|Type of paper:||Report|
|Categories:||Human Resources Unemployment Technology|
Organization Type, Products and Consumer Base
Unilever is a transnational consumer goods that claims British and Dutch as its primary home and has its headquarters in Rotterdam in the Netherlands and London in the United Kingdom. Unilever as an organization has 400 brands under its belt and in 2017 reported 53.7 billion euros returns (Unilever. n.d). The organization has a dual is a dual listed company with Unilever N.V which is based in Rotterdam and Unilever plc. in London. The two companies are united by having a single board of directors and operate as a single business. Unilever is a manufacturer and produces goods that move fast in the United Kingdom and globally. Unilever is a significant global organization that provide consumer goods in different areas such as home care, personal care and food products. Unilever food products include sauces, noodles, Bertolli pasta, packet tea, dressings, ice cream, and bouillons. Other products include the hygiene products, deodorants, skin cleansers and antiperspirants (Unilever. n.d).
Unilever was founded through the merger of the Dutch Margarine Unie which was a margarine producer and Lever Brothers a soap making organization from Britain. Over the years Unilever has invested in market diversification and increased its portfolio brands through corporate acquisitions. Unilever consumers are in personal care products, food, refreshment products and home care products which shows a diversified consumer base. Unilever appeals to its consumers worldwide through its Sustainable Living Plan which promotes market growth while promoting environmental sustainability and increasing overall social impact through health and well-being of the organization consumer base. Unilever believes in making the world a better place through partnerships with governments, consumers, suppliers and other businesses to address the world problems such as climate change (Unilever. n.d).
Technological Unemployment Concept
Technological unemployment refers to the loss of jobs due to the mechanization and automation of services and product production (Frey and Osborne, 2017). The innovation and invention of new technological products in the industrial era has significantly reduced the number of jobs available for human beings as more organizations mechanize and automate their production process with the aim of reaping more returns by reducing their production costs and increasing their overall production scale (Campa, 2017). Technological unemployment is a major problem in the 21st century as more advanced technological applications replace human labor such as artificial intelligence enabled robots (Peters, 2017). Most organizations are being pulled towards technology because of its advantages over human labor in convenience, flexibility, and quality (Frey and Osborne, 2017). As a result, technology unemployment is more real today than it was 20 years ago. In the future, more computerized production units will reduce organizations overdependence on human labor which will significantly increase unemployment across the globe (Peters, 2017). The rise of unemployment is due to the inability of human labor to adjust and move towards occupations that are human labor intensive (Matuzeviciute et al., 2017).
Technology unemployment can be traced back to the 17th century after the introduction of the ribbon weaving machine which attracted protest from the people due to unemployment. In the 1630s, the wind-driven sawmill further worsened the situation, but in the 18th-century water driven saw mills overcome opposition from people. Ludic reactions towards technology in the 17th and 18th century but going forward more people started to appreciate technology (Campa, 2017). Supported by Adam Smith perspective technology developed and machinery and business automation was viewed as an approach to increase productivity. Through the John Maynard Keynes Keynesian theory, the concept of technological unemployment was introduced in 1929 (Frey and Osborne, 2017). In the 1930s unemployment became a serious issue during the great depression as more people realized that technology caused unemployment (Onuonga, 2016). Today, artificial intelligence has increased the criticism of technology as a factor causing unemployment. In the last 40 years, the robots and computers have dominated areas where human labor was needed which increases the threat of technology on employment (Campa, 2017). The fourth industrial revolution which is being experienced will increase unemployment further in the future as more robots, and artificial intelligence applications are made.
In the 21st century, humankind is on the blink of the fourth industrial revolution that will increase technologies such as intelligence robots, internet of things, and data science application in industrial production and other aspects of life activities that are currently being undertaken by human beings (Brandes and Wattenhofer, 2016). The fourth industrial revolution is being driven by the innovations and inventions in science as well as private and public investments in technology with the aim of increasing efficiency by economizing on labor (Vermeulen et al., 2018). As the use of technology to increase production exponentially increases the people becoming unemployed are many because of the mismatch between their skills and jobs that are not yet mechanized. Mass unemployment as a result of mechanization and the use of artificial intelligence, 3D printing and virtual assistants in the production process will lead to income inequalities and also has the ability of negatively affecting market economies due to the reduction of human purchasing power (Campa, 2017).
Low Impact Scenario of Unilever Technological Adoption
In a low impact scenario, technology can be used to increase yields and compensate labor inefficiencies. Technology adoption in the manufacture of self-care and food products will affect 70% of Unilever labor in the assembly line. Unilever employs 169,000 employees and in a scenario where 70% of the jobs will be automated 118,000 employees will be laid down. The following as are the impacts of a 70% jobs automation at Unilever.
|Total Cost Annually||$8,811,660,000|
Table 1: Unilever Human Resources Cost Estimation
Positive Impacts of Assembly Line Automation
Reduced Cost of Labor
In a scenario where 118,000 employees are discharged from their positions, Unilever will reduce the cost of labor annually by $6,152,520,000. Reduced cost of labor can significantly increase Unilever shareholders returns and financial standing of the business. Reduced cost of labor will help the organization to concentrate on other matters such as improving the quality of the products and increasing the customer value on the products (Matiskova and Mura, 2012). More money can be channeled towards the research and development department which will significantly improve Unilever products and increase the competitive organization edge over other competitors. Therefore, regarding increasing the organization returns and its competitive edge, the automation of the organization assembly line jobs will significantly have positive impacts on the organization. The reduction of the cost of labor will enable Unilever to create competitive prices for its products which will further improve the competitive organization edge (Schneider, 2005).
|Assembly Line Workers||118,000 Employees|
|Total Cost Annually||$6,152,520,000|
Table 2: Assembly Line Workers Cost
Higher Output and Production Convenience
Automation of the assembly line jobs will increase output because unlike humans machines can work continuously without stopping (Mishev, 2006). Higher output will increase Unilever returns and sales which will help reduce the amount of cost incurred per unit in the production of the products (Nimawat and Shrivastava, 2016). Increased convenience will ensure that Unilever can produce goods in large quantities which will help the organization to take advantage of globalization and expand its market reach. The expansion of the market will have a significant positive impact on Unilever revenues.
Negative Impacts of Automation
High Cost of Early Retirement Plans
Early retirement is a costly option for an organization to retrench some of its employees due to automation of the areas that the employees were working on previously (West, 2015). As such, in a scenario where Unilever will carry out low impact automation and mechanization of the production process 118,000 employees will need to be compensated for early retirement. The retirement plan would cost Unilever a lot of money which will temporarily affect the organization liquidity in the short term.
High Cost of Automation
Unilever should consider the cost of automation and the future gains from automation. In such a case, the short-term cost of acquiring technology and training employees to use the technology will be significantly high (West, 2015). However, in the long term, Unilever will be able to get more revenues because of the high production outcomes and reduced cost of production.
Unilever Public Reputation
In cases where organizations lay down a high number of employees, there is a significant public outcry from labor organizations which can negatively affect the organization reputation in the market (Oschinski and Wyonch, 2017). A negative reputation adversely affects an organization sales because consumers may opt to buy Unilever substitutes. However, if Unilever will compensate the employees to be retrenched on fair terms the public image of the organization can be protected.
Technological unemployment is a significant problem that continues to affect millions of employees as more organizations embrace technology. A low impact scenario of technology adoption can affect the organization positively and negatively, and it is essential for Unilever to manage the negative impacts of technology adoption in the assembly line. Compensating the employees who will lose their jobs can help Unilever to retain its public reputation and reap the advantages of automation in the long term.
Brandes, P. and Wattenhofer, R., 2016. Opening the Frey/Osborne black box: Which tasks of a job are susceptible to computerization?. arXiv preprint arXiv:1604.08823.
Campa, R., 2014. Technological growth and unemployment: a global scenario analysis. Journal of evolution and technology, 24(1), pp.86-103.
Campa, R., 2017. Automation, education, unemployment: a scenario analysis. Studia Paedagogica Ignatiana, 20(1), pp.23-39.
Campa, R., 2017. Technological unemployment: a brief history of an idea.
Frey, C.B., and Osborne, M.A., 2017. The future of employment: how susceptible are jobs to computerisation?. Technological forecasting and social change, 114, pp.254-280.
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