Research Papers

Where Do We Start? Guidance for Technology Implementation in Maintenance Management for Manufacturing

[+] Author and Article Information
Michael P. Brundage

National Institute of Standards and Technology,
Systems Integration Division,
Gaithersburg, MD 20814
e-mail: mpb1@nist.gov

Thurston Sexton

National Institute of Standards and Technology,
Systems Integration Division,
Gaithersburg, MD 20814
e-mail: thurston.sexton@nist.gov

Melinda Hodkiewicz

Department of Mechanical Engineering,
University of Western Australia,
Crawley, WA 6009, Australia
e-mail: melinda.hodkiewicz@uwa.edu.au

KC Morris

National Institute of Standards and Technology,
Systems Integration Division,
Gaithersburg, MD 20814
e-mail: kcm@nist.gov

Jorge Arinez

GM Research and Development Center, Manufacturing Systems Research Lab,
Warren, MI 48090
e-mail: jorge.arinez@gm.com

Farhad Ameri

Department of Engineering Technology,
Texas State University,
San Marcos, TX 78666
e-mail: ameri@txstate.edu

Jun Ni

Department of Mechanical Engineering,
University of Michigan,
Ann Arbor, MI 48109
e-mail: junni@umich.edu

Guoxian Xiao

GM Research and Development Center,
Manufacturing Systems Research Lab,
Warren, MI 48090
e-mail: guoxian.xiao@gm.com

1Corresponding author.

Manuscript received May 7, 2019; final manuscript received May 23, 2019; published online July 22, 2019. Assoc. Editor: Y. Lawrence Yao. The work was authored in part by a U.S. Government employee in the scope of his/her employment. ASME disclaims all interest in the U.S. Government's contribution.

J. Manuf. Sci. Eng 141(9), 091005 (Jul 22, 2019) (16 pages) Paper No: MANU-19-1268; doi: 10.1115/1.4044105 History: Received May 07, 2019; Accepted May 23, 2019

Recent efforts in smart manufacturing (SM) have proven quite effective at elucidating system behavior using sensing systems, communications, and computational platforms, along with statistical methods to collect and analyze the real-time performance data. However, how do you effectively select where and when to implement these technology solutions within manufacturing operations? Furthermore, how do you account for the human-driven activities in manufacturing when inserting new technologies? Due to a reliance on human problem-solving skills, today’s maintenance operations are largely manual processes without wide-spread automation. The current state-of-the-art maintenance management systems and out-of-the-box solutions do not directly provide necessary synergy between human and technology, and many paradigms ultimately keep the human and digital knowledge systems separate. Decision makers are using one or the other on a case-by-case basis, causing both human and machine to cannibalize each other’s function, leaving both disadvantaged despite ultimately having common goals. A new paradigm can be achieved through a hybridized system approach—where human intelligence is effectively augmented with sensing technology and decision support tools, including analytics, diagnostics, or prognostic tools. While these tools promise more efficient, cost-effective maintenance decisions and improved system productivity, their use is hindered when it is unclear what core organizational or cultural problems they are being implemented to solve. To explicitly frame our discussion about implementation of new technologies in maintenance management around these problems, we adopt well-established error mitigation frameworks from human factors experts—who have promoted human–system integration for decades—to maintenance in manufacturing. Our resulting tiered mitigation strategy guides where and how to insert SM technologies into a human-dominated maintenance management process.

Copyright © 2019 by ASME and GM
Topics: Maintenance , Errors
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Grahic Jump Location
Fig. 1

SRK framework (adapted from Reason [24])



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