Transformation

Conclusion

The present is characterized by technological advances that affect the society, businesses, economy and also freight transport sector. This phenomenon is referred to as the fourth industrial revolution. The fourth industrial revolution is the fourth major industrial era since the initial industrial revolution of the 18^th century. The fourth industrial revolution

is marked by emerging breakthroughs in a number of fields, including robotics, artificial intelligence, the industrial internet of things, 3D printing, autonomous vehicles, quantum computing, and so on. This wave of new technological advancements brings with itself new challenges that we as a society face. One of these challenges is the impact of these modern digital technologies to the labor market. Labor market in the field of transport industry will be affected by the technological factors that will make sector more innovation-friendly than it used to be during the last century. IT applications of these technological factors span for example from online monitoring of speed, consumption, optimal path, congestion avoidance, driving help and assistance (Madlenak et al 2018). These technologies will also require a higher degree of qualification and skills of workers in the transport sector.

Industries are changing and the number of robots delivered to the businesses across the world has risen from 100000 per year to 300000 in 2016. Adoption of industrial robots will lead on one hand to labor productivity growth, creation of new types of jobs, rising wages and making some of the jobs more pleasant to do. On the other hand robots will make some jobs less and less important and some jobs will be expendable altogether. Jobs that are set to experience increasing demand are data analysts, scientists, software developers, ecommerce and social media specialists. An increased demand is expected for variety of new jobs such as AI and machine learning specialists, big data specialists, process automation experts, information security analysts, user experience and human-machine interaction designers, robotics engineers and blockchain specialists. Redundant jobs are those that are routine-based. For example data entry clerks, accounting and payroll specialists, secretaries, auditors, bank tellers, cashiers and so on.
On a global scale share of jobs that are at the risk of automation varies across the countries. In general, Northern Europe, North America and New Zealand are less susceptible to the risk of job automation. Countries of Eastern and Southern Europe face much higher risk of job automation. Based upon our own calculations, countries with high unemployment rate have higher risk of job automation. On the contrary, the lower the unemployment the level of risk of job automation decreases. From the employment standpoint of view, the countries where there is low employment, the risk of job automation is rising and therefore, on the other hand, the higher the employment rate, the risk of job automation decreases. Impact of automation can be crucial in countries where the unemployment is high and the employment is low and so we can conclude that it is necessary for countries to prepare themselves for the potential risks associated with the impact of automation on the labor market. On a basis of realized measurement of correlation coefficients (figure 2, figure 3), which indicate only moderate correlation between examined variables, we can conclude that further analysis is needed for a deeper knowledge of the problem, because several research problems exist in relation to our own and OECD’s statistical measurements. For example one of the problems that analysis should be aimed at finding out is related to why is this correlation moderate and not strong. Is it because the process of automation is already underway in several countries or the structure of employment in different sectors of GDP is significantly different in these countries?
Based on the research we have carried out, we can conclude that not only from a global perspective, digital technologies may have an impact on the employment and unemployment on the labor market in the OECD countries, but they also can have an impact on the labor market of transport sector. In the field of freight transport it is estimated that 3 to 6 % of the workforce in the OECD counties is employed in the transport sector. The impact of digital technologies on this workforce is, according to Blix, determined by three scenarios. These scenarios focus on the area of autonomous vehicles.
First scenario is called “Medium skills, medium autonomy”. In this scenario the share of autonomous vehicles is limited. Furthermore, the scenario points out that not all jobs are subject to automation and in some cases human flexibility can be more cost effective than multi-purpose automation. In another words, loading and unloading of cargo outside of cities can be easily automated, but the same tasks carried out inside the cities could prove costly and ineffective.
Second scenario is called “low skill, high autonomy”. In this scenario a large share of tasks is still performed by humans, but the skills needed to perform the tasks are low. For example a human driver is less able to keep the same speed of the optimal speed for fuel consumption compared to machines. Modern software can calculate the most fuel efficient driving in ways that are impossible for humans. In this case the technology, if implemented can further reduce the need for human driving. In this scenario the human work will be concentrated around non-routine work that is costly to automate. It is also estimated that some work will even require more skills than today, for example, repairing machines and vehicles could require a combination of software skills as well as understanding of mechanics.

Third scenario “high skill, high autonomy” points out that if the freight transport labor goes towards automation, the decline in the amount of jobs could be similar to how jobs in the manufacturing industry have declined overtime. The driving forces behind the lob losses in this scenario could stem from: substantially better safety records than human drivers, lower labor costs and less traffic congestions if transport logistics improved and vehicles also drive during the night.

Based upon the facts we have stated in chapter 4, we can conclude that the labor market in the freight transport sector can potentially change. A large degree of this change depends on the skill level of jobs and the amount of less skilled work could decline dramatically