Present Status and Future Growth of Advanced Maintenance Technology and Strategy in US Manufacturing

Published

September 30, 2016

Author(s)

Xiaoning Jin, Brian Weiss, David Siegel, Jay Lee, Jun Ni

Abstract

The goals of this paper are to 1) examine the current practices of diagnostics, prognostics, and maintenance employed by United States (U.S.) manufacturers to achieve productivity and quality targets and 2) to understand the present level of maintenance technologies and strategies that are being incorporated into these practices. A study is performed to contrast the impact of various industry-specific factors on the effectiveness and profitability of the implementation of prognostics and health management technologies, and maintenance strategies using both surveys and case studies on a sample of U.S. manufacturing firms ranging from small to mid-sized enterprises (SMEs) to large-sized manufacturing enterprises in various industries. The results obtained provide important insights on the different impacts of specific factors on the successful adoption of these technologies between SMEs and large manufacturing enterprises. The varying degrees of success with respect to current maintenance programs highlight the opportunity for larger manufacturers to improve their maintenance practices and consider the use of advanced prognostics and health management (PHM) technology. This paper also provides the existing gaps, barriers, future trends, and roadmaps for manufacturing PHM technology and maintenance strategy.

Citation

International Journal of Prognostics and Health Management (IJPHM) – Special Issue: PHM for Smart Manufacturing Systems

Volume

7

Issue

12

Pub Type

Journals

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Keywords

Maintenance strategy, Prognostics and health management, Preventive and predictive maintenance

Citation

Jin, X. , Weiss, B. , Siegel, D. , Lee, J. and Ni, J. (2016), Present Status and Future Growth of Advanced Maintenance Technology and Strategy in US Manufacturing, International Journal of Prognostics and Health Management (IJPHM) – Special Issue: PHM for Smart Manufacturing Systems, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=920633 (Accessed September 15, 2024)