AWA: Academic Writing at Auckland

Production industries in the late nineteenth century became increasingly difficult to organise human effort regarding efficiency and effectiveness. As such, there has been an increase in the size and complexity of industrial organisations (Koumparoulis, 2012). To compensate for this matter, Frederick Taylor instituted his view to the growing concern which became known as the scientific management– this was based on four fundamental principles which encompass around standardisation, productivity and division of labour as a form of efficiency. Taylor described scientific management as “a theory of management that analyses and synthesizes workflows with the principal objective of maximizing the prosperity of both management and the workers (Taylor, 1895, 1903 as cited in Vijai 2015, p. 447). In essence, this would allow companies to achieve greater through an improved quality of life in the workforce, a positive factor for stakeholders involved (Koumparoulis et al.). However, according to Freedman (1992), “new technologies are transforming products, markets, business processes, and entire industries, revolutionizing the business environment; thereby, the possibility that scientific management will become less relevant today than it was in the 20th century. As such, this essay will discuss how the principles of Taylor’s scientific management are becoming more relevant today due to technological advances and artificial intelligence (AI) with appropriate examples from organisations.

Taylor aimed to implement scientific methods into work procedures as part of his scientific management theory. Such scientific methods would surpass the “rule of thumb” method (assumption and estimation) given its lack of role in efficiency towards improving the performance of the employees (Blake & Moseley, 2010 as cited in Huang, 2012). Similarly, AI works on the basis of scientific procedures (Kolbjørnsrud, 2016). AI could be described as “the science concerned with the creation of machine intelligence which is able to perform tasks heretofore only performed by people” (Turksen, 1986, pp. 142). Simply put, AI can perform tasks which could only be previously done by humans. For example, Motivo, a firm which specialises in semiconductor manufacturing, was able to shorten their design processes from years down to months by placing a greater emphasis on AI using analytical tools. This would assist fabs in replacing guesswork and human intuition with fact-based knowledge, pattern recognition, and structured learning. As a result, this would reduce errors during the design process by correcting errors in “physical designs and improve yield and reliability without running a single wafer or making a mask” (Burkacky, 2017, para. 5). In other words, this assists engineers in making computerised chips using scientific methods on the basis of analytical tools as a form of automation. Rather than using approximation and guessing, analytical tools would provide a set of useful information regarding the design process. As a result, adopting AI measures will ensure that Taylor’s fundamental principle regarding scientific methods will still be more relevant than it was in the 20^th century.

The fundamental similarity between AI and scientific management is that both work in the most efficient way possible. However, AI is based on a set of algorithms automated by a programmer (Upstone, 2017) whereas, scientific management “analyzes the exact series of operations needed while doing the work under investigation, as well as understanding the tools which are used.” (Huang, et al, pp. 81). Apple, a multinational firm which sells consumer electronics, had explored automation when it came to disassembling their own products. Apple argues that today’s practices which is based on human activity, would “end up with a very heterogeneous mix of component pieces that are difficult to sort into individual materials”. (Rujanavech, 2016, pp. 2). In more simplified terms, components that are still stuck together as a piece. On the contrary, the automation system developed by Apple allows individual components to be removed from such devices and “separated into like-material component streams”. (pp. 3) As such, Apple have programmed an automation system which disassembles iPhones efficiently based on given parameters such as components and processes. Such parameters which have been developed and automated by workers which would make the disassembling process much more efficient and more sustainable. Because of AI, it complements along with the key principle of scientific management making it more relevant than it was in the 20^th century to an extent.

Part of the scientific management theory “calls for the workman’s scientific education and development” (Wagner-Tsukamoto, 2007 as cited in Huang, 2012, pp. 81) which simplifies as allowing employees to remain relevant at their jobs. Simply put, employees would need to be monitored and assessed afterwards in order to become more efficient in their roles. Similarly, technology would ensure that employees remain relevant at their jobs by making their roles more efficient, rendering scientific management more relevant today. For example, Tesco, a multinational grocery firm, keeps track of its employees work rate at one of its distribution centre using a digital arm-band which constantly monitors their performance. (Walsh, 2013). These arm bands would grade employees through collection assignments such as lifting supplies on a forklift. Such grades are determined by the rate of assignments done – assignments which are done quicker yields in a better grade whereas; assignments are done slower (or employee taking a break) yields in a weaker grade. As a result, employees would be graded and assessed on their work performance based on the digital arm-band. Thereby, management would have a more simplified role in ensuring that scientifically developed methods are being followed for their employees. This is because management would be able to utilise more time with employees on areas that need to be improved on, rather than utilising time on monitoring employees.

It is becoming increasingly evident that the principles of scientific management are more relevant today than it was in the 20th century due to the influence of technological advances and AI. Taylor’s principles call for efficiency by largely emphasising on tasks which are to be done scientifically, rather than generating assumptions and guesses from the rule of thumb. Using scientific methods can become efficient as it can minimise costs by cutting any form of unnecessary productivity. As such, AI and technology advances allow organisations such as Motivo and Apple to be more efficient in slimming down their processes involved in assembling and disassembling respectively. Furthermore, technology advances also play a role in ensuring that work performances from employees are being monitored. As such, management can spend more time providing specialised training to employees individually based on the feedback given from technology advances rather than monitoring the employees as they go. The idea of spending more time with employees through specialised training links back to Taylor’s fundamental principle – where processes are more simplified due to technological advances.

References

Burkacky, O., Patel, M., Sergeant, N., & Thomas, C. (March 2017). Reimagining fabs: Advanced analytics in semiconductor manufacturing. McKinsey & Company.     para. 5. Retrieved from http://www.mckinsey.com/industries/semiconductors/our-insights/reimagining-fabs-advanced-analytics-in-semiconductor-manufacturing

Freedman, D. H. (1992, November-December). Is management still a science? Harvard Business Review. 70(6), 26-38. Retrieved from Business Source Premier database.

Huang, K.P, Tung, J., Lo, S.C., & Chou, M.J. (2013, April 4). A review and critical analysis of the principles of scientific management. International Journal of Organizational Innovation. 5(4). Retrieved from Business Source Premier database.

Kolbjørnsrud, V., Amico, R., & Thomas, R, J. (2016, November 2). How artificial intelligence will redefine management. Harvard Business Review. p2-6. Retrieved   from Business Source Premier database.

Koumparoulis, D.N., & Solomos, D.K. (2012). Taylor’s scientific management. UGSM-Monarch Business School. 8(4). Retrieved from Business Source Premier database.

Rujanavech, C., Lessard J., Chandler, S., Shannon, S., Dahmus, J., & Guzzo, R. (September 2016). Liam – an innovation system. pp. 2. Retrieved from https://www.apple.com/environment/pdf/Liam_white_paper_Sept2016.pdf

Turksen, I.B. (1987, September 12). ­Computer Integrated Manufacturing – Current status and challenges. NATO Scientific Affairs Division. pp. 142.

Upstone, S. (2017, April 21). Introduction to algorithms, machine learning and AI. Campaign. Retrieved from http://www.campaignlive.co.uk/article/introduction-algorithms-machine-learning-ai/1431094

Vijai, J.P, Somayaji, G.S.R, Swamy, R.J.R, & Aital, P. (2015, February 24). Relevance of F.W. Taylor’s principles to modern shop-floor practices. Emerald Publishing. pp. 447. Retrieved from Business Source Premier database.

Walsh, A-M. (2013, February 11). Tesco staff forced to wear arm monitors that track work rate. Independent Ireland. Retrieved from http://www.independent.ie/irish-news/tesco-staff-forced-to-wear-arm-monitors-that-track-work-rate-29060257.html