2017

Kortelainen, Helena; Hanski, Jyri; Kunttu, Susanna; Kinnunen, Sini-Kaisu; Marttonen-Arola, Salla

Digitalization and the industrial internet are transforming business as technologies enable the gathering, processing and utilization of increased amounts of data. At the same time, technology companies have shifted their focus from product delivery to life-cycle services which are knowledge-intensive by nature. Companies are interested in developing global fleet-based industrial services, which could be seen as means to increase competitiveness. The targets of these fleet services, the customers' asset fleets, are typically complex and expensive assets, and they are characterized by high profitability, efficiency and safety demands. In order to provide value-adding fleet services, these knowledge-intensive services require data collection from globally distributed fleets. Technical and economic life-cycle data are often fragmented in business ecosystems and the full business potential of data is rarely utilized. Therefore, innovative technological and business model solutions are needed to upgrade the accumulated data into business knowledge that can be used to support decision-making and service delivery. This offers new opportunities not only for typical fleet services such as maintenance but also to support different types of fleet decision-making situations regarding different types of fleets. This publication presents and discusses the findings from the project "Fleet information network and decision-making", which is a part of the DIMECC Service Solutions for Fleet Management (S4Fleet) research program. The project addresses this issue by developing ways to upgrade the accumulated fleet data into valuable business knowledge that can be used in decision-making both on the level of individual companies and the whole ecosystem. The research is conducted in collaboration with companies involved in the research project, and the findings result from multifaceted cooperation with the case companies over a three-year period. The project results in a Data to Business Knowledge (D2BK) model, and documents pathways for industrial ecosystems to implement the model. The main ambition is to increase understanding on fleet data based services and develop ways to create value from fleet data at ecosystem level.