Employment and Technology in Manufacturing

Employment and Technology in Manufacturing

Alister McLeod (Indiana State University, Indiana, USA)
Copyright: © 2019 |Pages: 16
DOI: 10.4018/IJSKD.2019070102

Abstract

Manufacturing has acted as a modern-day bellwether for the impact robotic systems have on labor. Technical systems have been known to reduce employment in certain sectors of the economy while boosting it in others. Currently, the most pervasive line of thought is that robotic systems are reducing the need for labor-intensive jobs in the United States (U.S.) and other The Organization for Economic Co-operation and Development (OECD) nations. However, the findings of this article suggest that robotic systems and labor are codependent upon one another with respect to the manufacturing sector. Further examination of the factors driving or at the root of this codependency may likely provide further insight as to how robotic systems and their usage will affect future employment.
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Introduction

The diminishing need for lower skilled labor in the field of manufacturing has been widely documented as the elimination of labor-intensive processes, whether by outsourcing or technological innovations (Frey & Osborne, 2013; Jeserich, Mason, & Toft, 2005; Mckinsey Global Institute, 2017; Sirkin, Zinser, & Rose, 2014). Modern manufacturing techniques deploy automated machinery and specialized work environments in an effort to increase productivity. These systems however, fail to capture the nature of future computerized systems. Automated systems are currently utilized extensively for routine tasks; however, robotic systems have the potential to upend the notion that only routine tasks are at risk of being void of human input. The purpose of robotic systems is to overcome issues surrounding intelligence and adaptability. Their forms are difficult to demarcate; however, their presence can take the form of both an embodied and unembodied systems. Their embodied nature undertakes physical tasks that require a high level of skill, analytics, and knowledge. Their unembodied nature, on the other hand is solely concerned with undertaking tasks that require human-like intelligence that generates creative or innovative outcomes. It is important to remain cautious regarding the manner in which the present-day impact of these systems upon the workforce is assessed.

On the surface, one can observe that high-technology (HT) jobs have been on the increase, while a majority of the lost jobs are those which appear to be monotonous and labor-intensive in nature (Tanzi, 2013). The explanation here is that tedious jobs are being undertaken by automated systems and labor-intensive jobs are being moved to China [1 million to 2.4 million of the lost jobs during this time, 2000 -2016, are attributed to this particular competitor according to Acemoglu and Restrepo (2017); D. Autor and Salomons (2018)]. On the other hand, this scenario is a possible example of how Multi-National Corporations MNCs have utilized technology to increase profitability and decrease employment within a developed economy, while moving capital investments and increasing employment in a developing economy. Productivity, a measure which is crucial to the understanding of the health of the manufacturing sector, may be incorrect, due to the vague nature of these relationships. Improvements arising from product design and production processes skew the perception that the U.S. manufacturing sector is healthy, as improvements in design take place, while improvements to the production process occurs in low-capital economies, where new arrangements can be deployed easily. Improvements in processes, while they increase productivity, do not lead to a direct ascription of job loss. However, if viewed from a multinational perspective, job shifting occurs from high to low capital economies, enabling increased profitability and a decreased need for a large local labor pool. This makes the determination of the manufacturing sectors health exceedingly difficult to measure (Houseman, Bartik, & Sturgeon, 2014).

This paper seeks to understand the connection between robotic systems in manufacturing environments and the potential impact at the local, regional and national level on labor. Regional economic development is an important issue, that in today’s knowledge-based economy, undergirds the future of many communities. A multifaceted approach to community development seeks to engage all stakeholders in such a manner that regional strengths are leveraged to make communities stronger and more flexible in adverse economic conditions. However, as knowledge and technology intensive (KTI) opportunities are pursued there is a tendency for human capital to become more specialized, negating the flexible nature of a community’s economic structure (Tanzi, 2013). The purpose of this paper is to outline a theory for understanding how robotic systems and the knowledge base of the humans utilizing them, will affect the nature of work in manufacturing environments and local communities.

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