The effectiveness of the maintenance scheduling decision depends on the availability of information regarding the remaining useful life of the asset in addition to the asset operating schedule and spares and resources required to carry out the maintenance action. In this paper, we illustrate an integrated asset maintenance approach using a lab-based demonstrator where a washing machine unit is deployed as the “asset”. The condition of the washing machine is assessed using data from a number of embedded wireless sensors to monitor critical parameters. The configuration of the washing machine is monitored using Radio Frequency Identification (RFID) tags. In addition to the condition monitoring platform, the demonstrator includes a supply chain platform with RFID infrastructure to provide real-time tracking information to predict the availability of spare parts within a given time horizon. Information from the condition monitoring platform and the supply chain tracking platform is then fed to a decision support system (DSS) which uses the available information to optimise the total cost of maintenance and decide the following parameters: (a) timing of maintenance, (b) choice of the appropriate spare part, (c) choice of the repair personnel.
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