Sensing the Future: Designing Predictive Analytics with Sensor Technologies

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 environmentspecific sensor based predictive models that can out-perform the traditional state of the art predictive methods especially in volatile and uncertain environments.

[1]  Helmut Merkel,et al.  Ersatzteillogistik : Theoretische Grundlagen und praktische Handhabung , 1999 .

[2]  Theo Notteboom,et al.  Slow steaming in container liner shipping: is there any impact on fuel surcharge practices? , 2013 .

[3]  Zhongzhen Yang,et al.  Slow steaming of liner trade: its economic and environmental impacts , 2014 .

[4]  Ruud H. Teunter,et al.  Intermittent demand: Linking forecasting to inventory obsolescence , 2011, Eur. J. Oper. Res..

[5]  Rob A. Zuidwijk,et al.  On the use of installed base information for spare parts logistics: a revieuw of ideas and industry practice , 2010 .

[6]  Stefan Minner,et al.  Forecasting and Inventory Management for Spare Parts: An Installed Base Approach , 2011 .

[7]  Bernd Hellingrath,et al.  Conceptual approach for integrating condition monitoring information and spare parts forecasting methods , 2014 .

[8]  Abbas Tashakkori,et al.  Mixed Methodology: Combining Qualitative and Quantitative Approaches , 1998 .

[9]  Diane J. Cook,et al.  Smart environments - technology, protocols and applications , 2004 .

[10]  Galit Shmueli,et al.  Predictive Analytics in Information Systems Research , 2010, MIS Q..

[11]  J. Woo,et al.  The effects of slow steaming on the environmental performance in liner shipping , 2014 .

[12]  M. N. Jalil Customer Information Driven After Sales Service Management: Lessons from Spare Parts Logistics , 2006 .

[13]  J. D. Croston Forecasting and Stock Control for Intermittent Demands , 1972 .

[14]  Jing-Sheng Song,et al.  Evaluation of Base-Stock Policies in Multiechelon Inventory Systems with State-Dependent Demands . Part 11 : State-Dependent Depot Policies , 2006 .

[15]  Janni Nielsen,et al.  European Conference on Information Systems (ECIS) , 2008 .

[16]  Alan R. Hevner,et al.  Design Science in Information Systems Research , 2004, MIS Q..

[17]  Alexandre Dolgui,et al.  Demand forecasting for multiple slow-moving items with short requests history and unequal demand variance , 2008 .

[18]  Paul H. Zipkin,et al.  Evaluation of base‐stock policies in multiechelon inventory systems with state‐dependent demands part I: State‐independent policies , 1992 .

[19]  John Kervin Tashakkori, Abbas and Charles Teddlie, Mixed Methodology: Combining Qualitative and Quantitative ApproachesTashakkori, Abbas and Charles Teddlie, Mixed Methodology: Combining Qualitative and Quantitative Approaches , 2000 .

[20]  Pär J. Ågerfalk Embracing diversity through mixed methods research , 2013, Eur. J. Inf. Syst..

[21]  Behzad Ghodrati,et al.  Operating environment-based spare parts forecasting and logistics: a case study , 2005 .