Product: Management and Development
Product: Management and Development
Research Article

Value of concept of operations analysis for digital transformation using digital twins

Joana Lacerda da Fonseca Pinto Cardoso, Eric Scott Rebentisch, Donna Hagstrom Rhodes, António Lucas Soares

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Structured sociotechnical approaches are increasingly important to digital transformation given digital technology not only changes the way companies operate, but also brings to light new business strategies. Indeed, companies have ramped up operational data collection opportunities and adopted digital practices to facilitate new flows of design information, enabling teams to interoperate in ways that might lead to better system performance. More structured approaches are needed to overcome challenges faced in digital transformation, as present efforts are often rather ad-hoc and poorly structured. Such approaches better enable tying transformation to the organization’s strategic objectives, and leverage operational strengths while mitigating limitations. As digital transformation occurs under certain sociotechnical contexts and with specific purposes, success can critically depend on the ability to unambiguously describe this context and the intended transformation as new operational scenarios. This paper discusses digital transformation using digital twins as a transdisciplinary challenge, presents a sociotechnical system analysis framework for digital twins, and offers insight on the value of applying an existing method called Concept of Operations Analysis, producing operational scenarios. Our ongoing work shows that the aforementioned method may accelerate the sociotechnical system redesign cycle and generate actionable decisions aligned with strategic goals and operational strengths and limitations.


digital transformation, digital twins, sociotechnical systems.


Abdallah, Y. O., Shehab, E., & Al-Ashaab, A. (2021). Understanding digital transformation in the manufacturing industry: a systematic literature review and future trends. Product: Management & Development, 19(1), 1-12.

Barricelli, B. R., Casiraghi, E., & Fogli, D. (2019). A survey on digital twin: definitions, characteristics, applications, and design implications. IEEE Access : Practical Innovations, Open Solutions, 7, 167653-167671.

Bickford, J., Van Bossuyt, D. L., Beery, P., & Pollman, A. (2020). Operationalizing digital twins through model-based systems engineering methods. Systems Engineering, 23(6), 724-750.

Bockshecker, A., Hackstein, S., & Baumöl, U. (2018). Systematization of the term digital transformation and its phenomena from a socio-technical perspective – A literature review. Research Papers, 43.

Brown, N., & Brown, I. (2019). From digital business strategy to digital transformation - how? A systematic literature review. In Proceedings of ACM SAICSIT Conference (pp. 1-8). New York: Association for Computing Machinery.

Glaessgen, E., & Stargel, D. (2012). The digital twin paradigm for future NASA and US Air Force vehicles. In Proceedings of the 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference (pp. 1-14). USA: AIAA/ASME/AHS.

Govindarajan, V., & Immelt, J. R. (2019). The only way manufacturers can survive. MIT Sloan Management Review, 60(3), 24-33.

Grieves, M., & Vickers, J. (2017). Digital twin: mitigating unpredictable, undesirable emergent behavior in complex systems. In F. J. Kahlen, S. Flumerfelt & A. Alves (Eds.), Transdisciplinary perspectives on complex systems: new findings and approaches (pp. 85-113). USA: Springer.

International Organization for Standardization – ISO. (2018). ISO/IEC/IEEE 29148:2018(E): Systems and software engineering - Life cycle processes - Requirements engineering. Geneva: ISO.

Jones, M. D., Hutcheson, S., & Camba, J. D. (2021). Past, present, and future barriers to digital transformation in manufacturing: a review. Journal of Manufacturing Systems, 60, 936-948.

Kritzinger, W., Karner, M., Traar, G., Henjes, J., & Sihn, W. (2018). Digital Twin in manufacturing: a categorical literature review and classification. IFAC-PapersOnLine, 51(11), 1016-1022.

Mathieu, J. E., Heffner, T. S., Goodwin, G. F., Salas, E., & Cannon-Bowers, J. A. (2000). The influence of shared mental models on team process and performance. The Journal of Applied Psychology, 85(2), 273-283.

Nightingale, D. J., & Rhodes, D. H. (2015). Architecting the future enterprise. Cambridge: MIT Press.

Rasheed, A., San, O., & Kvamsdal, T. (2020). Digital twin: values, challenges and enablers from a modeling perspective. IEEE Access: Practical Innovations, Open Solutions, 8, 21980-22012.

Rebentisch, E., Rhodes, D. H., Soares, A. L., Zimmerman, R., & Tavares, S. (2021). The digital twin as an enabler of digital transformation: a sociotechnical perspective. In Proceedings of the 2021 IEEE 19th International Conference on Industrial Informatics (INDIN) (pp. 1-6). USA: IEEE Industrial Electronics Society.

Rouse, W. B., & Morris, N. M. (1986). On looking into the black box: prospects and limits in the search for mental models. Psychological Bulletin, 100(3), 349-363.

Semeraro, C., Lezoche, M., Panetto, H., & Dassisti, M. (2021). Digital twin paradigm: a systematic literature review. Computers in Industry, 130, 103469.

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