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.
manufacturing, maintenance, costs, benefits, economics, machinery
Alsyouf, Imad. 2009. “Maintenance Practices in Swedish Industries: Survey Results.” International Journal of Production Economics. 121: 212-223.
Bevilacqua, M. and M. Braglia. 2000. “The Analytic Hierarchy Process Applied to Maintenance Strategy Selection.” Reliability Engineering and System Safety. 70, no 1: 71-83.
Census Bureau. 2020a. Economic Census. https://www.census.gov/programs-surveys/economic-census.html
Census Bureau. 2020b. Annual Survey of Manufactures. https://www.census.gov/programs-surveys/asm.html
Chowdhury, C. 1995. “NITIE and HINDALCO give a new dimension to TPM.” Udyog Pragati, Vol. 22 No. 1: 5-11.
Eti, M.C., S.O.T. Ogaji, and S.D. Probert. 2006 “Reducing the Cost of Preventive Maintenance (PM) through Adopting a Proactive Reliability-Focused Culture.” Applied Energy. 83: 1235-1248.
Federal Energy Management Program. 2010. Operations and Maintenance Best Practices: A Guide to Achieving Operational Efficiency. https://energy.gov/sites/prod/files/2013/10/f3/omguide_complete.pdf
Feldman, K., Sandborn, P. & Jazouli, T. 2008. “The analysis of return on investment for PHM applied to electronic systems.” International Conference on Prognostics and Health Management, Denver, pp. 1-9.
Harris, C. M. 1984. Issues in Sensitivity and Statistical Analysis of Large-Scale, Computer-Based Models, NBS GCR 84-466, Gaithersburg, MD: National Bureau of Standards.
Helu, M., & Hedberg, T. 2015. Enabling Smart Manufacturing Research and Development using a Product Lifecycle Test Bed. 43rd North American Manufacturing Research Conference, NAMRC 43, 1, 86-97. doi:10.1016/j.promfg.2015.09.066.
Helu, M., and Weiss, B. A. 2016 'The current state of sensing, health management, and control for small-to-medium-sized manufacturers' ASME 2016 Manufacturing Science and Engineering Conference, MSEC2016.
Herrmann, C., S. Kara, S. Thiede. 2011. “Dynamic Life Cycle Costing Based on Lifetime Prediction.” International Journal of Sustainable Engineering. 4, no 3: 224-235.
Hölzel, N. B., & Gollnick, V. 2015. “Cost-benefit analysis of prognostics and condition-based maintenance concepts for commercial aircraft considering prognostic errors.” Annual Conference of the Prognostics and Health Management Society, Coronado, pp. 1-16.
Hou-bo, H., Jian-min, Z. and Chang-an, X. 2011. Cost-benefit model for PHM. Procedia Environmental Sciences, vol. 10, part A, pp. 759-764.
Jin, X., Siegel, D., Weiss, B. A., Gamel, E., Wang, W., Lee, J., et al. 2016a. The present status and future growth of maintenance in US manufacturing: results from a pilot survey. Manuf Rev (Les Ulis), 3, 10. doi:10.1051/mfreview/2016005.
Jin, X., Weiss, B. A., Siegel, D., & Lee, J. 2016b. Present Status and Future Growth of Advanced Maintenance Technology and Strategy in US Manufacturing. Int J Progn Health Manag, 7(Spec Iss on Smart Manufacturing PHM), 012. https://www.ncbi.nlm.nih.gov/pubmed/28058173.
Kolberg, D., & Zuhlke, D. 2015. Lean Automation enabled by Industry 4.0 Technologies. Ifac Papersonline, 48(3), 1870-1875. doi:10.1016/j.ifacol.2015.06.359.
Komonen, Kari. 2002. “A Cost Model of Industrial Maintenance for Profitability Analysis and Benchmarking.” International Journal of Production Economics. 79: 15-31.
Kumar, A. 2018. Methods and materials for smart manufacturing: additive manufacturing, internet of things, flexible sensors and soft robotics. Manufacturing Letters, 15, 122-125.
Mobley, R. Keith. 2002. An Introduction to Predictive Maintenance. (Woburn, MA: Elsevier Science, 2002). 1.
McKay, M. C., Conover, W. H., and Beckman, R.J. 1979. “A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code,” Technometrics 21: 239-245.
Nakajima, S. 1988. Introduction to Total Productive Maintenance (TPM). (Portland, OR: Productivity Press).
National Institute of Standards and Technology. 2019. Monte Carlo Tool. https://www.nist.gov/services-resources/software/monte-carlo-tool
Pellegrino, J., Justiniano, M., Raghunathan, A., & Weiss, B. A. 2016. Measurement Science Roadmap for Prognostics and Health Management for Smart Manufacturing Systems. NIST Advanced Manufacturing Seriess (AMS). https://dx.doi.org/10.6028/NIST.AMS.100-2.
Pinjala, Srinivas Kumar, Liliane Pintelon, and Ann Vereecke. 2006. An Empirical Investigation on the Relationship between Business and Maintenance Strategies.” International Journal of Production Economics. 104: 214-229.
Tabikh, Mohamad. 2014. “Downtime Cost and Reduction Analysis: Survey Results.” Master Thesis. KPP321. Mӓlardalen University. http://www.diva portal.org/smash/get/diva2:757534/FULLTEXT01.pdf
Thomas, D. S., 2017 “Investment Analysis Methods: A practitioner’s guide to understanding the basic principles for investment decisions in manufacturing.” NIST AMS 200-5. https://doi.org/10.6028/NIST.AMS.200-5
Thomas, D. S., 2020. “Smart Investment Tool.” https://www.nist.gov/services-resources/software/smart-investment-tool
Thomas, D. S., & Weiss, B. A. 2018. “The Costs and Benefits of Advanced Maintenance in Manufacturing.” NIST AMS 100-18. https://doi.org/10.6028/NIST.AMS.100-18
Thomas, D., and Weiss, B.. 2020. “Economics of Manufacturing Machinery Maintenance: A Survey and Analysis of U.S. Costs and Benefits.” NIST Advanced Manufacturing Series 100-34. https://doi.org/10.6028/NIST.AMS.100-34