0
Research Papers

A Framework for the Capture and Analysis of Product Usage Data for Continuous Product Improvement

[+] Author and Article Information
Henning Voet

Laboratory for Machine Tools and Production
Engineering (WZL),
RWTH Aachen University,
Campus-Boulevard 30,
Aachen D-52074, Germany
e-mail: Henning.Voet@wzl.rwth-aachen.de

Max Altenhof

Laboratory for Machine Tools
and Production Engineering (WZL),
RWTH Aachen University,
Campus-Boulevard 30,
Aachen D-52074, Germany
e-mail: Max.Altenhof@rwth-aachen.de

Max Ellerich

Laboratory for Machine Tools and Production
Engineering (WZL),
RWTH Aachen University,
Campus-Boulevard 30,
Aachen D-52074, Germany
e-mail: M.Ellerich@wzl.rwth-aachen.de

Robert H. Schmitt

Laboratory for Machine Tools and Production
Engineering (WZL),
RWTH Aachen University,
Campus-Boulevard 30,
Aachen D-52074, Germany
e-mail: R.Schmitt@wzl.rwth-aachen.de

Barbara Linke

Department of Mechanical and
Aerospace Engineering,
University of California Davis,
One Shields Avenue,
2052 Bainer Hall,
Davis, CA 95616-5294
e-mail: BSLinke@ucdavis.edu

Manuscript received April 29, 2018; final manuscript received November 5, 2018; published online December 24, 2018. Assoc. Editor: William Bernstein.

J. Manuf. Sci. Eng 141(2), 021010 (Dec 24, 2018) (11 pages) Paper No: MANU-18-1278; doi: 10.1115/1.4041948 History: Received April 29, 2018; Revised November 05, 2018

Product improvement, usually through changes in design and functionality, is relying more and more on the continuous analysis of large amounts of data. Product data can come from many sources with varying effort in obtaining the data, e.g., condition monitoring and maintenance data. Intelligent products, also known as “product embedded information devices” (PEID), are already equipped with sensors and onboard computing capabilities and therefore able to generate valuable data such as the number of user interactions during the use phase. The internet of things (IoT) makes data transfer possible at any time to close the loop for the product lifecycle data and methods like machine learning promote new uses of those data. This paper proposes a methodology to capture the most relevant data on product use and human–product interaction automatically and utilize it as part of data-driven product improvement. Product engineers and designers will gain insights into the use phase and can derive design changes and quality improvements. The methodology guides the user through research on product use dimensions based on the principles of user-centered design (UCD). The findings are applied to define what usage elements, such as specific actions and context, need to be available from the use phase. During systems development, machine learning is suggested to fuse sensor data to efficiently capture the usage elements. After product deployment, use data are retrieved and analyzed to identify the improvement potential. This research is a first step on the long way to self-optimizing products.

FIGURES IN THIS ARTICLE
<>
Copyright © 2019 by ASME
Your Session has timed out. Please sign back in to continue.

References

Roblek, V. , Meško, M. , and Krapež, A. , 2016, “ A Complex View of Industry 4.0,” SAGE Open, 6(2), p. 11.
Schlick, C. , Stich, V. , Schmitt, R. , and Schuh, G. , 2017, “ Cognition-Enhanced, Self-Optimizing Production Networks,” Integrative Production Technology: Theory and Applications, Brecher, C. , and Özdemir, D. , eds., Springer International Publishing, Cham, Switzerland.
Swan, M. , 2012, “ Sensor Mania! the Internet of Things, Wearable Computing, Objective Metrics, and the Quantified Self 2.0,” J. Sens. Actuator Networks, 1(3), pp. 217–253. [CrossRef]
Milwaukee Tool, 2018, “ ONE-KEY Tool Tracking, Customization and Security Technology,” Milwaukee Tool, Brookfield, WI, accessed Sept. 16, 2018, https://www.milwaukeetool.com/OneKey
Nike Inc, 2018, “ Nike HyperAdapt 1.0 Manifests the Unimaginable,” Nike, Beaverton, OR, accessed Sept. 16, 2018, https://news.nike.com/news/hyperadapt-adaptive-lacing
Independent, 2018, “ Hello Barbie Is Now Connected to Wifi - and Can Chat Back,” The Independent, Northcliffe House, London, accessed Sept. 16, 2018, https://www.independent.co.uk/life-style/gadgets-and-tech/ai-enabled-toys-hello-barbie-is-now-connected-to-wifi-and-can-chat-back-a6721666.html
Luchs, M. , and Swan, K. S. , 2011, “ Perspective: The Emergence of Product Design as a Field of Marketing Inquiry,” J. Prod. Innovation Manage., 28(3), pp. 327–345. [CrossRef]
Jiao, J. , 2006, “ Customer Requirement Management in Product Development: A Review of Research Issues,” Concurrent Eng., 14(3), pp. 173–185. [CrossRef]
Sonderegger, A. , 2013, “ Smart Garments—The Issue of Usability and Aesthetics,” ACM International Joint Conference on Pervasive and Ubiquitous Computing, Zurich, Switzerland, Sept. 8–12, pp. 385–392. http://www.ubicomp.org/ubicomp2013/adjunct/adjunct/p385.pdf
Schmitt, R. , ed., 2016, Smart Quality—QM im Zeitalter Von Industrie 4.0: 20. Business Forum Qualität; 12. und 13, September 2016, 1st ed., Apprimus Verlag, Aachen, Germany.
Jun, H.-B. , Kiritsis, D. , and Xirouchakis, P. , 2007, “ Research Issues on Closed-Loop PLM,” Comput. Ind., 58(8–9), pp. 855–868. [CrossRef]
Stark, J. , 2015, Product Lifecycle Management, Springer, Cham, Switzerland.
Kiritsis, D. , 2011, “ Closed-Loop PLM for Intelligent Products in the Era of the Internet of Things,” Comput.-Aided Des., 43(5), pp. 479–501. [CrossRef]
Igba, J. , Alemzadeh, K. , Gibbons, P. M. , and Henningsen, K. , 2015, “ A Framework for Optimising Product Performance Through Feedback and Reuse of In-Service Experience,” Rob. Comput. Integr. Manuf., 36, pp. 2–12. [CrossRef]
Kiritsis, D. , 2009, “ Product Lifecycle Management and Embedded Information Devices,” Springer Handbook of Automation, Springer, Berlin, pp. 749–765.
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, 15(12), pp. 32079–32122. [CrossRef] [PubMed]
Abramovici, M. , and Lindner, A. , 2013, “ Knowledge-Based Decision Support for the Improvement of Standard Products,” CIRP Ann. Manuf. Technol., 62(1), pp. 159–162. [CrossRef]
Shin, J.-H. , Kiritsis, D. , and Xirouchakis, P. , 2014, “ Design Modification Supporting Method Based on Product Usage Data in Closed-Loop PLM,” Int. J. Comput. Integr. Manuf., 28(6), pp. 551–568. [CrossRef]
Magniez, C. , Brombacher, A. C. , and Schouten, J. , 2009, “ The Use of Reliability-Oriented Field Feedback Information for Product Design Improvement: A Case Study,” Qual. Reliab. Eng. Int., 25(3), pp. 355–364. [CrossRef]
Gould, J. D. , Boies, S. J. , and Lewis, C. , 1991, “ Making Usable, Useful, Productivity-Enhancing Computer Applications,” Commun. ACM, 34(1), pp. 74–85. [CrossRef]
ISO, 1998, “ Ergonomic Requirements for Office Work With Visual Display Terminals (VDTs)—Part 11: Guidance on Usability,” International Organization for Standardization, Geneva, Switzerland, Standard No. ISO 9241-11.
Baxter, K. , Courage, C. , and Caine, K. , 2015, Understanding Your Users: A Practical Guide to User Research Methods, Morgan Kaufmann, San Francisco, CA.
Chisnell, J. R. A. D. , 2008, Handbook of Usability Testing, Wiley, Hoboken, NJ.
Simmons, E. , 2005, “ The Usage Model: A Structure for Richly Describing Product Usage During Design and Development,” 13th IEEE International Conference on Requirements Engineering (RE'05), Paris, France, Aug. 29–Sept. 2, pp. 403–407.
Wellsandt, S. , Hribernik, K. , and Thoben, K.-D. , 2015, “ Content Analysis of Product Usage Information From Embedded Sensors and Web 2.0 Sources: A First Analysis of Practical Examples,” IEEE International Conference on Engineering, Technology and Innovation/ International Technology Management Conference (ICE/ITMC), Belfast, UK, June 22–24.
Xiao, M. , Yin, G. , Wang, T. , Yang, C. , and Chen, M. , 2015, Requirement Acquisition From Social Q&A Sites, Requirements Engineering in the Big Data Era Requirements Engineering in the Big Data Era, Springer, Berlin, pp. 64–74.
Sun, D. , and Peng, R. , 2015, “ A Scenario Model Aggregation Approach for Mobile App Requirements Evolution Based on User Comments,” Requirements Engineering in the Big Data Era Requirements Engineering in the Big Data Era, Springer, Berlin, pp. 75–91.
Kang, Y. , and Zhou, L. , 2016, “ RubE: Rule-Based Methods for Extracting Product Features From Online Consumer Reviews,” Inf. Manage., 54(2), pp. 166–176. [CrossRef]
Thoben, K.-D. , and Lewandowski, M. , 2015, “ Information and Data Provision of Operational Data for the Improvement of Product Development,” Product Lifecycle Management in the Era of Internet of Things Product Lifecycle Management in the Era of Internet of Things, Springer International Publishing, Cham, Switzerland, pp. 3–12.
Fayyad, U. M. , Piatetsky-Shapiro, G. , and Smyth, P. , 1996, “ From Data Mining to Knowledge Discovery in Databases,” AI Mag., 17(3), pp. 37–54.
Gabriel, R. , Gluchowski, P. , and Pastwa, A. , 2009, Data Warehouse & Data Mining, 1st ed., W3 L-Verl, Herdecke, Germany.
Kantardzic, M. , 2011, Data Mining: Concepts, Models, Methods, and Algorithms, 2nd ed., Wiley, Hoboken, NJ.
Jørgensen, A. , Hauschild, M. , Dornfeld, D. , and Kara, S. , 2014, “ Sustainability,” CIRP Encyclopedia of Production Engineering CIRP Encyclopedia of Production Engineering, Springer, Berlin, pp. 1203–1204.
Clark, G. , Kosoris, J. , Hong, L. N. , and Crul, M. , 2009, “ Design for Sustainability: Current Trends in Sustainable Product Design and Development,” Sustainability, 1(3), pp. 409–424. [CrossRef]
Wang, J. , Chen, Y. , Haoc, S. , Peng, X. , and Hu, L. , 2018, “ Deep Learning for Sensor-Based Activity Recognition: A Survey,” Pattern Recognit. Lett., (in Press). https://www.sciencedirect.com/science/article/abs/pii/S016786551830045X
Ravi, D. , Wong, C. , Lo, B. , and Yang, G.-Z. , 2016, “ Deep Learning for Human Activity Recognition: A Resource Efficient Implementation on Low-Power Devices,” Body Sensor Networks Conference: UCSF Mission Bay Conference Center, San Francisco, CA, June 14–17, pp. 71–76.
Viola, N. , Stesina, F. , Fioriti, M. , and Corpino, S. , 2012, Functional Analysis in Systems Engineering: Methodology and Applications, IntechOpen, London.
Bales, G. L. , Das, J. , Tsugawa, J. , Linke, B. , and Kong, Z. , 2017, “ Digitalization of Human Operations in the Age of Cyber Manufacturing: Sensorimotor Analysis of Manual Grinding Performance,” ASME J. Manuf. Sci. Eng., 139(10), p. 101011. [CrossRef]
Lawhern, V. , Hairston, W. D. , and Robbins, K. , 2013, “ DETECT: A MATLAB Toolbox for Event Detection and Identification in Time Series, With Applications to Artifact Detection in EEG Signals,” PLoS One, 8(4), pp. 1–13. [CrossRef]
Al-Fuqaha, A. , Guizani, M. , Mohammadi, M. , Aledhari, M. , and Ayyash, M. , 2015, “ Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications,” IEEE Commun. Surv. Tutorials, 17(4), pp. 2347–2376. [CrossRef]
Saadeh, H. , Almobaideen, W. , and Sabri, K. E. , 2017, “ Internet of Things: A Review to Support IoT Architecture's Design,” Second International Conference on the Applications of Information Technology in Developing Renewable Energy Processes and Systems (IT-DREPS), Amman, Jordan, Dec. 6–7, pp. 1–7.
Weyrich, M. , and Ebert, C. , 2016, “ Reference Architectures for the Internet of Things,” IEEE Software, 33(1), pp. 112–116. [CrossRef]
Di Martino, B. , Rak, M. , Ficco, M. , Esposito, A. , Maisto, S. A. , and Nacchia, S. , 2018, “ Internet of Things Reference Architectures, Security and Interoperability: A Survey,” Internet Things, 1–2, pp. 99–112. [CrossRef]
Lu, Y. , and Da Xu, L. , 2018, “ Internet of Things (IoT) Cybersecurity Research: A Review of Current Research Topics,” IEEE Internet Things J., (epub). https://ieeexplore.ieee.org/document/8462745
Das, J. , Bales, G. L. , Kong, Z. , and Linke, B. , 2018, “ Integrating Operator Information for Manual Grinding and Characterization of Process Performance Based on Operator Profile,” ASME J. Manuf. Sci. Eng., 140(8), p. 081011. [CrossRef]

Figures

Grahic Jump Location
Fig. 2

Experimental setup

Grahic Jump Location
Fig. 3

Data collection overview

Grahic Jump Location
Fig. 4

Training data collection

Grahic Jump Location
Fig. 5

Applied forces from usage scenario: actual compared to predicted labels

Tables

Errata

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In