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Cyber Technologies, Machine Learning, Additive Manufacturing, and Cloud in the Box to Enable Optimized Maintenance Processes in Extreme Conditions

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HCI for Cybersecurity, Privacy and Trust (HCII 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14045))

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Abstract

Routine maintenance processes (e.g., peacetime conditions) are not optimized for extreme maintenance conditions (e.g., aircraft or ship battle damage repair, extreme cold Alaska pipeline repair, and COVID-19 depot repair processes). In extreme contexts, modern information technology (e.g., machine learning [ML], additive manufacturing [AM], and Cloud in the Box [CIB]) is typically not being leveraged to optimize productivity and cycle time in these maintenance processes. Literature on process optimization does not address the use of modern technology for optimization in extreme maintenance conditions. This research aims to test the value added to information technology to optimize process productivity and cycle time for extreme maintenance conditions. It will extend the use of process optimization theory to include the effect of modern information technology as well as extreme maintenance contexts. This research is critical because failure to make correct repairs can affect the organization at its most vulnerable cyber infrastructure.

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Authors and Affiliations

  1. Information Sciences Department, Naval Postgraduate School (NPS), Monterey, CA, USA

    Kasey Miller & Johnathan Mun

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  1. Kasey Miller
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  2. Johnathan Mun
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Correspondence to Kasey Miller .

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  1. San Jose State University, San Jose, CA, USA

    Abbas Moallem

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The views expressed in this paper are those of the authors and do not reflect the official Navy policy or position of the NAVAIR, Department of Defense, or the U.S. Government. This paper is not a product of NAVAIR.

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Miller, K., Mun, J. (2023). Cyber Technologies, Machine Learning, Additive Manufacturing, and Cloud in the Box to Enable Optimized Maintenance Processes in Extreme Conditions. In: Moallem, A. (eds) HCI for Cybersecurity, Privacy and Trust. HCII 2023. Lecture Notes in Computer Science, vol 14045. Springer, Cham. https://doi.org/10.1007/978-3-031-35822-7_43

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  • DOI: https://doi.org/10.1007/978-3-031-35822-7_43

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  • Online ISBN: 978-3-031-35822-7

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