Abstract
As digital technologies become prevalent and embedded in the environment, "smart" everyday objects like smart phone 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 paper, we argue that extending these data streams, referred to as "predictive analytics", provides a solid basis for the design and development of IS artefacts that can generate additional value. Subsequently, we introduce a model for Designing Information Systems with Predictive Analytics (DISPA), extending Design Science Research specifically towards predictive analytics. The model is evaluated based on a case study of MAN Diesel and Turbo, a leading designer of marine diesel engines. The case illustrates that the framework provides useful guidelines for developing environment-specific sensor based predictive models that can out-perform the traditional state of the art predictive methods especially in volatile and uncertain environments.
Originalsprog | Engelsk |
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Titel | ECIS 2015 Proceedings |
Redaktører | Jörg Becker, Jan vom Brocke, Marco De Marco |
Antal sider | 14 |
Udgivelsessted | Atlanta, GA |
Forlag | Association for Information Systems. AIS Electronic Library (AISeL) |
Publikationsdato | 2015 |
Artikelnummer | 54 |
ISBN (Trykt) | 9783000502842 |
DOI | |
Status | Udgivet - 2015 |
Begivenhed | The 23rd European Conference on Information Systems (ECIS) 2015 - Westfälische Wilhelms-Universität Münster, Münster, Tyskland Varighed: 26 maj 2015 → 29 maj 2015 Konferencens nummer: 23 http://www.ecis2015.eu/ |
Konference
Konference | The 23rd European Conference on Information Systems (ECIS) 2015 |
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Nummer | 23 |
Lokation | Westfälische Wilhelms-Universität Münster |
Land/Område | Tyskland |
By | Münster |
Periode | 26/05/2015 → 29/05/2015 |
Internetadresse |
Navn | Proceedings of the European Conference on Information Systems |
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ISSN | 0000-0034 |
Emneord
- Predictive analytics
- Design science research
- Forecasting
- Sensors