Hostname: page-component-586b7cd67f-dlnhk Total loading time: 0 Render date: 2024-11-27T15:54:35.943Z Has data issue: false hasContentIssue false

Towards an Approach Integrating Various Levels of Data Analytics to Exploit Product-Usage Information in Product Development

Published online by Cambridge University Press:  26 July 2019

Patrick Klein*
Affiliation:
University of Bremen;
Wilhelm Frederik van der Vegte
Affiliation:
Delft University of Technology;
Karl Hribernik
Affiliation:
BIBA - Bremer Institut für Produktion und Logistik GmbH
Thoben Klaus-Dieter
Affiliation:
University of Bremen;
*
Contact: Klein, Patrick, University Bremen, Faculty of Production Engineering, Germany, [email protected]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

By applying data analytics to product usage information (PUI) from combinations of different channels, companies can get a more complete picture of their products’ and services’ Mid-Of-Life. All data, which is gathered within the usage phase of a product and which relates to a more comprehensive understanding of the usability of the product itself, can become valuable input. Nevertheless, an efficient use of such knowledge requires to setup related analysis capabilities enabling users not only to visualize relevant data, but providing development related knowledge e.g. to predict product behaviours not yet reflected by initial requirements.

The paper elaborates on explorations to support product development with analytics to improve anticipation of future usage of products and related services. The discussed descriptive, predictive and prescriptive analytics in given research context share the idea and overarching process of getting knowledge out of PUI data. By implementation of corresponding features into an open software platform, the application of advanced analytics for white goods product development has been explored as a reference scenario for PUI exploitation.

Type
Article
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
© The Author(s) 2019

References

Armstrong, J.S. (2010), Long-Range Forecasting, 2nd Edition, Available at SSRN 666990. Wiley, New York.Google Scholar
Armstrong, W.W. (1985), “The dynamics of articulated rigid bodies for purposes of animation”, The Visual Computer, Vol. 1, pp. 231240.Google Scholar
Banerjee, A., Bandyopadhyay, T. and Acharya, P. (2013), “Data analytics: Hyped up aspirations or true potential”, Vikalpa, Vol. 38, pp. 111.Google Scholar
Battle, R. and Benson, E., (2008), “Bridging the semantic Web and Web 2.0 with Representational State Transfer (REST). Web Semantics: Science, Services and Agents on the World Wide Web”, Semantic Web and Web 2.0, Vol. 6, pp. 6169. https://doi.org/10.1016/j.websem.2007.11.002Google Scholar
Chaudhuri, S., Dayal, U. and Narasayya, V. (2011), “An overview of business intelligence technology”, Communications of the ACM, Vol. 54, pp. 8898.Google Scholar
Davenport, T.H. (2006), Competing on analytics. harvard business review, 84, Vol. 98.Google Scholar
Delen, D. and Demirkan, H. (2013), “Data, information and analytics as services”, Decision Support Systems, Vol. 55, pp. 359363.Google Scholar
Geppert, J. (2011), Modelling of Domestic Refrigerators’ Energy Consumption under Real Life Conditions in Europe, Friedrich Wilhelms University, Bonn.Google Scholar
Han, J., Pei, J. and Kamber, M. (2011), Data mining: concepts and techniques, Elsevier.Google Scholar
Harrington, L., Aye, L. and Fuller, R.J. (2018), “Opening the door on refrigerator energy consumption: quantifying the key drivers in the home”, Energy Efficiency, Vol. 11, pp. 15191539. https://doi.org/10.1007/s12053-018-9642-8Google Scholar
Holler, M., Uebernickel, F. and Brenner, W. (2016), “Understanding the Business Value of Intelligent Products for Product Development in Manufacturing Industries”, in: Proceedings ICIME. Presented at the Proceedings of the 2016 8th International Conference on Information Management and Engineering, ACM, pp. 1824.Google Scholar
Hribernik, K., Franke, M., Klein, P., Thoben, K.-D. and Coscia, E. (2017), “Towards a platform for integrating product usage information into innovative product-service design”, in: Proceedings of ICE/ITMC. Presented at the Engineering, Technology and Innovation (ICE/ITMC), 2017 International Conference on, IEEE, pp. 14071413.Google Scholar
Hyndman, R.J. and Athanasopoulos, G. (2014), Forecasting: principles and practice. OTexts.Google Scholar
Jackson, J. (2002), “Data Mining; A Conceptual Overview”, Communications of the Association for Information Systems, Vol. 8, p. 19.Google Scholar
Kandel, S., Heer, J., Plaisant, C., Kennedy, J., van Ham, F., Riche, N.H., Weaver, C., Lee, B., Brodbeck, D. and Buono, P. (2011), “Research directions in data wrangling: Visualizations and transformations for usable and credible data”, Information Visualization, Vol. 10, p. 271288.Google Scholar
Koch, R. (2015), “From business intelligence to predictive analytics”, Strategic Finance, Vol. 96, p. 56.Google Scholar
Kotu, V. and Deshpande, B. (2015), “Predictive analytics and data mining: concepts and practice with rapidminer”, Morgan Kaufmann.Google Scholar
Lehmhus, D., Wuest, T., Wellsandt, S., Bosse, S., Kaihara, T., Thoben, K.-D. and Busse, M. (2015), “Cloud-Based Automated Design and Additive Manufacturing: A Usage Data-Enabled Paradigm Shift”, Sensors (Basel), Vol. 15, pp. 3207932122. https://doi.org/10.3390/s151229905Google Scholar
Lützenberger, J., Klein, P., Hribernik, K. and Thoben, K.-D. (2016), “Improving Product-Service Systems by Exploiting Information From The Usage Phase. A Case Study. Procedia CIRP”, Product-Service Systems across Life Cycle, Vol. 47, pp. 376381. https://doi.org/10.1016/j.procir.2016.03.064Google Scholar
Park, J., Han, S.H., Kim, H.K., Cho, Y. and Park, W. (2013), “Developing elements of user experience for mobile phones and services: survey, interview, and observation approaches”, Human Factors and Ergonomics in Manufacturing and Service Industries, Vol. 23, pp. 279293.Google Scholar
Porter, M.E. and Heppelmann, J.E. (2015), “How smart, connected products are transforming companies”, Harvard Business Review, Vol. 93, pp. 96114.Google Scholar
Puget, J.F. (2015), “Analytics Landscape”, IT Best Kept Secret Is Optimization.Google Scholar
Seliger, G., Gegusch, R., Müller, P. and Blessing, L. (2008), “Knowledge generation as a means to improve development processes of industrial product-service systems”, In Manufacturing Systems and Technologies for the New Frontier, Springer, pp. 519524.Google Scholar
Van der Vegte, W.F., Kurt, F. and Sengöz, O.K. (2019), “Simulations based on real product-usage information to support redesign for improved performance: exploration of practical application to domestic refrigerators”, Journal of Computing and Information Science in Engineering, Vol. 19 No. 3, http://doi.org/10.1115/1.4042537.Google Scholar
Watson, H.J. (2014), “Tutorial: Big data analytics: Concepts, technologies, and applications”, Communications of the Association for Information Systems, Vol. 34, pp. 12471268.Google Scholar
Yang, X., Moore, P.R., Wong, C.-B., Pu, J.-S. and Kwong Chong, S. (2007), “Product lifecycle information acquisition and management for consumer products”, Industrial Management and Data Systems, Vol. 107, pp. 936953.Google Scholar