Measuring service outcomes for adaptive preventive maintenance

Abstract

Measuring Service Outcomes for Adaptive Preventive Maintenance AbstractServices 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 2 productivity gains in making sure that you meet required standards for service output but do not exceed them.

Frontlog scheduling in aircraft line maintenance: From explorative solution design to theoretical insight into buffer management

“…Predetermined policies (Khazraei & Deuse, 2011) include age‐, time‐, and use‐based maintenance, in which maintenance timing is based on respective proxies of the equipment's condition. These maintenance policies result in periodic maintenance (Blakeley, Argüello, Cao, Hall, & Knolmajer, 2003; Grigoriev, van de Klundert, & Spieksma, 2006): The periods or intervals are fixed and predetermined by equipment manufacturers or regulations (Öhman, Finne, & Holmström, 2015). The most common predictive policy (Khazraei & Deuse, 2011) is condition‐based maintenance.…”

Overcoming the challenges that hinder new service development by manufacturers with diverse services strategies

“…SecurCo lacks capabilities to link complex services to their products. ChemCo has these capabilities but are wary of becoming too service focused (Kowalkowski et al, 2015), which they link to a productivity risk (Öhman, Finne & Holmström, 2015). They adopt a services-led strategy; but with a very strong focus on their own products, to avoid the 'tail wagging the dog'.…”