Ph. D. Project
Title:
Modularity and Reusability in Model-Based Systems Engineering supporting Digital Transformation of Engineering
Dates:
2025/05/15 - 2028/05/14
Student:
Supervisor(s):
Other supervisor(s):
Dr. Pascale Marangé (pascale.marange@univ-lorraine.fr)
Description:
In a subcontracting company in the aerospace industry, how can the deployment and adoption of model-based
systems engineering (MBSE) be facilitated by using modeling to formalize and capitalize on the company's
expertise while simplifying the daily work of engineers, modelers, and exchanges with other project
stakeholders? This topic unfolds across three axes: modeling, digital environment, and adoption.
Modeling
This involves applying principles of standardization and modularity to design modular model architectures,
promoting reuse and enabling faster product design by composing different models. It is worth noting that
models can be product models. The models considered are either descriptive, specifying architectures in terms
of structure and behavior, or analytical, supporting simulations to verify all or part of the systems.
Implementing these models to conduct impact analyses related to changes in customer requirements is also
important.
To achieve this, we can rely on desired properties of these models in the company's specific context: scalable
to the size of the problems, actionable to support decisions, aligned with the system's lifecycle, reusable, agile,
trustworthy, reconfigurable, and modular. These properties should be formally defined to facilitate their
verification.
For reusing models from design to product manufacturing, it is essential to define model and product design
patterns that integrate the company's expertise, linking product models to industrial system models.
To encourage reuse, the second area of research concerns the identification of models (model identity cards,
intent models) to be capitalized on or sub-models for archiving and retrieval. Several studies have addressed
this topic in different contexts and deserve further exploration, combining them with defining a model's
maturity for reuse and associated processes (verification/validation of descriptive or simulation models).
The relevance of using AI and Machine Learning tools for extracting modeling patterns and their capitalization
should be analyzed as part of potential solutions.
Digital Environment ⬓ Digital Continuity ⬓ Authoritative Source of Truth (ASOT)
The digital environment supporting the implementation of MBSE is traditionally limited to the choice of
modeling software but should extend to modeling methods and the digital environment for modeling and
sharing.
The method should be chosen based on the company's goals and modeling objectives. Therefore, analyzing
the literature to determine decision criteria for selecting a method aligned with modularity and reuse
requirements will be pertinent.
The digital environment must be adapted to the challenges the company will face. Building an Authoritative
Source of Truth (ASOT) is crucial for validating, qualifying, and sharing models and data among various
stakeholders. This source should reference reliable models for teams to use. It should enable industrial system
development, simulations and optimizations, and the production of reports with shared reference models and
data.
For the exchange of simulation models with external project partners, special attention must be given to
intellectual property and safeguarding the company's expertise within the models.
Associated with the ASOT, digital continuity raises issues of model and application integration and
interoperability, extensively documented in the literature but still a source of implementation difficulties.
Current standards (such as Open Services for Lifecycle Collaboration ⬓ OSLC or Open Model Based Engineering
Environment ⬓ openMBEE) should be considered, leveraging web technologies to connect different
engineering artifacts views throughout the lifecycle or to link various engineering tools.
Adoption
The final scientific point of interest concerns MBSE adoption within engineering teams. Recent literature
highlights the critical nature of the MBSE adoption process and the organizational and operational
transformations it induces.
It is relevant to use enterprise architecture frameworks such as TOGAF (The Open Group Architecture
Framework) or UAF (Unified Architecture Framework), as they provide proven methodologies for structuring
and managing information system transformations and organizational processes. By leveraging these
frameworks, potential barriers to adopting new solutions (obstacles) and factors facilitating adoption
(enablers) can be systematically identified. These frameworks also allow modeling the current state ("As-Is")
and the target state ("To-Be"), while defining a detailed roadmap to guide the transition from the current state
to the desired state, considering strategic priorities, available resources, and key milestones.
The adoption roadmap should be developed along organizational and technical dimensions, addressing
alignment challenges between these axes, including training sessions, workshops, and sandboxes.
Subsequently, it is essential to measure the level of MBSE adoption and analyze its impact on the company's
performance. This involves defining and implementing indicators for MBSE adoption maturity (e.g., the
number of trained personnel, personnel involved in projects) and defining and implementing indicators to
measure the impact of adoption maturity on internal and external company performance (design,
industrialization, delivery, etc.).
systems engineering (MBSE) be facilitated by using modeling to formalize and capitalize on the company's
expertise while simplifying the daily work of engineers, modelers, and exchanges with other project
stakeholders? This topic unfolds across three axes: modeling, digital environment, and adoption.
Modeling
This involves applying principles of standardization and modularity to design modular model architectures,
promoting reuse and enabling faster product design by composing different models. It is worth noting that
models can be product models. The models considered are either descriptive, specifying architectures in terms
of structure and behavior, or analytical, supporting simulations to verify all or part of the systems.
Implementing these models to conduct impact analyses related to changes in customer requirements is also
important.
To achieve this, we can rely on desired properties of these models in the company's specific context: scalable
to the size of the problems, actionable to support decisions, aligned with the system's lifecycle, reusable, agile,
trustworthy, reconfigurable, and modular. These properties should be formally defined to facilitate their
verification.
For reusing models from design to product manufacturing, it is essential to define model and product design
patterns that integrate the company's expertise, linking product models to industrial system models.
To encourage reuse, the second area of research concerns the identification of models (model identity cards,
intent models) to be capitalized on or sub-models for archiving and retrieval. Several studies have addressed
this topic in different contexts and deserve further exploration, combining them with defining a model's
maturity for reuse and associated processes (verification/validation of descriptive or simulation models).
The relevance of using AI and Machine Learning tools for extracting modeling patterns and their capitalization
should be analyzed as part of potential solutions.
Digital Environment ⬓ Digital Continuity ⬓ Authoritative Source of Truth (ASOT)
The digital environment supporting the implementation of MBSE is traditionally limited to the choice of
modeling software but should extend to modeling methods and the digital environment for modeling and
sharing.
The method should be chosen based on the company's goals and modeling objectives. Therefore, analyzing
the literature to determine decision criteria for selecting a method aligned with modularity and reuse
requirements will be pertinent.
The digital environment must be adapted to the challenges the company will face. Building an Authoritative
Source of Truth (ASOT) is crucial for validating, qualifying, and sharing models and data among various
stakeholders. This source should reference reliable models for teams to use. It should enable industrial system
development, simulations and optimizations, and the production of reports with shared reference models and
data.
For the exchange of simulation models with external project partners, special attention must be given to
intellectual property and safeguarding the company's expertise within the models.
Associated with the ASOT, digital continuity raises issues of model and application integration and
interoperability, extensively documented in the literature but still a source of implementation difficulties.
Current standards (such as Open Services for Lifecycle Collaboration ⬓ OSLC or Open Model Based Engineering
Environment ⬓ openMBEE) should be considered, leveraging web technologies to connect different
engineering artifacts views throughout the lifecycle or to link various engineering tools.
Adoption
The final scientific point of interest concerns MBSE adoption within engineering teams. Recent literature
highlights the critical nature of the MBSE adoption process and the organizational and operational
transformations it induces.
It is relevant to use enterprise architecture frameworks such as TOGAF (The Open Group Architecture
Framework) or UAF (Unified Architecture Framework), as they provide proven methodologies for structuring
and managing information system transformations and organizational processes. By leveraging these
frameworks, potential barriers to adopting new solutions (obstacles) and factors facilitating adoption
(enablers) can be systematically identified. These frameworks also allow modeling the current state ("As-Is")
and the target state ("To-Be"), while defining a detailed roadmap to guide the transition from the current state
to the desired state, considering strategic priorities, available resources, and key milestones.
The adoption roadmap should be developed along organizational and technical dimensions, addressing
alignment challenges between these axes, including training sessions, workshops, and sandboxes.
Subsequently, it is essential to measure the level of MBSE adoption and analyze its impact on the company's
performance. This involves defining and implementing indicators for MBSE adoption maturity (e.g., the
number of trained personnel, personnel involved in projects) and defining and implementing indicators to
measure the impact of adoption maturity on internal and external company performance (design,
industrialization, delivery, etc.).
Keywords:
MBSE, Digital Transformation, Model Reusability and Maturity, MBSE Adoption
Department(s):
| Modelling and Control of Industrial Systems |
Publications: