Tag Archive for: MBSE Adoption

In this blog, we recap the “Moving from Modules to Models – Is it finally time to leave IBM® DOORS® behind?” webinar.

As products grow increasingly complex, many teams are asking themselves if they have become complacent in how we approach requirements management and the value it delivers.

Regardless of the industry, requirements are critical to a shared understanding of the solution. They provide the foundation for traceability and regulatory compliance across the product or software’s lifecycle. Many companies are finding that using IBM® DOORS® for requirements management limits their ability to lay that common foundation.

In this webinar, experts from Persistent Systems and Jama Software® will discuss issues and challenges around the continued use of IBM® DOORS® and show how adopting a model-based requirements approach enables best practices to realize greater value from your engineering initiatives.

In this session, we will cover:

  • An overview of why requirements management is critical
  • Common challenges and pitfalls companies face
  • Best practices enabled by a model-based engineering approach
  • The path to successful adoption, reducing migration uncertainty and risk
  • How to start your migration journey

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

Moving from Modules to Models – Is it finally time to leave IBM® DOORS® behind?

Richard Watson: Hi folks, my name is Richard Watson. I work with Jama Software. I’m a practice director with Jama. My history is around systems engineering. I’ve been a systems engineer, I guess, for about 30 odd years, mostly with DOORS and DOORS Next. So my previous role was the product manager of DOORS before I moved into Jama Software. I’m thrilled today to be joined by Kathryn Fryer. Kathryn works for Persistent Systems and she too has got about 30 years of systems engineering experience. And she and I worked for a long time together inside of IBM, working on both DOORS and DOORS Next Generation. We’re here today to talk about data model consistency, and perhaps migrating your data to a consistent data model. So we’re going to start with Kathryn. So Kathryn, over to you.

Related: Jama Connect® Solution for IBM® DOORS®

Kathryn Fryer: Okay. Thanks, Richard. So I just wanted to start, for those of you who might not be familiar with Persistent Systems, we are a global company, been around for actually about 30 years, over 700 million in revenue and that was a huge increase year over year. And if you move to the next slide, we cover many different industries and solutions, and we partner with many different companies, everyone from Salesforce to Microsoft, to IBM and Jama.

So with that brief introduction, let’s move on to the topic at hand and talk about requirements management and model-based engineering. So these quotes might be familiar to you, certainly, the sentiment that they convey is familiar, requirements management is key to the success of software and systems projects, and poor requirements management is the single largest reason for project failure. And the costs of failure can be billions of dollars or pounds or euros, the cost of product recalls, of missing regulatory compliance, and never mind as we get into some of these industries, the cost affecting human life. So I know you’re all familiar with that and you know how important requirements management is, or you wouldn’t be here today. So if we move to the next slide, Richard?

Related: The Comprehensive Guide to Successfully Adopting Model-Based Systems Engineering (MBSE) 

Kathryn Fryer: So what makes it hard? Why are we still talking about it? And there’s four CS here, I think I refer to them in the past as well. And I don’t think these are new, this list is familiar. This has been true for a number of years now, and it continues to be true. Solutions become increasingly complex. We have more components, more moving pieces, more integrations, more dependencies. We have more contributors involved in delivering the solutions. So we need to collaborate, not even just within your organization, across the teams in your organization, but across different suppliers and then their suppliers. We have these diverse engineering teams, diverse tools, diverse processes, and we need that collaboration, the handshakes, the clear handoffs, and the understanding of the context, there’s another C I didn’t put on the slide, and how all the things must work together.

We need to manage change. Change is the only constant, right? So we need to stay on top, not just of changes in our project, but also changes in technologies and changes in standards and compliance. Compliance seems to be growing and evolving all the time. The specifics are going to vary by your industry and by your geography, but compliance is necessary in almost every industry now, I think, and that’s only going to grow with things like AI and autonomous capabilities and all that fun stuff. So there’s lots of things that we need to deal with.

Watch the full webinar to learn more: Moving from Modules to Models – Is it finally time to leave IBM® DOORS® behind?

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Data Migration Towards MBSE

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


The Real Intent of 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


 

Adopting MBSE

This blog contains excerpts from a whitepaper titled, The Comprehensive Guide to Successfully Adopting MBSE, written by Lou Wheatcraft. 

Adopting MBSE: Challenging the Status Quo in Product Development

For product development teams to successfully implement a practice such as model-based systems engineering (MBSE), it requires the willingness of an organization to perhaps change processes and even tooling. Often, companies choose to stay with the status quo, but at what cost? Here’s a look at how adopting MBSE might help your teams, and the cost of not adopting MBSE. 

Understand the Need to Move for Change 

What is the Risk of Staying with the Status Quo? 

For the MBSE Implementation Project Team to be successful, management must recognize the need to change. How can management be convinced? Three words – RETURN ON INVESTMENT (ROI)!  

Think about these questions: 

  •  What has been the impact of the current, poorly executed product development efforts? 
  • What is the overhead associated with the current document-based approach?  
  • What are the current quality issues facing the organization that is catering it to profits: Failures, recalls, returns, warranty costs, lawsuits, negative reviews on social media, decreasing market share?  

The ROI argument usually works with management especially when they can be convinced that by investing in a data-centric practice of SE tailored to the organization’s needs, the overall product development process, product quality, time to market, and profitability can be improved as discussed previously. 

What is the ROI of Adopting MBSE? 

To entice anyone — especially an entire organization — to make a change, proving ROI on the resource investment is key. Here are some key points to consider: 

  • The more effective the Systems Engineer (SE) processes, the less rework and fewer cost and schedule overruns.  
  • By moving to a data-centric practice of SE, the probability of achieving a competitive advantage can be improved by removing obstacles to being able to deliver products on time, on budget, and that meet or exceed customer and quality expectations. 
  • From a cultural perspective, the personnel responsible for product development and will be most affected by the change must be shown, and how the change will benefit them.  

Visit our interactive content page for ROI Calculators and more! 

Combating Opposition to Change

A good process is not one that is something people have to do in addition to their job, rather it is one that helps people do their job more effectively. – LOU WHEATCRAFT

RELATED POST: Webinar: Eliminating Barriers to MBSE Adoption with Jama Software

Get Team Buy-In

To get buy in from the product development teams, the MBSE Implementation Project Team must: 

  • Understand what problems the product development teams are having and show them how moving to a more data-centric practice of SE will address those problems and make their job easier than the current document-centric approach. If the change results in more work or makes communication harder, the battle will be lost. For example, the lead engineer or project manager may already be over their head and working 50–60-hour weeks. Requiring them to learn how to use a new tool or set of tools and implement a new process may be too much of a load for them to bear! However, if they are provided with a dedicated person that has the training, knowledge, and experience in the new processes and tools to help implement the changes and train other team members, they will be much more receptive. They will also be much more receptive if this results in them having to work fewer hours and having fewer crises to deal with each day! 
  • Be convinced of the utility of the changes, how the changes result in a better product and result in less rework for them. Frequently the reason project team members are working long hours is because they are always fighting fires, going from one crisis to another, which resulted from the lack of the proper SE tools, processes, data, and information in the first place! The culture needs to be changed from one of firefighting to one of fire prevention. As time passes, they will become advocates for the changes that have been made and welcome further change. 

The MBSE Implementation Project Team’s mission statement will be something like: “Improve our product development processes by adopting MBSE within the organization by moving from a document-centric to a data-centric practice of systems engineering.” Along with this mission statement, they will need to define a set of specific goals and objectives along with measures of success. Once defined, they will need to get agreement from management on these goals, objectives, and measures. 

RELATED POST: Systems Engineers Career Path – How to Elevate

Get Management Buy-In 

Project success is dependent on having a high-level, C-suite project champion and getting management buy in. A major challenge for the project will be convincing management and other key stakeholders that it is time for adopting MBSE and moving from a document-centric to a data-centric practice of SE and knocking down the walls of resistance. Some common reasons for them not wanting to move to a data-centric practice of SE include: 

  • “We have been doing product development using our current processes for years, why should we change?” 
  • “Implementing SE from a data-centric perspective may work for others, but not for us.” 
  • “This all seems very complicated, we don’t have the knowledge, experience, or tools.” 
  • “Our current SE work products, like requirements, are managed in an RMT. FFBDs, and other diagrams we are currently using are models, so aren’t we already adopting MBSE?” 
  • “It is too expensive to procure the needed SE toolset, maintain the tools, and train our people to use those tools.” 
  • “We don’t have the budget to incorporate SE from a data-centric perspective at this time.” 
  • “The expense and associated process to get new SE toolset installed on organizational computers is too great.” 
  • “We would have to make signification IT infrastructure upgrades to accommodate the additional volume of data and performance requirements of the new SE tools.” 
  • “We deal with the development of classified systems; controlling access and maintaining security will be too difficult.” 

Sound familiar? Often the pushback can be attributed to a lack of understanding the risks associated with the current state of the organization, understanding the benefits of moving toward a more data-centric practice of SE, and what level of SE capability is appropriate for the organization.

RELATED POST: Whitepaper: A Path to Model Based Engineering (MBSE) with Jama Connect

Adopting MBSE: The Road to Success 

To inspire a shift from a document-centric to a data-centric practice of SE in your organization, it’s vital to show both teams and management the value and expected ROI in doing so. Moving away from a current way of doing things isn’t always an easy road, but the risks of staying with the status quo are often great—and the rewards for changing processes and culture are often even greater.  

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.   

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.

RELATED POST: MBSE Is Not SYSML

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.

 

RELATED POST: The Real Intent of Model-Based Systems Engineering

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: 

  1. 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. 
  2. Invest in training in the proposed chosen processes and SE tools. 
  3. Pick a pilot project to apply the process and assign the grass roots data-centric SE advocates to that project. 
  4. Define and use measures so metrics can help document the ROI can be clearly communicated concerning the agreed-to level of data-centricity. 
  5. 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. 
  6. Encourage team members to be actively involved in organizations like INCOSE and join working groups whose members can aid the implementation process.
  7. 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

This is Part 3 of a blog series covering a whitepaper titled,The Comprehensive Guide to Successfully Adopting Model-Based Systems Engineering MBSE. Visit these links for the rest of this series: Part I, Part II, and Part IV.

Developing a roadmap to successfully adopting MBSE within your organization

In this section, we provide a general approach or roadmap to be used in your company’s journey in successfully adopting MBSE within your organization. We say “journey” because that is what it is. Transforming an organization doesn’t happen overnight. There are a lot of pieces of the puzzle to address so it is important to take small steps and not try to do everything at once. Success includes addressing the key factors of success discussed earlier and define a roadmap. This journey could take several years depending on the size of your organization.

As stated earlier, the organization should form a dedicated MBSE Implementation Project Team with membership from each of the key organizational elements that have a stake and role in the adoption of MBSE within the organization. A general roadmap the project team can follow is shown in Figure 1:

Figure 1: Roadmap to Successful Adoption of MBSE

RELATED POST: The Real Intent of Model-Based Systems Engineering

01: Get Management Buy-In

As discussed earlier, project success is dependent on having a high-level, C-suite project champion and getting management buy in. A major challenge for the project will be convincing management and other key stakeholders that it is time to adopt MBSE and move from a document-centric to a data-centric practice of SE and knocking down the walls of resistance. Some common reasons for them not wanting to move to a data-centric practice of SE include:

  • “We have been doing product development using our current processes for years, why should we change?”
  • “Implementing SE from a data-centric perspective may work for others, but not for us.”
  • “This all seems very complicated, we don’t have the knowledge, experience, or tools.”
  • “Our current SE work products, like requirements, are managed in an RMT. FFBDs, and other diagrams we are currently using are models, so aren’t we already doing MBSE?”
  • “It is too expensive to procure the needed SE toolset, maintain the tools, and train our people to use those tools.”
  • “We don’t have the budget to incorporate SE from a data-centric perspective at this time.”
  • “The expense and associated process to get new SE toolset installed on organizational computers is too great.”
  • “We would have to make signification IT infrastructure upgrades to accommodate the additional volume of data and performance requirements of the new SE tools.”
  • “We deal with the development of classified systems; controlling access and maintaining security will be too difficult.”

Sound familiar? Often the pushback can be attributed to a lack of understanding the risks associated with the current state of the organization, understanding the benefits of moving toward a more data-centric practice of SE, and what level of SE capability is appropriate for the organization (Goldilocks principle discussed earlier).

02: Understand the Need to Move Toward a Data-Centric Practice of SE

For the MBSE Implementation Project Team to be successful, management must recognize the need to change. How can management be convinced? Three words – RETURN ON INVESTMENT (ROI)! Think about it… what has been the impact of the current, poorly executed product development efforts? What is the overhead associated with the current document-based approach? What are the current quality issues facing the organization that is catering it to profits: Failures, recalls, returns, warranty costs, lawsuits, negative reviews on social media, decreasing market share? The ROI argument usually works with management especially when they can be convinced that by investing in a data-centric practice of SE tailored to the organization’s needs, the overall product development process, product quality, time to market, and profitability can be improved as discussed previously.

The more effective the SE processes, the less rework and fewer cost and schedule overruns. By moving to a data-centric practice of SE, the probability of achieving a competitive advantage can be improved by removing obstacles to being able to deliver products on time, on budget, and that meet or exceed customer and quality expectations.

From a cultural perspective, the personnel responsible for product development and will be most affected by the change, must be shown how the change will benefit them. Changing culture is often met with opposition and existing stovepipes/silos often resist change.

To combat this opposition, determine which stakeholders are for and against the change and why. For each individual or group, identify their concerns and devise a strategy to get the change adversaries to become change advocates. Start with those stakeholders that have the most influence and convince them by addressing their concerns. The aid of other stakeholders may need to be obtained to help influence those that oppose the change.

A good process is not one that is something people have to do in addition to their job, rather it is one that helps people do their job more effectively. – LOU WHEATCRAFT

To get buy in from the product development teams, the MBSE Implementation Project Team must understand what problems the product development teams are having and show them how moving to a more data-centric practice of SE will address those problems and make their job easier than the current document-centric approach. If the change results in more work or makes communication harder, the battle will be lost. For example, the lead engineer or project manager may already be over their head and working 50–60-hour weeks. Requiring them to learn how to use a new tool or set of tools and implement a new process may be too much of a load for them to bear! However, if they are provided with a dedicated person that has the training, knowledge, and experience in the new processes and tools to help implement the changes and train other team members, they will be much more receptive. They will also be much more receptive if this results in them having to work fewer hours and having fewer crises to deal with each day!

People at all levels must be convinced of the utility of the changes, how the changes result in a better product and result in less rework for them. Frequently the reason project team members are working long hours is because they are always fighting fires, going from one crisis to another, which resulted from the lack of the proper SE tools, processes, data, and information in the first place! The culture needs to be changed from one of firefighting to one of fire prevention. As time passes, they will become advocates for the changes that have been made and welcome further change.

The MBSE Implementation Project Team’s mission statement will be something like: “Improve our product development processes by adopting MBSE within the organization by moving from a document-centric to a data-centric practice of systems engineering.” Along with this mission statement, they will need to define a set of specific goals and objectives along with measures of success. Once defined, they will need to get agreement from management on these goals, objectives, and measures.

RELATED POST: Webinar: Eliminating Barriers to MBSE Adoption with Jama Software

03: Access the Organization’s Current Practice of SE

To do this, they need to first assess the organization’s current state of practice of SE in terms of the ten areas of capability associated with data-centricity discussed previously. Table 1 shows an example of what the results may look like for organizations that are firmly rooted in a document-centric practice of SE. Sadly, this is the state of many of today’s organizations!

 

Visit these links for the rest of this series: Part I, Part II, and Part IV.

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

This is Part 2 of a blog series covering a whitepaper titled,The Comprehensive Guide to Successfully Adopting Model-Based Systems Engineering MBSE. Visit these links for the rest of this series: Part I, Part III, and Part IV.

Adopting MBSE

What does it mean to practice SE from a data-centric perspective?

Successfully adopting MBSE and moving toward a data-centric practice of SE is much more than just acquiring and using a specific tool, set of tools, or focusing on the use of a specific type of model. As stated previously, MBSE is not just about the development of SysML or other language-based models nor just practicing model-based design. MBSE is itself made up of puzzle pieces, all of which contribute to the successful adoption of MBSE. To be successful, the following ten areas of capability associated with data-centricity must be addressed.

01: Holistic Product Development

A key tenet of data-centricity is taking a holistic view of product development and managing data and information within an integrated/federated environment. The focus is on multidiscipline, collaborative, project teams (e.g., integrated product teams). Many organizations still operate in organizational silos, with team members’ loyalty toward their specific silo rather than to the project team. When issues occur, the tendency is to blame those in other silos. Each silo often has its own processes, specific toolsets, data, and artifacts. Often the data and information are generated independently from those in other silos and are not in a form to enable sharing. This can result in inconsistencies, correctness, completeness, and currency issues of the data maintained in these artifacts. When moving toward data-centricity, organizations must have a holistic view of product development, minimizing the silos, encouraging collaboration, and improving communications not only between team members but between different tools used to generate and maintain data and information. Rather than treating Systems Engineering separate from Project Management (PM), projects must integrate both sets of functions such that there is a single project team that does both functions.

02: Manage Product Development Across the Lifecycle

Rather than having tools that are specific to a given organizational silo, a key characteristic of data centricity it that related data and information that represents lifecycle activities and associated artifacts can be linked resulting in “digital threads” that link related information together across the product lifecycle. This linkage enables project team members to work collaboratively and establish traceability between needs, design input requirements, system analysis artifacts, diagrams, models, architecture, design, system verification artifacts, and system validation artifacts. Traceability aids in change impact assessment across the product lifecycle helping ensure completeness, correctness, consistency, and currency of the data and information and resulting artifacts.

03: Enterprise Level Data and Governance Policy, Processes, & Procedures

Because of the dependence of not just the project teams, but the overall organization on electronic forms of data and information and increasing threats associated with the security of this data and information; enterprise-level policies, processes, and procedures concerning data governance and information management must be defined, implemented, and enforced.

04: Project Level Data and Information Management

Within the context of the enterprise-level data governance and information policies, each project must include their specific implementation of these policies within their Project Management Plan (PMP) and Systems Engineering Management Plan (SEMP). Because of the importance of managing the project’s data and information, the project is encouraged to develop and enforce a project-level Information Management Plan (IMP). Other supporting plans (e.g., requirements management plan) need to comply with the data management policies within the higher-level plans for both the project and enterprise.

RELATED POST: The Real Intent of Model-Based Systems Engineering

05: Master Ontology

Terminology and language are key to successful communications not only between team members but between tools. For a given enterprise, an enterprise-level ontology (data dictionary and glossary) must be developed to clearly define specific terminology and relationships of various terms used within the organization and the project. This is critical when there are product lines, multiple project teams, and the need to share data and information between current projects as well as reuse data and information for future projects. Within the enterprise-level ontology, individual project teams can define their project-specific ontology consistent with the enterprise-level ontology.

06: Master Schema

Here the word “schema” is used to describe how the data and information are organized and managed within individual tools and associated databases. It includes the naming of individual data and information items, defining relationships between data items, and the import and export of data and information. From both an enterprise and project perspective it is important to define a master schema that the SE and PM tools within their toolset are compliant in order to enable data integration, shareability, and reuse.

07: Use of Attributes and Associated Measures

Data centricity enables the project to define and use attributes that can be used to manage project activities across all system life cycle stages. For needs and requirements, attributes can include rationale, priority, criticality, source, owner, traceability, risk, maturity of needs definition, needs and requirements definition status, design implementation, system verification, and system validation. Attributes can be defined to aid in reusability and product line management. Attributes can also be associated with key measures defined by stakeholders within their goals and objectives. These measures include key performance indicators (KPI), measures of suitability (MOS), measures of effectiveness (MOE), measures of performance (MOP), key performance measures KPP), technical performance measures (TPM), and leading indicators (LI). Data representing these measures and attributes can be used within the SE and PM tools to generate reports, dashboards, etc. which are used to better manage the project and system engineering processes providing managers near real-time status information and enabling them to identify and correct possible issues before they become problems.

08: Configuration Management

Adopting data-centricity, the project’s artifacts and underlying data and information are developed, analyzed, and managed holistically within the data and information model. Because the data and information are managed within the project’s data and information model, this model represents a single source of truth (SSoT) for the project. Rather than configuration control of each individual artifact represented by the data and information in the model, the project team can configuration control the model which represents the baseline state of the artifacts represented by the data and information in the model at any given time. “Visualizations” of the data and information in the form of the various artifacts represent the baseline version of that artifact. Even when these visualizations are extracted as reports, the SSoT is still the data and information model from which they were generated.

Note: for many organizations, this is often their biggest challenge in that it requires the organization to redefine its concept of configuration management. However, as stated previously, configuration management of individual artifacts requires significant overhead in both cost and time to individually configuration manage individual documents as compared to managing the data and information model that is representative of moving towards a data-centric practice of SE and PM.

RELATED POST: Webinar: Eliminating Barriers to MBSE Adoption with Jama Software

09: Systems Engineering (SE) Tool Set

Data centricity requires projects to move beyond the use of common office applications: word processing, spreadsheets, presentations, basic drawing and diagraming tools, and requirement only management tools to define, analyze, record, and manage needs and requirements and other SE artifacts. Rather, projects must transform their SE process such that SE artifacts are developed using SE tools that are fully compliant with interoperability and data sharing standards, are consistent with the enterprise and project ontology, stores the data and information consistent with the project’s master schema, and allows linking of data and information across lifecycle activities and resulting artifacts. This data and information must be managed in a form that is shareable between the SE tools within the project’s toolset as well as shareable with the project’s PM tools. When selecting specific SE tools to be included in the project’s toolset, it is important that the project determine the types of information and methods of analysis that are needed based on their specific product line, culture, and workforce.

10: Project Management (PM) Tool Set

Data centricity also requires projects to move beyond the use of common office applications for project management e.g., budgeting, scheduling, cost management, risk management). Rather, projects must transform their PM process such that most of the PM artifacts are being developed using PM tools that are fully compliant with interoperability and data sharing standards, are consistent with the enterprise and project ontology, stores the data and information consistent with the project’s master schema, and allows linking of data and information across lifecycle activities and resulting artifacts. This data and information must be managed in a form that is shareable between the PM tools within the project’s toolset as well as shareable with the project’s SE tools. For example, Work Breakdown Structures (WBS) can be linked to Product Breakdown Structures (PBS) and physical architectures to enable management of budgets, schedules, resources, and risks associated with each system and system element within the product physical architecture.

Visit these links for the rest of this series: Part I, Part III, and Part IV.

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


This is Part I of a blog series covering a whitepaper titled, The Comprehensive Guide to Successfully Adopting Model-Based Systems Engineering MBSE. Visit these links for the rest of this series: Part II, Part III, and Part IV.

Introduction 

In a previous paper, we discussed key questions concerning Model-based Systems Engineering (MBSE) including what MBSE is, its true intent, why organizations should adopt MBSE, and the benefits. If you haven’t read that paper, it’s worth taking a look. 

We made the point that the goal of an organization when adopting MBSE, is to move from a document-centric to a data-centric practice of Systems Engineering (SE) to realize the real intent of MBSE which is to develop, maintain, and manage a data and information model of the system being developed — along with a model of all the system life cycle process activities, resulting in artifacts, and their underlying data and information. 

This paper will go into more detail as to key factors associated with successfully adopting MBSE, what it means to practice SE from a data-centric perspective, and provide a methodology to define a road map tailored to your organization resulting in the successful adoption of MBSE. 

RELATED POST: The Real Intent of Model-Based Systems Engineering

Key factors associated with successfully adopting MBSE 

Sadly, the attempts of many organizations to successfully adopt MBSE often end in failure. The process of adopting innovative technology like MBSE and moving toward a data-centric practice of SE can be considered to be a project in its own right. There have been numerous studies and reports concerning factors of why projects fail, and factors associated with projects that succeed. When adopting MBSE, these factors must be considered. Organizations that are able to successfully adopt MBSE and move to a data-centric practice of SE address the following key factors: 

01 – Getting Corporate Level Management Buy-In and Support – Success Starts at the Top! 

In an earlier paper, we discussed issues associated with a document-centric approach to product development of today’s increasingly complex, software-centric products along with the benefits of adopting MBSE from a data-centric perspective to address these issues. There must be a project champion that can clearly communicate these issues and benefits at the corporate level in order to get buy-in across the organization. 

A key consideration when getting this buy-in is how these issues and benefits are communicated. The adage “know your audience” is important. A common mistake when approaching higher-level management is using terminology that does not address their needs in a language they understand. When getting buy-in concerning the company adopting MBSE, you must clearly communicate to them what MBSE is and how the organization will benefit in terms of outcomes they can relate to. Giving them a demonstration of a specific tool using a lot of technical jargon can result in them quickly losing interest. They are interested intangible outcomes of a proposed solution that addresses business-related issues (problems): less overhead, decreased time to market, higher quality, decreases in post-launch issues, fewer issues associated with a product being approved for use, increased profits, rising stock prices, and a growing company. They want to understand how adopting MBSE will result in these types of outcomes. 

02 – Forming a Dedicated Project Team 

Rather than leaving it up to individual project teams to each attempt to adopt MBSE, a corporate level dedicated MBSE Implementation Project Team (IPT) is needed. For smaller organizations and startups this IPT might be a single person. MBSE is just one puzzle piece in the larger set of organizational puzzle pieces. To be successful, the larger, integrated puzzle must be considered to ensure the MBSE puzzle piece will fit. Other puzzle pieces include data governance policies, information management plan procedures and work instructions, information technology (IT) infrastructure (networks, internet, clouds, applications, computing devices, etc.), the product line, product development processes, procurement processes, company culture, workforce, etc. A dedicated project team can deal with possible issues in all these areas from a corporate, holistic perspective across organizational silos enabling a successful adoption of MBSE, helping to ensure the MBSE puzzle piece can be integrated within the overall corporate puzzle. 

RELATED POST: Webinar: Eliminating Barriers to MBSE Adoption with Jama Software

03 – Involving Key Stakeholders 

The various stakeholders involved in adopting MBSE must be included. These stakeholders must not only include the users, but other stakeholders that will be affected by the adoption of MBSE including those that will benefit, those involved in the activities required to adopt MBSE, and those from enabling and supporting organizations. Referring back to the puzzle analogy, stakeholders must be included that represent each of the above-listed puzzle pieces. Each stakeholder has expectations that must be addressed by the project team along with key drivers and constraints a successful project must consider in order to achieve a successful outcome. 

04 – Defining The Problem, Opportunity, Outcomes, Needs, and Requirements at the Beginning of the Project 

The project team and stakeholders at all levels of the organization must be aligned to a common understanding of the problem/opportunity that is driving the adoption of MBSE, to a common mission statement, goals, objectives, clear outcomes, needs, and requirements. Like any other project, these must be defined and agreed to from the beginning so that there is a clear roadmap to success and well-defined outcomes against which success can be measured. 

05 – Understanding the “Goldilocks Principle” 

The Goldilocks principle is about doing what is “just right” – not too little, not too much. When adopting MBSE and moving towards a data-centric practice of SE, the project team must understand the needs of the organization, what it means to practice SE from a data-centric perspective, and develop a practical and feasible roadmap. Delivering an MBSE capability that is too little can result in stakeholder expectations not being met, disappointment, and a failure of project teams to successfully adopt MBSE. Going overboard and implementing more than is needed can be overwhelming, turning people off to the concept and again a failure of project teams to adopt MBSE. 

This last point is especially important. “Just right” must be defined from a user perspective. The users are the product development project team members who will be conducting their project based on the processes and tools provided that will enable them to adopt MBSE for their project and move to developing their products from a data-centric perspective. They have expectations concerning being able to be more productive and effective. Meaning the processes and tools provided should not be viewed as things they have to do and use in addition to their job – resulting in more work; rather processes and tools they can follow and use that are an integral part of how they do their job – resulting in less work, a higher quality product with a shorter time to market. The new processes and tools enable them to deliver winning products: those that meet the needs of their customers, within budget and schedule, with the required quality. 

From a user perspective the following attributes must be addressed within the processes defined and tools selected for use: 

  • Full functionality; does what is needed, nor more, no less
  • Intuitive; easy to learn and use
  • Easy and fast to implement
  • Enable collaboration between team members, no matter their geolocation
  • Enable traceability of data, information, and artifacts across the system lifecycle
  • Enable change impact assessment across the system lifecycle
  • Reduces the time to define and manage needs and requirements
  • Supports verification and validation planning and execution
  • Tailorable to the organization’s product line, work instructions, and workflow
  • Helps ensure compliance with standards and regulations
  • Helps manage risk across the lifecycle
  • Enables management to track project status across the lifecycle 

Visit these links for the rest of this series: Part II, Part III, and Part IV.

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


In January 2020, NASA reported that Model-Based System Engineering (MBSE), “has been increasingly embraced by both industry and government as a means to keep track of system complexity.”

And in order to help facilitate accelerated adoption of MBSE, we’re proud to introduce Jama Connect® for Traceable MBSE™.

Jama Connect for Traceable MBSE is a single platform that combines requirements, architectures, behaviors, and V&V into a single model of the system by applying: structure for the data; rules for the data; and a consistent interface language between parts of the system. A Jama Connect model provides engineers, project managers, business analysts, or any stakeholder, an organized way to address all aspects of the system consistently with the assurance of completeness.

Accelerate Your Systems Development

Jama Connect for Traceable MBSE provides a path to companies embracing MBSE where the application of collaboration, modeling, and methods reduces time and effort across the lifecycle.

With this solution, you’ll get:

  • Framework aligned to industry best practices from INCOSE and SEBoK
  • MBSE Quick Start Guide
  • Document export templates and reports aligned with systems development
  • Standard features of Jama Connect including requirements management, test management, live traceability, review center, realtime collaboration, reuse & baseline management, workflow & configuration management, diagramming
  • Consulting and training customized to your teams’ MBSE processes

Download the solution overview to learn more about Jama Connect for Traceable MBSE.

An organizational paradigm shift from document centric to model-based systems engineering can be daunting. The learning curve for systems engineers can be steep and the return on investment is often not immediately apparent.  

In a recent workshop on MBSE adoption, we walked through how Jama Software for Traceable MBSE™ can be used to eliminate common MBSE challenges and provide tactics to help teams eliminate the barriers to broader adoption. We will demonstrate how Jama Connect’s Traceable MBSE can be used to streamline a collaborative, data-driven approach and provide more immediate systems engineering value to larger numbers of stakeholders.  

In this demonstration, we cover: 

  • Using Jama Connect to facilitate implementation of MBSE
  • Benefits of Traceable MBSE to the broader stakeholder community  
  • Keys to eliminating documents
  • Leverage OSLC and REST for Cross Tool linking

Below is an abbreviated transcript and a recording of the workshop.

In today’s hyper-connected world, we’re not only seeing increasing complexity of systems that are being built, but also experiencing increasing technological and sociological challenges in the work setting to develop those systems. Jama Connect helps address these challenges. Organizations can execute their MBSE journey without the headache of modeling languages or the licensing and rolling out of complex systems modeling tools.

Jama Connect for Traceable MBSE is a completely web-based application that is designed to make it easy for any stakeholder technical or non-technical to create models, consume data and participate in a collaborative systems engineering process. The Traceable MBSE template comes with data types to capture and segregate different types of requirements, behaviors, architecture, interfaces, and V & V data. It also has a structural template already laid out to organize the data and help users get started quickly.

We’re going to cover using Jama to facilitate that implementation of MBSE, then we’re going to talk about keys to eliminating documents. We’ll look at how you can leverage OSSE and rest for cross tool linking. And we’ll talk about the benefits of Traceable MBSE to the broader stakeholder community model based systems. Engineering is more than a modeling tool. It is not simply a tool but a process that demands change to take place, not only at the systems level, but at the project and program levels.

RELATED: Getting Started with Model-Based Systems Engineering (MBSE)

Document centered thinking and business practices needs to change at all levels in order to not only deal with system complexity, but to also become more agile organizationally, but leaping to a purely SysML tool is not a leap every organization is mature enough to tackle. MBSE is not all only about moving from document centric to a model. It’s also about being able to communicate the clarity that the modeled information gives to the stakeholders on other teams, whether that be at the implementation level or the program or customer level.

The first step every organization can take is to become more information centric. Our Traceable MBSE is designed to do just that. Under the hood, Jama Software’s Traceable MBSE is composed of an easy to navigate structure for the data. Automatic version control of the data and baselining, a model language, a diagram editor, a workflow engine, a collaboration engine, document import and export and model integration with wide varieties of tools in your ecosystem like other SysML tools, test execution and more.

Traceable MBSE is pre-configured with a meta-model that govern which elements were allowed to be connected together. And which type of TraceLink names are used when elements are connected. For instance, a need can be related to a validation test and its relationship name is validate. A system requirement can be related to a system architecture block and its relationship name is satisfies. Jama Connect automatically chooses the correct relationship name for the user. Users do not need to know any syntax or language.

They just associate one element with another and connect a prize applies the pre-configured trace rules to those elements. You can also easily extend these data element types and relationship names by creating your own. There are distinct benefits from moving away from a document centric to information centric paradigm. Discrete item types, and link names, make it easy to analyze and query the model. Teams using documents, wikis or legacy tools often go through a manual process to relate all that data together, which can be error prone, introduced inconsistent data, or when performed after the fact yield stale inaccurate data.

RELATED: MBSE Tool Maturity – Where does your organization stand?

Jama Connect lets you surface in real time data and display it on the model dashboard. Dashboards can be a central place for any stakeholder technical or nontechnical to come away with an understanding of the current status of the systems engineering effort. Let’s take a look at Jama in action. So I’ve opened up my model, my model essentially is just a Jama Connect project. We have the building blocks of a structure. So on the left, you see the structure of the model, all the different types of data types that we’re collecting in this model.

The thing important data types that we have that come out of the box with Traceable MBSE are our needs. So a need item type, that’s where you would capture, you would use this type of element to capture a stakeholder need or an expectation or a requirement. Some regulatory requirements or things like that. Then we have a system requirements. So we’re segregating different types of requirements. System requirement has its own object. So you can demonstrate traceability between the need. So if you have customer requirements that are coming in as need as a need, you can really easily demonstrate that traceability.

We have a third requirement item type is a subsystem requirement. You are not constrained to use our specific nomenclature. You can extend these item types. You can rename them as well. We also then have a use case type. So, this particular use item type in Jama Connect is used to capture textural based more traditional use case flows in a more verbal textural. We also have that the capability to more granularly define the elements within a use case. So if you have actors, if you have different states or activities and you need to granularly break those out of a textual use case so that you can trace them in some way you can do that.

We also have a part. A part is more like a SysML 2.0 construct where a part might be used to capture structural elements, such as system architecture elements, or function blocks or physical objects or abstract objects. We have the capability to capture an interface as well as various different verification validation objects. So we had Jama segregate, verifications and validations. So it’s really easy to demonstrate that you have done both verification and validation of the objects. The model and the other types are extensible. So while the Traceable MBSE comes with just a handful out of the box, the ones on the top, you can create your own additional ones to add to your model and extend it for your specific needs. And you can also rename things.

So if you don’t call something a subsystem requirement, maybe you call it a component requirement, or maybe you call it a software requirement, or maybe you even call it a user story. You can rename things as well. Now, Jama Connect does come with a method, we call it our Traceable MBSE method. It’s a top-down approach top down where you have at the most abstract within my model, I have my needs but it supports analysis, design and specification. And it starts off with a needs analysis. So, when I want to look at my needs analysis I can click on something in the tree and then it shows in context in the main area of the system.

Now my needs analysis I’m capturing each need as an individual item, typically like you would in a spreadsheet. So I could look at the different views of the needs, whether I have a spreadsheet view or a rich text view. Our diagramming capability is built right in as well. So, when you want to communicate more clearly to your audience a diagrammatic expression of your needs users don’t have to go to external tools. You don’t have to go to a modeling tool per se, but you can build those diagrams right here in Jama.

You want a Traceable MBSE is designed for anyone to get started with your MBSE journey and then bringing in and marrying up diagrams right here in the context of the needs is a really powerful thing. You don’t need to be an expert to know how to draw boxes and lines and add textual information to communicate the information that you need to your audience. You also have the ability to do the collaboration that Jama is really famous for. So, if I need to bring people in to look at this particular diagram, we have the capability to add that comment even use at mentioning. So, if I wanted to add, mention a coworker, I can just mention their name.

Can you verify whether we need the X, Y, Z and the act that the comments within Jama are actionable? So it’s not a static comment but I can tell Alex, Hey, I need you to answer this question specifically, or maybe I’m just a read only user of this Jama model, and I’m looking at something and I want to raise an issue, or they be an engineer and I need someone to make a decision about something I’m unclear. I need some direction, just help me make a decision about that. Under the covers comments go into people’s email, they can reply back through email. They can answer questions, or they could click on the link in that email that would essentially take them right to this context, and then they can answer back.

So now we can have that sort of a conversation live captured part of the auditable trail and bringing more people into participate. If I wanted to, I could have even had mentioned my customer by just putting in their email address, and then my customer could have replied back to the comment thread. Well, it’s all about having more people being able to participate in the MBSE process.

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