Strategic Roadmapping using Model Based Systems Engineering in Digital Transformation

Digital Transformation is the process of shifting an organization's culture and mindset to the use of digital technologies. This will fundamentally change the multidisciplinary way an organization operates and delivers value to its customers. It involves the integration of digital technologies and related business processes into all areas of the business, from the front-end customer experience to its back-end systems and provides dividends from the derivative digital currency (asset) it produces. As such, it is a complex and challenging process, requiring careful analysis, planning, integration and execution.

Model-Based Systems Engineering (MBSE) is a systematic approach to the design and development of complex systems, involving the use of models to represent the various aspects, elements, their relationships in, and impacts on a given system or systems of work. It allows for a comprehensive and systematic approach to forecasting and planning, addressing and integrating the concerns of various stakeholders, and the use of tools and techniques such as simulation and analysis. When shifting to a Model Based Enterprise during a Digital Transformation, MBSE provides valuable benefit when formulating strategic Roadmapping of Digital Transformation initiatives, as it allows for a more comprehensive and systematic approach to the planning, traceability and achievement of desired outcomes. When combined with model-based measures, business intelligence analysis and analytics, MBSE can provide even greater insight and knowledge realization in a Roadmapping practice. 

MBSE also provides visibility and traceability into a Digital Context, its Digital Threads and related Digital Twins. This context, when combined with a Roadmapping practice, then enables the opportunity for learning and knowledge insights that can be gleaned from that Digital Context. This might be considered a Digital Continuum, which is to say, a decision information base that considers historical events in relative context, evaluated over time. This information can serve as the basis for complex knowledge systems used to facilitate simulation, learning, analysis and optimization. When we learn throughout our educational and life journeys, we do much the same thing. We evaluate current decisions by considering historical events and circumstances from our life experience. This is in order that we may reflect on, learn from and improve our ability to make well informed current and future decisions based on those related historical events.

Another benefit of MBSE is that it allows for and considers the integration of various interdependent domains, such as engineering, business, technology and their respective concerns for a given system integration; be they enterprise or discrete. This approach and methodology ensure that all relevant perspectives and concerns are taken into account. In addition, the roadmap should reflect the needs and goals of all parties involved, from the enterprise executive to program participant and practitioner.

In the context of Digital Transformation, MBSE can be used to create a strategic, integrated roadmap that outlines the steps needed to achieve desired outcomes. This can include identifying the specific digital technologies that will be used, the maturity requirements of those technologies (and that of their human operators), mapping out the processes and systems that will need to be changed or created, and addressing the concerns and defining the roles, responsibilities and functional maturity requirements of relevant stakeholders.

One of the key benefits of using MBSE in Roadmapping vision, objectives and goals which motivate a Digital Transformation, is that it provides a more holistic and comprehensive approach to planning, analysis and achievement understanding. By using models to represent the various aspects of a system, we are able to gain a better understanding of how different technologies and processes will fit together, and how they will impact the organization as a whole (architecture). This aids in identifying potential issues and risks early on, allowing for proactive problem-solving and the creation of contingency plans. 

Make no mistake however, roadmapping and planning, while interdependent, are not the same. Roadmapping helps determine and address the long-range, multi-year, time-phased needs and impacts on an enterprise (the demand), while plans derive from the roadmap and respond to, define and satisfy shorter term objectives; ultimately satisfying roadmap goals at the right maturity level, at or before the required milestone. Also, Strategic Roadmapping is an ongoing practice, not a one-off effort. In the context of a Digital Transformation for example, the vision, goals and objectives for the enterprise (if that is the scale of transformation) are taken into account, along with other contributing or impacting resources like Governmental roadmaps, Vendor (3rd party) roadmaps, Regulatory roadmaps, etc., as well as taking into consideration ongoing socio-economic trends and occurrences (COVID for example). Some of these things are more enduring and have much longer lifecycles, like ISO Standards roadmaps for example, while others are far more volatile (like the economy). Each must be considered, evaluated and analyzed on an ongoing basis if the overarching roadmap is to provide any real intelligence or benefit. With this in mind, a Roadmapping practice should allow and empower an enterprise to become more agile and adaptable as it pursues its aims and position in its market segment.

Business Intelligence analysis involves the use of data and analytics to gain insights and inform decision-making. In the context of Digital Transformation, this can involve the analysis of data on customer behavior, market trends, and organizational performance, among other things. By incorporating business intelligence analysis into the Roadmapping process, it is possible to make more informed decisions about which technologies and processes to prioritize and how to best allocate resources at the right maturity and capability level to achieve optimal results.

Analytics, on the other hand, involves the use of advanced techniques such as artificial intelligence, machine learning and predictive modeling to analyze data and identify patterns and trends. This can provide even greater insights into the potential outcomes of different options and help identify the most promising options for an organization.

In summary, the use of MBSE in strategic Roadmapping for Digital Transformations can help organizations to plan and execute complex initiatives more efficiently and effectively. By using models to represent the various aspects of the system and integrating the perspectives of all concerned stakeholders, it is possible to create a comprehensive and well-informed roadmap that can guide the transformation process towards success after success. By combining MBSE with business intelligence analysis and analytics, organizations can create a more comprehensive and informed roadmap and knowledge system for and from their Digital Transformation initiatives. It can also support the identification of the most promising technologies and processes and inform the necessary allocation of resources. While unique to each context, it is reasonable to argue that the use of Roadmapping initiatives leveraging MBSE, business intelligence analysis, and model measures can significantly help organizations to plan and execute while reducing risk during their Digital Transformation initiatives, thereby with greater confidence, efficacy and efficiency.