Digital Engineering is a relatively new term and almost exclusively used in the Department of Defense, creating a great deal of confusion about what it means. Program managers and other leaders within defense acquisition recognize the existence of a new Digital Engineering Strategy (Department of Defense, 2018) from the Pentagon, and want to comply, but may not be certain what can or ought to be digitalized. It is easy to get caught in a trap of simply considering it model-based systems engineering (MBSE) with some enhancements.
Digital Engineering discussions often include unfamiliar and somewhat fluid terms. These may include Digital Thread (Kraft, 2020), Digital Twin (Madni et al., 2019), Digital Surrogate (Chakraborty et al., 2020), Electronic Prototype (Rieken et al., 2020), Authoritative Source of Truth (Kraft, 2019), Government Reference Architecture (Department of Defense, 2010), Open Architecture (Keller, 2021), and Agile Software (SAFe 5, n.d.).
INCOSE (2020) defines MBSE as “…the formalized application of modeling to support system requirements, design, analysis, verification and validation activities beginning in the conceptual design phase and continuing throughout development and later life cycle phases.” It is doing classic systems engineering using the traditional systems engineering methods (analyzing requirements, performing a functional analysis, design synthesis, with a verification feedback). But instead of using documents MBSE is done with models.
The DE Strategy focuses on decision making: using models and data that endures with the program over years, crosses functional areas, communicating across stakeholders, and performing activities. Whereas MBSE is a digital substitute for a list of engineering specifications, or a replacement for an engineering blueprint, digital engineering can include the digitalization of the logistics supply chain, math model of the cost estimate, Monte Carlo simulation of risk causal factors, test and evaluation discrepancy reporting, or the business process for managing training plans.
Business process is the most important target of digitalization. Specifically, the value of digitalization is realized through the transformed underlying business processes (Antonucci et al., 2021). Further, lean production is most affected by process technology (Tortorella et al., 2021). Lastly, process is a critical component of Industry 4.0 implementation in supply chains (Ghadge et al., 2020). A typical program office may own more than 50 processes that create reports or plans which in turn impact their product. Any one of them is a candidate for digital engineering.
Three examples might be the Life-Cycle Sustainment Plan (LCSP), Systems Engineering Plan (SEP), and the Test and Evaluation Master Plan (TEMP). A typical program office may have an assistant program manager for each functional area whose job is to draft that plan, then execute the processes and use the resulting data to make decisions. That is a good test to determine if something is a candidate for digitalization: there is a functional manager, with a written plan that describes processes, a data repository and decisions to be made. But not everything is a good candidate, there are traps.
Linde et al., (2021) found several common traps that must be avoided. First, offices in a rush may not understand the customer value they are creating, and fail to satisfy customer needs. Second, not understanding the value delivery process and how the new digitalized process fits within the rest of the organizational context has risks. Last, offices may not understand the new operations model and means of realizing value, simply trusting that digitalization will have made things better. An office can understand the value the process creates (why we do it), the value delivery process (how we do it), and value realization (what we and they get out of it) and those typical traps will be escaped.
The commercial goal of digitalization is to arrive at a new set of processes that use a new set of data to achieve value. It is easy to see digitalization merely as a problem of new applications, or the introduction of Artificial Intelligence (AI) into processes, or new data models depending upon personal perspective or experience. However, none of those solutions alone will have sustained or meaningful impact. New models may be better, but may not result in better decisions if disconnected from a unified data model. An office may be able to house petabytes of data for decades, but if it is not designed for people to use with their digital supply chain its value is limited. Using AI as support infrastructure to communicate with customers is common, but without integration with the business process it may not deliver value.
The commercial world has paved a wide path of opportunity for digitalization. Program offices, matrix organizations and others within DoD need to leverage those lessons as they modernize their business operations as they modernize their weapon systems. Digital Engineering is a means to that end.
References
Antonucci, Y. L., Fortune, A., & Kirchmer, M. (2021). An examination of associations between business process management capabilities and the benefits of digitalization: all capabilities are not equal. Business Process Management Journal, 27(1), 124–144. https://doi-org.ezproxy.umgc.edu/10.1108/BPMJ-02-2020-0079
Department of Defense. (2018). Digital Engineering Strategy. https://ac.cto.mil/digital_engineering
Ghadge, A., Er Kara, M., Moradlou, H., & Goswami, M. (2020). The impact of Industry 4.0 implementation on supply chains. Journal of Manufacturing Technology Management, 31(4), 669–686. https://doi-org.ezproxy.umgc.edu/10.1108/JMTM-10-2019-0368
Keller, J. (2021). The latest trends in power electronics: It’s not just about stand-alone devices anymore, as power systems designers tackle power density, thermal management, open-architecture standards, systems integration, and obsolescence management. Military & Aerospace Electronics, 32(4), 23–29.
Kraft, E.M. (2015, January 5-9). HPCMP CREATE-AV and the Air Force Digital Thread. [Paper presentation]. AIAA SciTech Forum Kissimmee, FL.
Linde, L., Sjödin, D., Parida, V., & Gebauer, H. (2021). Evaluation of Digital Business Model Opportunities: A Framework for Avoiding Digitalization Traps. Research Technology Management, 64(1), 43–53. https://doi-org.ezproxy.umgc.edu/10.1080/08956308.2021.1842664
Madni, A. M., Madni, C. C., & Lucero, S. D. (2019). Leveraging digital twin technology in model-based systems engineering. Systems, 7(1), 7.
Rieken, F., Boehm, T., Heinzen, M., & Meboldt, M. (2020). Corporate makerspaces as innovation driver in companies: a literature review-based framework. Journal of Manufacturing Technology Management, 31(1), 91–123. https://doi-org.ezproxy.umgc.edu/10.1108/JMTM-03-2019-0098
Scaled Agile Framework 5.0 (n.d.). https://www.scaledagileframework.com
Tortorella, G., Sawhney, R., Jurburg, D., de Paula, I. C., Tlapa, D., & Thurer, M. (2021). Towards the proposition of a Lean Automation framework: Integrating Industry 4.0 into Lean Production. Journal of Manufacturing Technology Management, 32(3), 593–620. https://doi-org.ezproxy.umgc.edu/10.1108/JMTM-01-2019-0032