The study findings by Frey and Osborne suggest that with the recent developments in Machine Learning, the employment opportunities across various occupations will be at risk in the future. In the future decades, machines will be taking over significant tasks, and the rate of technological unemployment will be determined by the pace of computerization across the various industries (Ford,2016). However, the speed is interpreted by two waves of automation often separated by a "technological plateau." The first wave encompasses the facts that human capital in routine areas such as transportation and logistics, office and administrative support, production, and manufacturing occupation will be taken over by machine capital (Frey and Osborne, 2017, p. 265). This wave will then be followed by a slowdown of the pace through which machine capital will be substituting human capital as a result of the underlying engineering bottlenecks that hinder computerization. The slowdown thus defines the "technological plateau" which is the gap between the first wave and the second wave of computerization discussed above (Bessen, 2015). The second wave encompasses the strategies adopted to overcome the existing engineering bottlenecks that hinder the development of automation usually associated with creativity and social intelligence (Frey and Osborne, 2017, p. 266).
As a manufacturing and construction company, Honda is at risk to face the impact of technological unemployment as exhibited by Frey and Osborne findings. Appendix table A of the research findings connotes that the occupations that are commonly found at Honda Corporation have low probabilities towards experiencing technological unemployment. For instance, professions such as mechanics, repairers, and installers have a 0.003 chance of being substituted by machines while mechanical engineers have a 0.011 probability (Frey and Osborne, 2017, p. 269). Considering a scenario where there is a high risk of computerization where more than 70% of the jobs are highly probable to be substituted by machines, the operations of the corporation will significantly change (Meiners et al., 2018). For example, the introduction of machine learning equipment such as robots helps to undertake more complex tasks that could be done by many workers. These machines will be capable of performing tasks more quickly and in an efficient manner (Liker, 2004). At the long-run, the Corporation cuts the demand of labor since less human capital is required which may in turn help to reduce costs. Moreover, the introduction of the robot, Asimo Honda, in the early 2000s saw the loss of many employment opportunities among the people working in Honda, Japan. Although the robot is currently not operational, Asimo Honda is a perfect example of how the future automation of tasks will lead to technological unemployment (Cowen, 2013).
While some jobs are at risk to be substituted by machine learning, other occupations face a low risk to the bottlenecks that may hinder technological development in the future. As Frey and Osborne suggest, although computerization will be inevitable in the future, these bottlenecks make some occupations that require skills such as social intelligence, creativity, perception, and manipulation safe from automation (Bessen, 2015). The findings assert that no robots can perform tasks that need these skills. In this case, therefore, occupations such as management, engineering, financial services, media, legal services, healthcare, education, and computer science are currently safe from computerization. The occupations at risk include transportation, manufacturing, production, office services, and sales (Frey and Osborne, 2017, p. 267).
Performance metrics
The crucial part of Honda's suppliers involves continuous evaluation as well as analysis of supplier performance. Just like the other organization, the company must have the capacity to measure, manage, and develop the performance of the suppliers. The company needs to determine which criteria need to be employed to have a qualitative and quantitative approach. The deference of these two approaches reflects in metrics (Mintzberg and Quinn, 2015). Therefore, most of the variables fall within the following group:
- Quality performance
- Cost reduction
- Delivery performance
- These categories may also involve the following metrics
- Delivery performance based on request date.
- The cost of material acquisition
- The cost of managing the order.
- Perfect order to fulfill and deliver customer orders
Conclusion
In summary, with the continuing relationship between Honda's supplies and Honda company, more opportunities for expansion and growth is expected (Bessen, 2015). With Honda's involvement in purchases during the manufacturing and sale of their products, most of the suppliers demonstrate competitiveness to export some of their spares to other nations across the globe. In this manner, the company is encouraging it, customer, to understand the way it operates right from it central or headquarters to other branches across the globe (West, 2018). Depending on this research, it is clear that partnership with other entities will benefit the company so much which may make it prosper more shortly.
References
Abernathy, W. J., & Ronan, L. (2010). Honda Motor Company's CVCC engine. Final report.
American Society of Mechanical Engineers. (2013). TS. New York: AMSE.
Berger, A. (2011). Global corporate strategy - Honda case study. Place of publication not identified: Grin Verlag Ohg.
Bessen, J. (2015). Learning by doing: The real connection between innovation, wages, and wealth. New Haven [Connecticut: Yale University Press.
Business review weekly: BRW. (2011). Melbourne: Business Review Weekly.
Calvino, F., &Virgillito, M. E. (2017). THE INNOVATION-EMPLOYMENT NEXUS: A CRITICAL SURVEY OF THEORY AND EMPIRICS. Journal of Economic Surveys, 32(1), 83-117. Doi:10.1111/joes.12190
Cleland, D. I. (n.d.). Strategic management of teams. Wiley & Sons., 1996.
Cowen, T. (2013). T...
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