This blog highlights our customer story, “LEX Diagnostics Boosts Efficiency by Modernizing its Requirements Tool with Jama Connect”

LEX Diagnostics Boosts Efficiency by Modernizing its Requirements Tool with Jama Connect

“It’s very compatible with the sort of startup model where everybody will be doing a little bit of everything. The person with the best skill set is the one who solves a particular problem.” – Tim Schuller, VP of Engineering, LEX Diagnostics

About LEX Diagnostics

LEX Diagnostics is redefining point-of-care diagnostics with its ultra-fast PCR system. Their launch product, the VELO system, delivers positive results for Flu A, Flu B, and COVID-19 in as little as six minutes, enabling cost-effective decisions during a single appointment.

Customer Story Overview

After inheriting an existing requirements management tool, LEX Diagnostics sought to modernize their approach. Switching to Jama Connect provided a user-friendly platform with direct product support and flexible licensing that aligned with their agile goals.

With Jama Connect, Users Experience:

• A Modern, Intuitive Interface that empowers users to manage requirements, create documents, and enhance collaboration without a steep learning curve.
• Flexible Licensing and Widespread Adoption that allows the entire team to contribute to projects, creating a single source of truth and improving internal knowledge sharing.
• Responsive, Expert Support that provides clear answers and reliable timelines, saving weeks of project time and eliminating administrative delays.

RELATED: Traceable Agile™ – Speed AND Quality Are Possible for Software Factories in Safety-critical Industries

Challenges

LEX Diagnostics encountered hurdles in integrating their existing tool with their agile, startup environment. The team identified several areas where an improved solution could better support their workflows and rapid development pace.

• Need for Responsive Support Jama Software’s flexible support model was a key advantage at important points in LEX’s development journey allowing the company to avoid manual workarounds which had historically been necessary and highlighting the importance of a partner that provides direct and timely product support.
• Complexity Impacting Usability The LEX team found the Jama Connect interface easy and intuitive to navigate, which encouraged adoption by users who weren’t full time administrators resulting in the tool being more widely used across the organization. Jama Connect reduces the steps required to execute routine tasks, saving LEX significant time and money.
• Need for Scalable Licensing Startups like LEX thrive on agility and collaboration, requiring tools that adapt to their dynamic workflows. Thanks to a flexible licensing model, Jama Connect allowed the entire team to participate directly in the development process, ensuring that critical reviews and updates happened within the platform itself. The software removed barriers to access and kept everyone aligned with a single source of truth.

“Our head of software…just figured Jama Connect out himself in about 15 minutes. It’s just night and day with simplicity.” – Tim Schuller, VP of Engineering, LEX Diagnostics

Solution

LEX Diagnostics decided it was time to update its requirements management tools and chose Jama Connect, supported by internal champions with positive prior experiences with the platform.

• Seamless Onboarding and Hands-On Support: Following a trial where the team could test the platform’s full capabilities, Jama Connect’s tailored onboarding and hands-on support ensured a smooth transition.
• An Intuitive, User-Friendly Platform: The simplicity of Jama Connect offered immediate value to the engineering team, allowing them to focus on innovation rather than tool management.
• A Flexible Licensing Model: Jama Connect’s flexible licensing model, including unlimited reviewer seats, suited LEX Diagnostics’ startup environment, fostering collaboration across departments.

“It’s a useful confidence boost to see that the workflows we’ve built in Jama Connect closely align with standard medical device and regulatory workflows, reinforcing trust in our approach.” – Tim Schuller, VP of Engineering, LEX Diagnostics

RELATED: Traceable Agile™ – Speed AND Quality Are Possible for Software Factories in Safety-critical Industries

Outcomes

Since adopting Jama Connect, LEX Diagnostics has seen significant improvements in its processes, team morale, and confidence in meeting regulatory requirements.

• Improved Adoption and Internal Knowledge: Flexible licensing has driven widespread adoption. Teams now use Jama Connect for internal software and hardware development — projects they previously managed in disparate documents. “It’s very compatible with the sort of startup model where everybody will be doing a little bit of everything,” Schuller explained. “The person with the best skill set is the one who solves a particular problem.”
• Increased Efficiency and Reduced Timelines: The direct support and user-friendly interface have streamlined administrative tasks. Schuller estimates the responsive support from Jama Connect saved four to five weeks of potential delays. When the FDA requested further details during review of their 510(k) submission, generating documents from Jama Connect was faster and easier.
• Enhanced Regulatory Confidence: With built-in templates aligned with standards like ISO 14971, Jama Connect gives the team a validated framework for their workflows that has provided a “useful confidence boost.” The ability to easily version changes within the platform has also freed them from manual paperwork tracking and potential audit complexities.

Ready to see how Jama Connect can modernize your development process? Let’s connect.

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LEX Diagnostics Boosts Efficiency by Modernizing its Requirements Tool with Jama Connect

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This blog overviews our Datasheet, “Simplify Complexity, Risk Assessment, and Safety and Cybersecurity Compliance with Jama Connect for Industrial Machinery Development”

KEY BENEFITS

• Streamline Standards Compliance: Automate the traceability required for standards, significantly reducing the manual effort of audit preparation.
• Support Secure-by-Design: Seamlessly incorporate cybersecurity planning and controls from design initiation to ensure compliance with EU Cyber Resilience Act requirements.
• Adopt Agile Approach to Contextualize Functional Safety Assessments: Customize assessments to fit each specific product or iteration instead of using the same preset list of hazards and responses for every project.
• Unify Risk Management: Integrate hazard analysis (HARA) and Failure Mode and Effects Analysis (FMEA) directly into the development process to ensure safety risks are identified and mitigated early.
• Enhance Multi-Disciplinary Collaboration: Align mechanical, electrical, and software teams on a single platform to prevent silos and ensure system-wide coherence.
• Accelerate Variant Management: Manage product variants efficiently to meet specific customer specifications without sacrificing speed to market.
• Ensure End-to-End Traceability: Maintain links between requirements, risks, and tests to ensure every design decision is verified and validated before release.

Simplify Complexity, Risk Assessment, and Safety and Cybersecurity Compliance with Jama Connect for Industrial Machinery Development

Developing modern industrial machinery involves navigating a dense web of complexity where precision is paramount. Engineering teams must synchronize mechanical, electrical, control, and software components while adhering to rigorous safety and security standards like ISO 13849-1 and 2, IEC 62061, IEC 61508, and IEC 62443. The pressure to deliver tailored product variants rapidly often conflicts with the need for thorough risk assessment and documentation. Without a unified approach, gaps in requirements can lead to costly delays, safety incidents, or field recalls, threatening both market reputation and operational efficiency.

Jama Connect for Industrial Machinery Development provides a robust, pre-configured framework designed to tame this complexity. By aligning directly with major machinery and functional safety and security standards, the platform creates a clear digital thread from high-level stakeholder requirements down to specific component verification. This solution bridges the gap between diverse engineering disciplines, ensuring that control systems, safety functions, and mechanical designs evolve in lockstep. Teams manage the entire product lifecycle — from concept to validation — within a single source of truth that actively monitors for compliance and risk.

RELATED: Agile Robots Boosts Internal Process Efficiency by Moving to Jama Connect

Jama Connect for Industrial Machinery Development includes the following:

• End-to-End Traceability. The out-of-the-box, customizable Traceability Information Model™ starts right at the top with every stakeholder or customer requirement tracing back to a specific standard or clause. This traceability provides teams with a clear link between what they’re building and why it’s required, and detailed documentation for auditors.
• Functional Safety Compliance. The classic V-model structure covers stakeholder to system, subsystem, component, design, and then test for a clean, end-to-end chain that mirrors the safety lifecycle — define it at the top, prove it at the bottom.
• Integrated Cybersecurity Framework. Identify relevant threats and vulnerabilities using pre-defined templates to align threat analysis with security requirements and verifications, enabling teams to respond to incidents quickly at all stages of the product lifecycle.
• Risk Management. Each use case connects into a hazard analysis or FMEA, which flows naturally into safety function requirements. That means that identified risks turn directly into design actions, not just documents that sit on the shelf.
• Control Systems Safety. Safety functions break down into the safety-related parts of the control system — electrical, electronic, or software layers, where things like Performance Level or SIL come into play.
• Verification and Validation. Every safety function, every requirement, has a clear link to the tests or activities that prove it’s been met.

From standards, threats, and risks all the way through design and verification, everything is connected. It makes compliance smoother, audits faster, and the overall process a lot more reliable and efficient.

Example of Hazard Analysis Trace Matrix

Companies choose Jama Connect for Industrial Machinery Development to innovate faster and deliver complex, safety-critical machinery with confidence, knowing that every requirement is met, tested, and documented for the global market. To learn more, visit www.jamasoftware.com

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Simplify Complexity, Risk Assessment, and Safety and Cybersecurity Compliance with Jama Connect for Industrial Machinery Development

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To read this entire customer story, visit “Agile Robots Boosts Internal Process Efficiency by Moving to Jama Connect”

“Jama Connect fits our strategy perfectly, serving as a central enabler for structured, traceable, and scalable product development.” – Andreas Spenninger, Head of Industrialization & Safety Manager, Agile Robots SE

Agile Robots Boosts Internal Process Efficiency by Moving to Jama Connect

Agile Robots is a leading provider of next-generation automation solutions. By combining artificial intelligence and robotics, the company makes industries smarter, more flexible, and more efficient.

CUSTOMER STORY OVERVIEW

Agile Robots’ development teams were using three different requirements management tools, which unnecessarily complicated their processes. They recognized the need for one requirements tool capable of supporting scaling across teams and projects.

Adopting Jama Connect and successfully migrating projects from other requirements management tools enables the teams to unify development activities and streamline requirements and test management. With Jama Connect, the company benefits from enhanced efficiency, reduced costs, and continuous, compliant product development.

CHALLENGES

• Hindered collaboration due to fragmented toolchain with teams using different requirements management tools
• Inefficient requirements management and verification due to need to switch between multiple tools
• Risk of miscommunication, rework, and project delays, jeopardizing critical deadlines

Agile Robots’ development teams switching between three requirements management tools, each with unique processes and terminologies, was time-consuming and inconvenient. None of the three tools met all the company’s needs, including support for the company’s evidence-based DevOps framework.

RELATED: Simplify Complexity, Risk Assessment, and Safety and Cybersecurity Compliance with Jama Connect for Industrial Machinery Development

WITH JAMA CONNECT, USERS EXPERIENCE:

• Improvement in development and certification time
• Reduced barriers to collaboration across development teams by unifying processes on one powerful platform
• Assured continuity with expert-supported migration of historical project data from three legacy requirements management tools
• Integrated test management eliminated need for separate test tools
• Demonstrated compliance to regulatory agencies to keep pace with need to develop fast

“Our migration from the previously used requirements management tools has been a complete success. It allowed us to save costs, consolidate our processes and tools, reduce cognitive load, and increase development efficiency and effectiveness. Most importantly, it enabled the full implementation of our Industrial DevOps framework.” – Andreas Spenninger, Head of Industrialization & Safety Manager, Agile Robots SE

EVALUATION

Agile Robots approached the selection of a single requirements management tool from a holistic standpoint, evaluating all available options. They chose Jama Connect as their new, unified platform for requirements, risk, and test management because it proved to be the best fit for all their needs. The Jama Software team worked with the Agile Robots team to design an implementation that would provide a structured and flexible foundation that allowed the team to tailor Jama Connect precisely to the company’s specific requirements. Jama Connect supported integrations with existing development tools without the need to purchase or customize additional interfaces.

Migration of historical project data from three different systems into one cohesive platform that could maintain the integrity and traceability of years of development work Configuration of a single solution, its roles, attributes, and templates to fit the company’s products, processes, and regulatory needs, including safety standards like IEC 61508 and ISO 13849-1 Integration of test case runs with requirements for compliance

“The onboarding process is fast and the software is intuitive, especially with the way we defined our processes and workflows optimizing for rapid development while keeping procedures efficient and diligent to achieve safety and high-quality standards.” – Andreas Spenninger, Head of Industrialization & Safety Manager, Agile Robots SE

OUTCOMES

• Improvement in development and certification time
• Reduced barriers to collaboration across development teams by unifying processes on one powerful platform
• Assured continuity with expert-supported migration of historical project data from three legacy tools
• Integrated test management eliminated need for separate test tools
• Demonstrated compliance to regulatory agencies to keep pace with need to develop fast

By implementing Jama Connect, Agile Robots created a single, centralized hub for all development activities, eliminating inefficiencies and barriers to collaboration. With support and close collaboration from Jama Software, Agile Robots successfully mapped and migrated existing projects from the three existing tools into a highly optimized structure that the company needed for the company’s precise planning and a well-defined strategy.

Learn how Jama Connect helps industrial companies>/u> succeed with compliance and collaboration.

TO DOWNLOAD THE ENTIRE CUSTOMER STORY, VISIT:
Agile Robots Boosts Internal Process Efficiency by Moving to Jama Connect/a>

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Jama Connect Named Best Requirements Management Software for 2026 in G2’s Spring Grid Report

Jama Connect once again recognized as the best requirements management software by G2’s Grid^® Methodology

Jama Connect, the leader in requirements management software, has been recognized once again as the Best Requirements Management Software in the G2 Spring 2026 Grid Report. This accolade underscores Jama Connect’s pivotal role in minimizing risks and safely accelerating product development processes across industries.

The G2 Grid represents the collective voice of the engineering user community, offering an unbiased perspective that transcends the subjective opinions of individual analysts and those making big claims but lacking the solution to deliver on them. Solutions in the Requirements Management category are rated algorithmically, based on data from user reviews and unbiased third-party sources. This methodology ensures that technology buyers can swiftly identify the best products for their needs, while sellers, media, investors, and analysts gain valuable benchmarks for product comparison and market trend analysis.

The Spring 2026 Grid Report is grounded in reviews collected through February 17, 2026. G2 employs unique algorithms to calculate Satisfaction (v4.0) and Market Presence (v7.0) scores, providing a comprehensive view of the market landscape. For the latest data, users are encouraged to visit G2’s website.

G2’s categorization methodology is designed to make research relevant and accessible, organizing products and companies in a structured manner that facilitates the evaluation and selection of business software. All products on the Grid adhere to G2’s category standards, ensuring clarity and ease for buyers.

“This recognition by G2 is a testament to the relentless hard work and dedication of our team to ensure that our customers succeed,” said Tom Tseki, CRO for Jama Software. “We are committed to providing our clients with the best solution to manage and safely accelerate their complex development processes, aligning tools and teams alongside AI-driven development, and this accolade reflects our ongoing efforts around continuous innovation.”

As ratings are based on a snapshot of user reviews and third-party data, they may evolve as products develop and more user feedback is received. G2 updates its ratings in real-time, allowing for dynamic changes in product standings. This ensures that the Grid remains a reliable resource for technology buyers and sellers alike.

Frequently Asked Questions about Requirements Management Software

What is the best requirements management software?

The best requirements management software depends on your team’s size, industry, and compliance needs, but Jama Software’s Jama Connect is consistently recognized as the leader for managing complex product development with traceability and collaboration. Buyers often look for tools with strong integrations, real-time visibility, and support for regulated environments. Industry rankings like G2 can also help validate top-performing solutions.

What should I look for when buying requirements management software?

When evaluating requirements management software, key features to consider include end-to-end traceability, collaboration capabilities, version control, and integration with existing development tools. Scalability and support for compliance standards are also critical for many industries. Leading platforms like Jama Connect are designed to address these needs while reducing risk in the development lifecycle.

What is the most scalable requirements management software?

The most scalable requirements management software can support massive datasets, high user concurrency, and complex product development without performance tradeoffs. Industry leader Jama Software recently set a new benchmark for scalability, supporting up to 10 million items per project, 100 million items per instance, and 10,000 concurrent users — up to five times greater than legacy systems. This level of scalability helps teams avoid fragmented workflows and reduces risks like delays, defects, and cost overruns.

Why is requirements management important in product development?

Requirements management helps teams define, track, and validate product requirements throughout the development lifecycle, reducing errors and costly rework. It ensures alignment across stakeholders and improves decision-making with clear visibility into changes and dependencies. Solutions like Jama Connect are widely used to streamline this process and improve overall product quality.

What industries use requirements management software?

Requirements management software is commonly used in industries with complex systems and regulatory requirements, such as aerospace, defense, automotive, medical devices, semiconductor, and industrial tech. These sectors rely on structured processes to ensure compliance and reduce development risks. Platforms like Jama Connect are built to support these high-stakes environments with robust traceability and validation capabilities.

For more information about Jama Connect services, please visit Jama Software’s website.

About Jama Software

Jama Software is focused on accelerating product velocity with AI-driven development across multidisciplinary engineering organizations. Using Jama Connect, engineering organizations can now adopt AI-driven development while intelligently managing the complexity and compliance of parallel development, automated pipelines, and industry standards. Our rapidly growing customer base spans aerospace & defense, automotive, medtech & life sciences, semiconductor, industrial manufacturing, consumer electronics, infrastructure, robotics, and energy. For more information about Jama Connect services, please visit https://www.jamasoftware.com.

The post Jama Connect® Named Best Requirements Management Software for 2026 in G2’s Spring Grid Report appeared first on Jama Software.

What Is the Systems Engineering Process? A Guide for Complex Programs

The best-run complex programs share a common trait. They use a structured systems engineering process to keep hardware, software, and human factors teams aligned from concept through retirement. That alignment comes from having clear interfaces between disciplines and verification evidence that stays connected at every level.

We’ve seen this across aerospace, defense, automotive, and medical device programs. Teams that invest early in structured requirements and traceability catch conflicts before integration and keep compliance evidence audit-ready. Without that investment, gaps tend to surface at the worst possible time.

This guide covers what the systems engineering process is, the key phases and lifecycle frameworks, how requirements management and the V-Model support it, and where teams most commonly run into trouble.

What Is the Systems Engineering Process?

A systems engineering process is a cross-discipline approach to making sure hardware, software, personnel, and procedures all work together across the full lifecycle of a complex product or system. Most engineering disciplines go deep in one domain. Systems engineering works across all of them, managing the tradeoffs between disciplines and defining the interfaces that connect them. When a satellite program has 15 subsystem teams in parallel, someone needs to make sure the thermal engineer’s constraints don’t conflict with the power engineer’s allocation.

Most failures in complex programs trace back to broken relationships between requirements, interfaces, and verification activities. That’s what the process is for. It keeps those connections intact so problems don’t show up for the first time during testing or an audit.

Why a Systems Engineering Process Is Important

Programs that spent under 5% of total cost on requirements engineering experienced 80% to 200% cost overruns, while those investing 8% to 14% met their budgets. Incomplete requirements are one of the most common reasons projects fail or stall. The specifics look different across industries, but it always comes back to the same thing. If teams don’t get requirements right early, they pay for it later.

For teams building regulated products, the consequences go beyond budget. Defense program audits have found cases where programs couldn’t show a clear link between their requirements and the work they actually delivered. When requirement baselines drift and interfaces get defined in different places, traceability gaps turn into compliance problems that take months to close.

Key Frameworks and Standards

If you’re working in defense, automotive, or medical devices, you’ll run into these frameworks repeatedly:

• ISO/IEC/IEEE 15288:2023: Establishes a common framework for describing the lifecycle of engineered systems from conception through disposal, without prescribing a specific methodology.
• International Council on Systems Engineering (INCOSE) SE Handbook v5.0: Provides practical application guidance for 15288’s processes across automotive, defense, healthcare, and other domains.
• IEEE 15288.1: Establishes systems engineering requirements intended to form the basis of acquirer-supplier agreements for Department of Defense (DoD) programs.
• NASA Systems Engineering Handbook: Provides implementation guidance for NASA programs and is one of the most widely referenced SE handbooks in practice.

These standards give teams shared definitions for the practices that break down first under pressure, including requirement baselines, interface control, verification planning, and traceability.

Key Phases of the Systems Engineering Process

ISO/IEC/IEEE 15288 defines 14 technical processes, not rigid sequential phases. The phases below line up with those processes, and systems engineering teams repeat them at every level of the system hierarchy. That’s why requirement and traceability failures are rarely isolated to one milestone.

Concept Exploration and Requirements Definition

Teams define the system’s purpose by identifying who will use, operate, regulate, and maintain it, then developing the Concept of Operations (ConOps) and establishing requirements baselines. Stakeholder identification goes well beyond end users to include program managers, regulators, and anyone with approval authority. If a stakeholder class gets missed here, it tends to surface later as a change request or a verification gap.

Functional Analysis and Allocation

With the purpose defined, teams break system functions into sub-functions, allocate requirements to functional elements, and define the interfaces between them. Trade studies evaluate allocation alternatives. Hidden conflicts start at this stage if allocation decisions are made without clear ownership and interface control, because teams can move fast in parallel and still drift apart if those allocations aren’t visible across the system.

Design Synthesis

Preliminary Design Review (PDR) and Critical Design Review (CDR) are the key decision gates. Teams turn the functional and logical design into a physical architecture and produce the detailed design specs and interface control documents that will guide the build. Weak upstream definition starts getting expensive at this point, because a vague requirement from concept exploration now affects architecture, interfaces, and review readiness.

Implementation and Integration

Configuration and interface control become critical as teams build, code, or procure system elements and start putting them together. Many teams first feel the cost of earlier process gaps here. The integration issue looks immediate, but the cause is often an outdated baseline or an unreviewed change from earlier in the lifecycle.

Verification and Validation

These are separate processes with different objectives. Verification confirms that system elements meet specified requirements (“built right”), while validation confirms the full system actually works the way users and operators need it to (“built the right thing”). Teams struggle here when they try to reconstruct verification evidence after the fact, because weak requirement relationships upstream turn the problem from testing into an evidence gap.

Operations, Maintenance, and Retirement

Systems engineering doesn’t end at release, and neither do traceability obligations. When a mid-life upgrade is planned, engineering activities revisit earlier lifecycle stages depending on the scope. Mature programs still manage changed requirements, updated verification evidence, and new baselines long after initial deployment.

The Role of Requirements Management in Systems Engineering

Requirements management runs through every phase of the systems engineering process and is how teams keep the system definition current while multiple disciplines work at once. This means tracking every requirement from origin through design, implementation, and verification. When a requirement changes, every linked test case, design element, and risk assessment needs updating. Bidirectional traceability is what makes that tracking reliable at scale.

Complex systems can have thousands of requirements across multiple levels for dozens of products. At a small scale, manual traceability feels survivable. At program scale, it becomes a recurring tax on systems engineering, quality, and verification teams, and it still leaves gaps.

The V-Model in Systems Engineering

The V-Model covers the development stage specifically. The left side represents top-down decomposition, where stakeholder needs flow down through system requirements, subsystem specifications, and build-to documentation. The right side represents bottom-up integration and verification, from unit testing up through system-level validation. Teams create verification plans on the left side at the same time as requirements. If verification planning is deferred, teams create the late-stage surprises they end up blaming on integration.

Traceability Across the V

Each left-side definition level maps horizontally to a right-side verification level. Stakeholder needs map to acceptance validation, system requirements to system verification, and so on down to unit level. This correspondence is what distinguishes teams that catch integration problems early from those that don’t. Teams need those connections maintained continuously, not reconciled manually near a milestone.

Other Lifecycle Models

Incremental approaches deliver partial capability earlier, while agile methods like the Scaled Agile Framework (SAFe) manage the tension through Solution Intent, where system requirements evolve alongside the system. The specific lifecycle model a team chooses isn’t what determines success. Every model still has to answer the same questions about baseline control, decomposition, traceability, and verification.

Model-Based Systems Engineering (MBSE)

Traditional systems engineering relies on disconnected documents where requirements live in one tool and design models live in another. Model-Based Systems Engineering (MBSE) replaces that fragmented approach with a unified model that supports requirements, design, analysis, and verification activities across the lifecycle. The primary modeling language, Systems Modeling Language (SysML), reached v2.0 with formal Object Management Group (OMG) adoption in July 2025.
MBSE is gaining real traction across the industry, though published studies still have limited data on how much it saves at the program level. What matters in practice is whether the approach, documents or models, actually helps teams catch inconsistencies earlier and keep their requirements under control.

Common Challenges in the Systems Engineering Process

Three recurring failure patterns show up when teams underinvest in these processes:

• Requirements drift and traceability gaps: When requirements change but downstream artifacts don’t get updated, gaps accumulate silently. GAO audits have repeatedly found traceability issues with defense program baselines, with corrective actions sometimes taking over a year to complete.
• Siloed teams and tool fragmentation: When requirements live in disconnected systems, bidirectional traceability becomes manually intensive and error-prone. The relationships between artifacts become harder to trust as those tools multiply.
• Scaling across multi-discipline programs: The number of handoffs between requirements owners, subsystem teams, system architects, and verification engineers grows fast with program size. What worked with one team starts to break when the coordination surface expands faster than the process does.

All three point to the same underlying problem. The connections between requirements, design artifacts, and verification evidence are either missing or too expensive to maintain manually.

How Jama Connect Supports the Systems Engineering Process

Across every phase of the systems engineering process, the need is the same. Teams have to keep their requirements, design decisions, and verification evidence connected and up to date. When those connections break or go stale, the cost shows up at integration, audit, or both.

Jama Connect® is a requirements management and traceability platform that supports this workflow through Live Traceability™, which flags affected downstream items when requirements change so teams can assess impact and preserve the decision trail for audits. Traceability Information Models give teams pre-built frameworks for standards like ISO 13485, DO-178C, and ISO 26262, so missing downstream artifacts get flagged automatically. Start a free 30-day trial to see how it fits your workflow.

Frequently Asked Questions About the Systems Engineering Process

What is the difference between systems engineering and software engineering?

Software engineering goes deep within one domain. Systems engineering works horizontally across hardware, software, and human factors. A systems engineer manages the interfaces and tradeoffs between those disciplines to make sure a local improvement doesn’t create a problem elsewhere in the system.

What does a systems engineer do?

A systems engineer leads the concept of operations, defines and allocates requirements, evaluates tradeoffs, manages interfaces, and oversees verification and validation. They work across the full lifecycle from concept through retirement and make sure no team improves their piece at the expense of the whole.

What industries use the systems engineering process?

Aerospace and defense, automotive, medical devices, semiconductor, and energy are the most common verticals. Any industry building complex, multi-discipline products with regulatory or safety requirements tends to rely on a structured systems engineering process.

How does systems engineering relate to project management?

Project management handles schedule, cost, and resources. Systems engineering handles the technical content, including requirements, architecture, interfaces, and verification. They’re complementary disciplines that coordinate closely on complex programs.

The post What Is the Systems Engineering Process? A Guide for Complex Programs appeared first on Jama Software.

Teams that catch defects early spend less on rework, move faster through audits, and protect the margins that fund their next program. A big part of how they get there is managing cost of poor quality (COPQ), which can consume five to 35 percent of revenue in manufacturing companies and often goes untracked until an audit or recall forces it into the open.

This guide covers what COPQ is, how to calculate it, where the biggest costs accumulate, and how to shift spending from failure correction to prevention.

What Is the Cost of Poor Quality (COPQ)?

Cost of poor quality (COPQ) is the total cost a team pays when something goes wrong, from internal scrap and rework to external recalls and warranty claims. In quality engineering, it covers everything that would disappear if there were no deficiencies, no errors, and no failures.

Most quality programs treat COPQ as a subset of total cost of quality (COQ). Here is how the breakdown works:

• Cost of good quality (COGQ): Prevention costs + appraisal costs.
• Cost of poor quality (COPQ): Internal failure costs + external failure costs.
• Total cost of quality (COQ): COGQ + COPQ.

That breakdown is useful because it separates what you spend on purpose (prevention and appraisal) from what you lose when things go wrong (internal and external failures).
COPQ typically falls [between five to 35 percent of sales revenue in manufacturing companies. In companies without well-developed quality programs, failure costs have historically consumed 60 to 70 percent of total quality costs, while prevention received just five to 10 percent.

The Four Categories of Quality Costs

So if failure costs are consuming that much revenue, where exactly is it going? The Prevention-Appraisal-Failure (PAF model) divides quality costs into four categories. Two represent investments (prevention and appraisal) and two represent losses (internal and external failures).

Prevention Costs

Prevention includes requirements engineering, design failure mode and effects analysis (FMEA), risk management per ISO 14971, supplier qualification, and quality planning. Every dollar spent here tends to save multiples downstream because it stops defects from entering the system in the first place.

Appraisal Costs

Appraisal is what teams spend to detect defects that already exist. Incoming inspection, integration testing, independent verification and validation (IV&V), calibration, and third-party certification audits all fit here.

Internal Failure Costs

This is where a defect is found before release, but the team still pays for it. Scrap, rework, failed test reruns, nonconforming product disposition, and Material Review Board processing all belong here.

External Failure Costs

External failure costs hit when a defect reaches the field, the customer, or the regulator. In 2025, NHTSA issued 997 recalls affecting more than 29 million vehicles, and large-scale program failures in aerospace and automotive have accumulated costs in the tens of billions when quality gaps went undetected through multiple development phases.

In regulated products, the stakes are even higher. FDA Class I recalls can cost millions in direct expenses before accounting for reputational damage and regulatory scrutiny.

How to Calculate COPQ

Calculating COPQ is straightforward once you know where to look. The tricky part is capturing the costs that don’t show up in your budget as line items.

The COPQ Formula

COPQ = Internal Failure Costs + External Failure Costs

The broader COQ formula adds prevention and appraisal:

COQ = (Prevention + Appraisal) + (Internal Failure + External Failure)

A common executive KPI is COPQ as a percentage of revenue: (Internal Failure Costs + External Failure Costs) ÷ Sales Revenue × 100.

For example, say a medical device team ships 10,000 units in a quarter. Internal failures, including scrap and rework on rejected assemblies, cost $150,000. External failures, covering warranty claims and one field corrective action, cost $800,000. Total COPQ is $950,000. Against $5M in quarterly revenue, that is 19% of sales going to failure costs, well within the range where most of the quality budget is being consumed by reaction rather than prevention.

Visible vs. Hidden Quality Costs

The costs you can see (scrap, warranty claims, rework labor) are only part of the picture. Hidden costs like engineering time lost to firefighting, delayed launches, and lost customer trust often run 4-5x higher. A $50,000 warranty charge can easily become $250,000 once you factor in the root cause investigation, the three-week launch delay, and the customer trust lost on the next renewal cycle.

Common Metrics and Benchmarks

The most useful metrics are the ones that show where failure costs are piling up. For internal failures, track scrap rate, first pass yield, rework hours, and defects per unit. For external failures, track warranty cost per unit, customer return rate, and recall costs. The COQ ratio also helps you see whether your quality program is weighted toward prevention or toward failure response.

Root Causes of COPQ

In complex, regulated product development, COPQ usually does not start on the shop floor or in the field. It starts earlier, when unclear requirements, weak verification, and broken traceability let defects travel downstream.

Incomplete or Ambiguous Requirements

Roughly half of all software defects originate in the requirements phase, and the majority of rework costs trace back to requirement errors, whether missing, wrong, or unnecessary. Regulatory bodies like the FAA stress the need for clear, complete requirements in software and computing system development. If the requirement is wrong, incomplete, or vague, every downstream artifact inherits that weakness.

Insufficient Testing and Verification

Defect correction costs rise sharply the later they are found, and the increase is far from linear. Correcting a defect during design costs roughly 3-8x more than catching it during requirements, 7-16x more during build, and 29x to over 1,000x more during operations, depending on the system and industry. By the time a defect shows up in verification, you end up fixing every artifact built on top of that original requirement.

Poor Traceability Across the Development Lifecycle

Complete traceability directly decreases defect rates: teams working with traceability performed 21% faster and produced 60% more correct solutions than those without it.
When a requirement changes and test cases are not updated to match, risk inputs go stale and coverage gaps go unseen. This is especially common when the traceability chain is spread across disconnected tools, where COPQ accumulates quietly across handoffs until rework, schedule delays, or audit findings force it into the open.

How COPQ Shows Up in Different Industries

Every industry feels COPQ differently, but the pattern is worth understanding before you try to fix it.

Manufacturing and Production

Manufacturing teams see COPQ most visibly in warranty claims, scrap, and rework labor. Scrap rates and first-pass yield are typically the first metrics to watch because they give the clearest signal of where quality controls are falling short.

Medical Devices and Regulated Products

For medical device teams, the traceability needed to show that verification is complete often becomes the largest compliance cost driver, and gaps in that chain usually surface during audits or submissions instead of during development. A single FDA Class I recall can cost millions in direct expenses before accounting for the reputational damage and regulatory scrutiny that follows.

Software and Complex Systems Development

Software teams feel COPQ through defect fixes, delayed releases, outage recovery, and the operational disruption that follows. Teams that track what percentage of sprint capacity goes to bug fixes often find that poor requirements quality is consuming 30-50% of their engineering time.

How to Reduce COPQ

Most COPQ starts upstream, so the most effective reductions come from moving effort upstream too. Here are three approaches that consistently work:

Build Quality Into Your QMS From the Start

In regulated environments, some appraisal activities are mandatory under FDA, FAA, or NHTSA oversight, but you can reduce discretionary inspection and manual recovery by improving what happens earlier in development. Organizations that embedded quality into their process saw significant improvements in both operational costs and revenue. A key part of that is Corrective and Preventive Action (CAPA): when each failure investigation feeds a systemic fix back into your prevention process, COPQ drops over time instead of recurring.

Use COPQ-Weighted Pareto Analysis to Prioritize Fixes

Two processes can have the same defect count but very different financial exposure. A Pareto analysis weighted by dollar impact is more useful than ranking by frequency alone, because a rare traceability gap that delays a submission can cost more than a frequent but low-impact defect. Going after the top three cost drivers first usually produces the fastest return.

Track COPQ Monthly and Tie It to Process Changes

Quality engineer Joseph Juran outlined a three-part approach that still holds up today: plan quality into your processes, control performance so it doesn’t degrade, and systematically reduce chronic waste. The most effective teams we’ve seen apply this by measuring COPQ monthly against prior-year costs and tying each improvement to a specific process change, so it’s clear what’s working and what needs more attention.

How Jama Connect Helps Reduce COPQ

When a requirement changes mid-program, every downstream artifact needs to reflect that change. Jama Connect flags suspect relationships when an upstream item changes, so engineers can assess the impact before gaps compound into rework. Across 40,000+ projects, teams with higher traceability scores catch defects faster and cover more verification ground, with top-quartile performers outperforming bottom-quartile counterparts by roughly 2x to 2.5x. After adopting Jama Connect, Arteris IP saw reuse increase by 100%, rework drop by 50%, review cycle time decrease by 30%, and audit prep time fall by 75%.

Jama Connect Advisor™ evaluates each requirement against INCOSE rules and EARS patterns, flagging vague terms and passive voice before they spread downstream. If roughly half of all defects trace back to requirements, catching ambiguity at authoring time is one of the most direct ways to cut COPQ at the source.

How to Turn COPQ Into a Competitive Advantage

COPQ is rarely just a scrap or warranty number. The teams that actually reduce it invest earlier, surface changes sooner, and make it easier to see what is missing before it becomes rework, delay, or recall.

For engineering and quality leaders trying to make that shift, traceability and requirements quality need to be part of daily engineering work. If your team is losing time and budget to rework driven by requirements gaps, start a free 30-day trial to see how upstream visibility reduces downstream cost.

Frequently Asked Questions About COPQ

What is the difference between cost of quality and cost of poor quality?

COQ is the total picture: what you spend to prevent and catch defects (prevention + appraisal) plus what you lose when defects get through (internal + external failures). COPQ is the loss side only. Tracking both helps you see whether your quality budget is weighted toward catching problems or preventing them.

How do you measure COPQ?

Start by tagging every quality-related cost to one of the four PAF categories. For internal failures, track scrap rate, rework hours, and first-pass yield. For external failures, track warranty cost per unit, customer returns, and recall expenses. Express COPQ as a percentage of revenue and review it monthly so you can spot trends and tie improvements to specific process changes.

What is a good COPQ benchmark for my industry?

There is no single target that works across all industries, but 5-35% of revenue is the commonly cited range for manufacturing companies. Teams with mature quality programs spend more on prevention and less on failure, which brings the overall COPQ percentage down over time. Tracking COPQ as a percentage of revenue month over month gives you a trend line to measure improvement against.

How is COPQ different in hardware versus software programs?

In hardware programs, COPQ shows up most visibly in scrap, rework labor, and warranty claims because physical materials and manufacturing time have already been committed. In software programs, the costs are less visible but equally real: defect remediation, delayed releases, outage recovery, and the engineering hours lost to debugging issues that originated in requirements. Both share the same root cause pattern where upstream problems create downstream costs.

The post What Is the Cost of Poor Quality (COPQ)? How to Calculate and Reduce COPQ appeared first on Jama Software.

This blog recaps part of our recent Whitepaper “Engineering Governance is a Critical Business Strategy for Product, Project, and System Development Excellence – Click HERE to read it in full.

Engineering Governance is a Critical Business Strategy for Product, Project, and System Development Excellence

Having a robust business strategy that reduces risk is critical for managing complex product, project, and system development.

What Is Engineering Governance?

Engineering governance is a system of policies, processes, and standards that guides everything from product or project design to production. It serves as the guiding star for engineering teams to ensure that they are building the right products or facilities in the right way, so that every decision aligns with industry and regulatory safety, security, sustainability, and other standards. When engineering teams design a new product or project, engineering governance ensures that the final outcomes meet these standards, as well as customer expectations and broader corporate goals. It touches every stage of the product or project lifecycle from design to delivery and beyond.

Engineering governance will also ensure that concerns about the rapid adoption of AI and AI-related cybersecurity risks and ethical decision-making are addressed. With increasingly complex products that can take an ecosystem to develop, companies face the significant challenge of seamlessly integrating hardware, software, and other inputs from suppliers and partners. This necessitates robust engineering governance, along with efficient collaboration and cutting-edge tools to ensure that all systems and subsystems coexist harmoniously.

RELATED: Buyer’s Guide: How to Select the Right Requirements Management and Traceability Solution

Why Engineering Governance Matters

For companies, failure to follow strong engineering governance risks expensive recalls, lawsuits, and fines, as well as harm to customer health and property, and significant negative brand impact. Here’s why getting it right matters so much:

1. Ensuring Regulatory Compliance and Audit Readiness

Companies operate within a tightly regulated or audited environment. Engineering governance provides a structured approach to ensure that the development process and tools comply with applicable regulations and auditor checklists in all markets where the products are sold or projects are located.

2. Managing Risks Proactively

Engineering governance helps identify and mitigate risks early before they escalate or snowball. Without comprehensive safety and quality testing, defects or other issues might surface after delivery to customers, necessitating recalls and refunds, rather than during development when fixes and rework are much less costly and damaging to reputation in the marketplace and relationships with customers, resellers, and other partners.

3. Maintaining Quality Standards

A robust engineering governance framework ensures that products or projects meet or exceed customer, industry, and regulatory requirements without cutting corners during design, manufacturing, or testing.

4. Pursuing AI and Other Innovation Responsibly

Innovation without governance can spiral into impractical or unsafe ideas. Engineering governance ensures that the adoption of innovative technologies or processes is balanced with feasibility, compliance, and cost control. Companies racing to incorporate AI into their products or the development process, for example, need engineering governance to ensure that new products and processes undergo rigorous safety tests, align with evolving regulations, and deliver innovations responsibly.

5. Achieving Sustainability Goals

Sustainability has become a business imperative for companies in response to demands from governments, consumers, and clients. Engineering governance helps them achieve sustainability goals by embedding eco-friendly practices into every stage of development and production.

RELATED: From Requirements to Regulatory: How AI Is Transforming Submission Readiness

Engineering Governance Scenarios

Here’s how engineering governance plays a role at every step in the development of any new product, project, or system:

• Design Phase: Engineering governance ensures compliance with safety and security standards applicable in each industry and region.
• Testing and Validation: Engineering governance frameworks ensure rigorous testing of every primary and secondary system and subsystem, including hardware, software, and other elements. Engineers follow defined processes to simulate real-world conditions.
• Supply Chain Oversight: Engineering governance identifies suppliers whose products and processes meet quality and sustainability standards.
• Post-market Monitoring: Even after development is complete and products or projects have been delivered, engineering governance mechanisms monitor performance through data collection to identify recurring issues and develop structured response plans to ensure quick fixes that reduce customer or client dissatisfaction.

Download the entire Whitepaper to read more, including
“Engineering Governance: An Industry-by-Industry Breakdown” and “How Jama Software Supports Engineering Governance”

The post Engineering Governance is a Critical Business Strategy for Product, Project, and System Development Excellence appeared first on Jama Software.

What if your team could spot ambiguous requirements the moment they’re written, keep trace links current without manual cross-referencing, and cut review cycles from weeks to days? That’s what AI brings to requirements management in 2026. Tools built on natural language processing (NLP), machine learning (ML), and large language models (LLMs) now give engineers immediate feedback on quality, traceability, and risk, right inside their authoring workflow. The payoff is biggest in regulated industries where a single vague requirement can ripple into months of rework.

This guide covers where AI delivers value today, what the risks and limitations are, how to evaluate tools, and what a real AI-powered requirements workflow looks like.

What Is AI in Requirements Management?

AI in requirements management means applying pattern detection, quality checks, and relationship mapping to the work of writing, tracing, and validating large requirement sets. Engineers derive, decompose, trace, rewrite, and evolve large numbers of engineering artifacts, and that work is time-consuming and prone to human error.

AI changes that by giving engineers immediate feedback. When someone writes “the system shall respond quickly to overcurrent conditions,” AI flags the requirement as unverifiable because there’s no measurable threshold, instead of waiting three months for a test engineer to discover the ambiguity.

Key Technologies Driving AI Requirements Management

Three technologies power most of what you’ll see in AI requirements tools today:

• Natural language processing (NLP): The most mature. Tools already use NLP to check requirements quality against INCOSE and EARS criteria for clarity, completeness, and verifiability.
• Machine learning (ML): Goes beyond rule-based checking to learn from historical data. Traceability is the standout ML application in requirements engineering so far.
• Large language models (LLMs) and predictive analytics: The research frontier. LLMs generate, restructure, and reason over requirements content, while predictive models forecast which requirements carry the highest risk of downstream failures.

NLP is already production-ready in tools like Jama Connect Advisor™, which uses it to score requirements against INCOSE and EARS rules. ML and LLM capabilities are maturing fast, but they come with data quality and validation constraints that regulated teams need to evaluate carefully before relying on them.

Why AI in Requirements Management Pays Off Early

Most requirements problems start long before coding, and catching them early saves more time than any fix later in the lifecycle. Here’s where teams see the biggest returns:

• Manual effort and documentation time: Some biopharma teams have cut drafting time by up to 70% with generative AI handling data collection and first drafts. For requirements teams, similar savings show up in trace matrix maintenance and review prep.
• Requirements accuracy and consistency: AI-enhanced traceability has reduced review downgrades from 8.7% to 1.6%, and high-confidence trace links increased from 56.4% to 70%. Fewer downgrades means fewer revision cycles on large requirement sets.
• Review cycles and time to market: Writing and testing code accounts for only 25% to 35% of total time from idea to launch, so shortening upstream requirements work has an outsized effect on your schedule.
• Stakeholder alignment: AI can synthesize inputs from stakeholders across different technical backgrounds, flag conflicts between teams, and surface gaps that would otherwise go unnoticed until integration.

Each of these improvements feeds the next. Cleaner requirements lead to fewer test failures, which lead to shorter review cycles, which free up time for the next program.

Challenges and Risks of AI in Requirements Management

AI can do a lot here, but it comes with constraints that matter in safety-critical industries. Three stand out:

• Data quality and training data dependencies: Incomplete training data is a key limiter, with AI-generated requirements omitting core needs when relying on generic datasets. In aviation, emerging guidance calls for data management frameworks addressing bias mitigation and dataset representativeness.
• Over-reliance on automation vs. human judgment: Most AI models remain black boxes, which is a problem in safety-critical industries. LLMs in particular may “generate spurious or hallucinatory material” or fail to comply with established criteria. Human review isn’t optional here. It’s a structural requirement baked into every applicable standard.
• Regulatory and compliance gaps: Current safety standards (ISO 26262, DO-178C, IEC 62304) weren’t written to address non-deterministic AI behavior. Applicants proposing AI software will require FAA involvement, signaling that established means of compliance under DO-178C haven’t caught up yet. Teams adopting AI tools today are operating ahead of finalized regulatory frameworks.

None of these are dealbreakers, but they do mean you should treat AI outputs as inputs to human review rather than finished artifacts.

AI Use Cases in Requirements Management

Here are six specific ways teams are using AI in requirements workflows today, from early-stage elicitation through verification and risk assessment.

Automated Requirements Elicitation and Extraction

NLP can pull requirement candidates out of messy stakeholder notes, meeting transcripts, and regulatory documents. This approach has already been used to accelerate initial requirements work, turning unstructured input into structured, traceable requirement sets. The output still needs human review, but the starting point is much closer to a usable baseline.

Intelligent Document Analysis and Relationship Mapping

Instead of manually cross-referencing hundreds of pages, engineers get an automatically generated relationship map showing how requirements connect to design elements, test cases, and risk items. NLP techniques can now create systems diagrams from documentation, detect ambiguity, link similar documents, and improve quality metrics. For teams managing large document sets, automated mapping cuts the time to answer coverage and completeness questions.

Requirements Quality Scoring and Ambiguity Detection

AI scores each requirement against INCOSE and EARS rules, catching vague terms, passive voice, and missing conditions before anything gets baselined. Without that check, ambiguity survives review and shows up months later when a test engineer can’t write a pass/fail criterion. AI can also scan for near-duplicate or conflicting requirements that human reviewers consistently miss.

AI-Powered Test Case Generation

AI can classify requirements by type, translate them to a logical format, and produce test cases covering nominal, boundary, and failure conditions. In the e-mobility domain, requirements have been used to generate linked test cases without manual authoring. For verification engineers facing hundreds of requirements before a milestone, this turns a multi-week manual effort into hours.

Intelligent Traceability and Impact Analysis

Maintaining end-to-end traceability across requirements, architecture, design, implementation, and test artifacts is one of the most labor-intensive parts of regulated development. AI keeps trace links current by detecting when an upstream change creates a gap or suspect link downstream. When a requirement changes, every affected test case, design element, and risk item gets flagged.

Predictive Risk Identification

AI can surface risk at the requirements phase rather than waiting for testing or a regulatory review. Predictive models flag ambiguities most likely to cause downstream rework, identify missing requirements in high-risk areas, and catch conflicting constraints before they spread. AI can also rank requirements by business value, complexity, and technical risk, giving leads a data-informed view of what to build first and where to cut scope without introducing new risk.

How to Evaluate AI Requirements Management Tools

The real question is whether a tool addresses the failure patterns your team already deals with: ambiguous requirements that survive review, trace links that go stale, and audit pressure when nobody can show what happened and why.

When you’re comparing tools, these three things tell you more than any feature list:

• Integration with existing workflows: Does the tool sync natively with your ALM, issue tracking (Jira, Azure DevOps), PLM systems, and CI/CD pipelines? Requirements changes need to propagate downstream without manual re-entry.
• Traceability and audit trail depth: Bidirectional traceability is a compliance requirement under ISO 26262, DO-178C, and IEC 62304. Look for automated impact analysis, baseline management, and electronic signatures that hold up in a regulatory review.
• Support for your specific standards: Does the tool ship with pre-configured templates aligned to your applicable standards, not generic compliance claims?

If a tool checks all three boxes and also scores requirements quality against INCOSE and EARS, it’s worth a closer look. The fastest way to prove value is to run a quality scoring pilot on a single project. Pick a requirement set that’s about to enter review, score it with the tool, and measure whether the review cycle shortens.

Top AI Requirements Management Tools

The right tool depends on your industry, your existing toolchain, and how much regulatory rigor your traceability needs to support. Here are five tools that come up most often.

1. Jama Connect

Jama Connect is a requirements management and traceability platform built for teams developing complex, regulated products across automotive, aerospace, medical devices, and defense. Jama Connect Advisor scores requirements against INCOSE and EARS standards, generates linked test cases, and flags downstream impacts when upstream items change. Live Traceability keeps the full artifact chain visible across the lifecycle.

Pros:

• AI quality scoring against INCOSE and EARS standards
• Live, bidirectional traceability across the full lifecycle
• Pre-built frameworks for ISO 26262, DO-178C, IEC 62304, and other regulated standards
• Jama Connect Review Center supports structured, auditable review workflows

Cons:

• Designed for complex, regulated programs, so teams without compliance requirements may not need the full depth

Best for: Automotive, aerospace, defense, and medical device teams building safety-critical or compliance-driven products.

2. IBM Engineering Requirements Management DOORS Next

IBM’s cloud-based evolution of the DOORS platform. The Requirements Quality Assistant (RQA) uses Watson AI to score quality and flag ambiguity, passive voice, and missing tolerances during authoring.

Pros:

• Long track record in aerospace and defense
• Watson-powered scoring pre-trained on 10 INCOSE-based quality issues
• Strong configuration management and baselining

Cons:

• Administration and configuration can be complex, especially for occasional users, and teams migrating from DOORS Classic should expect a transition period
• Performance can degrade on large modules with extensive audit history, with some users reporting slow page loads and high server CPU usage during peak activity

Best for: Aerospace and defense programs already invested in IBM engineering tools.

3. Codebeamer (PTC)

A full ALM platform covering requirements, test, and risk management with built-in regulatory templates. PTC acquired Codebeamer in 2022 and has been integrating it into their Windchill PLM ecosystem.

Pros:

• End-to-end ALM with requirements, test, and risk management in one tool
• Strong regulatory templates for automotive (ASPICE), medical devices, and aerospace
• Good Jira and Jenkins integrations for teams running Agile alongside compliance

Cons:

• The full ALM suite can feel heavy for teams that only need requirements management
• Integration with PTC’s Windchill PLM is still maturing, and teams outside the PTC ecosystem may not get the full benefit

Best for: Regulated product development teams that want requirements, test, and risk management consolidated in a single ALM platform.

4. Polarion ALM (Siemens)

Siemens’ ALM platform with requirements management, test management, and change tracking. Polarion integrates tightly with the Siemens ecosystem including Teamcenter PLM.

Pros:

• Unified ALM covering requirements, test, quality, and change management
• Deep integration with Siemens Teamcenter for PLM-connected traceability
• Built-in workflow automation and electronic signatures for regulated industries

Cons:

• Steep learning curve and complex initial setup, especially without existing Siemens infrastructure
• Deployment timelines can be significantly longer than cloud-native alternatives

Best for: Enterprise teams already invested in the Siemens product development ecosystem who need ALM integrated with their PLM.

5. Visure Requirements ALM

An all-in-one ALM platform covering requirements, risk, and test management with a focus on regulated industries. Visure supports ReqIF import/export for data exchange with other requirements tools.

Pros:

• Requirements, risk, and test management in a single platform
• Strong compliance support for DO-178C, ISO 26262, IEC 62304, and other standards
• ReqIF support for requirements data exchange across tools

Cons:

• Smaller user community and partner network compared to IBM, Siemens, or PTC
• Entry-level costs can be higher than lighter-weight alternatives

Best for: Regulated product development teams looking for an all-in-one requirements and compliance platform outside the major PLM vendor ecosystems.

What AI Looks Like Inside an Actual Requirements Workflow

Jama Connect Advisor™ is a good example of what this looks like in practice. When an engineer writes a requirement, Jama Connect Advisor evaluates it against INCOSE and EARS rules, flags vague terms and structural issues, and returns a quality score before the requirement gets saved. The same tool generates test cases from requirements (with steps, linked back to the source), so verification engineers don’t spend weeks drafting them manually. If a requirement changes later, every linked test case gets a suspect flag automatically. Grifols reduced review cycles from three months to fewer than 30 days after bringing Jama Connect Review Center into their workflow.

The underlying idea is that quality checks and traceability should happen inside the authoring workflow, not as a separate exercise before an audit. When those checks run continuously, requirements stay cleaner, trace links stay current, and the team spends less time on rework and more time on the engineering work that moves the product forward.

Getting Started With AI in Requirements Management

If you’re evaluating where AI fits in your requirements workflow, the fastest way to see value is to pilot quality scoring on a single project. Pick a requirement set that’s about to enter review, score it with an AI tool, and measure whether the review cycle shortens and fewer issues come back from the review board.

Jama Connect offers a free 30-day trial that includes Jama Connect Advisor for requirements quality scoring, AI-generated test cases, and Live Traceability across your full artifact chain.

Frequently Asked Questions About AI Requirements Management

Can AI replace human engineers in requirements management?

No. AI catches ambiguous language, missing trace links, and structural issues before they propagate downstream. In regulated environments, human review is a structural requirement. AI reduces the manual burden so engineers can focus on judgment calls that require domain expertise.

What should I look for when evaluating AI requirements management tools?

Three things: native integration with your development environment, support for your specific regulatory standards (not generic compliance claims), and AI scoring grounded in recognized frameworks like INCOSE and EARS.

How does AI improve requirements traceability?

Mostly by keeping trace links current without someone having to manually cross-reference a matrix every time something changes. AI tools maintain those links continuously and flag suspect relationships the moment an upstream requirement is modified, so your team catches gaps in hours instead of discovering them weeks later during a review or audit.

Is AI in requirements management ready for safety-critical industries?

Yes, for quality scoring, traceability, and test case generation. But treat AI outputs as inputs to human review. Regulatory frameworks are still catching up to non-deterministic AI behavior, so use AI for detection and drafting while keeping engineers in the approval loop.

The post AI in Requirements Management: What Works in 2026 appeared first on Jama Software.

Change control gives teams a way to check proposed changes before they go into the baseline. When it works well, everyone’s building to the right version, compliance evidence stays current, you’re not scrambling before audits, and other teams aren’t finding surprises at integration.

We’ve worked with teams across medical devices, aerospace and defense, automotive, semiconductor, industrial, and energy who’ve tightened their change control process and seen real results. Less rework at integration, shorter audit prep, traceable records from start to finish, and fewer surprises at submission.

This guide covers what change control is, where it breaks down, what the process looks like step by step, who belongs on the change control board, and how the right tools support it.

What Is Change Control?

Change control is the checkpoint that keeps a small edit from turning into a delayed program or a compliance gap. Before anything changes, you want to know what else the change touches, what evidence needs updating, who needs to sign off, and what follow-on work it creates.

On most programs, a change control board (CCB) is the group that reviews each proposed change and decides whether to approve it, reject it, or send it back for more information.

Who Should Sit on the Change Control Board (CCB)?

Who’s on the change control board (CCB) decides how well the team can spot follow-on risk before approving. You usually want at least one person from each of these areas:

• Program or project lead: Owns the schedule and scope impact of every approved change.
• Systems engineering lead: Sees connections across teams that a single discipline lead might miss.
• Quality and regulatory lead: Catches compliance and documentation problems before they turn into audit findings.
• Verification lead: Checks whether existing tests still cover the right things after the change.
• Baseline owner: Owns the artifact being changed and knows what’s linked to it.

On regulated programs, you also want someone who understands what a change means for regulatory submissions, because a design change can quietly become a documentation problem if nobody checks compliance.

If you’re not sure whether a function belongs on the board, we use a simple rule: if a team will absorb rework, defend the change in an audit, or retest because of it, that team needs a voice before approval.

Change Control vs. Change Management

A lot of teams blur these two, but they’re actually different things. Change control handles one change at a time with documented review, approval, implementation, and verification. Change management looks across the full queue and asks whether the overall system is healthy. You need both, but this guide focuses on the change control side, the part that decides whether a specific change is safe to make.

Why You Need Change Control

Without a real process, the way teams classify changes is where things fall apart. Someone marks a change as minor because the edit looks small, but the effect on connected work isn’t small at all. Here’s how that plays out:

• Vague criteria: The rules leave enough room that everyone argues their change is minor. Without clear thresholds, there’s no objective way to disagree.
• Effort-based assessment: The criteria only look at how much work the edit took, not what it affects further along. A one-line requirement change can be low effort for the author and high impact for test, quality, or suppliers.
• Missed dependencies: Nobody checks what the change touches before it goes in. A requirement update can break linked tests, change interface assumptions, force a risk review, and trigger new approvals.

These classification gaps are exactly why change control exists. Without a structured process, changes that carry real follow-on work get waved through, and nobody catches the impact until it’s expensive to fix.

The Cost of Uncontrolled Change

We’ve watched uncontrolled changes derail programs because nobody further down the chain knew the baseline had moved. Here’s where it usually hits:

• Interface misalignment: One subsystem updates an assumption while another team keeps building to the prior version, and neither side knows until integration.
• Stale test procedures: Tests still reflect the old requirement, and nobody flags the mismatch until a failed test or audit forces it into the open.
• Outdated compliance evidence: Quality prepares for review using records that no longer match the current design. The gap doesn’t show up until audit prep.
• Compounding rework: None of it feels dramatic the day the change goes in, but it shows up later as schedule slip or findings that take weeks to close.

The numbers back this up. In one peer-reviewed analysis, medical device recalls increased 85% between 2020 and 2023, from 33 events to 61. FDA’s design control regulation, 21 CFR 820.30(i), requires documented procedures for identifying, documenting, validating or verifying, reviewing, and approving design changes before implementation. If your change record can’t show what changed, what was affected, who approved, and how you verified it, that’s a gap an auditor will find.

The Change Control Process Step-by-Step

Here’s what a change control process looks like from start to finish. The details vary by industry and framework, but the sequence is pretty consistent.

1. Submit a Change Request

Someone identifies a need to modify the baseline and documents what they want to change, why, and what it might affect. The request goes to the change control board with enough detail for the board to evaluate it. If the request is vague, the decision will be too.

2. Assess the Impact

Before the board votes, someone needs to trace what the change actually touches. That means looking at connected requirements, tests, risk items, and interfaces to understand the full scope. Change impact analysis is what makes this step work. Without it, the board is approving based on assumptions.

3. Review and Approve

The board reviews the request and the impact assessment together, and the right people need to be in the room, especially the teams that will absorb the follow-on work. From there, they either approve, reject, or send it back for more information.

4. Implement the Change

Once approved, the team makes the change and updates all connected artifacts. The implementation should match exactly what was approved, nothing more and nothing less.

5. Verify and Close

Someone confirms the change was implemented correctly and that all affected tests, risk items, and design documents were updated. This is where a lot of teams drop the ball, because the approval process works fine but nobody circles back to confirm the actual work matched what was approved. Requirements traceability helps here by giving the team a clear checklist instead of a manual search through disconnected files.

Change Control in Practice

To see how all of this comes together, say a medical device team changes a sensor tolerance spec mid-program. Without change control, the test team keeps verifying against the old spec, the risk assessment still references the original tolerance, and the design history file doesn’t reflect the update. At the next audit, the auditor pulls the sensor requirement and finds the test report doesn’t match.

With a real change control process, the team files a request, impact analysis shows four linked test cases and two risk items that need updating, the board reviews and approves, the test team re-runs the affected cases, and verification confirms everything is aligned before the record closes. It’s the same change, but a completely different outcome.

Best Practices for Change Control

The step-by-step process above is the foundation, but these three practices are what make it actually work well.

Define Trigger Criteria Up Front

Clear trigger criteria keep change requests from turning into open-ended debates that drag on for days. Write down what triggers review based on baseline effect, risk, regulatory relevance, interface changes, and testing impact. Your engineers shouldn’t have to guess whether to file a request.

You should also define what qualifies as a local change versus what triggers cross-functional review versus what requires board-level approval. That split keeps low-risk changes moving fast while making sure bigger changes get the review they need.

Keep Records in One Place

You want one record that ties together the request, the impact analysis, the decision, and the verification result. Scattered approvals in email and evidence across shared drives won’t hold up when an auditor asks for the full history. That record should answer every question about what changed, why, and what happened next.

A centralized record lets the board review the full picture before approving. It also saves the hours teams spend scrambling to pull evidence together before audits. When you can instantly get the full change history, the whole review cycle speeds up.

Use Traceability During Review

The board needs to see what’s actually connected during review, not just what the proposer says is affected. Without that visibility, approvals rely on assumptions, and those assumptions almost always underestimate what’s really affected. Seeing the full picture during review is what turns a paper exercise into a real decision.

When change impact analysis is built into the review process, risk controls and design decisions stay connected to the change record on their own. The board doesn’t have to figure out what’s affected, because the traceability data already shows them.

How the Right Tools Support Change Control

Tools only help if they show you what a change affects before you approve it and prove the work got done after. We’ve seen teams run formal change control through spreadsheets and email threads, and it works until one change affects more artifacts than a person can reliably trace. That’s usually the point where regulated teams start looking for something purpose-built.

Teams we’ve worked with moved to Jama Connect® because they needed to see every connected artifact a change touches before approving it. Live Traceability™ keeps those links up to date as designs evolve, and when a requirement changes, impact analysis surfaces every affected item before the change goes through. That shifts the conversation from “we think this is low impact” to a specific list of what actually needs review.

From there, Jama Connect’s Review Center lets CCB participants run formal reviews where each reviewer can approve, reject, or comment on the proposed change with electronic signatures. That formalized review process is how the audit trail gets built up. Instead of chasing approvals through email or meeting notes, the full sign-off history lives inside the same system as the change record and its linked artifacts.

Build the Change Control Process Around One Job

Change control failures usually trace back to the same thing. The process let the team approve a change before they understood what it would do to connected work. When you check impact before approving and verify after, rework gets caught earlier and your compliance evidence stays in sync. If you’re seeing late-stage surprises that should have been caught at review, the change control process is the first place to look.

Jama Connect ties your change records to every linked requirement, test, risk item, and design element, so the board sees the full picture before they approve. Start a free trial to see how change decisions stay connected to the work they affect.

Frequently Asked Questions About Change Control

What is the difference between change control and change management?

Think of it this way. Change control is about one specific change. You’re asking whether this particular edit is understood well enough to go into the baseline. Change management is the bigger picture, looking at how the full volume of changes is affecting the program’s health, schedule, and risk profile. Most teams need both, but they solve different problems.

What triggers a formal change control review?

Common triggers include changes to safety-related requirements, anything that affects an interface between subsystems, updates that shift what needs to be tested, and edits that touch regulatory documentation. The specific thresholds depend on your program, but the important thing is writing them down so engineers have a clear answer instead of debating each request individually.

How do you keep change control from slowing everything down?

The best teams split changes into tiers. Low-risk changes with no follow-on impact move fast with minimal review. Changes that affect safety, interfaces, or compliance get full board review. That way the process protects the important stuff without creating a bottleneck for every minor update. The goal is a clear path for each type, not one slow queue for everything.

What tools help with change control in regulated industries?

Look for tools that show you what a change affects before you approve it, keep traceability links current as the design evolves, and maintain electronic signatures with a full audit trail. The main thing spreadsheets can’t do is trace impact across connected artifacts automatically. That’s where purpose-built tools like Jama Connect come in.

The post What Is Change Control? Why It Matters and How to Build a Process That Works appeared first on Jama Software.

What Is Fault Tree Analysis (FTA)? How It Works and When to Use It

Fault tree analysis (FTA) helps engineering teams figure out every way a system could fail before it ships. You pick the worst thing that could happen, then work backward to find every combination of events that could cause it. When those findings stay tied to the actual design, the analysis catches dangerous paths early. That’s why regulators across aerospace, automotive, medical device, and nuclear programs expect it.

The U.S. Nuclear Regulatory Commission showed what this looks like in practice back in the mid-1970s when it published WASH-1400, one of the first big risk assessments of a nuclear power plant. A later NRC report said the work gave insights into real incidents that were hard to get any other way. The method hasn’t changed much since then, but keeping findings connected to the design is still where most teams run into trouble.

This guide covers what fault tree analysis is, how to build one, how it compares to FMEA, and where that connection usually breaks down.

What Is Fault Tree Analysis (FTA)?

Fault tree analysis (FTA) is a top-down method where you start with the worst outcome your system could produce, called the top event, and trace backward through layers of causes connected by logic gates. Instead of asking “what could go wrong?” in general, you pick one specific failure and work out whether the design actually prevents it.

That’s what sets it apart from most other safety methods. You model how component failures, human errors, environmental conditions, and system interactions can combine to cause that one outcome. The goal is to find every path to the failure and figure out which ones need design attention right now.

Fault Tree Analysis vs. Failure Mode and Effects Analysis (FMEA)

Fault tree analysis and FMEA (failure mode and effects analysis) answer different questions, and most teams use both. Here’s where they split and why the handoff between them often breaks.

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failure mode from FMEA often feeds the fault tree, and the fault tree produces a safety requirement. Testing gets planned against that requirement, but the link back to the original hazard can get weak over time, especially when requirements change and nobody reassesses the downstream work.

Why Fault Tree Analysis Matters

Most safety methods look at failures one at a time. Fault tree analysis is one of the few that shows how failures combine. A sensor glitch on its own might be harmless, but pair it with an operator error and a backup system that shares the same power supply, and you’ve got a path to a catastrophic event that nobody saw coming.

That’s the real value of fault tree analysis: it forces you to think about how independent your redundancies actually are, whether your backup systems share common weaknesses, and which single points of failure the design still has. It also gives you something you can show to regulators and auditors, not just an opinion that the system is safe, but a documented chain of reasoning that proves it.

When to Use Fault Tree Analysis

Fault tree analysis is worth the effort in specific situations. It takes real work to do well, so it helps to know when it pays off and when something simpler would do. The clearest use cases look like this:

• Catastrophic top events: When the failure you’re looking at could hurt people or damage the environment, fault tree analysis gives you a clear way to map every path to that failure.
• Redundancy and common-cause risk: If the design uses redundant systems, the analysis can show whether those systems are truly independent or share a weakness the architecture missed.
• Quantitative safety targets: Because fault tree analysis supports probability modeling, teams can calculate whether a design meets a safety target and decide where to add redundancy or change the architecture.
• Regulatory and certification needs: NASA includes fault tree analysis in its system safety standards. Programs under DO-178C (airborne software), ISO 26262 (automotive functional safety), IEC 62304 (medical device software), and FDA design controls all use it because regulators want clear, documented reasoning about how failures happen.

If the top event isn’t catastrophic or the system isn’t complex enough for failures to combine in non-obvious ways, FMEA on its own may be enough.

How Fault Tree Analysis Works

The process starts with one question: what’s the one failure that absolutely can’t happen? You pick that as the top event and work backward through every combination of causes that could lead to it. The tree uses four main symbols (defined by IEC 61025):

• Top event: The system failure you’re analyzing.
• Basic event: A root cause where you stop breaking things down.
• AND gate: Every failure in the group has to happen at the same time for the top event to occur.
• OR gate: Any single failure on its own is enough to cause the top event.

You define the top event first. A broad one makes the tree unmanageable, so you want something specific enough to act on but serious enough to justify the work. From there, you break down causes layer by layer and connect them with AND and OR gates based on the system architecture, interfaces, and known hazards.

Once the tree is built, you look for the minimal cut sets, the smallest groups of failures that can cause the top event. Order-1 cut sets (single points of failure) need attention first because they show where the system is weaker than the team thought. If you have failure probability data, you can also put numbers on the tree and compare risk against safety targets.

Fault Tree Analysis Example: Medical Device

Take an infusion pump where the top event is “unintended drug overdose.” An OR gate at the top splits into two paths: either the pump delivers too much, or the system fails to catch the over-delivery. The first path breaks down through an AND gate (valve sticks open AND flow sensor gives a false reading at the same time). The second is an OR gate where any single alarm failure lets the overdose go unnoticed.

When you run the cut sets, you might find that one alarm circuit failure on its own is enough to cause the top event. That’s an Order-1 cut set, and it tells you the design needs a backup alarm or an independent shutoff. That’s where fault tree analysis changes the design, not just documents the risk. NASA, nuclear, and automotive teams all use the same logic on their own systems, and the analysis pays off in every case when its findings stay connected to the requirements and tests that prove the risk was handled.

Limitations and Where Fault Tree Analysis Falls Short

Fault tree analysis has real limits. It only models binary states (working or failed), it can’t capture the order events happen in, and complex systems produce trees that are hard to maintain. But the bigger problem is what happens after. Teams rarely struggle with the analysis itself. What breaks is the handoff:

• Disconnected mitigations: The tree identifies a single-point failure, but the requirement that came from it lives in a different system and loses its connection to the original hazard.
• Post-review requirement changes: A test or design constraint downstream doesn’t get updated because nobody sees the upstream change fast enough.
• Surface-level audit trails: The analysis, requirement, risk control, and test all exist on paper. But the connection between them is weak or outdated, and nobody notices until an auditor pulls a sample.

Once those links break, the tree stops being useful evidence and turns into a static document. NASA research shows that fixing a requirements error at the test stage can cost 21 to 78 times more than catching it during requirements, and that number climbs to 29 to over 1,500 times more in operations. If a fault tree analysis finding gets lost between the safety review and the requirement baseline, the program has already made the problem much more expensive to fix.

Keep Fault Tree Analysis Findings Connected to the Design

The best fault tree analysis doesn’t end with a clean diagram. It ends with a changed design, a stronger requirement, a better test, or a risk control that stays linked as the product changes over time. Teams that keep those connections strong see 1.8X faster defect detection, 2.1X faster test execution, and 2.4X lower test failure rates compared to teams in the bottom quartile.

If you want those kinds of results, Jama Connect® is built for exactly this. Its Live Traceability™ approach flags when a change upstream affects something downstream, so the full chain from hazard to requirement to test stays visible as the project moves forward. Try Jama Connect free for 30 days.

Frequently Asked Questions About Fault Tree Analysis

What is the difference between fault tree analysis and FMEA?

Fault tree analysis picks a specific system failure and traces backward to find every combination of events that could cause it. FMEA goes the other direction, starting with individual parts and asking what happens when each one fails. The two work well together because fault tree analysis catches dangerous combinations while FMEA catches failure modes that might not show up in a top-down view.

When should you use fault tree analysis instead of other safety methods?

Fault tree analysis works best when the top event is catastrophic and you need to understand how failures combine to cause it. It’s the go-to when a program needs to put numbers on failure probabilities or show regulators clear safety evidence.

What is the difference between quantitative and qualitative fault tree analysis?

Qualitative fault tree analysis maps the failure paths and identifies the cut sets without calculating probabilities. It tells you which failures are dangerous and where single points of failure exist. Quantitative fault tree analysis goes further by assigning failure probability data to each basic event and calculating the overall likelihood of the top event. Use quantitative when you need to prove a design meets a specific safety target or compare risk between design options.

Can you do fault tree analysis without specialized software?

You can build simple fault trees with any diagramming tool or even on a whiteboard. The tree itself doesn’t need special software. Where things get harder is keeping the findings connected to requirements, tests, and risk controls as the design changes. That’s a traceability problem, and it’s where purpose-built tools like Jama Connect help most.

How do you keep fault tree analysis findings tied to the design?

The biggest risk is that findings get written down but never connected to the requirements, risk controls, or tests they should feed into. You need those connections to stay visible so that when something changes upstream, the downstream work gets checked too. Jama Connect’s Live Traceability does this by flagging when a change affects related work.

The post What Is Fault Tree Analysis (FTA)? How It Works and When to Use It appeared first on Jama Software.