Tag Archive for: systems engineering


Streamlining Defense Contract Bid Document Deliverables with Jama Connect®

In the defense sector, whether it is a large prime, a subcontractor, or one of the thousands of other organizations under the defense umbrella, winning a contract bid for the United States Government comes with as astronomical amount of document deliverables that can be daunting and cumbersome. These documents are listed from the get-go in the Contract Data Requirements List (CDRL) which links directly to the Statement of Work (SOW). When designing and building these complex programs, as part of the CDRL, the contractor delivers countless Data Item Descriptions (DIDs).

These DIDs are formatted with very particular structures and arrangements as defined by MIL-HDBK-245 so that each unique deliverable defines the necessary data. Examples of these documents are Interface Design Description (IDD), System/Subsystem Design Description (SSDD), and Operation Concept Description (OCD) just to name a few of the variations. Below you can see a list of DIDs that are offered with Jama Connect®. The Software Requirements Specification (SRS) DID contains the formatted document below in the explorer tree so the export is pre-built and only the pertinent information can be filled in.

Figure 1: Jama Connect DID Template Library

Prior to entering the world of Jama Connect, my experience at the largest government contractor as a Systems Engineer exposed me to countless hours of document writing, specifically Interface Design Descriptions (IDD’s). The team had to create 80 documents from scratch for two different programs. Mind you, these 80 documents were solely for the navigation data that our systems sent to the rest of the ship. These documents are just a small segment of all the other similar documents that numerous teams worked on. These IDD’s were kept on the Local Area Network (LAN) where revision management was adding a most recently edited date to the end of the file name and completion was tracked in a excel workbook. Upon completing a document, the team would sit in room together to review a few IDD’s, try to remember if any system changes that were made, cross reference Cable Block Diagrams and Cable Run Sheets to see if all connector types and port numbers were correct, and struggle to make sure all interfaces for the connected systems were captured.

RELATED: Digital Engineering Between Government and Contractors

There was no relationship capability, no way to ensure up to date references, and minimal visibility into change incorporation. This led to unnecessary hours spent reviewing these items ultimately leading to late delivery of documents and the possibility of incorrect or out of date documents which the customer was not appreciative of. With the navigation system being the core of all ship operability, the risk of incorrect documentation had the potential to lead to countless issues on ship in an environment where the stakes are already so high. The premise of being able use Jama Connect to build these DID’s with the information already created in the software that establishes traceability and ease of review in the review center is lightyears easier and faster than the process we had in place.

Now that the stage is set for the effort that goes into delivering all the CDRL items, by utilizing Jama Connect, customers no longer must burn thousands of hours meticulously revisioning items in Word documents. The DID structure can be built into a Jama Connect project so that the DID can be broken down into individual components to be versioned on. Utilization of the REUSE capability allows for requirements and other text items from the project to be simply implemented in the DID structure so that the content is the same and the exported deliverable meets the criteria. Alongside the simplicity of creating DID’s within Jama Connect, the overall authoring process is reduced tenfold since numerous users can work in conjunction on their own elements of the DID, the out of the box templates offered give users the ability to immediate start creating the content, and overall deliverable time is cut down.

RELATED: How Jama Connect® Helps Program Managers with DOD 5000 Adaptive Acquisition Framework


The daunting task of delivering countless documents to the government is much less alarming when streamlined with the use of Jama Connect. With Jama Software’s DID’s Library, customers are able to dive into their deliverable creation while simultaneously working in an environment that provides perfect versioning and change management for the items that make up the DID. The ability to deliver documentation to the customer in a faster, more concise manner yields companies the ability to stop wasting time of writing documents from scratch and to instead complete other tasks leading to overall acceleration of a program.

digital engineering

Digital Engineering Between Government and Contractors

How does a digital thread work when tool ecosystems are disconnected from each other? For the defense contracting world, THIS is the elephant in the room. The DoD (Department of Defense) wants its Digital Engineering (DE) vision to become a reality and they do realize it cannot happen overnight, so how is this being addressed today?

The short answer is that the DoD wants their contractors to set up their own DE ecosystems and then exchange deliverables. This is no different than what they have done for the past 50 years but the types of deliverables are changing. Namely, they want DE content to be delivered as models which means SysML, UAF (an enterprise architecture format), Computer Aided Design (CAD) I– if the government purchased the technical data for this during contracting – models. The DOD still requires deliverables in Word/PDF/Excel/MS Project for various project management deliverables and engineering analysis that is only representable in textural form as well.

“Within the new Digital Engineering Ecosystem, there are three possible scenarios for information access between customer and contractor. The scenarios are: 1. Provide access to controlled baselines in contractor environment. This is considered a low IP (Intellectual Property) and data rights loss risk option two. Provide access to controlled baselines in the contractor environment and provide selected models and data in accordance with contract data requirements. This is considered a medium IP and data rights loss risk option 3. Provide all required baseline models and data in accordance with contract data requirements. This is considered a medium-high IP and data rights loss risk option.” – (AIA Aerospace whitepaper)

Nightly Check-in Technique:

PLM tools are deemed the center of the ecosystem since these already have the digital twin information (hardware CAD, configuration management, and product management backbone) in place. Nightly check-ins of files (SysML model, CAD, requirements, Ansys, etc.) are thought to be timely enough to keep things in sync. With this model, both the government and contractors would utilize a shared or their own PLM systems.

SAIC ReadyOne – a ready-made DE ecosystem of tools that can be purchased and installed in one click. It is managed on AWS GovCloud and is available up to CUI level four today. It is meant to answer the call for an easy to deploy, cost effective, and powerful integrated environment that delivers Digital Transformation – SAIC created Ready 1. ReadyOne is comprised of two main components

  • The infrastructure which includes the servers, application stacks, and build automation
  • And the Digital thread platform with a custom data model – which you can think of as the plumbing that defines the available connections between the various data artifacts – like system models, parts, and simulations – within the ecosystem

ReadyOne is Built on a model based, low code, platform supporting openness, flexibility, and customization. OOTB applications allow domain specific content to be mapped to Items and artifacts as well as customization – ensuring uniqueness of process, instead of forcing synthetic commonality.

Developers use familiar domain specific applications for System Modeling, CAD, and simulation to author content. Domain tools utilize pre-configured connectors with business rules to ensure all data is connected and the single source of truth remains persistent. Enterprise configuration management is a foundational component ensuring each domain is utilizing the proper data and relationships to remove opportunity for errors.

Digital Thread

SAIC Proprietary

INCOSE Digital Engineering Information Exchange Working Group (DEIX-WG)

Group Goal = “The DEIX WG primary goal is to establish a finite set of digital artifacts that acquiring organizations and their global supply chains should use to request an exchange with each other as well as internally between teams/organizational elements.”

Product Descriptions:

  • DEIXPedia: Micropedia of digital engineering topics to explain relevant DEIX topics. STATUS = In place and updating
  • Primer: A Narrative that describes the concepts and interrelationships between digital artifacts, enabling systems and exchange transactions. STATUS = DRAFT
  • Digital Engineering Information Exchange Mode (DEIXM): A prescriptive system model for exchanging digital artifacts in an engineering ecosystem. STATUS = DRAFT
  • DEIX Standards Framework (DEIX-SF): A framework for official standards related to MBSE (Model Based Systems Engineering) Information Exchanges. STATUS = DRAFT

Digital Thread Chart

RELATED: Traceability Score™ – An Empirical Way to Reduce the Risk of Late Requirements

Defense Systems Integrator – Digital Engineering Use Case and Lessons Learned

At the NDIA 25th Annual Systems and Mission Engineering Conference one large defense contractor presented their working tool ecosystem and explained their use case and lessons learned.

Use Case:

  • Provide system stakeholders with visibility into the system
  • For Example:
    • Determine impact of a Change to the System (e.g., requirement, model, part.)
    • Determine impact of Simulation to the System (e.g., validate or invalidate a requirement.)
  • To do this, I will need a digital engineering ecosystem that:
    • Enables the integration of repositories, i.e., requirements database, SysML models, PLM system.
    • Provides a framework for creating digital threads across data repositories.
    • Provides a mechanism for querying / visualizing digital threads.
    • Provides a way to compare/sync data repositories.
    • Can perform model/data transformations, e.g., DNG requirement → SysML requirement.

Dassault Cameo Systems Modeler is being used as the main user interface to coordinate with other models in other engineering domains, e.g., Creo, DNG etc.

Intercax’s Syndeia Cloud and Cameo plugin is good at connecting artifacts across different engineering repositories (DNG, Teamcenter, etc.) This group felt that Syndeia was the easiest to use and offered the most connections to their various tools. (Cameo, Teamwork Cloud, DNG, Teamcenter, Creo, Jira, GitHub, MySQL, Volta, Matlab/Simulink, Excel) They felt that in most cases Reference Connections were ideal and that reporting and analysis of the digital thread in the Syndeia Cloud webapp was more robust than anything other tool in the marketplace. They felt that Syndeia Cloud’s export of digital threads relationships into Excel to give to government customers was satisfactory.

Their Lessons Learned:

  • Define the Process for creating “reference connections”:
    • Who creates and manages the links.
    • Directionality of the link are consistent. Note: SysML element should always be identified as the source of the link.
  • Identify what types of links (digital threads) you want to create, for example:
    • Create reference connections from DNG functional requirement(s) to its SysML <<functional>> block to show a <<satisfy>> relationship.
    • Create reference connections from a Teamcenter part/item/assembly to a <<physical>> SysML block to show a <<satisfy>> relationship.
  • Establish operational and QA environments for Syndeia:
    • For testing out new patches and upgrades.
    • QA environment for training/experimentation.
  • Use caution of using “Local Repositories” (because they are local!)
  • Configuration manage your data repositories:
    • Teamwork Cloud for Cameo.
    • Global Configuration Management (GCM) for DNG.

RELATED: Write Better Requirements with Jama Connect Advisor™

Model-Based Acquisition (MBAcq)

An increasing number of RFPs are not only requiring MBSE but RFPs themselves are now starting to be created as SysML models and responses are expected to be returned in a SysML model file. Yes, there are SysML tool vendors (PTC, Spec Innovations) and even contractors (L3Harris) asking the DoD to drop its language so that the file format is compatible with Cameo. These vendors are trying to ensure they can export and import tool-agnostic SysML that is interoperable with each other.

The challenge to both the supplier and provider is the lack of standardization in the approach, resulting in a learning curve for every proposal as well as response. To address this concern, the OMG UAF MBAcq Working Group was formed to survey the current landscape with participation from government, industry, FFRDC’s, tool vendors, NDIA, and OMG standards org. The goal is to come up with process guidance and a SE (Systems Engineering) and architecture guidance.

Planned Deliverables:

  • ARM template and guidance (how to specify model-based DIDs, CDRLs.)
  • GRM template and guidance (includes guidance for how NOT to over-specify a system.)
  • Sample model (as part of UAF sample model.)
  • UAF Process Guide for Acquisition will:
    • Define the CONOPS for how a program office will use all the models they will receive over the lifetime or a system.
    • Demonstrate how to make models available for reuse for other/new systems.
    • Provide portfolio management for the models/programs.
    • Provide process and guidance that describes how to integrate MBSE approaches into pre-acquisition (before request for proposal release), request for proposal, contract award, and contract execution steps.
  • Impact to existing policy with recommendations for change.
  • Descriptions of what Sections K, L, and M could look like for model delivery.
  • Taxonomies with precise definitions for concepts and terms.
MBSE Digital Thread Chart

NDIA 2021 Systems & Mission Engineering Conference

Digital Engineering Tool Suites

According to SBE Vision, digital engineering tools are a mixed bag of silos:

  • Not all tools lend themselves to remote linking data at rest.
  • Some tools don’t have a web server.
  • Many detail design tools require a “local” model data as a basis for initial processing.
  • Sometimes a transformative capability of some kind is needed.
  • Certain use cases require a mashup of three or more systems.

Two Worlds Apart

SBE Vision is also developing techniques for both OLSC and synced data approaches. Complications can arise from linking vs syncing, such as company product, use case dependent, and can change over time.

OSCLS – Linked Data:
  • Remote linking of data
  • Peer-to-peer
  • Human oriented
  • Benefits:
    • Data stays at rest
    • Clean paradigm
    • Standards
  • Challenges:
    • Semantics (relational)
    • Configuration management
    • Change management
    • Consistency of standards implementation
  • Creating (temporary) copies of data
  • Hub-and-spoke
  • Human & machine Oriented
  • Benefits:
    • Enables many use cases
    • Simple, easy to use
    • Rich transformation when needed
  • Challenges:
    • Semantics (relational & transformative)
    • Configuration management
    • Change management
    • Managing correlation/sync state


Jama Connect enables real-time team collaboration through traceability and digital threads. To learn more about achieving Live Traceability™ on your projects, please reach out for a consultation.

IEC 15288

A comprehensive look at ISO/IEC/IEEE 15288 goals, standards, and tools to achieve compliance.

Product development is evolving quickly; over the past few years, it’s become increasingly complex. A study of nearly 300 design and engineering professionals found that 92% of respondents say they’re experiencing at least one form of increased complexity. Moreover, 76% say they’re experiencing at least three.

A set of standards, such as those found in ISO/IEC/IEEE 15288:2015, can help manage increased complexities using established frameworks. But if you aren’t familiar with the standards, you might have many questions such as: What is ISO/IEC/IEEE 15288:2015? What organizations use it? And how can it help with product development?

We’ve created a guide to help answer these questions so you can determine whether using this standard is right for your organization and explore other tools that can help.

RELATED: A Guide to Good Systems Engineering Best Practices: The Basics and Beyond

Why was ISO/IEC/IEEE 15288:2015 developed?

Building a new system is a large undertaking that involves a variety of moving parts and components. The success of any project, of course, relies on those parts working in synergy and solving for any potential disconnects. That’s why having a common set of practices can help. ISO/IEC/IEEE 15288:2015 was designed to create a standard reference of activities to be executed within a specific system engineering process. The standards are designed for those in systems engineering leadership, such as:

  1. Systems Architects
  2. Systems Developers
  3. Project Managers
  4. Computer Scientists

The standards are commonly used to guide internal work on systems development but can also be used as an external reference. For example, if you work with a partner, you might use ISO/IEC/IEEE 15288:2015 to help create agreements about how work is completed.

Building a New System is Growing More Complex

A recent study of almost 300 design and engineer professionals found that not only are engineering systems getting more complex, but many organizations aren’t equipped with the right tools to manage the intricacies of complex system development.

  • 92% of respondents reported experiencing at least one form of increased complexity.
  • 76% report dealing with three or more increased measures of complexity.
  • 25% report their products are becoming more complex in five or more ways.

How is ISO/IEC/IEEE 15288:2015 used?

During product and system development, you’re working to solve a specific customer challenge. Using ISO/IEC/IEEE 15288:2015 helps you accomplish this goal by providing a framework for your processes. But how exactly are standards typically used? Here are a few examples.

  • Used by an organization. An organization might use ISO/IEC/IEEE 15288:2015 to create an environment of desired processes. An infrastructure or method, procedures, technologies (and more) typically support these processes.
  • Used by the project. You might decide to use what is found in ISO/IEC/IEEE 15288:2015 as internal standards to support the deployment of an existing environment or offer a new system or service. In addition, standards are used to judge the performance of a project in a specific environment.
  • Supports partner agreements. Agreements are the foundation of any successful relationship, including those with suppliers or other external parties. You might partner with a supplier, for example, to select relevant processes and activities within the standards and create agreements based on those elements.
  • Used to evaluate processes. ISO/IEC/IEEE 15288:2015 can serve as a process reference model to determine whether your existing processes support a specific goal around process improvements.

As you can see, you have flexibility when using standards. You can implement all frameworks or just a few of them. ISO/IEC/IEEE 15288:2015 can serve as a starting point, selecting what fits best for your project, processes or organization to guide your decisions.

To learn more about what’s included in each of the six parts of ISEO/IEC/IEEE 15288 and how Jama Software can help, download our Comprehensive Guide here. 


In this blog, we define GAMP®5, the framework for a risk-based approach, with an introduction and conclusion provided by Jakob Khazanovich, Medical Device Solutions Consultant at Jama Software®.

This blog contains details sourced from the International Society for Pharmaceutical Engineering.

What is GAMP®5 and How Does Its Guidance Help Regulated Companies Using Computerized Systems?

When medical product companies decide to implement a new software tool, an important question arises regarding the level of computer system validation required to ensure the latest software complies with regulatory requirements and industry standards.

One major guidance document companies should reference to answer this question is Good Automated Manufacturing Practice (GAMP®5): A Risk-Based Approach to Compliant GxP Computerized Systems.

RELATED: Convergent Dental Selects Jama Connect® For Its Live Requirements Traceability


What is GAMP®?

GAMP® is a set of guidelines for producing quality equipment using the concept of prospective validation following a life cycle model. It was specifically designed by the International Society for Pharmaceutical Engineering (ISPE) to aid suppliers and users in the pharmaceutical industry.

GAMP stresses the use of critical thinking and risk-based assessments to justify the testing approach of a software tool. Its guidelines are widely supported by regulatory agencies and are used globally by regulated companies using computerized systems for compliance and validation.

What is GAMP®5?

GAMP®5 refers to the ISPE’s guidance document, “GAMP®5: A Risk-Based Approach to Compliant GxP Computerized Systems”. This GAMP®5 guide offers a framework for a risk-based approach to computer system validation in which a system is evaluated and assigned to a predefined category based on its intended use and complexity.

Though the steps in this guidance document are not mandatory, the framework provides a comprehensive approach to computer system validation that is generally accepted within the industry.

Additionally, its approach falls in line with the European EMA and US FDA regulations governing computer system validation, Annex 11 and 21 CFR Part 11.

Based on input from experienced IT, automation, and software practitioners, one of the reasons GAMP® guidance has always been successful is because it reflects the good practices for modern IT and software engineering teams.

RELATED: Jama Connect® and FDA 21 CFR Part 11

GAMP®5 Second Edition

Released in July 2022, GAMP® 5 Second Edition prioritizes patient safety and product quality over compliance and encourages the application of critical thinking. The overall GAMP® 5 framework, key concepts, and ICH Q9 aligned Quality Risk Management approach remain unchanged from the First Edition.

GAMP® 5 Second Edition supports standards set for forth by the FDA CDER (Center for Drug Evaluation and Research). Those standards call for “maximally efficient, agile, flexible manufacturing sector that reliably produces high-quality drug products without extensive regulatory oversight, where the vision requires moving beyond simply meeting minimum CGMP standards and towards robust quality management systems.”

This updated version of the guideline aims to help teams meet compliance expectations by offering best practices for IT teams, recommendations for optimal Quality Risk Management approaches, and ways to excel in software engineering, all while achieving better product quality and safety.


The risk-based approach to validation is a best practice seen in guidance documents and used by best-in-class medical industry organizations, many of which are Jama Connect® customers. In general, few tools require full validation but should have functionality confirmed through a subset of tests, the scope of which is determined based on the potential risk to the patient or product.

When your organization implements Jama Connect, consider consulting GAMP®5 guidance to avoid non-value-added over-validation and unnecessary constraints on your processes and systems.

DOD 5000

DOD 5000 Series

DOD 5000 Series consists of policy guidance and is backed by a collection of directives that describe a disciplined management approach for acquiring systems and materiel to satisfy valid military needs.

Six pathways of the adaptive acquisition framework: urgent capability acquisition; middle tier of acquisition (MTA); major capability acquisition; software acquisition; defense business systems (DBS); and defense acquisition of services. Up until a few years ago, it used to be that every program large and small had to follow the exact same acquisition process. Today, programs that are small and short-lived can follow a different process than that of a large multi-decade weapons system acquisition program. DOD 5000.02 policy encourages program managers to tailor, combine, and transition between pathways to create their own program strategies.

All programs regardless of the pathway share functional areas that must be considered during program execution. Acquisition Intelligence, Cybersecurity, Intellectual Property (IP) Policy, Mission Engineering, Systems Engineering, and Test & Evaluation are key areas every program must practice. Digital Engineering is a key constituent of program execution, and its vision is articulated by the Department of Defense 2018 Digital Engineering Strategy. It clearly highlights model-based systems engineering (MBSE) as a new approach to take. Digital Engineering crosses these functional areas and is where Jama Connect® assists program managers the most.

RELATED: Jama Connect®: Accelerating Systems Development with Requirements Management and Live Traceability™

Jama Connect® gives program managers and their acquisition team the ability to use Digital Engineering MBSE right from the start in an easy-to-use browser interface

The early phases of all DOD 5000.02 acquisition pathways require the definition of mission capabilities and needs. Instead of capturing this information in Word, Excel, or a SysML tool which requires a deep level of expertise; the Jama Connect solution will provide an Excel and Word-like experience but also segregate data as discrete model elements. An early acquisition MBSE approach in Jama Connect provides numerous benefits to the program team such as categorization of information; prioritization; version history of changes; status monitoring through workflow; real-time metrics on dashboards; exportable dashboard widgets for PowerPoint presentations; built-in document generation; activity monitoring; and more.

These MBSE data capabilities provide real-time monitoring of progress of the definition of mission needs and capabilities and more importantly gives all stakeholders the opportunity to participate in the capture and writing of the data. The learning curve of Jama Connect is five minutes to half a day compared to six months for SysML tools. This is especially important when there are urgent operational needs and other quick reaction capabilities need to be acquired. DoDD 5000.71 and DoDI 5000.81 both provide instruction for program management techniques to follow.

Jama Connect dashboard widgets.

DOD 5000.02 Acquisition Lifecycles

DOD 5000.02 instructions call for numerous reviews to take place throughout the acquisition lifecycles.

Acquisition Intelligence example use case: DOD 5000.86 Instruction provides policy and guidance on how to integrate intelligence information into the acquisition lifecycle. Typically, this is performed as a siloed activity with information captured in documents, passed back and forth, and reviews taking place in face-to-face meetings. Jama Connect enables the extension of the needs and requirements in the MBSE data model to threats, adversary capabilities, and adversary intentions. Jama Connect’s Live Traceability™ gives the program and intelligence teams the ability to share information and analyze it in a single model. Contextual collaboration mechanisms such as Review Center reduce cycle times spent on document review and approval. This integration of intelligence supports: (1) Characterization of the threat. (2) Identification of intelligence supportability plans, risks, and cost drivers. (3) Residual risk to inform stakeholders.

Cybersecurity example use case: DOD 5000.90 Instruction provides policy and guidance on how to incorporate cyber threat information produced by the Intelligence Community in development of their cyber security strategy and assessment of risk. Jama Connect lets the acquisition team see the relationships between designs and architectures and the identified risks. Live Traceability enables more informed decision making and could act as a conduit to the risk management framework (RMF) system. When new threats emerge, Jama Connect can provide instant impact analysis to a program’s existing mission requirements.

Systems Engineering example: DoDI 5000.88 begins by stating that engineering begins “at the identification of a military need and continues throughout sustainment of the end item.” Jama Connect can be used during all phases of the acquisition lifecycle and allows a systems engineering discipline to become central to program management rather than a siloed activity. Mission needs are captured in Jama Connect as discrete elements rather than Word documents or PowerPoint slides and can be reviewed, approved, and captured in the concept baseline. An element approach (aka digital engineering) allows for the easy consumption of data by digital tools in the tool ecosystem.

As ME products are developed in response to the mission needs and provide mission-based inputs to the requirements process, Jama Connect will establish trace relationships between needs, ME products, and the requirements. Live Traceability gives the ability for any stakeholder at any time to see the most up to date and complete upstream and downstream information for any requirement — no matter the stage of systems development or how many siloed tools and teams it spans. This enables the engineering process to be managed through data, and its performance improved in real time.

DoDI5000.88 calls for many technical and assessments throughout the life of a program. Digital engineering reviews in Jama Connect give the technical and non-technical engineer to not only redline, comment on, provide approval for the data itself, but allows for teams to give traceable reference. If the technical review is that of requirements, then reviewers would have the context to see the related mission needs, any analysis, architecture, any tests, as well as understand the evolution of change to each individual requirement in that review. Reviews in this manner provide significant reduction of cycle times and retains a traceable audit trail of commentors and approvers.

Jama Connect Review Center

Jama Connect Review Center, participant progress view.

RELATED: Ensure Product Quality with These Review Process Best Practices


Jama Connect can provide capabilities to assist government program management teams execute parts of the adaptive acquisition framework and tie together information via Live Traceability and collaboration mechanisms. Program decisions are informed by real time data and is accessible to engineering and non-engineering stakeholders alike.

In summary, there are many opportunities to use Jama Connect as a key enabler of Digital Engineering across all phases of the adaptive acquisition framework no matter which pathway is chosen.

Risk Traceability

Incorporating Risk Traceability into Manufacturing Production Software and Preparing for the Transition from CSV to CSA


Over the years, the burden of Computer Systems Validation (CSV) has resulted in medical device manufacturers avoiding implementation of automated manufacturing production systems or upgrading long-outdated versions of software. As part of the FDA’s ‘Case for Quality’ initiative in 2011 to study quality best practice in medical device manufacturing, the FDA found that the burden of compliance, such what is expected for Computer Systems Validation (CSV), deterred technology investments and as a result, inhibited quality best-practice.

As an outcome, the FDA is expected to release a draft guidance in 2022 that outlines their new approach of Computer Software Assurance (CSA) for Production and Quality System Software. The goal is to improve software quality by focusing on the software’s impact to patient safety, impact to product quality, and impact to quality system integrity. Using a risk-based approach, manufacturers can spend more time testing to ensure software meets its intended use, instead of ensuring test protocols and reports are fully scripted are error free.

As the key to right-sizing CSA activities is based on risk, managing risk, and risk traceability and incorporating is a key activity. And there’s no need to wait for the publication of the FDA CSA draft guidance, medical device manufacturers can incorporate these risk management and traceability practices now in their CSV activities.

Keep reading below for best practices on how to extend risk management and risk traceability from the medical device development to your manufacturing production software used to manufacture your devices.

Main Points for How

Consider the product risks and hazards

Even though we are discussing manufacturing production software and the software used on manufacturing equipment, the first step is to consider the product risks and hazards. Start with your master list of hazards, hazardous situations, and associated harms developed during medical device development and determine where and how failures in the manufacturing production software and production equipment can result in those risks and hazards. These are the areas to consider as the highest risks to control, mitigate, and validate.

Next, perform additional risk analysis from the lens of the environment and manufacturing personnel that will operate said equipment and software. It is important to also protect the environment and prevent injury and harm to those interacting with the equipment. You don’t want the unintended release of hazardous chemical reagents or equipment fires that could have been prevented with built-in high temperature shut-down feature.

RELATED: Managing Complexity in Systems Engineering for Product Development with Live Traceability

Create manufacturing production software intended uses and requirements

Just as one determines the intended use and design input requirements of a medical device, the same is to be done for the manufacturing production software and equipment. Incorporate the necessary features as manufacturing requirements (analogous to the design inputs of your medical device) that address the risks identified in the previous step.

One example is based on risk of the product and demonstrates how risk traces from the development of the medical device. Consider the design of a semi-automated assembly machine for a drug-delivery device. The machine aligns and press-fits together two plastic injection-molded halves of the delivery sub-assembly. One of the main product associated risks is ensuring proper flow efficiency to ensure the proper amount of drug is delivered correctly and to the proper anatomical location.

In this case the risk traceability and translation to the assembly machine design inputs is:

RELATED: Application of Risk Analysis Techniques in Jama Connect® to Satisfy ISO 14971

Perform software testing and equipment validation based on risk

Once ready to test the production equipment software and perform equipment validation, incorporate, you guessed it, risk. All manufacturing requirements then need to be verified, however, scale the amount and type of testing for each manufacturing requirement based on the risk. Aspects of the software and equipment that are of high risk, such as preventing severe harm to operators, or the end users and patients of the medical device will have more testing and documentation than those of lower risk.

Document risk traceability of testing and equipment validation

Just as documenting risk traceability for medical device is important, it’s also important for manufacturing production software and equipment. Using a tool like Jama Connect® makes it easy to link your master list of hazards, hazardous situations, and harms to your manufacturing requirements for manufacturing production software and equipment. This increases consistency and efficiency in the risk analysis and prioritizing efforts on where it matters for patient and operator safety. The interface of Jama Connect® also makes the traceability and documentation of the associated testing and validation easy to visualize, alerting teams to gaps and incomplete testing.


Extending risk traceability from your medical device development to your manufacturing production software and equipment brings focus to what can impact patient safety and scales the testing and validation work appropriately. Incorporate risk and you’ll also be well on your way for the upcoming FDA transition from CSV to CSA.

Systems Engineering

Editor’s Note: This blog was originally published through IEEE Innovation at Work, “Managing Complexity in Systems Engineering for Product Development with Live Traceability,” by author Nikhil Rai, IEEE Senior Member, Senior Director of Product and Solutions Marketing at Jama Software.

Systems Engineering for Product Development with Live Traceability

Complexity of Product Development is Exploding

In today’s world, technological innovations across embedded systems, the Internet of Things (IoT), and artificial intelligence (AI) are aggressively pushing the envelope on product complexity and accelerating digital transformation. The next generation of autonomous cars, electric vehicles, robots, and space shuttles are all complex systems that need to seamlessly interact with multiple layers of software, hardware, and humans across different environments.

Because complex technology products require intricate interfaces with various systems or sub systems, there is an inherent challenge of coordinating the multiple development teams, who are often geographically spread across the world. Furthermore, large teams typically work on different stages of product development within their own silos. The different development tools, architecture, and IT environments used across various teams and organizations can significantly add to the challenges of developing new technologies.

Manufacturing sectors will reap the AI technology benefits of enhanced monitoring and supply chain optimization. In financial institutions, AI techniques can be used to identify which transactions are likely to be fraudulent. Through AI, the industry could also adopt fast and accurate credit scoring while automating manually intense data management tasks. Disruptions in the transport and logistics realm could include autonomous trucking and delivery on the roads and automated picking in warehouses.

RELATED: Systems Engineers Career Path – How to Elevate

Reality of Today’s Complex Systems Development

Most complex system development starts with defining user needs and requirements, which involves design, software, and hardware teams developing deliverables that ultimately get tested and integrated for delivering the product. Given the disparate nature of these teams, there is an inherent need to optimize and continuously keep track of systems information from concept to development and delivery. However, there is often a gap in terms of how information is consumed across engineering departments, as well as its availability and traceability. The digital thread in engineering today is still a collection of different tools, which means traceability is usually accomplished in a cumbersome and manual fashion via time-consuming meetings and disconnected documents.

Challenges in Traceability

Traceability is a key both to product development and software engineering for tracking the systems development lifecycle. Helpful in achieving regulatory compliance, traceability can also be used within software engineering for running test cases and gaining valuable insights.

While some organizations make it a point to map product development stages and improve traceability to gain these benefits, traceability is often considered after-the-fact by others. This means that once an issue is identified, there is effort required to trace back where the error originally occurred to prevent that same failure from occurring in the future. This approach can cause multiple cost overruns, product delays, and inefficient management of change impacts. Think of addressing a car recall or fixing a complex medical device or industrial equipment— the costs can be enormous. By emphasizing the importance of traceability from the start, organizations can avoid the extra effort and cost associated with implementing it after an error has been made.

RELATED: Requirements Traceability – How to Go Live

Live Traceability™

The solution lies in bringing a practical approach of Live Traceability™ to the systems engineering world. Optimizing and measuring the systems development process with Live Traceability is a crucial competitive advantage that companies can leverage. For any complex product, systems, or software development, the requirements and user needs form the first level of abstraction. Live Traceability can be measured and monitored by using systems requirements as an anchor to manage information used in different stages of the systems development lifecycle.

At Jama Software, we define live requirements traceability as the ability for any engineer at any time to see the most up-to-date and complete upstream and downstream information for any requirement— no matter the stage of systems development or how many siloed tools and teams it spans. Live Traceability of system requirements is required by industry standards to ensure product safety and forms the foundation for digital engineering and model-based systems engineering. This enables the engineering process to be managed through data and its performance improved in real time.

Through better requirements management and a focus on traceability, companies can improve their systems development performance with higher quality products and improved cycle times. The industry is now embracing the idea that to improve engineering efficiency within the systems development process, we need to continuously monitor and measure traceability.


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.   


Systems Engineers Career Path

Most Systems Engineers we speak with have a common perspective on their role within their organization. Systems engineering as a concept is understood and supported by leadership. And, if systems engineering best practices are followed across all engineering disciplines, leadership also acknowledges the benefits the organization can realize. However, leadership will not force change on engineering disciplines (software, hardware, electrical, risk, verification, and validation) to remove barriers to systems engineering best practice adoption. This leaves most Systems Engineers in the challenging position of possessing responsibility for achieving systems engineering benefits without the authority to ensure engineers adopt best practices. 

The good news is that there is a way out of this situation. The first step to elevating your career is to realize that managing the product engineering process through data is the key and that the Systems Engineering role is the natural role to lead this. For most organizations, the product engineering process is the only process in the company that is not managed through data. No one can go to a system and see the status of all product requirements and where development, integration, risk, and testing stand at any point in time. There is no way to manage by exception. There are no alerts that requirements are not covered, test cases are missed or that hardware made a change that now impacts software. Most organizations have come to accept this state of affairs and try to manage the process through endless meetings, emails, and Slack messages.   

Until the product engineering process is manageable through data, Systems Engineers will be stuck in their current trap — endlessly trying to address issues after the fact, holding unwanted meetings, and the uphill battle of trying to persuade changes in behavior. Below is a 3-step approach that Systems Engineers we work with have used to solve this organizational challenge and have thereby elevated their careers. As with any process change, it is best to do it in stages. You can start with the following steps. 

  1. Baseline current process performance 
  2. Build the business case for change to gain support 
  3. Deliver quick wins

RELATED POST: How to Overcome Organizational Barriers to Live Requirements Traceability

Step One | Baseline Current Traceability Performance 

The first step towards moving the organization to manage the product engineering process through data is to baseline current process performance.  The best place to focus the baseline effort is on traceability since it spans the entire product development process, is a data management concept that is understood, enables systems engineering benefits, and is required by industry standards. To ease the baselining effort, we’ve developed a Traceability Diagnostic that you can use to assess your current situation. The Diagnostic inventories traceable data, the systems in which they reside, and your current Traceability Score™. This is a few-hour effort and forms the basis of the business case in step two.

In this no-cost, guided process, we’ll help you:

  • Understand the monetary risk of your current Traceability Debt™
  • Uncover the quantifiable ROI of moving to Live Traceability
  • Develop a clear plan of action, cost, and timing to achieve Live Traceability

To Get Started With Your Free Traceability Diagnostic,  CLICK HERE

Step Two | Build Business Case for Live Traceability™ 

Once you have established a baseline, it is now possible to build a business case for change that will resonate with leadership. Based on your baseline, the Traceability Diagnostic determines the probability of negative product outcomes (defects, delays, rework, cost overruns, etc.) and the magnitude of these events. This quantifies the risk of maintaining the status quo and doing nothing. In addition to the risk reduction potential of Live Traceability, the Diagnostic also calculates the engineering productivity gains from eliminating the need for time-consuming, manual, after-the-fact traceability efforts. 

Step Three | Start with Quick Wins 

Once you have secured support to move forward, it is common to be able to deliver some quick wins to the organization shortly after project kick-off.  The typical place to start is the painful and time-consuming after-the-fact traceability efforts.  For example, continuous syncing between requirements and software development task management in Jira or Azure DevOps can be set up quickly to automate traceability from requirements to user stories, eliminating a large source of risk and manual, after-the-fact traceability effort. Once quick wins are shown, organizational momentum increases even further and puts you on the success path to begin managing the product engineering process through data.     

Clients of ours that have taken this approach have received significant recognition, been promoted into roles with greater leadership, and have increased their external visibility through speaking engagements. Live Traceability is a unique opportunity to elevate one’s career. Don’t miss the chance. 


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