Measuring service outcomes for adaptive preventive maintenance

Mikael Öhman*, Max Finne, Jan Holmström

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

12 Citations (Scopus)

Abstract

Services account for an increasing share of economic activity in the western world. As part of this, preventive maintenance (PM) service volumes are constantly growing as a result of a growing (and aging) asset population and maintenance outsourcing. While the pursuit of improved service productivity is in the interest of both firms and nations, the challenges of measuring service performance, and more specifically service outcomes, persist. This paper presents an outcome-based measure for fleet PM, which has far-reaching implications considering service productivity and performance measurement. We develop a statistical process control based measure that utilizes data typically available in PM. The measure is grounded in reliability theory, which enables generalization of the measure within PM services but also outlines the limitations of its application. Finally we apply the measure in a PM field service process of a servitized equipment manufacturer. Based on actual maintenance records we show that the service provider could reduce their service output by at least 5-10% without significantly affecting the aggregate service outcome. The developed measure and control process form the basis for adaptive preventive maintenance, which is expected to facilitate the transition towards outcome-based contracts through complementing condition-based maintenance. One of the key benefits of the approach is that it provides a cost-effective way of revealing the scarcely studied phenomenon of service overproduction. Based on our case, we conclude that there are significant productivity gains in making sure that you meet required standards for service output but do not exceed them.

Original languageEnglish
Pages (from-to)457-467
Number of pages11
JournalInternational Journal of Production Economics
Volume170
DOIs
Publication statusPublished - 1 Dec 2015
MoE publication typeA1 Journal article-refereed

Keywords

  • Design science
  • Outcome measurement
  • Preventive maintenance
  • Service performance
  • Statistical process control

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