Hostname: page-component-8448b6f56d-dnltx Total loading time: 0 Render date: 2024-04-18T09:50:32.727Z Has data issue: false hasContentIssue false

DEMYSTIFYING DIGITAL X

Published online by Cambridge University Press:  27 July 2021

Chris Cox*
Affiliation:
Design Manufacturing Futures Lab, School of Civil, Aerospace and Mechanical Engineering, University of Bristol;
James Gopsill
Affiliation:
Design Manufacturing Futures Lab, School of Civil, Aerospace and Mechanical Engineering, University of Bristol; Centre for Modelling and Simulation, Bristol, UK
Ben Hicks
Affiliation:
Design Manufacturing Futures Lab, School of Civil, Aerospace and Mechanical Engineering, University of Bristol;
*
Cox, Christopher Michael Jason, University of Bristol, Mechanical Engineering, United Kingdom, christopher.cox@bristol.ac.uk

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.

The rapid pace of development in Digital Engineering has led to an explosion of ideas and new practice in how it can support Engineering Design and Manufacture. You may have heard of the terms Digital Transformation, Digital Twin, Digital Thread, Digital Tapestry and Digital Footprint amongst many other forms of “Digital X” but how have these come about and how do they come together to provide the landscape of what Digitalisation has to offer?

In this paper, we analyse the emergence, definition, use and co-occurrence of “Digital X” terminology from an academic dataset of 19,627 papers curated from Scopus. The results reveal that these terms are being used without being fully contextualised in terms of a hierarchy or equivalent to effectively articulate the Digital landscape.

Through this analysis, an emerging “Digital X” framework is proposed, with evidence given to support suggested links, and knowledge gaps highlighted for further investigation. Once this framework is complete, a rich lexicon describing the Digital Landscape will pave the way for the future in Digital Engineering.

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), 2021. Published by Cambridge University Press

References

Aghaei Chadegani, A., Salehi, H., Yunus, M., Farhadi, H., Fooladi, M., Farhadi, M., and Ale Ebrahim, N., 2013. A comparison between two main academic literature collections: web of science and scopus databases. Asian social science, 9(5), pp.1826.Google Scholar
Bullen, G.N., 2014. Digital manufacturing: the digital tapestry. (Technical report). SAE Technical Paper.10.4271/2014-01-2267CrossRefGoogle Scholar
Callon, M., Courtial, J.-P., and Laville, F., 1991. Co-word analysis as a tool for describing the network of interactions between basic and technological research: the case of polymer chemsitry. Scientometrics, 22(1), pp.155205.10.1007/BF02019280CrossRefGoogle Scholar
Callon, M., Courtial, J.-P., Turner, W.A., and Bauin, S., 1983. From translations to problematic networks: an introduction to co-word analysis. Information (international social science council), 22(2), pp.191235.10.1177/053901883022002003CrossRefGoogle Scholar
Deloitte, 2016. 3d opportunity and the digital thread.Google Scholar
Deloitte, 2018a. Digital enablement, turning your transformation into a successful journey.Google Scholar
Deloitte, 2018b. Expecting digital twins.Google Scholar
Deloitte, 2019a. Aerospace & defense 4.0, capturing the value of industry 4.0 technologies.Google Scholar
Deloitte, 2019b. What is digital economy?Google Scholar
DePaolo, C.A. and Wilkinson, K., 2014. Get your head into the clouds: using word clouds for analyzing qualitative assessment data. Techtrends, 58(3), pp.3844.10.1007/s11528-014-0750-9CrossRefGoogle Scholar
Feeney, A.B., Frechette, S.P., and Srinivasan, V., 2015. A portrait of an iso step tolerancing standard as an enabler of smart manufacturing systems. Journal of computing and information science in engineering, 15(2).10.1115/1.4029050CrossRefGoogle Scholar
Gainsburg, J., Rodriguez-Lluesma, C., and Bailey, D.E., 2010. A “knowledge profile” of an engineering occupation: temporal patterns in the use of engineering knowledge. Engineering studies, 2(3), pp.197219.10.1080/19378629.2010.519773CrossRefGoogle Scholar
Gaska, M.T., Bobinis, J.S., and Galluzzo, V., 2015. Application of system design for operational effectiveness for architectural modeling of the sos relationship between primary and enabling systems. Procedia computer science, 61, pp.240245.10.1016/j.procs.2015.09.204CrossRefGoogle Scholar
Girardin, F., Calabrese, F., Dal Fiore, F., Ratti, C., and Blat, J., 2008. Digital footprinting: uncovering tourists with user-generated content. Ieee pervasive computing, 7(4), pp.3643.10.1109/MPRV.2008.71CrossRefGoogle Scholar
GmbH, P.H. and Co., K., 1996. Digital thread counter - camscan 5200. German; English. Melliand textilberichte, 77(9), 570571+E116Google Scholar
Gopsill, J., Humphrey, M., Thompson, D., and Garcia, E., 2020. Co-word graphs for design and manufacture knowledge mapping. Proceedings of the design society: design conference, 1, pp.12751284.10.1017/dsd.2020.94CrossRefGoogle Scholar
Hess, T., Matt, C., Benlian, A., and Wiesböck, F., 2016. Options for formulating a digital transformation strategy. Mis quarterly executive, 15(2).Google Scholar
Kim, D.B., Witherell, P., Lipman, R., and Feng, S.C., 2015. Streamlining the additive manufacturing digital spectrum: a systems approach. Additive manufacturing, 5, pp.2030.10.1016/j.addma.2014.10.004CrossRefGoogle Scholar
Kinberg, C. and Landeck, B., 1970. Integrated manufacturing systems. architectural considerations. Ibm journal of research and development, 14(6), pp.589604.10.1147/rd.146.0589CrossRefGoogle Scholar
Loper, E. and Bird, S., 2002. Nltk: the natural language toolkit. In proceedings of the acl workshop on effective tools and methodologies for teaching natural language processing and computational linguistics. philadelphia: association for computational linguistics.Google Scholar
Matt, C., Hess, T., and Benlian, A., 2015. Digital transformation strategies. Business & information systems engineering, 57(5), pp.339343.10.1007/s12599-015-0401-5CrossRefGoogle Scholar
Mckinsey, 2015. Industry 4.0, how to navigate digitization of the manufacturing sector.Google Scholar
Mckinsey, 2017. A roadmap for a digital transformation.Google Scholar
Mckinsey, 2021. Road work ahead: the emerging revolution in the road construction industry.Google Scholar
Mies, D., Marsden, W., and Warde, S., 2016. Overview of additive manufacturing informatics:“a digital thread”. Integrating materials and manufacturing innovation, 5(1), pp.114142.10.1186/s40192-016-0050-7CrossRefGoogle Scholar
O'Keeffe, G.S., Clarke-Pearson, K., Communications, C. on, and Media, 2011. The impact of social media on children, adolescents, and families. Pediatrics, 127(4), pp.800804.10.1542/peds.2011-0054CrossRefGoogle ScholarPubMed
Rosen, R., Von Wichert, G., Lo, G., and Bettenhausen, K.D., 2015. About the importance of autonomy and digital twins for the future of manufacturing. Ifac-papersonline, 48(3), pp.567572.10.1016/j.ifacol.2015.06.141CrossRefGoogle Scholar
Steuben, J.C., Iliopoulos, A.P., and Michopoulos, J.G., 2016. Implicit slicing for functionally tailored additive manufacturing. Computer-aided design, 77, pp.107119.10.1016/j.cad.2016.04.003CrossRefGoogle Scholar
Stuehler, J., 1970. Integrated manufacturing process control system. implementation in ibm manufacturing. Ibm journal of research and development, 14(6), pp.605613.10.1147/rd.146.0605CrossRefGoogle Scholar
Sturm, L.D., Williams, C.B., Camelio, J.A., White, J., and Parker, R., 2017. Cyber-physical vulnerabilities in additive manufacturing systems: a case study attack on the. stl file with human subjects. Journal of manufacturing systems, 44, pp.154164.10.1016/j.jmsy.2017.05.007CrossRefGoogle Scholar
Tao, F., Cheng, J., Qi, Q., Zhang, M., Zhang, H., and Sui, F., 2018. Digital twin-driven product design, manufacturing and service with big data. The international journal of advanced manufacturing technology, pp.35633576.10.1007/s00170-017-0233-1CrossRefGoogle Scholar
Yang, X., Dong, A., and Helander, M., 2012. The analysis of knowledge integration in collaborative engineering teams. Journal of engineering design, 23(2), pp.119133.10.1080/09544828.2011.567979CrossRefGoogle Scholar