Sensing the Future: Designing Sensor-based Predictive Information Systems for Forecasting Spare Part Demand for Diesel Engines - Szymon Furtak (Copenhagen Business School (CBS), 2018)
As digital technologies become prevalent and embedded in the environment, "smart" everyday objects like smart phones and smart homes have become part and parcel of the human enterprise. The ubiquity of smart objects, that produce ever-growing streams of data, presents both challenges and opportunities. In this dissertation, I argue that information systems extending these data streams, referred to as "predictive information systems with sensors", can generate added value and will be gaining momentum in academia and in the industry. Subsequently, seeing apparent complexity in designing IS artifacts with such functionality, I introduce a framework for Designing Information Systems with Predictive Analytics (DISPA), extending Design Science Research specifically towards rigorous design of predictive analytics. The framework is evaluated based on a case study of MAN Diesel and Turbo, a lead designer of marine diesel engines generating multiple applicable artifacts in the process. Additionally, the framework exemplification in the case context led to supplementing the framework with a set of Design Principles for Designing Predictive Information Systems as well as a matrix for pre-assessing financial feasibility of predictive information systems with sensor technologies. This work provides a contribution to information systems research, and in particular to design science research, by introducing a model for Designing Information Systems with Predictive Analytics (DISPA) that can serve as a method for developing IS artifacts. The framework constitutes an Information System Design Theory consistent with the established definitions from the literature (Gregor & Jones, 2007; Kuechler & Vaishnavi, 2012; Walls, Widmeyer, & El Sawy, 1992). In addition, the paper introduces and systematically evaluates a number of spare-part forecasting methods, which can be considered a contribution to operations research literature.
hdl:10398/9598
Keywords: 650