Building a foundation for the future of automation in U.S. manufacturing.
Date
2024-05
Authors
Benjamin, Remon
Major Professor
Advisor
Ryan, Saxon J.
Freeman, Steven A.
Shelley, Mack
Withers, James
Mosher, Gretchen
Committee Member
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Altmetrics
Abstract
With the demand for and research on the topic of automation continuously increasing, there still exists a lack of understanding within manufacturing industries around factors that contribute to successful or failed automation adoption. This lack of understanding highlights the necessity for research to explore multiple perspectives on the outcomes of automation adoption. Understanding the nuances of automation adoption is critical for optimizing its implementation and addressing challenges. By examining various facets of automation adoption, research can provide insights that inform strategic decision-making processes within organizations. Thus, this dissertation delves into management perspectives, worker viewpoints, and operator errors related to automation to construct a better understanding of automation.
The first objective in this study focused on the perspective of automation adoption success and failure through the lens of management within manufacturing industries.16 experienced managers from Midwest manufacturing sectors were surveyed using the online Delphi method, a structured communication technique. Through iterative rounds of surveys, key factors contributing to successful adoption were elucidated. These factors included clearly defined objectives, safety considerations, and strategic planning. Conversely, factors associated with failed adoption included technological misunderstanding, insufficient financial planning, and organizational capability mismatches. Understanding these management perspectives is crucial for devising effective strategies for successful automation integration.
With known difficulties gathering data from management perspectives, applicability of Delphi method to gather worker perspectives was questioned and spurred the formulation of the second objective: assessing the applicability of the same Delphi data collection method to gather workers' viewpoints on factors influencing successful or failed automation adoption. Hence, the second objective of this study aimed to evaluate the viability of employing the online Delphi method to gather insights from workers in the Midwest manufacturing sector regarding factors that affect successful or failed automation adoption. The involvement of workers in online Delphi surveys uncovered hurdles in participant engagement and retention. Despite these obstacles, the importance of exploring factors affecting automation adoption from workers’ point of view was further emphasized. Larger participant pools and alternative methodologies are recommended to effectively capture diverse workers’ perspectives on factors affecting successful or failed automation adoption.
In a parallel investigation, the third objective focused on investigation methods for operator errors using automation. It drew upon literature suggesting that education, training, and accountability measures are effective in mitigating bias and proposed methodologies for assessment. The study recommended the use of pre- and post-surveys, tests and focus groups to evaluate students' perceptions and behaviors regarding automated technologies. While education and training were proposed as tools to reduce these operator errors, the paper acknowledged the potential limitations of these interventions. It emphasized the importance of alternative measurement methods and potential policy interventions, such as accountability measures, to effectively address bias. The study's proposed methodology aimed to provide insights into the effectiveness of educational interventions in reducing operator errors related to automation. Limitations and areas for future research, including longitudinal studies and real-world impact assessment, were also discussed.
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Type
dissertation