Modelling the value of fleet data in the ecosystems of asset management

Abstract

The emergence of technologies, e.g. Internet of Things (IoT), cloud services and advanced analytics, have enabled diverse data utilization and service development based on the data. In asset management, assets, such as machinery and equipment, are equipped with sensors and abilities to be connected, which enables e.g. remote monitoring and control, and multiple other opportunities in asset management related decision-making. However, the full potential of increased data collection and availability of technologies has not been tapped, and research combining asset management, data, and value for business perspectives is scarce. To succeed in intense competition, companies need to collaborate with others in networks, and increasingly the term ecosystem is utilized to emphasize longterm collaboration and mutual aims. The objective of this thesis is to understand how data from a wide asset fleet can be turned into value in the ecosystems of asset management.

The research is conducted in close collaboration with industry as the research has a connection to the DIMECC S4Fleet research program (2015–2017). The research applies the design science approach, and the individual publications apply different research methods, including literature review, case study, framework building and modelling. The empirical data is qualitative data, including materials from seminars, meetings and other events related to the research program, but also interviews are conducted. Descriptive cases and illustrative numerical data are utilized for the testing of the developed models.

As results, a group of frameworks and observations are made and utilized as the basis for developing a model to evaluate the value of fleet data. The model is further developed into an extended model that enables the understanding and measuring of the value of fleet data in the ecosystems of asset management. It is essential to identify and define the fleet, the ecosystem around the fleet, what the decision-making situations are, and what the expected benefits and the costs of data utilization are, to create the basis for measuring the value of fleet data at ecosystem level. This thesis proposes a novel model that applies the cost-benefit approach and presents the logic of how to evaluate the value of fleet data. The proposed model can be used e.g. in developing ecosystem collaboration and data utilization, reasoning IoT investments and proving the value creation from data-based services. Development in data utilization at ecosystem level can result in increased competitive edge, improved data sharing, benefits and risks sharing, and deepened long-term collaboration e.g. in product and service development and sales. The results increase the scientific discussion on the topic, but further research is needed in measuring the value of data, defining the effects of data refining level on the value, and studying the opportunities of ecosystem level data management.