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In this blog, we recap a webinar discussing Best Practices for Data Migration Towards MBSE (model-based system engineering.)

Data is one of the most valuable assets for organizations, but are we giving it enough attention? The prospect of moving to Model-Based Systems Engineering (MBSE) is tantalizing but often seems difficult when considering the move from existing engineering assets.

In this webinar, Richard Watson, VP Practice Director at Jama Software, will discuss this topic and give best practices on how to formalize the process of MBSE migration to reduce risk of data loss. He will also walk through the risks of legacy requirements management tools and provide solutions for migration.

In this session we will cover:

What MBSE is and how it differs from SysML
Benefits of building a common data model for engineering
How to approach moving data towards MBSE
Best practices for data migration when moving away from legacy solutions

Below is an abbreviated transcript and a recording of our webinar.

Best Practices for Data Migration Towards MBSE

Richard Watson: Hi, everybody. Today, we’re going to look at what MBSE is, and also, how you can get data into MBSE to provide benefits to your organization to get more consistency. So we’re going to jump straight into looking a little bit about what exactly is MBSE. So MBSE, model-based systems engineering, is something that is a concept come up from the OMG. So I thought it would be a good idea to give you their definition. I’m going to read it out. It’s the formalized application of modeling to support system requirements, design, analysis, verification, and validation activities beginning in the conceptual design for… Oh, wait a minute. So, looking at this words, it’s really, really complicated to read, and really, by the end of it, you don’t fully understand well what actually is model-based systems engineering.

If you read between the lines, model-based systems engineering is the elimination of documents to combine all of your different engineering datas. So that’s requirements, architecture, behavior, verification, and validation into single data-driven model environments, all tied together with relationships. So, many people think MBSE is modeling, and modeling does form part of it but it’s not only visual modeling. It’s actually data modeling.

If we take another angle of what is MBSE, again, lots of words, this is, for this time, from Lou Wheatcraft. Lou is co-chair for the INCOSE’s Requirements Working Group, which is the largest working group in INCOSE. He explains that MBSE can’t be effectively practiced when viewed from just one perspective. Now, that means, if you do just look at MBSE from a modeling perspective, you don’t see the whole of MBSE. If you just look at MBSE from a requirements perspective, you don’t see the whole of MBSE. So, it’s lots of different types of data interconnected together with relationships, viewed from different angles or dimensions.

Drawing an Information Model

Richard Watson: I’ve tried to draw this in a picture. So here you can see the different site types of data that you might have, but we all know that you can’t look at architecture without seeing how it ties to behavior. You can’t do verification without seeing how it ties to requirements. All of these different disciplines tie themselves together, and they tie themselves together with relationships. Okay, this test case must verify that requirement or this architecture realizes that requirement, this design iterates that architecture. Once you understand how your data relates together, you can draw an information model. And that information model, the bit in the middle, is an MBSE systems model. This is MBSE, not the model itself, but the fact that it’s data and relationships with between data.

This is tool agnostic, you’ve not looked at any systems that will manipulate this yet. Once you have an understanding of all of the different engineering data in your organization, then you can break it down and say, “Well, what would be the best tool to look at and manipulate the data in your MBSE framework?” So you might have requirements, tools, modeling tools, change management systems, development tools, et cetera. All of those different applications will have some visibility into your MBSE data model. And collectively, this is MBSE. So MBSE is a collection of all of these different systems interacting with all of your engineering data and making sure that when you’re sitting in any of these systems, be it requirements, design, test, deployment, simulation, you have access, not just to the data that that system itself manages, but also you have a view of the relationships to the data that sits inside of other systems. So from the design, you can see relationships to the requirements. From simulation, you can see relationships back to the design, back to the requirements. So, MBSE is the data model-based systems engineering.

So if you’ve followed so far, it’s quite a strong structure, and once you have an understanding of it and once you can move your organization towards it, it brings a lot of benefits. Jama Software have actually produced an MBSE framework, it’s almost like a kick starting your process. So you can use Jama Software to have a beginning of an understanding of what your MBSE data model should look like. And then you can look to figuring out which aspects of it are tied to different tools to manipulate your engineering information. The benefit of that framework is that, it saves you time and it accelerates the speed that you can get to actually doing some engineering and the speed that you can produce actual systems at the end. It makes requirements and all of the data, all of the relationships between the data accessible across your organization and anybody that you work with. So these frameworks are a really good way of accelerating that process.

How do we get your legacy data into an MBSE framework?

Richard Watson: But there’s a big question. So if you start MBSE framework, is that assuming that you’re going to start with a blank page, that you’re going to start without any information or any data? And of course, it’s very rare these days for an organization or a project to sit in a room and say, “Right, we’ve got absolutely nothing. What are we going to create today?” More commonly, we’re finding that projects build themselves from other projects and existing datas.

So now, not only are you understanding what is an MBSE framework and how does your engineering data relate to each other, but also you’ve got existing data. How do you get that existing data into your MBSE framework, such that then you’ve got a consistent set of data that you can deploy to your teams? Consistent data is very significant because, firstly, it makes it far cheaper for your engineers. Engineers can move from project to project and understand what the data should look like. It also makes it far quicker to integrate other systems too. So you do want this consistent data approach, this model-based systems engineering approach, but you don’t want to forget your legacy, your original data.

So let’s see how we make that switch. How do we get your legacy data into an MBSE framework? If you consider the value of your data, it’s far higher, typically, than money that you’ve invested in anything else. The money that you spend in the tools to manipulate your data around MBSE, actually they’re not expensive compared to the value of the actual data that you’re manipulating. One reason migration typically either fails or it fails to leave users satisfied is that, we are not giving the data the respect it demands and deserves because of its value. Organizations have approached migration literally just with a give-it-a-go attitude. What I mean by that is, they simply press the button and say, “Okay, migrate.” They migrate until something goes wrong, and then they fix the thing that goes wrong.

Jama Connect® for MBSE

Richard Watson: And then they press the button again, and say, “Well, continue to migrate,” and they continue to do that over and over and over again until the migration process is finished. The difficulty with that is, it’s very, very difficult to predict how long will that migration process take. And even once you’ve done migration using this mechanism, when you go to your users, your users will be very distrusting because they will have witnessed the process that you just went through to do data migration, and they’ll have very little reassurance that the data that’s in the new system has any or all of the resemblance of the data that it had in your original place. So for example, if you’re migrating data from DOORS into Jama Connect, you need to know and recognize the data in Jama Connect was the data you originally worked on in DOORS.

Now, the problem is not necessarily about a single tool vendor. The process side of migration can be wholly generic. Okay? And that’s the key, having a process around migration gives you a way of making it predictable. And I’m just about to share with you a process for migration that is tool agnostic. It doesn’t matter where your data comes from or where your data goes to. It gives you a holistic process that you can use to migrate your data. And each step of the process, you just decide where is the data coming from and how do I take it and transform it into where it’s going to. Jama Software do provide migration tools towards an MBSE framework. And the examples through this presentation will be showing you how to migrate data from DOORS, IBM DOORS, into Jama Software’s Jama Connect product.

Watch the full webinar to learn more about Best Practices for Data Migration Towards MBSE

In this blog, we recap a webinar discussing the real intent of MBSE (model-based system engineering.)

Today’s products are becoming increasingly complex and software intensive. This presents major challenges for organizations being able to effectively manage all the data, information, and artifacts across the lifecycle for these types of systems — and one of the key reasons that Model-Based Systems Engineering (MBSE) is fast becoming the standard approach to systems engineering for digital transformation today.

Since one size doesn’t fit all, an organization needs to assess the SE capabilities that best fit its domain, product line (degree of complexity), and culture. The level of SE capability an organization establishes needs to be tailored to the size and complexity of systems developed by the organization, whether small, medium, or large projects.

In this webinar, we will cover the following topics:

Challenges associated using 20th century document-based approach to Systems Engineering
What is MBSE?
What is the real intent of MBSE?
Is MBSE the same thing as SysML?
Benefits of implementing Systems Engineering from a data-centric perspective
How Jama Connect helps SE practitioners address these challenges

This webinar will be especially beneficial for those in the aerospace, automotive, defense, and medical industries.

Below is an abbreviated transcript and a recording of our webinar.

The Real Intent of MBSE

Joseph Pitarresi: Well, hello everyone. This is Joseph Pitarresi and I’m the senior product manager for Jama Software labs. I’m very excited about today’s topic, The Real Intent of Model Based Systems Engineering and Keeping up With Complexity. And I’m pleased to introduce our two industry experts for this webinar. First is Lou Wheatcraft, he’s the managing of Wheatland Consulting. Lou has over 50 years experience in MBSE thought leadership and his specialties are in needs and requirements, definition and management and verification and validation. Lou currently co-chairs the INCOSE Requirements Working Group, and he has consulted with global leaders in the sectors of aerospace and defense, medical devices, consumer goods, transportation, and energy.

Very pleased to have Lou with us today. Our second area expert is Jama Software’s own Cary Bryczek. Cary serves as a principal systems engineer and aerospace defense for Jama Software. She’s been with Jama Software for over eight years and has over 15 years in systems engineering leadership roles. Now, with those introductions, let’s get started. Over to you Lou.

Lou Wheatcraft: Well, thank you for having me. It’s an honor to participate in this webinar. The subject is The Real Intent of Model-Based Systems Engineering- Keeping Up With Complexity. This presentation is a follow on to the Whitepaper published by Jama in early December. Today’s increasingly complex centric systems are becoming more than norm how we practice system engineering needs to evolve to help us better develop and manage these more software centric or software intensive systems. Yesterday’s electro-mechanical systems had fewer interactions both internally and externally. Interfaces could be shown on drawings. It isn’t too long ago for a lot of our electro-mechanical systems didn’t contain a single computer chip and now today’s systems like automobiles can have over a hundred computer chips and associated embedded software and all the complexities of interaction between the software in those computer chips and with the hardware mechanical systems.

For software centric systems, internal and external interactions have increased orders of magnitude as have threats and the vulnerabilities. Critical functions in today’s software centric systems are carried out mainly by the software and really the electro-mechanical parts of the system are enablers for the softwares that carry out their key functionality. In INCOSE vision 2025, they recognize this complexity, this set of constant themes throughout the evolution of systems engineering is the ever-increasingly complexity of systems, which can be observed in terms of the number of system functions, components and interfaces and their non-linear interactions, and emerging properties. Each of these indicators, the complexities increased dramatically over the last 50 years and will continue to increase due to the capabilities that stakeholders are demanding and advancement in the technologies that enable these capabilities.

Keeping Up with Complexity

Lou Wheatcraft: This complexity presents organizations with challenges that need to be addressed. Failures can result in tremendous loss. These challenges result in increases in complexity in our systems, increases the role software has in the software architecture. So software centric, software intensive systems are the norm. Dependencies and number of interactions has dramatically increased between parts of the system. Interactions also between the system and the macro system is a part. The number of threats across the interface boundaries and vulnerabilities to those threats, dependencies between project management and the systems engineering, dependencies between system engineering and life cycle process activities and artifacts. The increases in oversight, competition, the pressure and need to reduce development time and time to market. The increases in risk, not just program and project risk, but also development risk, manufacturing, risk, system integration, system verification, system validation, and risk during operations.

And the number of projects that are over budget and experiencing schedule slippage. To address these challenges we must change how we practice system engineering. To address these challenges, it’s important projects incorporate systems thinking into all phases of product development. Projects must manage the integrated system and associate SE artifacts from beginning with the focus on the interdependencies, the parts that make up the system, interactions, both internal and external, integrated system behavior and emerging properties. And this is important because the behavior of the system is a function of the interaction of the parts, both the parts internal, but also interactions of the system within the macro system as part. When we were developing integrated system, they have emerging properties that were not indicated within our needs and requirements and relationships between the engineer and artifacts across the system life cycle. During development of our system, the different life cycle phases involved in developing products each has its own set of artifacts and these artifacts are related to each other.

We need to think about the relationships of the different life cycle processes and the resulting artifacts. And shouldn’t be developed and managed in a silo. Understanding the role of textual needs and requirements as well as diagram/models, which form is the best to communicate specific thing. It’s like two sides of the same coin. One is not sufficient. We need both the textual needs and requirements as well as we need diagrams and models as key analysis tools from which the needs and requirements are derived. We need to establish traceability between all data information across the system life cycle. So the focus of the rest of this presentation is addressing the real intent of model based systems engineering or MBSE for short. From the INCOSE System Engineering handbook, they talk about MBSE versus a Document-Centric Approach System Engineering. And they say, “In a document based system engineering approach, there’s often considerable information generated about the system that’s contained in documents and other artifacts such as specifications, interface control documents, system description documents, trade studies, analysis reports, and verification plans, procedures, and reports.

An MBSE Approach

Lou Wheatcraft: The information that obtained within these documents is often difficult to maintain and synchronize and difficult to assess in terms of quality, correctness, completeness, and consistency.” The document centric approach to system engineering, there are several key issues. The sheer volume of documentation has become overwhelming. We have a large number of documents for a project, each containing key data and information. All this has to be documented, reviewed, baseline configuration managed. Often these documents are done thoroughly at different times, and because of this, it’s nearly impossible to keep data and information in sync, current, correct, consistent resulting in no real single source approved. The vast number of documents results in cost and time overhead that consume a significant part of development cost eating into profit margins.

For projects that are still using the document centric approach system engineering, the development times and time to market are longer for many products, making the company less competitive. To avoid these issues, organizations must move from a document centric to a data centric perspective of systems engineering. The model based system engineering approach addresses many of the problems from the document centric approach to SE. From the INCOSE System Engineering handbook and the MBSE approach, much of this information is captured electronically in the system model or set of models. The system model is a primary artifact of the system engineering process. MBSE formalizes the application, the system engineering through the use of models. The degree to which this information is captured in models and maintained throughout the life cycle, depends on the scope of MBSE effort

Watch the full webinar to learn more about The Real Intent of MBSE

This is Part 4 of a blog series covering a whitepaper titled, The Comprehensive Guide to Successfully Adopting Model-Based Systems Engineering MBSE. Visit Part I, Part II, and Part III.

04: Determine the Future State the Organization Needs to Be At

The descriptions made previously for each of the ten areas of capability represent the level of capabilities an organization should be at for a complete transformation from a document-centric to data-centric practice of SE. However, for each area, there is a range of capability levels, it is not just an either-or determination.

It is important for the enterprise to decide how, and to what extent, they are going to address each of the ten areas resulting in reaching the future state that is best for their organization. This decision must be based on the needs of the enterprise while being scaled to the level of rigor that allows the system development life cycle process activities to be performed by the projects with an acceptable level of risk and that will result in the needed outcomes.

It is important to realize that this journey towards practicing SE from a data-centric perspective can be made in a series of small steps. The enterprise doesn’t have to jump to a completely data-centric practice of SE at the beginning of their journey.

Some organizations may want to start with an electronic (vs. hard copy documents) requirement capability which supports allocation and traceability as well as can manage requirements (and other work products) across all system lifecycle process activities. This will help link requirements to the stakeholder needs from which they were transformed, to design outputs, and to verification and validation work products. The project can identify measures to track system development activities and identify and manage risks. The managers can then add the capability to use non-language-based diagrams as single entities without the underlying data, e.g., functional flow diagrams or context diagrams, and link the requirements to those diagrams. From there, the capability for analytical modeling can be added where the various diagrams, requirements, and other work products are visualizations of underlying sets of data (only if there is some benefit to be gained from doing so.)

The future state for each of the ten areas of data-centric capability can be expressed as a range of capability levels (CL) (e.g., 0 – 3). The MBSE Implementation Project team can define what capabilities are in terms of each of these levels. For each of the ten areas of capability, the level defined represents a goal which the project team will strive to meet along with any specific measurable objectives for each goal.

05: Identify the Gaps

Once the organization has assessed the current state of their practice of SE, they will need to address each of the ten areas in terms of what level of capability is best for the organization.

From an IT infrastructure requirements perspective, it is best for the projects to communicate the end state envisioned, so their IT department can provide the IT infrastructure and PM and SE toolsets that are scalable to be able to handle the needs of the organization for the envisioned end state.

Central to successfully implementing the levels of data-centric capabilities the organization needs to be at involves the selection of the SE tools that will be used. What capabilities are needed from an SE toolset depends on the product line, its complexity, green-field vs brown-field products, issues the organization is having and wants to address, and the workforce knowledge and experience.

Organizations need to understand what data and information best meets their needs and which set of SE tools they need to work with and manage this data. SE tools are like any other software application…one size does not fit all. The SE toolset that is best for an organization is the toolset that meets the organization’s requirements management, systems engineering, and modeling needs. Consider the outcomes needed as a result of using SE tools and the ROI resulting from these outcomes.

Table 2 shows the ten areas of capability, the present state (P) and future state (F) in terms of capability level as defined by the MBSE implementation project team. The difference between the present state and future state represents a “gap” which the project team will fill as part of their project.

06: Develop Action Plans

For each of the ten areas, the MBSE Implementation Project Team will develop an action plan. Each action plan represents a sub-project that will have its own mission statement, goals, objectives, measures of success, needs, requirements, budget, schedule, and stakeholders. Actions that will need to be addressed for each area include:

Developing enterprise, business management, and business operations level policies, processes, and procedures needed to implement SE from a data-centric perspective.
Providing requirements to the IT department concerning the IT infrastructure needed, so these capabilities can be realized.
Selecting and procuring PM and SE toolsets that support the level of data-centricity decided on.
Training their managers and systems engineers in the use of the PM and SE toolsets and processes from a data-centric perspective.

07: Implement the Action Plans

The project team will also need to assign a priority to each area of capability.It is important to realize that this journey towards practicing SE from a data-centric perspective can be made in a series of small steps.

Successfully providing the capability to practice SE from a data-centric perspective requires three key things to be addressed: people, process, and tools. All three are highly dependent. One of the first areas that should be addressed is the selection of the SE tools that will be used by all the product development teams. Tool selection will be influenced by the data governance policies, data and information management procedures, product line, culture, and work force. The product development process will need to be updated to reflect the levels of data-centricity decided on as well as the SE tools selected. The product development teams will then need to be trained in the processes and SE tools.

Use a Pilot Project

There is an old saying “The devil is in the details.” To bring the people, process, and tools together, the MBSE Implementation Project Team should choose a pilot project. A pilot project can be used to demonstrate the benefits and ROI of adopting MBSE and moving towards a data-centric practice of SE. It will allow the organization to gain an understanding of what works (provides value, ROI), what doesn’t, what is liked, what isn’t liked, and tailor the processes to best fit the needs of the organization.

This pilot project can develop an example PMP, SEMP, and IMP that can be used as a template for other projects. A project ontology and schema can be developed that can also be reused by other projects. If the product that is the focus of the pilot project is highly regulated, the pilot project can import standards and regulations into the SE toolset, enabling their reuse for future projects. Armed with the lessons learned from the pilot project, the organization can fine tune their various processes, policies, and work instructions to help the organization move closer to achieving their mission, goals, and objectives to practice SE from a data-centric perspective.

Several key steps include:

Develop a practical process that implements the chosen SCL. The process needs to fit the product line, domain, and culture of the organization. The implementation needs to be tailored to the project. Don’t try to follow a process developed by a tool vendor for some other organization or product line.
Invest in training in the proposed chosen processes and SE tools.
Pick a pilot project to apply the process and assign the grass roots data-centric SE advocates to that project.
Define and use measures so metrics can help document the ROI can be clearly communicated concerning the agreed-to level of data-centricity.
Show management how measures and metrics maintained within the PM and SE tools will help them better track the health and status of the project, enabling them to be better project managers and systems engineers.
Encourage team members to be actively involved in organizations like INCOSE and join working groups whose members can aid the implementation process.
Invest in an outside consultant who has a proven track record in implementing SE capabilities consistent with the chosen level of data-centricity and chosen SE toolset. Often tool vendors will be able to provide that person or persons who can work with both the MBSE Implementation Project Team as well as the product development pilot project team members. The tool vendor will be able to help integrate their tool into the organization’s IT infrastructure as well as help the pilot project team members tailor the tool to their needs and be readily available to assist when issues come up.

Once the pilot project is completed successfully (an assumption), the project can be used as an example for future projects. The core grass roots folks can be spread out among other projects and mentor other project managers and systems engineers and train them and their teams in the concept of practicing SE from a data-centric perspective and in the use of the chosen SE toolset.

In many of the cases where adoption has been successful, there has been both advocacy at the top as well as a strong grass roots support that has gradually gained acceptance across the organization, but typically only after one team has proven success.

Parting Thoughts

Success is possible if the organization addresses the key factors of success discussed above. Of particular importance is understanding the overall puzzle in which the MBSE puzzle piece must fit as well as understanding the importance of all the pieces concerning the various areas of data-centric capabilities that make up MBSE.

Developing the roadmap discussed as well as using a pilot project are key to success. Since one size doesn’t fit all, an organization needs to assess the data-centric capabilities that best fit its domain, product line (degree of complexity), and culture. The level of capability an organization establishes needs to be tailored to the size and complexity of systems developed by the organization, whether small, medium, or large projects.

Based on the needs of the organization and level of SE capability they choose, they will need to choose the appropriate SE toolset, update their processes, and train their people in these tools and processes.

An organization will be successful in practicing SE from a data-centric perspective when it is considered to be the “gold standard” for system development within the organization. However, the road to success is long — it takes very strong, unwavering leadership and experience to get this done right. It is human nature to try to push back and say that it isn’t possible, but it is.

Thank you for joining us for tips on successful MBSE adoption!

To download the entire paper, visit: Whitepaper: The Comprehensive Guide to Successfully Adopting Model-Based Systems Engineering MBSE