Human Resources Labor Employment: Automation and Labor in the Manufacturing Sector

Human Resources Labor Employment: Automation and Labor in the Manufacturing Sector
Introduction
Labor and automation often move in opposite directions. When automation is on the rise, labor moves in the opposite direction. The world is quickly tilting towards technology and America is at the forefront of this movement. The world is thus, full of promises but challenges waylay these promises. Cars that are driving themselves, machines that operate themselves, and medical equipment that read x-rays, as well as, algorithms that respond to customer-service inquiries are the new and powerful forms of automation. Despite the fact that these automatons increase productivity, as well as, make our lives easy, they present a problem because they are a substitute for some activities performed by humans. Jobs will be created out of the automation but more will be lost. This paper will analyze the trend resulting from automation using data, discuss how an HR planner in the manufacturing sector would use the information, and how such information fits into succession planning. Towards the end of the paper, an evaluation of why organizations should identify their requirements for personnel in the future will be done.
Literature Review
According to McKinsey Global Institute (2017), more than a third of the U.S. workforce will have their jobs taken over by automation. If it is escalated to a global level, between 400 million and 800 million individuals will be replaced by automation and will need to look for new work. The most affected industries as expressed in the McKinsey report are agriculture and manufacturing. The report outlines that more than 60% of occupations will have at least 30% of their constituent activities automated. By 2030, the study found out that office support workers demand in the U.S. labor market will also drop by approximately 20%. Most of the jobs that are under siege include those that are repetitive. Chui, Manyika, and Miremadi (2015) demonstrated that 45% of work activities in the United States can be automated using technology, and if such technology is further refined to understand human emotions and natural language, a further 13% of the jobs would be automated. Chui et al. further claimed that 5% of occupations could be entirely automated using existing technology. Manyika (2017) demonstrated that 51% of the U.S. wages are susceptible to replacement by technology and they net a total of $2.7 trillion in wages annually. That amount of money is about to be snapped up by robots. The most profound industries poised for this takeover are accommodation and food services, manufacturing, transport, agriculture, retail, and mining in that order (Manyika, 2017).
Acemoglu and Restrepo (2017) looked at important statistics in the U.S. labor market and found out that the U.S. robot usage has been on a steady increase. As of the 1990s, the U.S. had 0.4 robots and in the early 2000s, it increased to 0.7. In the late 2000s, the statistics increased rapidly to 1.4 robots per 1000 workers. These statistics show that robot use has almost been doubling after every decade. Acemoglu and Restrep however, note that robot use in the United States is not evenly distributed as the automotive industries have more robot use than any other industry. As such, states with more automotive manufacturers are likely to experience more disruption than others. It was also clear that robots are heavily used in industries such as automotive, plastic, metal, and electronics.
Acemoglu and Restrepo (2017) found out that people in blue-collar occupations, such as machinists, assemblers, welders, and material handlers are the most likely culprits of the new wave of change. The same study went ahead and predicted that since a majority of the holders of blue collar jobs are men, men will be more affected by the displacement and put the displacement ratio at -0.53, while for women, it was -0.3. Acemoglu and Restrepo further suggested that for males, the decline would be most concentrated in manufacturing while in women, it will be more pronounced in non-manufacturing industries. When wages are considered, it was evident that those with less than a high school diploma will face reduced wages, while those with postgraduate degrees will most likely be not impacted. This trend was attributed to reduced demand for the nontradable sector.
Acemoglu and Restrepo (2016) evaluated the effect of the growth of innovation and the growth of the labor market with relation to capital. The study first appreciated that automation is most likely to reduce employment and labor share and due to reduced efficiency of labor in comparison to automation. The study found out that when automation is stabilized, the consequence is a reduction in the cost of using labor, which further discourages the development of further automation; on the advantage, it increases the creation of new tasks. The flip side of this transition is that automation happens faster than the creation of new tasks, which has a net negative effect on labor.
David (2015) aimed at evaluating why despite the level of automation in the United States over the decades, a majority of the jobs have not been wiped out. The study argues that automation is not a substitute for labor. Instead, it raises the level of output through efficiency in ways that lead to higher demand for labor. David (2015) argued that what automation does is to alter the types of jobs available and what such kind of jobs pay. The study further suggests that the development of artificial intelligence and robotics will have an immense effect on labor and employment growth. David (2015) concluded that machines will continue to substitute workers in performing routine and codifiable tasks. At the same time, automation will raise the comparative advantage of workers who supply industries with problem-solving skills, creativity, and adaptability. As such, employees need to position themselves to gain skills that are hard for machines to replicate.
Grace, Salvatier, Dafoe, Zhang, and Evans (2017) aimed at unraveling when artificial intelligence will override human functions for the purposes of informing public policy. The study predicted that AI would outperform human beings by 2027 on tasks such as translation of languages, vehicle driving, working in retail by 2031, and working as a surgeon by 2053. The study demonstrated that scientists have a 50% belief that AI will completely be in a position to perform all human tasks in 45 years and all human jobs will be automated in 120 years.
Forecasts for Possible Shifts in Manufacturing Industry
From the literature review above, it has been demonstrated that the manufacturing industry is poised for a radical change due to automation. The first change that will be there is that automation will render many, especially those who work in blue-collar jobs in the industry, redundant. Statistics have put this number at a third of the entire workforce, but the manufacturing sector is likely to experience more than a third due to the repetitive nature of the industry. This effectively means that the workforce in the industry is likely to shrink considerably and this is likely to continue if artificial intelligence is put in action. In a century from now, Grace et al. predicted that the industry will be without workers, as machines and algorithms will have taken over.
The labor demographic dynamics are likely to shift. Acemoglu and Restrepo (2017) expressed that men are the most likely to be affected by automation than women. This is because men are the most dominant in the manufacturing sector, especially in areas that require the use of muscle in repetitive work. Ladies in manufacturing will work in areas such as administration where automation is not likely to hit as fast as it would to the sectors dominated by men. These statistics mean that the labor in the manufacturing sector is working against men and if automation gathers speed, their (men) numbers will shrink at a faster rate.
The other dynamic with regards to demographic aspects is the level of employment depending on education qualifications. Employees in the manufacturing sector with a high school diploma or less are likely to be edged out faster than everybody else, while the wages of those with postgraduate education are likely to remain stagnant. This has two possible implications. First, fewer education levels will not be feasible or attractive in any way in the manufacturing sector. Second, the manufacturing sector is likely to attract less highly educated workers since the wages for this caliber of employees is not raising. This offers them little or no motivation to be in the manufacturing sector. As such, the human resource managers will have a difficult time looking out for, and convincing talent to join the manufacturing sector. This development will create a scarcity that will force companies to pay more to attract talent. As such, a cycle of oversupply and undersupply of labor will ensue until equilibrium is achieved.
The manufacturing sector, as expressed by Manyika (2017) will make huge savings out of automation since wages will not be paid because machines will have taken over. This dimension will leave the manufacturing sector with huge capital reserves that can be invested in research and development. As a consequence of research activities, new tasks might come up as elaborated by Acemoglu and Restrepo (2016). The new tasks will raise the demand for new jobs, which raises labor prospects in the industry. The new tasks will have a new dimension in that they will require to be performed by specialized hands, which calls for more training. For instance, when a robot takes over the role of assembling a car, the assembler who was doing that is rendered jobless. On the other hand, the robot requires to be manufactured, maintained, and programmed. Therefore, the robot opens up another line of activities but, which are more specialized than what it is doing. Labor, in this case, becomes specialized.
David (2015) demonstrated that labor that is replaced in the manufacturing sector that is most likely to be replicated is labor which performs repetitive tasks. This means that the likely shift in the industry is that workers who shall feel threatened by automation will scamper for safety by going for training to perform non-repetitive tasks that require problem-solving skills, creativity, and adaptability. These skills are most needed high up the organizational structure in the management and decision-making levels. Due to the over flooding of available labor in these levels, as the law of demand and supply dictate, there will be a surplus in the market and the wages payable to those in these levels will shrink or remain constant, an observation that has been made by Acemoglu and Restrepo (2017).
How an HR Planner Would Use the Information Provided Above?
In a radically shifting labor market, as described above, an HR planner would be required to be a step ahead of the industry to ensure that when changes come, the company such an HR planner is working for, remains steady. Such preparations would require foresight and use of statistics provided by researchers and other industry leaders to make sound planning decisions. The following discussion explains what an HR planner in the manufacturing industry can do with the information provided in the different parts of this paper.
The very first thing is to develop a skill need plan for the industry for a period of 5years, 10 years, 15 years, and so on. Such planning would be important to identify what skills set would be replaced by automation and what the HR office would do with the affected employees. Further, the HR planner would identify the possible new skills that would be needed in a particular company within a particular timeframe. With collaboration with the executive and other levels of management in the company, the HR can determine the most likely types of investments that can be considered by the company.
From the information above, it has been determined that there is a particular group of employees, in terms of gender and education levels, who are likely to be phased out by automation. The HR planner can identify training needs and develop a training mechanism aimed at ensuring that such employees are equipped with the skills necessary to handle the new technology or are averse with any other new task that may arise out of the changes as expressed by Tsarouchi, Matthaiakis, Makris, and Chryssolouris (2017). This step would ensure that the employees are not stranded by the new development.
How does the Information Above fit in Succession Planning?
Succession planning is necessary for ensuring that there are business continuity and an improvement in business responsiveness to competition and the changing business environment. To properly plan succession planning, important leadership positions that have a significant effect on a company and its direction, success, and performance are considered. In a changing business world, especially in manufacturing, it is necessary to ensure that there is a huge delivery of the required level of performance.
The information above is necessary to identify where change is likely to be and how it should be handled. The people in the company that are likely to be affected and the new kinds of skills that should be provided to different level managers who would be incoming to manage the new technology and workers with new sets of skills. It will provide an insight into what kind of activities are necessary for ensuring that workers who are poised to be managers should be exposed to in ensuring that they are competent and conversant with all operations.
Importance of Identification of Personnel Requirement in the Future of Organizations
The level of preparedness of the HR department when change is looming, has an important dimension towards the success of such an organization. Identifying personnel resource needs ensures important advantages to an organization as follows. First, it eliminates the aspect of surprises associated with rapid talent needs, which are not as easy to fill. Second, it helps in smoothing out business cycles by avoiding delays, getting the right skills, and emphasizing on employee development. Third, it helps in the early identification of potential problems and this is important in helping the organization to prevent serious HR problems. Fourth, it helps in taking advantage of opportunities that arise. Talented employees are important in ensuring that there is a critical development of important infrastructure necessary in the identification and utilization of infrastructure.
Conclusion
It has been demonstrated that the manufacturing industry is facing a significant change emanating from automation. With automation, many employees will be deemed redundant. The HR department of any organization should ready itself for a change through conducting a needs assessment, which will help in the identification of necessary actions suitable to address the challenge being fronted by technology. Failure to be ahead of the change will wreak havoc in the organization in terms of personnel and the organization might lose in the market.

References
Acemoglu, D., & Restrepo, P. (2017). Robots and jobs: Evidence from US labor markets. MIT
Acemoglu, D., & Restrepo, P. (2016). The race between machine and man: Implications of technology for growth, factor shares and employment (No. w22252). National Bureau of Economic Research.
Chui, M., Manyika, J., & Miremadi, M. (2015). Four fundamentals of workplace automation. McKinsey Quarterly, 29(3), 1-9.
David, H. (2015). Why are there still so many jobs? The history and future of workplace automation. Journal of Economic Perspectives, 29(3), 3-30.
Grace, K., Salvatier, J., Dafoe, A., Zhang, B., & Evans, O. (2017). When will AI exceed human performance? Evidence from AI experts. arXiv preprint arXiv:1705.08807.
Manyika, J. (2017). A future that works: AI, automation, employment, and productivity. Extracts from McKinsey Global Institute Research.
Tsarouchi, P., Matthaiakis, A. S., Makris, S., & Chryssolouris, G. (2017). On a human-robot collaboration in an assembly cell. International Journal of Computer Integrated Manufacturing, 30(6), 580-589.