Factors Influencing the Quality of Manually Acquired Asset Data

Abstract

Sensors provide plenty of data about assets in use. However, efficient service operations require asset data that cannot be acquired through sensors. For example, maintenance actions must be manually reported. We have observed many challenges with the quality of this manually gathered data, such as missing or inaccurate data. We conducted two case studies to find out the factors influencing manual data gathering. We combined the results of these case studies with a literature review to create a framework of manual data gathering. The framework describes how the quality of manually collected asset data is affected by the organization and culture, the tools used, the tasks and competences, and, most importantly, the people and their motivation for collecting the data. This framework helps managers in organizing the data collection work by visualizing the aspects that need to be considered. Further work should test the framework in an industrial context.