project teams Archives

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

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.

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.

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 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.

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.

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

Tag Archive for: project teams