Asset data quality—A case study on mobile mining assets

Abstract

Good asset management decisions involve balancing cost, risk and performance requirements. Raw data on maintenance costs (a major contributor to total costs) and for estimating risks associated with asset failure is stored in an organisation’s Enterprise Resource Planning (ERP) system. However as this chapter demonstrates asset data is often erroneous, lacking requisite detail and therefore not fit for decision support. This chapter describes a project to clean data stored in computerised maintenance management systems (CMMS) that form part of ERPs. It looks in detail at the cleaning process, identifying key issues and developing of a set of recommendations for improvement. The major issues identified are to do with poor practice in assigning work to appropriate subsystems and maintainable items, ineffective use of standard text to describe work, inconsistent use of codes describing the type of work, and inability to identify suspensions and actual asset usage hours from the stored data. While focussing on asset data from mobile mining assets, the problems identified are similar in other sectors. Despite these issues, much of the required information is available once the data has been cleaned and forms a resource for the mining industry to assess how asset reliability and costs are changing with the introduction of new developments such as autonomous mobile equipment.