Maintenance Costs and Advanced Maintenance Techniques in Manufacturing Machinery: Survey and Analysis

Published

April 25, 2021

Author(s)

Douglas Thomas, Brian Weiss

Abstract

The costs/benefits associated with investing in advanced maintenance techniques is not well understood. Using data collected from manufacturers, we estimate the national losses due to inadequate maintenance and make comparisons between those that rely on reactive maintenance, preventive maintenance, and predictive maintenance. The total annual costs/losses associated with maintenance is estimated to be on average $222.0 billion, as estimated using Monte Carlo analysis. Respondents were categorized into three groups and compared. The first group is the top 50 % of respondents that rely on reactive maintenance, measured in expenditures. The remaining respondents were split in half based on their reliance on predictive maintenance. The top 50 % of respondents in using reactive maintenance, measured in expenditures, compared to the other respondents suggests that there are substantial benefits of moving away from reactive maintenance toward preventive and/or predictive maintenance. The bottom 50 %, which relies more heavily on predictive and preventive maintenance, had 52.7 % less unplanned downtime and 78.5 % less defects. The comparison between the smaller two groups, which rely more heavily on preventive and predictive maintenance, shows that there is 18.5 % less unplanned downtime and 87.3 % less defects for those that rely more on predictive than preventive.

Citation

International Journal of Prognostics and Health Management

Volume

12

Issue

1

Pub Type

Journals

Download Paper

https://doi.org/10.36001/ijphm.2021.v12i1.2883

Local Download

Keywords

maintenance, manufacturing, predictive maintenance, reactive maintenance, preventive maintenance

Citation

Thomas, D. and Weiss, B. (2021), Maintenance Costs and Advanced Maintenance Techniques in Manufacturing Machinery: Survey and Analysis, International Journal of Prognostics and Health Management, [online], https://doi.org/10.36001/ijphm.2021.v12i1.2883, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=931612 (Accessed April 24, 2024)