor’s thesis, University of Twente).

Faisal, A., Yigitcanlar, T., Kamruzzaman, M., and Currie, G., 2019. Understanding autonomous vehicles: A systematic literature review on capability, impact, planning, and policy. Journal of Transport and Land Use12(1), pp.45-72.

Fleetwood, J., 2017. Public health, ethics, and autonomous vehicles. American journal of public health107(4), pp.532-537.

Litman, T., 2017. Autonomous vehicle implementation predictions (p. 28). Victoria, Canada: Victoria Transport Policy Institute.

Pendleton, S., Andersen, H., Du, X., Shen, X., Meghjani, M., Eng, Y., Rus, D. and Ang, M., 2017.Perception, planning, control, and coordination for autonomous vehicles. Machines5(1), p.6.

Schmidt, D.K.M., 2018. Effects of Autonomous Driving on Cities and Their Urban Transport Systems.

Zhao, J., Liang, B. and Chen, Q., 2018. The key technology toward the self-driving car. International Journal of Intelligent Unmanned Systems6(1), pp.2-20.

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