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New Technology and the End of Jobs

New Technology and the End of Jobs

Man versus Machine in the Workplace:

Investigating the role of Artificial Intelligence in the Increasing Levels of Unemployment

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Date

Proposed Research Topic:

New Technology and the End of Jobs

Human beings are slowly being replaced with machines in almost every sector as well as industry by technology revolution. Many people are getting eliminated from their jobs permanently as the work category and job assignments continue to sink even further. Jobs are also being restructured and others are disappearing because of the continuous adoption of technology. There is continued unemployment and according to findings, global unemployment is at its record high since the great depression back in 1930s. An estimated 800 million people are un employed and this figure could even rise further. Millions of graduates who are hopeful for employment opportunities, who are continually entering the work force are continually finding themselves jobless. It is now clear that the rising figures of unemployment indicates a short term adjustment to a market so powerful and powerfully driven by forces that drive the global economy towards a new direction. The global market is looking forward towards an exciting world of high tech automated production, abundant materials that are unprecedented, and booming world commerce. In the US alone it is estimated that 2 million people are being eliminated annually from their jobs by corporations. In return the jobs that are created are low paying low sector jobs. The worrying factor is that this transition is all over the world. Even the developing nations are increasingly eliminating employees with built state of art which tech facilities used for production CITATION Bai13 l 1033 (Bailey, 2013).

Technology has changed to the disadvantage of human labor. There is what is referred to as “Big Data” by scientists. This is the use of computers to thrive on information from the international website, barcodes are placed on nearly every product. More information is passed across the internet every second twice the total amount of information that was stored on the entire internet some 20 years back. Giving an example with Wall-Mart, the store is capable of collecting approximately 50 million cabinets of information from the customer’s transactions every 1 hour. This is far much the capability of a human if they were to be left to handle the transactions, according to Andrew McAfee and Erick Brynjolfsson, (2012). Computers can make sense of so much data than human beings.

It is true that the world is changing technologically and this the major force towards unemployment, because machines are replacing human labor but corporations are taking this as an advantage to gain Return on Investment. ROI refers to the capital invested in a company and the return realized from the capital based on then net profit of the business. It is important to understand that profit and ROI are two different things. Profit is used to measure the performance of the business. ROI is not necessarily the same as profit. However ROI can be used to gauge the profitability of the business. It is used to identify the past and potential financial returns of a business looked by the managers as a project. This is because it can portray how successful a business is expressed in ratios or percentages. It is also used to describe financial returns and increased efficiencies in the organization. It is also used to calculate the much of a value an investment is.

ROI has been used in line with Artificial Intelligence (AI). It worth knowing that it is customer demand that drives today’s business and the demand patterns varies from period to period. Because of these variables it has become very difficult for organizations to develop accurate forecasts, which refers to the process of estimating future events. Forecasting reduces uncertainty and used to provide benchmarks used to monitor performance. Combinations of AI and emerging technologies have been used to improve the accuracy of forecasts to contribute to organization enhancement. It is perceived that the use of machines to replace human labor is more effective and contributes to profitability and improved ROI. This has also fueled investors to replace human labor with machinery and computer software.

There is a saying that goes that whatever is measured gets done. Human nature can also be measured. It is true that many workers constantly re-prioritize their work activities. It is also worth understanding that not everyone in an organization will work towards a common goal, that is, the success of the organization. It is therefore important to measure performance against input. Metrics have the attention of both manufacturing and business leaders. It is important to measure sectors in business activities and provide improvement where necessary. The following are some of the manufacturing metrics utilized mostly by process, discrete, and hybrid manufacturers:

Improving customer expectations and responsiveness such as on time delivery and manufacturing cycle time, Metrics to improve quality such as yield, consumer rejects, material returns, supplier quality incoming, Metrics for improving efficiency such as capacity utilization, throughput, overall equipment effectiveness, schedule of production effectiveness, Metrics for reducing inventory like WIP Inventory/ Turns, Metrics on increased flexibility and innovation like rate of new product introduction, and Engineering change order cycle time. Metrics for Ensuring Compliance, Metrics for reducing maintenance like percentage planned, Metrics for cost reduction and increasing profitability like net operating profit, productivity in revenue per employee, energy cost per unit, productivity in revenue per employee, and manufacturing cost as per percentage of revenue.

Variables Definitions Metric References

ROI Return On Investment-this is a business term used to identify the past and potential financial returns. It helps to indicate how successful a business is. Metrics on Improving Costumer Experience and Responsiveness-Manufacturing cycle time,

On-time delivery to Commit

Bailey, R. (2013, February 8). Were the Luddites right? Smart machines and the prospect of technological unemployment. Reason, 45(1), 48.

AI Artificial Intelligence-this is a computer science emphasizing on intelligent machines working and relating like humans. Metrics on improving quality-Customer rejects,

Yield

Brynjolfsson, E., & McAfee, A. (2012). Technology’s influence on employment and the economy. In Race against the machine: How the digital revolution is accelerating innovation, driving productivity, and irreversibly transforming employment and the economy (p. 10). Lexington, MA: Digital Frontier Press.

Metric These are quantifiable measures used to assess the position and status of a venture. Metrics on Improving Efficiency-effectiveness,

Through put,

Capacity utilization,

Grint, K., & Woolgar, S. (2013). The machine at work: Technology, work and organization. John Wiley & Sons.

Inventory These are stock held by a business in form of materials or goods for the purpose of repair, resale, or raw materials waiting processing. Metrics on Reducing inventory- Work in progress inventory turn Sachs, J. D., & Kotlikoff, L. J. (2012). Smart machines and long-term misery (No. w18629). National Bureau of Economic Research.

WIP Work In Progress- these are materials that are partly finished. They are materials that are already in the production process but have not yet turned to a fully finished product. Metrics on Increased flexibility and innovation-Engineering change,

Rate of new product introduction

Hart, M. (2013). Educating cheap labour’r. The learning society: Challenges and trends, 96.

Big Data This is the use of computers to thrive on information from the international website, barcodes are placed on nearly every product Metrics on Cost reduction and profitability-Productivity in revenue per employee,

Net operating profit

The agricultural industry has seen numerous changes in the previous 100 years. Since the conception of the modern horticulture in 1900, we have pushed ahead to the period of computerized improved farming where everything that is carried out before seeding and up to after harvesting produces data that can be broke down or analyzed. Big Data officially changed the horticulture industry a ton and in the impending decade this will get to be unmistakable or visible in every aspect of farming in the Western world and progressively likewise in the Developed world. There are three ranges that will be influenced the most by the chances of big data: reduced costs of operation and improved efficiency, crop and animal efficiency and improved productivity, optimization of crop prices and mitigation of weather conditions CITATION Gri13 l 1033 (Grint, 2013).

In the agricultural industry, the internet of things and industrial internet are adversely affecting agricultural equipment such as sprayers, tractors, harvesters, milking machines and soil cultivators. Farmers are now capable of getting information thanks to the sensors that have been deployed in machines such as tractors, cow milking and several others. These machines offer information in real time 24/7 even in the absence of the farmer. These machines act as smart machine that are capable of talking and can coordinate with each other to give the farmer the overall condition of the farm. They can predict problems and even take action before adamage can be realized. The farmer can take action immediately he sees a problem dismayed by the sensors and if the problem is very serious, a service employee will visit the farm.

These sensors have led to increased productivity in many processes of agriculture. They also predict failure and maintenance and also safe the farmer fuel and energy for harvesting and transportation by optimizing the best driving conditions especially in large farm because they can predict shorter routes to drive and help save a lot of fuel. These computers are integrated and they pass information to each other making the entire process manageable by only 1 person. These machines are managed by diagnostics to make sure that optimal settings are in place. These data are passed to the farmer who will then analyze them to ensure continuity of effective operation now and in future. Big Data technologies are continually making precision agriculture interesting. This includes recognition, understanding, exploitation of information capable of quantifying variations in crops and soil. This has helped the farmers a lot especially in optimization of the crop productivity CITATION Sac12 l 1033 (Sachs, 2012).

Not just yields and crops can be enhanced with huge information, additionally the farm animals will gain from enormous information innovation. Having sensors in the sheds will allow input on the states of the animals. Sensors can consequently measure the animal’s weight and conform bolstering if needed. Contingent upon the conditions in the shed or the states of the creatures, sustaining can be balanced too. The creatures will get the right nourishment and the perfect sum at the right minute. There are also chips inserted on the animals that can monitor their health conditions. Sick animals can receive medication through the food there are given and conditions of the sheds adjusted in any case they are affecting the animals. The heard can also be traced via the smart phone with the help of the chips placed on them. These sensors also display the mental health of the animals. Big Data flips around the customary horticulture industry. Despite the fact that the ventures can be significant for ranchers, the potential advantages of applying huge information advancements on the field are gigantic CITATION Har13 l 1033 (Hart, 2013).

I used both primary and secondary data collection methods to collect data. Under primary data collection, I collected the data myself by through qualitative and quantitative methods. I used observations, interviews, focus group interviews and questionnaires.

The following were the sourced of data I used:

Primary Data: interviews-I will use forms that will be completed by the respondents. Interviews are better for complex questions that I will be asking even though being expensive than questionnaires. Questionnaires: these are forms that are completed and returned by respondents. I will use this method of data collection because it is cheaper and they allow the respondents humble time to give feedback to the questions asked. Focus group interviews: I will identify a group of a particular group of people especially the farmers and people employed in the agriculture industry and conduct an interview on them. Observations: I will use direct observation to collect data. I will try and find observer programs to help me with the exercise.

Secondary data sources: Previous researches: I would use previous researches on how smart machines are affecting employment in Agriculture industry; official statistics: statistics published by government agencies or other public agencies on economic and social development and environment; Mass media products: data from media houses on development in the Agricultural and horticultural industry and how machinery is affecting employment in the industry; Government reports: the government publications and reports on Agricultural Industry and how smart machinery and Big Data is affecting employment; Web Information: searching the international network for data on Big Data and smart machinery and how they are affecting employment in Agricultural industry; Historical data and information: the history of smart machinery and Big data.

References

Bailey, R. (2013, February 8). Were the Luddites right? Smart machines and the prospect of technological unemployment. Reason, 45(1), 48.

Brynjolfsson, E., & McAfee, A. (2012). Technology’s influence on employment and the economy. In Race against the machine: How the digital revolution is accelerating innovation, driving productivity, and irreversibly transforming employment and the economy (p. 10). Lexington, MA: Digital Frontier Press.

Grint, K., & Woolgar, S. (2013). The machine at work: Technology, work and organization. John

Wiley & Sons.

Sachs, J. D., & Kotlikoff, L. J. (2012). Smart machines and long-term misery (No. w18629).

National Bureau of Economic Research.

Hart, M. (2013). Educating cheap labour’r. The learning society: Challenges and trends, 96.

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