Use Cases and Strategies for Simplifying Variant Management
Variant management enables organizations to efficiently tailor requirements for diverse markets while maintaining alignment across teams.
Jama Connect® offers flexible strategies to simplify creation, adaptation, and tracking of multiple variants. These approaches facilitate efficient reuse, reduce complexity, and maintain traceability across complicated product lines.
Identifying and adapting product variants based on evolving market dynamics, regulatory requirements, and unique customer needs to ensure consistent compliance.
Streamlining variant creation by configuring specific versions of product components, optimizing reuse, and fostering alignment across complex product lines.
Leveraging a structured feature model to effectively manage options and better understand complex product variations.
Below is an abbreviated transcript and a recording of our webinar.
The video above is a preview of this webinar – Click HERE to watch it in its entirety!
VIDEO TRANSCRIPT
Matt Mickle: We have a fun topic today, walking through variant management use cases with the goal of simplifying this sometimes complicated topic. I will start off by walking through some of the common use cases that we often hear, followed by some concrete examples of how we would see these within the industry. I’ll talk a little bit about how we’ll solve these within Jama Connect and then have some demonstration of this directly in the tool. I’ll do this for each use case as we proceed, and then we’ll move on to some Q&A and I’ll answer some of your questions.
So, what do I mean when I say variant management? Well, simply, I would describe variant management as any process or technique that is used to manage variability and assets within a project. This could be in the form of certain techniques, such as feature-based product line engineering, which we’ll talk a little bit more about later. Configuration management, product derivation, or branch and merge. A product can vary in many ways, such as different features, material or components, premium services, or levels of performance. Here are some examples you might recognize. Models of home appliances with different sizes or capabilities, like these refrigerators. Microcontrollers with a configuration of reusable IP blocks. Medical devices, such as insulin pumps or digital thermometers having an array of features based on setting, method of application or type of consumer. As well as everyday devices, such as smartphones or smartwatches with different uses or consumer profiles.
Nearly every product you could think of has some amount of variation. And the process of managing those variants extends from the conception of the products, all the way into their description at the point of sale, and maintenance thereafter. So, one of these methods, which we will mention in the discussion today, is product line engineering, or PLE for short. And for this, we’ll use the simple definition, a focus on engineering for a family of products with similar features, components or modules as a single product line to leverage commonality and variability, minimize the duplication of effort, and maximize reusability.
Mickle: Now, a couple of definitions that go along with that from the standards for product line engineering, from ISO 26550, the definition of a feature would be an abstract functional characteristic of a system of interest that end users and other stakeholders can understand. And from the product line engineering for feature-based product line engineering standard, ISO 26580, a product line would be a family of similar products with variations in features. So, product line engineering could be considered as the next step in maturity. Single system engineering. And as the ISO standard on software and system engineering for product line engineering and management states, product companies utilizing single system engineering and management approaches may end up with highly complex and low-quality products. Low productivity, high employee turnover, and less than expected customer satisfaction.
So, let’s instead talk about the benefits of moving from single-system engineering into product-line engineering. Product line engineering enables organizations to create product line architecture that allows for the systematic reuse of components, modules, and assets across different products within a product line. This promotes efficiency by reducing redundancy in the need to recreate similar functionalities for each product. By reusing existing components and assets, organizations can significantly reduce development costs. Product line engineering allows for economies of scale, as the investment in creating a core set of assets can be spread across multiple products, leading to cost savings in the long run.
With product line engineering, organizations can streamline the development process by leveraging existing components and architectures. Faster time to market for new products, since development efforts are focused on creating unique features, rather than rebuilding common functionalities. Product line engineering helps ensure consistency in products across the product line. By reusing well-tested and validated components, the likelihood of introducing defects or inconsistencies is reduced. And this will lead to higher overall product quality. As market demands change or new technologies emerge, product line engineering provides a framework that allows organizations to adapt and evolve their product line more easily. This enables the addition of new features or modification of existing ones without starting the development from scratch.
Mickle: Product line engineering supports efficient configuration management, allowing organizations to define and manage variations and products through configuration, rather than by creating separated versions or desynchronized copies of content. This simplifies the task of handling different customer requirements or market-specific adaptations. Product line engineering makes maintenance and upgrades more manageable. Changes or bug fixes can be applied to common components, and then the updates can be propagated to all of the products within the line, ensuring that each product benefits from the improvements without having to undergo individual modifications.
And finally, product line engineering helps mitigate the risks associated with product development by relying on well-established and proven components. Since these components have been used and tested across multiple products, the likelihood of critical issues arising is reduced. Now, of course, there are many benefits for product line engineering, but there are a lot of challenges that a company goes through in order to try and move towards product line engineering. For example, let’s say a company starts out with a single product and then begins to build variants on that product, turning it into a product line. As the number of variants and variation between them grows, the ability to manage them becomes more and more challenging.
When a change is made, it’s important to assess not only the impact of that change within the product, where the change is made, but also in any products that are part of the same product line. If the change is against common requirements, then the decision is needed on whether they need variation. New versions or configurations of components of a system will need to be thoroughly reviewed with regards to how they interconnect. This becomes even more challenging and complex when considered as the product development data moves from one development application to the next. Throughout the supply chain, information about progress and change needs to flow and be collected in order to see overall status.
2025 Expert Predictions for the AEC Industry: How Technology, Emerging Trends, and Innovation Will Shape the Industry in 2025 and Beyond
As we look toward the next five years in the Architecture, Engineering, and Construction (AEC) industry, emerging technologies are set to revolutionize how buildings are designed, constructed, and maintained. From the rise of digital twins to the growing integration of AI and machine learning, the tools and strategies transforming the industry promise to boost efficiency, sustainability, and collaboration. As companies prepare for these advancements, understanding how these technologies will shape the landscape and adopting the right tools will be critical.
In part five of our annual predictions series, Joe Gould, Senior Account Executive at Jama Software, shares his insights on the trends, challenges, and innovations shaping the future of AEC.
Question 1: What emerging technologies or digital tools do you believe will most significantly reshape the AEC industry in the next five years, and how can companies prepare to integrate these advancements effectively?
Joe Gould:
Digital Twins – The use of digital twins to create real-time, virtual representations of physical assets is set to revolutionize operations and maintenance. This technology provides actionable insights, predictive maintenance, and enhanced asset performance management. Implement IoT sensors and connect data streams to develop digital twin capabilities. Start with pilot projects to showcase value and gradually expand their use.
AI and Machine Learning – AI-driven tools will enhance project planning, risk management, and resource optimization. Machine learning models can analyze historical data to predict delays, optimize schedules, and reduce costs. Integrate AI into existing workflows, such as predictive analytics for scheduling or automated quality control checks, to reduce manual errors and inefficiencies.
Modular and Prefabrication Technologies – Offsite construction and prefabrication are becoming more efficient with advancements in design automation and digital manufacturing tools. Adopt software platforms that integrate modular construction workflows with design and scheduling tools. Establish partnerships with prefabrication facilities.
Sustainability Focused Tools – These are tools for energy modeling, lifecycle analysis, and carbon tracking will drive environmentally responsible design and construction. Embed sustainability metrics into project KPIs and adopt tools that facilitate compliance with green building certifications like LEED or BREEAM.
Question 2: As sustainability goals become increasingly prioritized, what role do you see software and product development playing in achieving more environmentally friendly and energy-efficient designs within the AEC sector?
Gould: Software and product development play a pivotal role in advancing sustainability and energy efficiency within the AEC sector by enabling more data-driven, holistic, and collaborative approaches to design and construction. Tools such as Building Information Modeling (BIM), energy simulation software, and lifecycle assessment platforms allow architects and engineers to optimize designs for energy performance, material efficiency, and reduced carbon footprints from the earliest project stages. Digital twins extend this capability by facilitating real-time monitoring and optimization of building performance throughout its lifecycle, ensuring long-term energy efficiency and reduced environmental impact. By leveraging these technologies, companies can not only meet regulatory demands but also position themselves as leaders in creating environmentally responsible and energy-efficient designs that contribute to a sustainable future.
Question 3: With remote and hybrid work now a permanent reality for many industries, how do you anticipate these work models impacting collaboration and innovation in the AEC space, especially regarding software and project management tools?
Gould: With remote and hybrid work becoming the norm, the AEC industry is seeing some interesting shifts in how teams collaborate and innovate. While it used to be all about in-person meetings and site visits, now software and project management tools are stepping up to bridge the gap. Cloud-based platforms make it easier than ever for teams to share updates, track progress, and stay connected no matter where they’re working from. This new way of working is also pushing companies to adopt more streamlined workflows and better communication practices, which can actually spark innovation!
Question 4: How do you foresee AI and machine learning influencing decision-making and risk management in AEC projects? What are some challenges or limitations the industry might face in adopting these technologies?
Gould: AI and machine learning are definitely shaking things up in the AEC industry, especially when it comes to decision-making and risk management! These technologies can analyze massive amounts of data — like project schedules, historical performance, and even weather patterns — to predict potential delays, budget overruns, or safety risks before they happen. It’s like having an early warning system that helps teams make smarter, faster decisions. On top of that, AI can optimize workflows, improve resource allocation, and even suggest more efficient designs.
Question 5: As a follow up question: Do you have any concerns or anticipate any negative impacts as it pertains to AI & ML.
Gould: I believe there are some challenges to getting these tools up and running. One big hurdle is the quality of data — if your data isn’t clean or consistent, the AI’s predictions won’t be reliable. There’s also a learning curve; not everyone in the industry is ready to fully embrace these new tools, so training and change management are crucial. Plus, while AI is great for identifying trends, it still relies on human expertise for context and final decisions. So, while the potential is huge, there’s still some work to do in terms of adoption and integration in my opinion.
Question 6: Given the current emphasis on data-driven project management and predictive analytics, what strategies would you recommend for AEC firms to better leverage data for optimizing project outcomes and resource allocation?
Gould: If AEC firms want to get more out of data-driven project management, it starts with organizing their data. Centralizing everything — budgets, schedules, progress updates —into tools like BIM or Procore makes it easier to analyze and act on insights. Predictive analytics can then help spot issues early, like delays or resource shortages, so teams can adjust before problems escalate. The key is to train people to use the data effectively and start with small pilot projects to build confidence. When everyone’s on the same page and using the same data, decisions get smarter, and projects run smoother.
Question 7: Are there any additional insights you have regarding predictions, events, or trends you anticipate happening in 2025 and beyond?
Gould: Looking ahead to 2025 and beyond, I think we’ll see a bigger push for sustainability in AEC, with more focus on net-zero buildings and carbon tracking tools. AI and automation will likely play an even larger role in design and project management, making workflows faster and more efficient. Plus, digital twins and smart buildings will continue to grow, especially as IoT tech gets better. The challenge will be adapting quickly while balancing innovation with practicality, but the opportunities for transformation are huge!
2025 Expert Predictions for Aerospace and Defense: AI, Sustainability, and the Next Frontier
Aerospace and defense are at the cusp of revolutionary changes, driven by advancements in artificial intelligence, autonomous systems, sustainable technologies, and digital transformation.
In part four of our annual predictions series, Vance Hilderman, CEO at AFuzion and Jama Software’s industry experts Cary Bryczek , Director of Solutions & Consulting; Karl Mulcahy, Global Sales Manager of Aerospace & Defense and Matt Macias, General Manager of Aerospace & Defense share their insights on the trends, challenges, and innovations shaping the future of aerospace and defense.
From the integration of AI in autonomous systems to the adoption of digital twins for operational efficiency and the pursuit of sustainable practices, these insights offer a glimpse into the opportunities and disruptions that lie ahead. Whether it’s navigating cybersecurity challenges or adapting to shifting geopolitical conditions, this year’s predictions provide a roadmap for industry leaders to thrive in 2025 and beyond.
We like to stay on top of trends in other industries as well. Read our predictions for Industrial & Consumer Electronics (ICE) HERE, Automotive HERE, and Semiconductor HERE – Plus, stay tuned for future topics, including Medical Device & Life Sciences, and AECO.
Editor’s Note: Responses reflect a mix of British and American English, depending on the respondent.
Question 1 – With the rising integration of AI, machine learning (ML), and autonomous systems, how do you foresee these technologies reshaping aerospace and defense operations? What are the most promising applications and potential challenges?
Vance Hilderman: AI & ML are already used for ground planning, flight plan optimization, flight deck monitoring, and assists. Militaries are using AI onboard UAVs (Unmanned Aerial Vehicle) and fighter aircraft but real-time AI on commercial aircraft is not yet allowed for safety-related operations.
Cary Bryczek: We will see an explosion in systems engineering utilizing AI. AI will not only be used to write requirements but decompose the requirements into lower-level requirements, create architecture models and establish traceability throughout. It’s beginning to happen right now! AI assistants for systems engineers will create enormous time savings so the actual engineering can be performed.
Karl Mulcahy: AI/ML I’m sure is of interest to these companies to make internal development practices more efficient, but also to enhance their offerings e.g., AI monitoring for better insights/decision making on a battlefield.
However, with ongoing security aspects a constant concern for sensitive projects within the defence world particularly, it may require more maturity and capabilities within customer environments for internal efficiency gains.
Matt Macias: The aviation industry is already demonstrating prototypes leveraging AI and autonomous operation with a large number of new and existing companies developing transformational vehicles to provide new ways for people and goods to utilize airborne mobility’s advantages. There is a strong desire to bring the consumer faster, safer and more cost-effective ways to travel. We see many new startups and innovative ideas in the work, which is very exciting. We also see a great rise in the pursuit of novel, innovative cyber-system approaches and new vehicle designs, propulsion and operations.
In the defense world we see AI/Autonomous systems enabling disruptive changes in the systems and total architectures utilized for security. These new technologies are enabling breakthroughs in new missions and exposing unexpected vulnerabilities. We saw this clearly in Ukraine with the successful use of inexpensive, modified consumer drones defeating far more expensive systems. We also see this in the changes and cancelations of some larger DOD systems programs, where there appears to be a shift in focus to very different, lower-cost systems. For example, drones that operate in a “constellation” of unique, adaptable, or “swarms” of “expendable” or essentially single-use systems that can potentially overwhelm more traditional manned or legacy systems. This is not only changing the approaches to military strategy, but it is revolutionizing the development of tomorrow’s military systems, leading (as in commercial aviation) to an explosion of new ideas and new programs. We also see a rapid growth of disruptive companies taking market share from traditional defense contractors.
All in all, this is a very exciting time for anyone who is interested in aviation, space and defense innovation.
Question 2 – As a follow-up question: Do you have any concerns or anticipate any negative impacts as it pertains to AI & ML?
Hilderman: When used on the flight deck for real-time flight controls, it needs to be certified which is not yet possible for commercial aviation. We’re working on this.
Bryczek: I would say none to be honest. The technology is there to protect intellectual property. Perhaps the only concern I have is do we have the energy infrastructure ready to drive some of the computing power behind it all.
Macias: Currently, the most immediate negative impacts of AI & ML is the disruption of well-established commercial markets and in the case of defense, the unexpected vulnerability of military systems that we have invested heavily into ensure our security.
We don’t know yet how advanced air mobility systems might change the flow of people and goods around our cities, but it is likely that not likely in 2025. In the mid-term future, we will see disruptions as we seek new norms, such as increased noise, safety challenges, privacy challenges, etc. We can also see that the major militaries of the world are very concerned about countering the asymmetric threats autonomous systems pose to our larger defense platforms, likely to accelerate as AI is applied in the future.
Question 3 – As global demand for sustainable practices intensifies, what innovations in product design, materials, or manufacturing processes do you think will most significantly impact sustainability efforts in aerospace and defense?
Hilderman: eVTOL. [Editor’s note: Electric Vertical Take-Off and Landing (eVTOL) aircraft are a type of VTOL (Vertical Take-Off and Landing) vehicle that use electric power for vertical takeoff, landing, and hovering. Unlike traditional VTOLs, eVTOLs rely solely on electric propulsion.]
Bryczek: We are going to continue to see more research and development efforts into alternative geological materials to mitigate the need to use rare earth elements. Systems will need to be redesigned, or new systems built altogether that utilize different materials. It’s not just global political unrest that is driving this but also socio-environmental resistance to the mining/extraction process that ruin the environment.
Mulcahy: Better collaboration across teams using tools to capture outcomes, integrate data sets, and ensure better decision-making/more efficient ways of incorporating science and research into the manufacture of products.
Macias: Aerospace and Defense is an industry that has struggled greatly with achieving solutions for sustainability. A significant innovation focus is being applied to this ongoing challenge. We can see major positive impacts already in more efficient structures (increased use of carbon fiber composites and advanced designs) and advancement in the efficiency of traditional propulsion systems. In work and over the horizon there is a strong desire to harness advanced, model-based design approaches (including AI, generative design, MDO, MBSE), and advanced manufacturing automations (3D printing, advanced robotics, etc.) to enable dramatic innovations that will increase the efficiency of flight and other operations.
However, what the industry most dearly seeks is a sustainable power source for A&D systems. This will have great value as these systems consume a great deal of energy and in the case of defense systems, the cost of getting fuel to the point of need is extremely high. The challenges of electrification, sustainable aviation fuels (SAF), hydrogen propulsion, etc. continue to be a major focus of the A&D industry but also continue to present very significant challenges of affordability, reliability, power density/weight, and the logistics of fuel delivery.
Question 4 – Cybersecurity remains a top priority in aerospace and defense. What proactive steps do you believe the industry should take to strengthen security measures, particularly in software development and data management for connected and autonomous systems?
Hilderman: Mandate formal usage of DO-326A and ED-202A for cybersecurity within Avionics.
Bryczek: We already have terrific security policies and guidelines as Vance has pointed out that both the US and Europe have crafted. Developers need to be held accountable to follow security by design and to leverage zero-trust architecture. Still too often do I see security performed as an afterthought.
Macias: Security assurance is critical as we advance our use of autonomous systems and integrated data networks. This is and will remain a subject of constant focus, priority and challenge. The application of careful and advanced cybersecurity approaches must be a primary focus of all parts of the A&D system lifecycle including IP protection and security in operational data. As our systems become more intelligent and as the leverage is greater and greater computing power, this will only increase.
Question 5 – Given the shift toward digital transformation, what role do you see digital twins and simulation technologies playing in enhancing operational efficiency, project accuracy, and training in aerospace and defense?
Hilderman: Aircrafts are increasingly automated meaning less pilot involvement which means less onboard “practice;” this means simulation-based training is even more important.
Mulcahy: With more complex products being designed and worked across companies to deliver a larger product/initiative, going digital will be important to ensure alignment.
It will be important to ensure ways to share data seamlessly across tools to understand wider impacts, relationships and identify risks at an earlier stage.
Macias: The A&D industry is seeking the total usage of comprehensive digital twins that harness simulations in near real-time to instruct all aspects of a system’s lifecycle. Simulation driven, model-based development when harmonized into a comprehensive digital twin will enable dramatic breakthroughs in program efficiency, quality, and innovative capabilities. Because of the dramatic increase in ability of the engineering teams to cycle through massive numbers of virtual design and operational scenarios leading companies are enabling dramatic improvements in optimization and deep insights into the function of the designed systems early and throughout ongoing design changes.
This will extend to every aspect of the lifecycle, first into manufacturing and sustainment/service, mission development and operations health monitoring. We can envision a future where every operation of a system/vehicle is both simulated before it happens and after to assess the most efficient operation and the overall health of the system, safety of its occupants/environment. This can also have a significant impact on sustainability if the digital twin is harnessed to optimize operations for minimum energy consumption and maximize life of the system.
Question 6 – How do you anticipate changing geopolitical conditions and regulatory demands influencing the development of next-generation aerospace and defense products? What strategies should industry leaders consider to remain agile and compliant?
Hilderman: Defense demands will only grow; Europe will need to greatly increase spending, and USA will need to counter increased China spending.
Bryczek: In the defense industry, meeting the mission requirements and providing capabilities quickly to the warfighter trumps regulatory safety compliance requirements. Since there is no “certification” activity as in civilian aerospace systems, there is less burden on development practices. I see very little regulatory changes that will greatly impact defense. On the civilian side, regulations continue to evolve still very slowly. Leaders need to remain agile with their business strategy and align with what the political conditions offer. If there is a way to morph your product to a different market; then be bold and make it happen.
Mulcahy: With the rise of more worldwide conflicts, especially in Europe and the Middle East, more countries are spending more of their GDP on defence spending.
In today’s world, defense now goes more than just weapons, but also into space, cyber security and of course ensuring systems are secure and reliable.
New threats require new solutions to help mitigate these threats. That’s where more companies will develop more solutions and start-ups will emerge.
We often hear of a grey area in the UAV world in terms of regulations, but with more focus on the SORA (Specific Operations Risk Assessment) / SAIL (Safety Assessment Integrity Level,) it will be interesting to see what standards emerge with more civilian/military uses for UAVs for both attack and defence purposes.
Macias: As the broader world adjusts to an accelerated rate of change, we will need to introduce innovative solutions faster and leverage solutions from global partners. This will demand secure, virtual collaboration methods, new ways of joint development while protecting IP and data security, and new standards for safety, communication, and joint operations. Industry leaders should continue to seek secure, virtual collaboration methods that can bring global/multi-disciplinary teams together and ensure harmonized efforts.
Question 7 – Are there any additional insights you have regarding predictions, events, or trends you anticipate happening in 2025 and beyond?
Hilderman: Demand for engineers is greater than supply and this will only worsen.
Mulcahy: More innovation in the UAV / Advanced Air Mobility (AAM) markets, but also more focus on the security of these solutions and the supporting infrastructure and regulations. It will be interesting to see how this combines with AI to develop fully autonomous and intelligent UAVs for civilian/military use cases. The need for larger companies to become more digital, deliver faster, and streamline operations will continue to be a focus.
Macias: The recent past has shown that innovative concepts are accelearating at such a high pace that we are continuously being surprised and amazed at new possibliities and impacts. The industry as a whole must seek faster awareness, greater agility and increase creativity to respond, leverage, and compete in the face of such dynamic times for Aerospace and Defense systems.
2025 Expert Predictions for the Semiconductor Industry: Innovations, Sustainability, and Globalization
The semiconductor industry is navigating a transformative era, marked by groundbreaking innovations and pressing challenges. As AI and machine learning demand faster, more efficient chips, semiconductor design and manufacturing are evolving at an unprecedented pace.
In part three of our annual predictions series, Michael Luciano, Senior Account Executive at Jama Software, explores the key trends shaping the industry. From advancements in silicon photonics and memory technologies to innovations in cooling systems and power delivery, these developments are poised to revolutionize chip performance while addressing critical energy efficiency needs.
Michael also addresses growing concerns about the environmental impact of chip production. With the immense power demands of AI-driven data centers and the continued use of harmful chemicals in manufacturing, the industry is turning to nuclear energy, novel materials, and refined processes as potential solutions. Emerging markets like India and China also play a pivotal role in future growth, highlighting the importance of global collaboration and infrastructure investment.
We like to stay on top of trends in other industries as well. Read our predictions for Industrial & Consumer Electronics (ICE) HERE, and Automotive HERE – Plus, stay tuned for future topics, including Aerospace & Defense, Medical Device & Life Sciences, and AECO.
With AI and machine learning driving demand for faster, more efficient chips, what key innovations in semiconductor design do you predict will transform these technologies, and how can companies balance performance with energy efficiency?
Michael Luciano: This is a great question. Key innovations in semiconductor design coming from increased demand with AI and machine learning (ML) will likely be on-chip optical communication using silicon photonics, continued memory innovation (i.e. HBM and GDDR7), backside or alternative power delivery, liquid cooling systems for Graphics Processing Unit (GPU) server clusters and superclusters.
Do you have any concerns or anticipate any negative impacts as it pertains to AI & ML?
Luciano: It’s understandable that people have concerns. Like every other tool that man has created, it’s important to create safeguards to prevent misuse and abuse. Agreeing on the exact safeguards and corresponding regulations is a highly contested and complex topic with wildly ranging global opinions. It’s undeniable that as AI systems and tools continue to evolve, these systems will replace some people’s jobs. This is already starting to happen. I am cautiously optimistic. As AI technologies become more advanced, with every negative impact I believe there will be an equal or greater level of positive impact for society and mankind elsewhere. Artificial superintelligence (ASI) is a hypothetical AI system with an intellectual scope beyond human intelligence. Mankind needs to see eye-to-eye before ASI comes to fruition or we are all in trouble. But don’t worry, we still have some time.
As chip production faces increased scrutiny for environmental impact, what role do you see for sustainable materials and manufacturing practices in the semiconductor industry, and how can software contribute to optimizing these efforts?
Luciano: In the context of the AI boom – the power required to operate gigawatt+ data centers is immense. Nuclear power is likely the most environmentally friendly way to go about it. Amazon and Google are currently investing heavily and recently formalized several key partnerships in this space. In the context of individual chip/device manufacturing – modern fabs also require a lot of energy/power. Nuclear powered systems will be the long-term answer. There are also a lot of nasty chemicals and gases that are used in chip production. I don’t see a clear way to fix this now, but as academia continues to study alternatives and companies continue to invest heavily in Research and Development (R&D) there is a possibility individual process steps can be adjusted/refined to incorporate novel materials or find other ways to help mitigate detrimental environmental impacts.
As the semiconductor industry becomes increasingly globalized, what emerging markets or regions do you see as pivotal to future growth, and how can companies foster effective cross-border partnerships and innovation?
Luciano: I identify Asia-Pacific (APAC) as the largest emerging market – specifically India and China, due to their populations. Companies can foster effective cross-border partnerships and innovation through significant investment in key infrastructure in those markets.
Are there any additional insights you have regarding predictions, events, or trends you anticipate happening in 2025 and beyond?
Luciano: AI Agents will mature and become widely used. This will significantly change how companies operate and go-to-market (GTM.)
2025 Expert Predictions for the Automotive Industry: AI, Sustainability, and the Road Ahead
The automotive industry is undergoing a seismic transformation, driven by advancements in AI, machine learning, electric vehicles, and sustainability initiatives.
In part two of our annual predictions series, Jama Software’s industry experts — Neil Stroud, General Manager of Automotive & Semiconductor; Stefan Stange, Managing Director; Matt Mickle, Director of Solutions and Consulting; and Ádám Gősi, Account Executive — share their insights on the most pressing challenges and groundbreaking innovations shaping the future of automotive.
From the rise of software-defined vehicles to overcoming supply chain disruptions and achieving ambitious sustainability goals, this year’s predictions offer a compelling roadmap for manufacturers looking to stay competitive and thrive in the years ahead.
We like to stay on top of trends in other industries as well. Read our predictions for Industrial & Consumer Electronics (ICE) HERE and stay tuned for future topics, including Aerospace & Defense, Medical Device & Life Sciences, Energy, and Semiconductor.
Question 1 – With the rising integration of AI, machine learning, and autonomous systems, how do you foresee these technologies reshaping automotive and semiconductor operations? What are the most promising applications and potential challenges?
Neil Stroud: The industry has undergone somewhat of a reset of expectations around autonomy. Solving the challenges related to autonomous vehicles is harder than we all thought. Generally, we are now laser-focused on the software-defined vehicle and developing the related systems that allow mass deployment of L2+ and L3 capable vehicles. This will ultimately lead into autonomy anyway.
It’s great to see robotaxi solutions gaining traction with L4 (ODD limited L5) and humans slowly becoming more open to getting in a vehicle with no driver. This is a massive mindset shift.
AI/ML has a massive role to play in all of these areas.
Stefan Stange: Development complexity and quality expectations will increase exponentially while the development time and cost must decrease, modern tools and processes supported by AI will support to solve these challenges.
Matt Mickle: With automotive, AI is exponentially increasing the development of systems that will enhance safety, enable convenience, and make maintenance more reliable. Overall, this is shifting the perspective of the driving experience entirely. This does come with concerns around risk especially in regards to cybersecurity, and with ethical decision making.
On the semiconductor side AI is helping to optimize design of chip architecture and enhance performance with AI-assisted tooling providing better analytics. Risks here are also in cybersecurity as well as supply chain risks due to things like export controls and potential tariffs.
Ádám Gősi: The challenge I think will define future development is intellectual property and how certain tools and models are handling sensitive data. Besides this, it is important to establish the responsibilities. In safety-critical development there always has to be a human expert to control the result. These factors will determine if it is worth investing in developing a new AI model or maybe adopting an existing one. Customers often don’t have their own definition of what they are expecting of an AI, they expect us to show them our best interpretation so far. This could lead to a trap of over-promising. With my limited knowledge, I see the current era as the “gold rush era” – some AI developers don’t have a clear target just hoping to hit the big prize.
Question 2 – As a follow-up question: Do you have any concerns or anticipate any negative impacts as it pertains to AI & ML?
Stroud: As someone who has worked in functional safety for almost 20 years, I’m still concerned about the industry’s ability to develop systems that have the appropriate levels of safety where AI is involved. As humans, we expect the autonomous world to be a safer world and reduce the number of accidents, injuries, and deaths on our roads. However, there are plenty of high-profile examples where we are falling short.
Stange: Safety and security are a must, not losing the data authority.
Mickle: Definitely many concerns with things like AI being used maliciously in cybersecurity attacks, Energy consumption and waste issues due to the massive amounts of computational energy needed, unpredicted failures or AI hallucinations, etc. but these need to be considered and worked through as progress is inevitable and unavoidable.
Gősi: In safety-critical development there always has to be a human expert to control the result.
Question 3 – With the rapid progression toward electric vehicles (EVs) and autonomous driving, what technological advancements do you think will be critical to automotive innovation over the next few years, and how can manufacturers stay competitive?
Stroud: The industry has to keep investing in battery technology to increase the range capabilities of vehicles as well as extending useful battery lifetime. It will be interesting to see how well alternative fuels such as hydrogen become mainstream. I do think there will be new data that comes to light that shows that electrification isn’t necessarily the holy grail that we expect. Building batteries is taking a major toll on the planet so there may well still be a combustion engine resurgence. Also, there is a macroeconomic challenge in that 80% of the world EV batteries come from China.
There is also a significant electrical grid infrastructure challenge to solve that is hugely expensive. As the number of EV’s grows, it will be theoretically possible to intelligently use power stored in unused vehicles to supplement the grid for supply. However, the grid we have today is fundamentally unidirectional (i.e. power station to consumer). Making this bidirectional is a massive and costly challenge.
Over the coming years, there is no doubt the industry will continue to get autonomous operation to a more reliable, safe, and therefore more mass-deployable state. As a result, new car ownership and usage business models will emerge enabled by technological advancements.
Stange: Development complexity and quality expectations will increase exponentially while the development time and cost must decrease. The use of modern and forward-thinking solutions will help.. Defining and following reliable processes and partnerships enabling a collaboration network by using best-in-class solutions with full traceable Agile for the development and manufacturing process should be the goal.
Mickle: AI has been coined as the next major platform shift or technology super cycle and will be the primary change for the upcoming years until we have greater machine intelligence or perhaps “machine consciousness” or Artificial Super Intelligence where AI actually outperforms humans across the board.
Gősi: As release cycles are ever shorter, products and software have to be released before fully developed. Over-the-air updates will be an important factor. Automakers will be able to update less developed rapidly depreciating models. ADAS sensors will become more refined, but incrementally. The current hardware is capable of fully self-driving. Software and regulation/local law are the limiting factors. Battery technology could see an improvement in general with the range increasing. Western Manufacturers will have to bring down the costs and improve on software quality, like Asian EV manufacturers.
Question 4 – Sustainability is a growing focus in the automotive industry. How do you see product design, materials, and manufacturing processes evolving to meet environmental goals and what role will software play in supporting these sustainable initiatives?
Stroud: There are many aspects to this. Making vehicles even more recyclable is fundamental. This impacts not only the materials used within the vehicle but also the design and construction of the vehicle. There is always the tradeoff between the value of recycled materials versus the cost of the effort to break the old car down into distinct recycled parts.
We are already seeing huge effort being put into efficient scalability and reuse when it comes to vehicle chassis platforms and software reuse.
Finally, manufacturing has a key role to play. The continuous march towards the truly smart factory that not only allows for mass-customization but also the ability to manufacture multiple models on the same production line even to the point where a single MaaS line can produce for multiple OEMs. These megafactories will be super-efficient and because there are fewer of them, the environmental impact will be lower.
Stange: The complexity also to guarantee sustainability will increase and must be handled with a professional and scalable software and process.
Also, the market expectation in regard to design, materials, individuality and related manufacturing processes will increase and be a game changer for success.
Mickle: Systems are being designed more with an end of life in mind and with reusability and modularity as a backbone. We’ve seen this shift for many years now with SDVs and it will only grow. Data provided by AI driven software will help drive optimization in the lifespan of all areas of the development process and supply chain, enhancing efficiency to reduce waste and understand better how things can be reused.
Gősi: Sustainability goals are not supporting the real cause in my belief. If EVs are going to continue being the supported trend by governments, and therefore manufacturers, then the battery manufacturing and recycling process needs to be improved to be more sustainable.
Question 5 – As software-defined vehicles become the new standard, what shifts do you anticipate in software development and cybersecurity practices to support seamless updates, driver experience, and vehicle safety?
Stroud: Software is eating the world. The challenges faced by OEMs in the software domain is immense. The modern vehicle contains hundreds of millions of lines of code. More than a modern commercial or military aircraft. This problem is compounded as the software comes from hundreds of third-party vendors and it is the responsibility of the vehicle manufacturer to integrate and test everything for correct operation and ensure it is still safe, secure and performant. This need must drive a mindset shift in the way systems are designed as well as breaking down the barriers between the OEM’s and the suppliers. The ones that will succeed are the ones that foster seamless interfaces between organizations. This will directly impact safety and security in a positive way as well as accelerating innovation.
Stange: A car becomes more and more a computer with wheels with exponential increase of requirements regarding software, updates on weekly basis also considering cybersecurity. Manufacturers have to balance between cost and security and develop cars that allow access to like a iPhone or computer but with higher and better security and safety strategies.
That means, car software platforms have to change and improve dramatically in the next years.
Mickle: Increased automation and traceability throughout the toolchain, using AI to analyze data and improve efficiency by providing that data intelligently to influence rapid change and execution.
Gősi: On the high level over the over-the-air updates and software updates and development need to be improved at the majority of OEMs and suppliers.
Question 6 – How is the automotive industry preparing to address challenges in the supply chain, particularly with semiconductor shortages, and what strategies could help improve resilience and adaptability in the face of future disruptions?
Stroud: The recent semiconductor supply constraints were a wakeup call for the industry. Vehicle shipments were being held up because of the lack of availability of the smallest sub-$1 components. As a result, I see two behaviours emerging. Some OEM’s are naturally building strategic partnerships with the semiconductor suppliers and trying to contractually guarantee supply. This gets interesting as the majority of semiconductor companies are ‘fabless’ and rely on companies such as TSMC, Global Foundries and SMIC. The other strategy is to take matters into your own hands and develop your own chips (ASIC) that provide exactly the functions that are needed versus the inevitable compromise that often happens with an off-the-shelf standard product. Companies like JLR are starting down this path. However, it’s not for the faint-hearted. Chip design is not easy and it’s expensive especially if you want to gain the benefits of the latest bleeding edge process nodes. Also, you will still be dependent on the same fab companies that serve the rest of the industry.
Stange: I see OEM collaborating more and more with start up´s in the semiconductor or EV market to assimilate the needed know how, strategies and flexibility reacting fast to the market needs. So, for example VW collaborates with Rivian and announced using their Software platform for the next Audi´s and VW´s and Aston Martin is collaborating with Lucid.
Mickle: Investment in local suppliers as well as diversifying the regions from which supplies are acquired. Also designing for flexibility and modularity such as with chiplets.
Gősi: Shortages don’t seem to be a problem nowadays. The supply chain needs to be more agile and detect risks sooner to avoid the delays caused by the JIT method.
Question 7 – The concept of “mobility as a service” is gaining traction in urban areas. How do you think automotive companies should adapt to this trend, and what new types of partnerships or innovations do you foresee in this area?
Stroud: MaaS will ultimately drive massive change in the way the mobility of the population happens. We already see that the current generation generally has a dramatically different view on driving and vehicle ownership. We’ve already witnessed the disruption that rideshare created. The car OEMs will have to adapt and develop new business models that go beyond the traditional purchase and lease models of today. There will be intermediary companies that emerge (that could be subsidiaries) that provide such services. I also envisage tighter integrations with the insurance companies as the volume of individual vehicle data that’s available will allow for hyper-tailoring of such services.
Stange: From my perspective, we have started mobility as a service already in urban areas with apps like FreeNow or Uber and more will come. Important is, that everything somehow has to be networked with the support of intelligent systems and software.
Also, the infrastructure, for example, intelligent charging points, parking, and availability has to improve. The way of thinking about mobility as a service has already changed for the younger generation, while my older son still likes cars for fun and as a status is my younger son just considering how to get from A to B fast, cheap, and safe.
Gősi: This is a business question between OEMs and local governments. From a product perspective, they would need cheap-to-produce and cheap-to-maintain and easy to drive vehicles that can serve those users. Manufacturers will have to further optimize their platform strategy for this specific application.
Question 8 – Are there any additional insights you have regarding predictions, events, or trends you anticipate happening in 2025 and beyond?
Stroud: I strongly believe that it is not the technology that will limit the deployment of these new technologies but the legislation. Every single country has a different set of rules and that will have a significant impact. We also have to overcome the challenges of dealing with ethical AI. Autonomous crash scenarios will have to be ‘calculated’ differently depending upon geographic location. Naturally, this is a highly sensitive topic but one that must be solved.
Stange: 2025 will be a challenging year for Automotive specially and Europe, unclear EV strategies, Asian competition, high taxes, energy and labor costs, and political struggles – everything will push the auto industry to slimline their way of thinking, developing, and manufacturing. On the other side, there will be a lot of opportunities for the innovators, the startups, and the companies, that reinvent themselves and restructure in a healthy way, these are the ones, we are happy to support.
Gősi: Unless there is a major shift in strategy and execution, the traditional European OEMs will lose their market-leading position. Innovative US and innovative and competitive Asian manufacturers will take over the leading position.
Editor’s Note: Responses reflect a mix of British and American English, depending on the respondent.
Jama Software is always looking for news that would benefit and inform our industry partners. As such, we’ve curated a series of customer and industry spotlight articles that we found insightful. In this blog post, we share an article from Med Device Online, titled “How The EU AI Act Impacts Medical Device Manufacturers”, written by Hilde Viroux, PA Consulting and published on November 18, 2024.
How The EU AI Act Impacts Medical Device Manufacturers
The EU AI Act (Regulation (EU) 2024/1689) is a landmark legislation that will shape the future of AI in Europe and is expected to be the baseline for similar legislation in other countries/regions. It will have a significant impact on the AI industry and society, as it will set new standards and rules for the development and use of AI systems, as well as create new opportunities and challenges for innovation and competitiveness. The EU AI Act attempts to regulate AI in a way that balances the benefits and risks of this transformative technology. The AI Act also will impact other industry sectors like the medical device industry for devices that include AI technology. Medical device manufacturers will have to comply with the AI Act.
All AI systems are classified in four risk classifications: unacceptable, high, limited, and minimal.
AI systems for which the risk is deemed unacceptable are banned from the market. These are, for example, untargeted scraping of facial images from the internet or CCTV footage, emotion recognition in the workplace and educational institutions, social scoring, and biometric categorization to infer sensitive data, such as sexual orientation or religious beliefs.
High-risk AI systems have a significant impact on people’s lives or rights, such as healthcare, education, law enforcement, or public services. These must comply with strict requirements, such as data quality, transparency, human oversight, and accuracy. High-risk AI systems will have to undergo a conformity assessment by a notified body that is specifically designated for AI systems before they can be commercially available.
Limited-risk AI systems can pose some risk to users, like chatbots, emotion recognition, or biometric categorization. These must provide clear information to users and allow them to opt out.
Minimal-risk AI systems like spam filters or video games are not expected to pose any risk. Although they are largely exempt from the regulation, they still must follow the general principles of safety and fairness.
The AI that is part of a medical device will fall in the high risk category and will require oversight by a notified body as it can have a significant impact on people’s lives.
The EU AI Act was published July 12, 2024, and will apply by August 2, 2026. However, some elements of the act become mandatory by August 2, 2025.
Providers of high-risk AI systems must implement a quality management system (QMS) that covers the following during the lifetime of the AI system:
Risk management to identify and mitigate the potential risks of the AI system to health, safety, and fundamental rights during the lifetime of the AI system
Data governance of training, validation, and testing data sets
Development and maintenance of the technical documentation: for products that already require technical documentation under other legislation (e.g., medical devices), the AI related technical documentation must be included in the existing technical documentation.
Data logging to ensure AI systems keep track of data during their lifetimes
Labeling providing information on the functioning of the AI system and its operation and maintenance
A design that ensures appropriate levels of accuracy, robustness, safety, and cybersecurity
Post-market monitoring, including collection and reporting of incidents and malfunctioning to the relevant authorities
The implementation of the QMS will be mandatory by August 2, 2025, as well as the identification of the economic operators.
The EU AI Act identifies a number of economic operators in the high-risk AI system life cycle, who all have specific obligations related to high-risk AI systems.
In addition to the obligations related to the QMS, providers of AI systems that are not based in the EU must appoint an authorized representative (AR) who is based in the EU and confirm the AR’s mandate from the provider.
The AR has to verify the DoC, the technical documentation, and that the appropriate conformity assessment procedure has been completed and is the liaison with the competent authority.
Importers of AI systems have to ensure the AI system conforms with the AI regulation and provide their name and address with the AI system. Importers have to work with the competent authorities on any actions initiated by them to reduce and mitigate risks posed by the AI system.
Distributors have to verify the AI system is accompanied by the appropriate instructions for use and bears the CE marking.
Users of AI systems have to use them in compliance with the instructions for use. They have to report any serious incident, unacceptable risks, or malfunctioning to the national supervisory authorities, the AI system provider, importer, or distributor.
Most medical devices manufacturers will have their economic operators (AR, importer, distributor) already identified and in place as this is also a requirement under the medical device regulations. However, it will be important that contracts with those economic operators are updated to include the obligations related to the AI Act.
The obligation to report complaints, as part of the post-market surveillance becomes mandatory by August 2, 2025.
The EU AI Act foresees the creation of a European database where all providers, ARs, and the AI systems will be registered. It establishes a governance structure for the oversight and enforcement of the regulation. It creates a European Artificial Intelligence Board (EAIB), composed of representatives from national authorities and the European Commission, to provide guidance, advice, and recommendations on AI matters. It also designates national competent authorities and notified bodies to monitor, audit, and certify AI systems and their providers.
To support innovation, member states will establish AI regulatory sandboxes where providers of AI systems can develop, test, and validate innovative AI systems in a controlled environment. Spain, for instance, has launched one of the first pilot sandboxes under its State Agency for the Supervision of Artificial Intelligence (AESIA). This pilot sandbox aims to align with Spain’s National AI Strategy and is anticipated to serve as a model for other EU member states. Other member states, such as Germany, are working on regulatory frameworks that would facilitate similar testing environments, and some countries are implementing sandboxes specifically for sectors like mobility, public procurement, and healthcare. The EU Commission also provides guidance and support to standardize these efforts across the region, aiming to accelerate safe AI development through regulated testing environments.
Full compliance to the EU AI Act will be mandatory by August 2, 2026, with AI systems meeting the requirements, the sandboxes established, and the notified bodies designated and fully operational.
The EU AI Act introduces a system of sanctions and remedies for non-compliance or infringement of the regulation. It empowers national authorities to impose administrative fines of up to 4% of the annual worldwide turnover of the provider, user, importer, or distributor, depending on the severity and duration of the breach. It also allows national authorities to order the withdrawal, recall, or modification of non-compliant AI systems, as well as to suspend or prohibit their use or supply. In addition, it grants the right to compensation to individuals or organizations that suffer harm or damage as a result of non-compliant AI systems.
Pitfalls To Avoid
From a high-level perspective, the requirements seem similar to the general requirements for medical devices, like QMS, economic operators, and notified bodies.
However, there are some things to watch out for. For example, the notified body designated for medical devices may not be designated for the AI Act, resulting in device manufacturers having to deal with two different notified bodies and inspections for the same product. Two notified bodies’ numbers will have to be referenced on the label and in the declaration of conformity.
Medical device manufacturers that are using AI as part of their device or device software should be assessing the AI Act requirements and ensuring they are built into their QMS, as the due date is less than a year out. Having a QMS compliant to ISO 13485 that satisfies medical device regulations is not sufficient. The QMS must cover cybersecurity requirements as well for high-risk AI systems to protect against tampering, data breaches and other security risks.
Conclusion
Although the requirements in the AI Act seem very similar to what is already required for medical devices, like having a QMS, control over economic operators, post-market surveillance, and conformity assessment with a notified body, compliance with EU medical devices regulations is not sufficient to meet the AI Act requirements.
Manufacturers should be aware and start their compliance journey sooner rather than later to meet the various due dates of August 2025 and 2026.
In this blog, we recap our webinar, “Key Systems Engineering Skills: Critical Thinking and Problem Framing” – Click HERE to watch it in its entirety.
Key Systems Engineering Skills: Critical Thinking and Problem Framing
Elevate your team’s success by exploring the role of critical thinking in a system engineering competency model.
In this insightful session, Chris Unger, Retired GE Healthcare Chief Systems Engineering Officer and Principal at PracticalSE LLC, and Vincent Balgos, Director of Medical Device Solutions at Jama Software®, discuss how critical thinking and decision-making skills are integral to systems engineering.
In this insightful session, you will learn:
Explore the vital role of critical thinking and decision-making in systems engineering.
Learn practical techniques for decision framing and closure.
Gain insight on how systems engineers should manage design decisions on a project.
See a simple model of how and when to engage with stakeholders in design decisions.
Below is an abbreviated transcript of our webinar.
Chris Unger: We’re going to talk today about a follow-up to the last webinar, where I’m going to talk about some of the most important systems engineering skills, critical thinking, and problem framing. So, how do skills in general, and soft skills, fit into improving systems engineering? So, in prior talks, I’ve suggested you keep your processes very simple but make them effective, and that’s easy to say but hard to do. That means you have to understand the system of the SE processes, how they connect, and where the diminishing value of the processes, the source process heading off, happens. As an example, a topic could be a technical risk, or it could be a trade-off between different possible solutions. So, we want to understand how those to the risk management and the decision process interact.
In order to do that, the best systems engineers have to have really good judgment. In addition, we have to influence people. Being simplistic, hardware and software engineers design things, things do what they’re told. I know it’s oversimplified, but our deliverables are instructions on how the software and hardware engineers do things. So, the best systems engineers here have an area of depth that they’re experts in, so they bring some technical credibility. They have systems of breadth, they understand all the systems processes and how they interact, and they have great interpersonal skills. Today I’m going to focus on how you achieve a balanced and optimized design, how you focus on your cost versus risk, and doing that through basically decision making.
So, first I want to talk about the Helix Model. So, the Helix Project was a project funded by the government and, the US government, and their concern was for big aerospace and NASA projects you tend to produce a major, billion-dollar development every 10 years, and then you do 10 years of support. So, people often move on. They were worried about how you create the truly brilliant leader systems engineers from a team that may be a little bit sparse. They developed this model up here in the front and simplistically, you start with things you learn in school, how to do good mechanical engineering, electrical engineering, and software engineering techniques. You then go into an organization, and so you spend the first five years learning about your company. Things like, well, if you’re going to be doing a say glucose monitor, what does blood chemistry look like? What does a sensor look like? What’s a workflow? So, you become a good organization-specific mechanical engineer.
Then you learn about lifecycle. How do you go from womb to tomb, from customer needs to disposal and disposition with all the regulations across the world in terms of chemical safety? So, after five, maybe 10 years, you understand your domain, you understand the lifecycle and you understand your technology. What differentiates after that? What they found was the skills on the bottom half of this page, the Systems Mindset, so big picture thinking, and paradoxical mindset. You’ve all heard that joke about fast, good and cheap, pick two of the three. Well, that’s the world in which systems engineers live. We make trade-offs between things that are inherently conflicting. The other thing is, we’ve got to make decisions quickly, so you’ve got to have a flexible comfort zone. You’ve got to be willing to wait till you have the critical information but make a decision without all the information you want.
Unger: In terms of the middle column, Interpersonal Skills, just the obvious stuff as I mentioned. You’ve got to influence the other engineers to make a good decision. Then finally here in Technical Leadership, balanced decision-making, and risk-taking. So, I had a general manager one time say, “We’re in the business of managing risks, not avoiding risks.” The least-risk program is also a boring one, but you also don’t want to take moonshots and everything. So, you really want to balance. It’s another case of a paradoxical mindset. Balance risk-taking with hitting a schedule predictably. So, these are the kinds of skills that really differentiate as systems engineering leaders, 10 to 15 years into your career. I’m going to talk more about these, decision-making, stakeholder management, and barrier-breaking.
So, I put together a very simple Systems Engineering Competency Model. I started with the NASA handbook and the NASA lifecycle. I simplified it, into that they had scope and requirements management separated, and I actually agree with those being different. But in reality, on the size of programs that we typically implemented, the people who did one typically did the other. Same thing, the architecture and the design, those were typically the same people. So, you have the upfront design, you have implementation. So, managing the subsystems actually do the implementation of what the design asks them to do, and you integrate it, such that you find your defects early. Then you manage all the lifecycle, the serviceability, manufacturability, disposability, and all the “ilities.”
Then leadership, obviously, there the interpersonal skills. This was developed for GE Healthcare, so I just picked it from our existing leadership skillset and I simplified it. What you’ll notice here is I put down at the bottom, critical thinking, as a technical skill. For many executives, and for other functional engineers, critical thinking is important, but as I mentioned, since we deliver instructions and designs to other engineers, framing decisions, taking vague things from product management and marketing, and turning them into clearer problems or functions to solve, I consider that a core technical excellence of systems engineering. But that’s vague. How do I actually measure that? So, I came up with this fairly simple set of observable behaviors. So, first of all, framing problems takes an ambiguous problem identifies the critical stakeholders, and turns them into a clear problem a more junior engineer can solve.
So, first, let’s talk about framing the problem. Even an entry-level person has to be able to understand a problem that’s been framed for them. But as you get to more senior people, the 10 to 15-year level, you have to be able to frame a complex problem, see around corners, use foresight to sort out essentials from the detail, and identify risks and emergent behavior that need to be incorporated in the decision, that other engineers might not see. Even at the strategist level, you can take a complex and ambiguous problem clarify the ambiguity, and turn it into simply just a complex and interconnected problem.
So, if we’re talking about maybe the 10 to 15-year-old person, not the most senior executives, you’ll be able to take a complex problem, identify ahead of time problems other people don’t see, and capture that. Balance cost, schedule, technical risk, and team capabilities, and make a trade-off based on sound evidence and data. Balance your intuition, when you don’t have all the data with waiting and gathering data where you need it. Then finally, making the decision is maybe the easy part. You have to make sure the team follows your leadership. Take accountability for making the right decisions, delegate where you can, and then ensure that the entire team buys into the decisions that the team or you have made. So, that’s the theory.
Unger: Let’s talk about how we manage design decisions. First of all, why? Why is this a critical skill? By identifying the critical design decisions, it allows the team to focus on the most important thing, and separate out the core from the distractions. It helps teams identify work items. So, for example, one time when I was working with the ultrasound team in Japan, we had a bunch of really experienced engineers and they were working on a new ultrasound probe. It had moved an active component into the probe and there was a thermal issue. They were talking in Japanese for about five, 10 minutes when I was asked to frame the problem and I said, “Yeah, you’re talking too fast and too much. This is not that easy. Come back to me and tell me what you’re actually doing.”
They were figuring out how to measure the thermal properties in the lab. I said, “Well, imagine you had a probe that was safe, with maybe 39°C, but that was uncomfortable to handle. Have you worked with the application people on how much value? If you spent $50 more and took the temperature down by 1°C, would that be worth a trade-off? The team, “Oh, that’s interesting.” They were actually focused on the technical feasibility, not the real market and customer acceptance problem. So, by doing this upfront, you can make sure that you have a complete work process for the team. Then once you’ve made the decision, it minimizes rework by making sure the decisions stay closed.
Now, this decision list and prioritization should start early. It would be comfortable to wait until you know everything, but that’s too late. So, it’s a living document. Don’t wait to get started until you have enough information to make a good plan. Start with what you know, and then build out as you continue. So, one of the first things I talk about is, what is a decision? As an example, I’ve had teams come to me saying, “The operating system selection is a decision.” It’s like, “No. It’s actually not typical. It’s typically a collection of decisions.” So, I draw this little arrow here. It’s basically a decision is a point in which you select between different paths going forward and you pick one way versus another. So, deciding whether to include a stretch item in scope or not is a decision. Deciding between very specific designs and implementing a feature is a decision. Setting a critical to-quality parameter or balancing between different parameters, so cost versus reliability or cost versus performance, is a decision.
In this blog, we’ll recap a section of our recent Expert Perspectives video, “A Method to Asses Benefit-Risk More Objectively for Healthcare Applications” – Click HERE to watch it in it entirety.
Expert Perspectives: A Method to Assess Benefit-Risk More Objectively for Healthcare Applications
Welcome to our Expert Perspectives Series, where we showcase insights from leading experts in complex product, systems, and software development. Covering industries from medical devices to aerospace and defense, we feature thought leaders who are shaping the future of their fields.
In the complex world of healthcare, evaluating benefit-risk is crucial to successful product development and patient outcomes. Our expert perspectives video, “A Method to Assess Benefit-Risk More Objectively for Healthcare Applications,” offers actionable insights for healthcare innovators aiming to meet rigorous regulatory requirements while ensuring patient safety and efficacy.
In this episode of Expert Perspectives, Richard Matt breaks down a streamlined, objective method for benefit-risk analysis. He explores a structured frameworks and data-driven approach that help teams make balanced decisions, mitigate risks early, and stay compliant with regulatory standards, including FDA and ISO guidelines.
This patent-pending approach helps organizations navigate challenges, foster innovation, and ultimately bring safer, more effective healthcare solutions to market.
Below is a preview of our interview. Click HERE to watch it in its entirety.
Kenzie Jonsson: Welcome to our expert perspective series where we showcase insights from leading experts in complex product, systems, and software development. Covering industries from medical devices to aerospace and defense, we feature thought leaders who are shaping the future in their fields. I’m Kenzie, your host, and today, I’m excited to welcome Richard Matt. Formerly educated in mechanical, electrical, and software engineering and mathematics, Richard has more than thirty years of experience in product development and product remediation. Richard has worked with everyone from Honeywell to Pfizer and is now a renowned risk management consultant. Today, Richard will be speaking with us about his patent pending method to assess benefit-risk more objectively in health care. Without further ado, I’d like to welcome Richard Matt.
Richard Matt: Hello. My name is Richard Matt, and I’m delighted to be speaking with you about our general solution to the problem of assessing whether the benefit of a medical action will outweigh its risk. I’ll start my presentation by saying a few words about my background and how this background led to the benefit-risk method you’ll be seeing in the presentation.
To understand my background, it really helps to go back to the first job I got out of undergraduate school. I graduated with a degree in mechanical engineering and an emphasis in fluid flow. And my first job was in the aerospace industry at Arnold Engineering Development Center, at a wind tunnel that Baron von Braun designed. I worked there as a project manager, coordinating various departments with the needs of a client who brought models to be tested. These are pictures of the ADC’s transonic wind tunnel with its twenty-foot by forty-foot long test section that consumes over a quarter million horsepower when running flat out. Those dots in the walls are holes, and a slight suction would pull the out on the outside of the wall to suck the air’s boundary layer through the holes. So a flight vehicle appeared more closely to match its flight air characteristics in free air. It was amazing place to work.
We could talk about aerodynamic issues and thermodynamic issues like why nitrogen condenses out of the air at mach speeds above six or why every jet fighter in every country’s air force has a maximum speed of about mach three and a half. But to stay on the topic of benefit-risk, the reason or my intro to this, the reason I was brought this up was that I saw here firsthand the long looping iterations that came from different technical specialties, each approaching the same problem from the respective of their technical specialty. I found it very frustrating and the, following analogy very apt, after getting, so each of our technical specialties would look at the same problem, the elephant from their own view. And I found myself getting frustrated with my electrical and software engineering coworkers, that they didn’t understand what I was talking about, but I knew realized soon I didn’t understand what they were talking about either.
So I decided I wanted to become part of the solution to that problem by going back to graduate school and getting myself rounded out and my education so I could talk to these folks from their perspective also. So I went back to grad after mechanical and undergraduate, went back to graduate school in electrical and mathematics and picked up enough software. I started teaching, programming also in college. I developed there a solution for the robot arms in those wind tunnels to to control a robot arm for every possible one, two, or three rotational degree of freedom arm, and that was my graduate thesis. After I completed my thesis, I felt empowered to start, my work doing going wherever I wanted doing whatever I wanted to do and realized that if I wanted to do anything significant, it would take many years, and I decided to focus on teamwork. Does that sound pretty good?
Matt: My ability to work across technical boundaries enabled me to bring exceptional products to the market. For instance, I brought an Internet of Thing (IoT) device to the market during the nineteen nineties before Internet of Things was a thing. And I rapidly advanced while I was working as a VP of engineering at a boutique design firm in the Silicon Valley. These are a few of the clients that I had, through the work that I’ve done over the years.
And, the combination of the breadth of my formal training and my system perspective for solving problems has really helped me work across continue to work across boundaries, so that I’ve worked for companies to help them establish their pro product requirements, trace requirements, do V and V work. I’ve done a lot of post-market surveillance work. I established internal audit programs. I’ve been the lead auditee when my firm is audited. Done had significant success accelerating product development and has been on work on. So mixed in with all of these works, I special I started specializing into risk management as consulting focus versus something I just did normally during development.
And since the defense of a patent requires notice, I’ll mention that the material here is being pursued on the patent, and, would like to talk with anyone who finds this interesting to pursue after you’ve learned about it. So let me start my presentation on benefit risk analysis by talking about how important it is to all branches of medicine and the many problems we have implementing it. The solution I’m gonna come up with, I’ll just outline here briefly so you can follow as we’re going through the presentation. I’m gonna first establish a single and much more objective metric to measure benefit and risk than people traditionally use. I’ll be accumulating overall benefit and risk with sets of metric values from this first metric. And finally, we’ll show how to draw a conclusion from the overall benefits and risk measurements of which is bigger benefit or risk.
So in terms of importance, historically, benefit-risk has been with medicine for millennia. It’s a basic tenant to all of medicine. The first do no harm goes all the way back to the quarter of Hammurabi 2,000 BC, and it legally required physicians to think not just about how they can help patients with treatment or what harm they might cause to treatment and making sure that the balance of those two favor the patient is very much the benefit-risk balance that we look at today. The result we’re gonna talk about is gonna be used everywhere throughout medicine with devices, with drugs, with biologics, even with clinical trials.
So is that fundamental cross medicine? How it’s used currently?
If you are in one of the ways developing new products, benefit-risk determinations have to be used in clinical trials to show that they’re ethical to perform, that we’re not putting people in danger needlessly. Benefit-risk determinations are the final gate before a new product is released for use to patients. And I have a quote here from a paper put out by AstraZeneca saying the benefit-risk determination is the Apex deliverable of any r and d organization. There’s a lot of truth to that. It’s the final thing that’s being put together to justify a product’s release. And so it has a very important role here for FDA and has a very important role for pretty much the regulatory structure of every country, including the EU.
Matt: In terms of creating a quality system, every medical company is required to have one. Benefit-risk determinations are used to assess a company’s quality system. This is per the FDA notice about factors on benefit-risk analysis. When regulators are evaluating company’s quality system, they’ll use benefit-risk to determine if nothing should be done, if a product should be redesigned, if they should take legal actions against a company of a range of possibilities from replacing things in the field to stopping products from being shipped. It’s also a key in favorite target for product liability lawsuits, because of how subjective it is, and we’ll get to that in a moment. It can also be used for legal actions against officers. So benefit risk is a really foundational concept for getting products out and keeping products out and keeping companies running well. Just a bit of historical perspective of medical documentation and development. We have here, I cited four different provisions of the laws, regarding medical devices in the United States. This is a small sampling.
The point I’m trying to make here is that each of these summaries of the laws discuss continually evolving, continually growing, more rigorous standards for evidence, more detailed requests for information from the regulators to the instrumentation development companies to the product development companies. So first, medical products are heavily regulated. We have the trend of increasing analysis and rigor. Per ISO 142471, and this is an application standard that is highly respected in the medical device field. A decision as to whether risks are outweighed with benefits is essentially a matter of judgment by experienced and knowledgeable individuals.
And this is our current state of the art.
Not that everybody does it this way, but this is the most common method of performing benefit-risk analysis. And benefit-risk analysis by this method, has a lot of problems because it’s based on the judgment and it’s based on individuals, and both of those can change with different settings. That’s why it’s a favorite point of attack for product liability lawsuits.
This quote was true in 1976, when medical devices were put under FDA regulation, but significantly remains unchanged nearly fifty years laters. Benefit-risk determinations are an aberration and that unlike the rest of medicine, they have not improved over time. They’ve remained a judgment by a group of individuals. In, twenty eighteen, FDA was, approached by congress to set a goal for itself of increasing the clarity, transparency, and consistency of benefit risk assessments from the FDA.
This was in human drug review as the subject, and the issue was that various drug companies had gotten very frustrated with the FDA for disagreeing with their assessments of what benefit-risk should look like. And to repeat again, when you have a group of individuals making a judgment, that’s gonna lead to inconsistencies because both the group and their own individual judgment will vary from one situation to the next. I have another, quote here from the article from AstraZeneca. The field of formal and structured benefit-risk assessments is relatively new.
Matt: Over the last twenty years, there’s still a lack of consistent operating detail in terms of best practice by sponsors and health authorities. So this is an understatement, but a true statement. We have had a lot of increasing effort over the last few years because if people are dissatisfied with the state of benefit-risk assessments, they want to do better than this judgment approach. And so there have been a plethora of new methods developed. I’ve found one survey here that summarize fifty different methods just to give you an idea of how many attempts there are. And I went through those fifty methods.
The other thing that’s interesting to see is the FDA’s attempt to clarify benefit-risk assessments. I have here five guidance documents from the FTA, and I would put forth the proposition that anytime you need five temps five attempts to explain something, it means you didn’t understand the thing well in the first place or failing about a bit trying to get it done right. I think this is also held up by the drug companies, pressure on congress to get FDA to improve their clarity and consistency of benefit-risk assessments.
So here’s the, fifty methods that I found in one study of benefit-risk assessments. They have them grouped into, a framework, metrics, estimate techniques, and utility surveys. These are the fifty different methods, and I’ve gone through each one of them. And they all have fundamental problems. They, I’m going through them a bit slowly. Like, here’s one, from the FDA, another benefit risk assessment. Health-adjusted life years are one of the few that uses the same metric for benefit and risk. Number needed to treat is a very popular indication for a single characteristic, but you can’t integrate that across the many factors that needed to do benefit-risk assessment.
And so we’ve gone down the rest of these, methods. If I group these fifty methods by how they accumulate risk, I get a rather useful collection. Most of the methods do not consider all the risk-benefit factors for benefit-risk situation. They will pick on just one factor. And you can’t combine the factors with themselves or with others. It’s simply looking at one factor by itself. So it’s an extremely narrow view of benefit-risk for most of these. The few methods that do look at all the risk-benefit factors, most of them start with what I call the judgment method, where you’re forced to distill all the factors down to the most significant few, only four maybe four to seven methods, four to seven factors.
So either the methods consider only one type of, one factor at a time, or they force you to throw away most of the methods and consider maybe four or seven factors is the second method. The third method is they assign numbers to the factors, they’ll add the factors together, and they’ll divide the benefit sum by the risk sum. And if the division is bigger than one, they’ll say the benefit’s bigger than the risk. And if the division is less than one, they’ll say the risk is bigger than the benefit.
In the world of automotive and semiconductors, where the pace of technological innovation seems to accelerate daily, staying ahead of trends is critical. That’s why we sat down with Neil Stroud, Jama Software’s industry expert with decades of experience spanning major players like Intel, Arm, and Samsung. Neil has been at the forefront of the functional safety and semiconductor evolution, witnessing firsthand the challenges and transformative changes that shape these industries.
In this exclusive interview, Neil shares his unique perspective on the latest industry dynamics, the impact of global supply constraints, and how the automotive industry’s strategic relationships with semiconductor vendors are evolving. He also discusses Jama Software’s role in helping both sectors address increasingly complex requirements and integration challenges, driving efficiency and reducing risk across the supply chain. Join us in exploring how Jama Connect empowers companies to manage complexity, enhance traceability, and accelerate their time to market.
Driving Innovation: Quarterly Automotive & Semiconductor Trends with Neil Stroud
Kenzie Jonsson: Thanks for sitting down with me today, Neil! I’d love it if you could spend a little bit of time telling us about your background and career path.
Neil Stroud: Prior to joining Jama Software back in April of this year, I’d spent most of my career in the semiconductor industry, working for companies like Samsung, NEC, and PMC-Sierra. I also spent 12 years with Intel, and then moved into the IP space with Arm who are one of the key players in semiconductor IP. Directly before joining Jama Software, I spent time with CoreAVI, a niche software company in the safety-critical graphics space. Almost twenty years of my career has been spent in the functional safety domain. It wasn’t by design; it was more by accident. I didn’t set out to get into that domain at all. It all came about through my time at Intel where I was calling on a big industrial automation company and they asked me the question, “Hey, so when are you going to start supporting functional safety with Intel architecture?”
Of course, at that point, I didn’t know what it was, what it meant, what it was all about. One thing led to another, and I stumbled into the world of functional safety and was given a great opportunity at Intel to go… I was going to say, go and lead it, but it was more me volunteering and saying, I think we should be doing this. And Intel the senior leadership at Intel saying, “Oh, go on then, go do it.” That’s exactly what I did. So, it was quite nice because you’re acting as a startup within the safety of a big corporation like Intel. At that point you start to look at the fundamentals – what does safety look like? What do we need to do as a company? How do we sell it? How do we make money out of it? What are the technical issues? What problems are the industry facing? That kind of stuff. So, I pretty much became a GM of my own startup at that point, which was a great experience.
That was back in the day when complex semiconductor functional safety wasn’t really a thing. So, we were blazing the trail, not just for Intel but for the whole industry. So, little did I know back then where it would lead. It’s been so much fun. That’s also what took me to Arm – to drive the whole functional safety strategy across their ecosystem. So, all of that obviously led me into adjacent businesses especially automotive, as safety is of paramount importance where I worked with the big OEMs and throughout the supply chain. Now here I am at Jama Software bringing all of that experience of semiconductor, automotive, and software and apply that into the requirements management tools domain to drive our presence and growth in the automotive and semiconductor segments.
Jonsson: What changes have you been part of at Jama Software recently to help us better meet the needs of our customers?
Stroud: It’s a really interesting time to join Jama Software. Obviously, we’ve been successful as a company over the preceding years. I’m amazed by the number of different market segments that are using Jama Connect. There are some obvious ones like automotive, semiconductor, medical, consumer electronics, and aerospace and defense. But there are some emerging segments as well, which is great to see, like insurance companies and state departments and beyond. Clearly, Jama Connect is a tool that transcends verticals. But of course, we need to be able to tweak and tailor that to accommodate the unique needs of each market segment. Functional safety and cybersecurity are great examples of these differences. That’s what’s exciting as part of the change with Francisco Partners acquiring us back in April for $1.2 billion. That to me is a leading indicator that they’re betting on us to continue growing and we are investing heavily to continue to delight our current customers and of course help new customers achieve new levels of innovation. Placing that bet is exciting for all of us at the company. As a result, one of the changes we made at that time was to really double down on the vertical focus. So, bringing in an organizational structure that allows us to do and in turn drive even more alignment with the needs of each market segment.
It’s good for us. But more importantly, it’s good for the customers because we can talk in their language, we can better understand their problems, and of course we can partner with them to solve their problems. And that in turn means tailoring our product to better suit their needs. So, it’s a win-win. It’s a confirmation of the importance of those verticals to Jama Software and sends a clear message to that we are listening and here to partner with them on their growth journey. So, it’s exciting for me and I see that excitement across the whole company.
Jonsson: Can you tell us what you’re seeing in the industry with the conversations that you’re having with our customers and prospects?
Stroud: Well, I cover both automotive and semiconductor industries. There’s obviously a lot of overlap between the two, and I think that’s an increasing trend we’ve seen over the last few years. The automotive guys have been building a lot more of a strategic relationship with the semiconductor vendors. Not least because when the supply constraints kicked in a couple of years ago, production lines were coming to a halt because they couldn’t get hold of the smallest, tiniest, cheapest components. And at that point, it is interesting how it created a real forcing function. The automotive segment said at that point, “Right, we aren’t going to get burnt again.”
So, they did one or two things. Some went out and tried to tie down the semiconductor vendors contractually to say, “Look, in the event that this happens again,..” and it will happen again because the semiconductor industry tends to work on about a seven-year cycle of oversupply versus constraint, “we want to guarantee our component supply.” The car OEMs and tier-one suppliers obviously didn’t want to get caught in that again. I don’t have visibility into how successful those discussions were, but I don’t think it will necessarily prevent a recurrence. The good news is that there is huge investment going into building new fabs that will provide significant capacity increases in the coming years.
The other interesting dynamic that happened was some of the auto guys said, “Well, screw that. We’re going to do our own silicon.” It sounds easy when you say it quickly, but there’s an awful lot to it when you commit to that solution. Questions like, “Okay, so how are you going to do that?”, “Are we going to go and engage with a design house or we’re going to hire a team of semiconductor design engineers,” “Which fab supplier will we use?” “Will they guarantee supply?”
It’s not a trivial undertaking and to make it work from an ROI perspective it’s probably a ten-year journey. And in the meantime, you’ve still got to work with what you’ve got. The other issue is once you get down that path, you are committed and it’s an expensive commitment to make. The downside is you don’t get the benefit of volume that the big guys like Qualcomm, Samsung, MediaTek, or NVIDIA can offer you. They build millions and millions of chips and can amortize the cost across many customers and markets. If you’re building your own, you don’t get that advantage, but you mostly own your own destiny. So, pros and cons.
So that’s one dynamic. I think the other dynamic we’ve seen in automotive generally over the last five years is a repositioning of what’s important. If we go back, even just five years, we all thought we would be driving autonomous vehicles right now. There’d be mass deployment. You and I would both have one on the drive. Of course, that hasn’t happened because we all realized how difficult it is. I think we were in denial for a while, but that forced us to pivot to solving the software defined vehicle challenge. If we can get that taken care of, then that kind of leads us to the autonomous world anyway. And we can solve it in bite-sized chunks. So thankfully the automotive industry and the semiconductor industry, and probably lots of other industries now are focused on a software-defined vehicle as an intermediate step.
Solving this challenge doesn’t just apply to road vehicles. I think when you look at industrial automation, that’s the same. Do they want to get full autonomy? Of course they do. Is it a challenge? Yeah, it is. So, software-defined has a role to play there. Same in A&D, same in a lot of the other verticals. So, there are a lot of synergies between the verticals as well. That created, I think, clarity, but it also created a seismic shift for the car OEMs in that the OEMs themselves, and I’m talking more about the incumbent suppliers, the big guys like VW, Mercedes, Ford, GM and others. History shows they’re so used to being completely in charge of their own destiny – when you need something, you just put a team together and you go build it. Those days are gone. You look at complexity in a modern vehicle, whether it’s the hardware or the software, you just can’t do that these days. It’s not scalable.
So, you have to rely on the supply chain to drive the innovation and deliver those pieces, those elements, and then you as the OEM have to integrate them. But that’s not a world they’re used to. And it obviously introduces a whole world of complexity.
Stroud: That’s another area where using Jama Software really pays dividends to ensure the whole supply chain is seamlessly connected from a requirements perspective resulting in faster design and delivery across multiple vendors and a better-quality product overall. A modern vehicle can have upwards of 100 million lines of code going into a modern high-end vehicle and this is increasing exponentially. Those software elements are coming from a hundred different vendors. Some of those are safety-related, and some of those are security-related. All of a sudden as an OEM, I’m responsible for integrating all of that, checking it works together, checking it’s still safe, checking it’s still secure, and then rolling it out through the door for consumers to go and purchase a new vehicle.
At the same time, vehicle suppliers can use this new SDV approach to drive new business models that allow post-sales upgrades and updates. If a car doesn’t have a feature on the day of sale, in a year’s time the owner could say, “Hmm, it’d be nice to have that new feature.” You log into your account, put your credit card details in, and as if by magic, the new feature arrives over the air to your vehicle the next day. That’s a whole new world and we are only scratching the surface today.
So, I guess the punchline is from our perspective, and doing what we do, it’s all about efficient requirements management and traceability. This applies not just to the OEMs, but throughout the supply chain as well, to ensure the elements from those hundreds of different vendors all come together. Those requirements have got to be exquisitely accurate and all the independent interdependencies mapped out correctly to be sure that you’re not violating a safety goal or creating a bug in the system.
This way you get into traceability… How well is my project going? How healthy is it? How many of those requirements are covered right now and tested and using that capability to reduce the number of recalls, drive efficiency in the design team, reduce the risk, all those good things. Of course, this level of detail isn’t just important to the engineering teams. It can also be rolled out to senior management who are likely more interested in risk, cost, time-to-market and so on.
So, the market’s really coming to us. Jama Software is now the largest supplier of requirement management solutions overall, which we’re immensely proud of. But we have to learn from the market and our customers how Jama Connect changes grows and morphs as a solution to enable that ubiquitous risk reduction and efficiency improvement. So, there are some big factors at play.
We’ve done very well in the semiconductor space overall, but it still frightens me to see how many spreadsheets are used to manage the business in the big semiconductor companies. And that’s speaking from experience because I lived in that world for a long time. There are way too many spreadsheets out there for doing requirements tracking. When you’re working that way, there’s no single source of truth and that will get you into trouble, guaranteed. It will cost you big with bugs in the silicon. So, it’s imperative to partner with the semiconductor industry and really drive change, accelerate innovation and solve tomorrow’s supply constraints. That’s on the chip design side, but also more recently, we’ve got the CHIPS Act, which is kick-starting a massive investment in the semiconductor industry to drive fab capacity to meet the huge growth in demand for chips.
So, we see the big players such as Intel, Samsung, and TSMC, all investing billions and billions of dollars to put fabs into place to meet this growth in demand and technology, which is exciting. The challenges are different to the auto market but guess what, these chip manufacturers need robust requirements management to run their business. And again, a lot of it’s been running on spreadsheets for a long time.
Now, we’re seeing, of course, headwinds in both industries. We still see that with EV vendors on the automotive side. We see even today challenges in the semiconductor industry with some consolidation of cost and trying to get costs under control. Jama Software has a critical role to play in that transformation. We can help drive efficiency and shorten cycles and time-to-revenue. All those things play into huge cost reductions for all. We are using our expertise in both product and deployment to educate and drive incremental success for our customers.
Kenzie Jonsson: Thank you for your time today, Neil! I really enjoyed this conversation, and I look forward to catching up with you next quarter!
Tackling Industrial Manufacturing’s Biggest Challenges: Solutions That Work
Industrial manufacturing is undergoing a transformation driven by technology, market demands, and a rapidly evolving workforce. However, this evolution brings its own set of challenges that manufacturers must navigate to remain competitive. Below, we’ll explore the top challenges in industrial manufacturing and offer practical solutions to address them.
1. Supply Chain Disruptions
The Challenge: Global events like the pandemic and geopolitical tensions have exposed the vulnerabilities of supply chains. Material shortages, delays, and fluctuating costs have become routine, making it difficult for manufacturers to meet production targets.
The Solution:
Diversified Sourcing: Manufacturers should explore multiple suppliers, ideally in different regions, to reduce the impact of disruptions in one area.
Advanced Analytics and Forecasting: By leveraging data analytics, manufacturers can predict potential disruptions and adjust procurement strategies to maintain inventory levels.
Digital Supply Chain Management: Implementing technology like real-time tracking and automated inventory management systems ensures better visibility and responsiveness across the supply chain.
2. Talent Shortage and Skills Gap
The Challenge: As industrial processes become more automated and technical, there’s a growing need for skilled labor, particularly in areas like robotics, data analytics, and equipment maintenance. However, the industry faces a shortage of qualified workers due to retirements and a lack of interest from younger generations.
The Solution:
Reskilling and Upskilling Programs: Companies can invest in training programs for existing employees, focusing on emerging technologies and technical expertise.
Collaboration with Educational Institutions: Partnering with local schools and universities to create apprenticeship programs and internships can help build a pipeline of future talent.
Adoption of Automation: Automating repetitive or dangerous tasks can offset the impact of labor shortages while enhancing operational efficiency.
The Challenge: Industry 4.0 technologies, including IoT, AI, and machine learning, offer vast opportunities for improving manufacturing processes. However, integrating these technologies can be expensive and complex, especially for small and medium-sized enterprises.
The Solution:
Start Small, Scale Gradually: Manufacturers should begin by digitizing a single aspect of their production (e.g., predictive maintenance) and expand as they see ROI.
Cloud-Based Solutions: Cloud platforms offer scalable, cost-effective ways to implement Industry 4.0 tools without a significant upfront investment in infrastructure.
Cross-Department Collaboration: Ensure alignment between IT, engineering, and operations teams to facilitate seamless integration and minimize disruptions during implementation.
4. Meeting Sustainability Goals
The Challenge: Governments and consumers are increasingly demanding sustainable practices from manufacturers. This includes reducing emissions, minimizing waste, and adopting environmentally friendly materials. However, transitioning to green manufacturing can be costly and complex.
The Solution:
Energy Efficiency Audits: Conduct regular audits to identify areas where energy consumption can be reduced, whether through upgrading equipment or adopting renewable energy sources.
Circular Economy Practices: Embrace recycling and remanufacturing to minimize waste, both in production and post-consumer use of products.
Collaboration with Stakeholders: Partner with suppliers and customers to promote sustainable practices across the entire value chain.
5. Cybersecurity Risks
The Challenge: With the growing adoption of digital technologies comes an increased risk of cyberattacks. These attacks can disrupt production, compromise sensitive data, and damage a manufacturer’s reputation.
The Solution:
Regular Security Audits: Conduct frequent assessments of your digital infrastructure to identify and address vulnerabilities.
Employee Training: Train staff on cybersecurity best practices, particularly in recognizing phishing attacks and securing devices.
Robust Incident Response Plans: Develop and test response plans to minimize downtime in case of a cyberattack, ensuring quick recovery and damage mitigation.
The Challenge: Manufacturers are under pressure to produce more custom products, reduce lead times, and improve quality—all while maintaining efficiency. Meeting these demands often strains existing processes and resources.
The Solution:
Lean Manufacturing: Implement lean principles to eliminate waste in production and streamline processes, improving both speed and efficiency.
Automation and Robotics: Invest in robotic process automation to handle repetitive tasks, reducing human error and speeding up production.
Flexible Manufacturing Systems: Adopt systems that can easily switch between different product types, accommodating the increasing demand for customization without sacrificing efficiency.
Conclusion
Industrial manufacturing is facing unprecedented challenges, but with the right strategies and technology, companies can navigate these obstacles and position themselves for long-term success. From investing in workforce development to embracing digital transformation, the solutions are within reach. By proactively addressing these challenges, manufacturers can enhance their competitive edge in an increasingly dynamic market.
Note: This article was drafted with the aid of AI. Additional content, edits for accuracy, and industry expertise by Steven Meadows and Kenzie Jonsson.
![[Webinar Recap] Key Systems Engineering Skills: Critical Thinking and Problem Framing](https://www.jamasoftware.com/media/2024/03/Key-Systems-Engineering-Skills-Critical-Thinking-and-Problem-Framing.png "[Webinar Recap] Key Systems Engineering Skills: Critical Thinking and Problem Framing")
