Making the value of technology clear
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For more insight on integrating ALM and PLM in any industry, refer to our buyer’s guide.
[post_title] => Automotive: ALM and PLM Better Together
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[post_content] => What are your thoughts on the state of product development?
We are researching the state of product development, including the challenges, capabilities, and future plans manufacturers have. We are exploring questions such as, what challenges do you face? How well do hand-offs between engineering and manufacturing work? Can you find the skilled workers you need? Has AI had any impact? How are you using simulation, if at all? The survey takes about 10 to 15 minutes.
If you work in engineering, product development, manufacturing, or manage those teams, please take the survey to share your thoughts. As a thank you, we will send you a copy of the report summarizing the findings.
In addition, respondents will be entered into a drawing for one of 20 $25 Amazon gift cards.*
Individual responses will be kept confidential. Please feel free to forward this survey to others you feel have an opinion to share.
Thank you for your support, please check out our Active Research page for additional Tech-Clarity survey opportunities.
*See survey for eligibility rules
[post_title] => The State of Product Development Survey
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[post_content] => ComplianceQuest made some significant announcements at its recent ConQuest customer conference. The first was to identify the space where they play, as a platform and integrated software suite to clean up the messy “middle office.” They also continued to add more agentic AI to the platform to support the people using the applications sitting on it. The other key news came before the event: the release of SupplierQuest, which reaches beyond supplier quality to supplier management, onboarding, collaboration, and performance management.
ComplianceQuest has named its vision the Connected Middle Office Platform. Think about how ERP long ago integrated back-office data and workflows, such as HR, Finance, and Supply Chain, for greater efficiency. Since then, CRM has helped optimize the front office activities, including sales, marketing, and service. What’s in the middle? ComplianceQuest says core operations, and they are fragmented, manual, and lack a way to collaborate among functions.
With a proven platform and multi-module solutions for QMS, EHS, Supplier Management, and PLM, ComplianceQuest has a strong story for this middle office. Our research supports the ComplianceQuest view. In our Making Manufacturing Analytics and AI Matter survey, manufacturers’ top investment aim is gaining easy access to high-quality, timely, and complete plant data. Today, many struggle to share operations data, put it in context, and use it to make sound decisions. The ComplianceQuest platform, with its single database, enables collaboration and closed-loop processes across these disciplines and data sets. Consider how a DFMEA can be connected to complaints, CAPAs, and regulatory reports.
AI agents to assist humans in this middle office work are trained only on each customer’s data. ComplianceQuest has incorporated its years of experience in developing these agents. CQ AI agents focus on activities such as generating content, finding and connecting relevant data across the system, forecasting future outcomes, leveraging chat to intake information and create records , and communicating with suppliers through intelligent emails that turn into actionable records . This reduces redundant and duplicated data, enhances worker productivity, and augments data-driven decisions.
ComplianceQuest has been releasing AI since 2022, primarily as categorization and prioritization and retrieve and relate capabilities. Predictive AI is already embedded and is part of the quality maturity index, supplier performance, and safety incidents functionality. AI is a top priority on the CQ roadmap to improve efficiency and costs and mitigate risk with predictions. All of this is embedded in the platform, not separately licensed, as are the advanced analytics.
In March, ComplianceQuest introduced its SupplierQuality suite, expanding beyond supplier quality to encompass more aspects of supplier relationship management. Examples include:
This expansion leverages the collaborative platform and other solutions and adds capabilities to foster greater success with suppliers during a time of supply chain change.
ComplianceQuest has always leveraged the Salesforce platform for connectivity. As the company builds out its middle office platform and suite, we will continue to watch for new releases, additions, and improvements. Thank you, Nikki Willett, for catching us up on the latest news from ComplianceQuest.
[post_title] => ComplianceQuest Claims Middle Office and Adds Agents [post_excerpt] => [post_status] => publish [comment_status] => open [ping_status] => open [post_password] => [post_name] => compliancequest-middle-office [to_ping] => [pinged] => [post_modified] => 2025-06-05 10:14:29 [post_modified_gmt] => 2025-06-05 14:14:29 [post_content_filtered] => [post_parent] => 0 [guid] => https://tech-clarity.com/?p=22080 [menu_order] => 0 [post_type] => post [post_mime_type] => [comment_count] => 0 [filter] => raw ) [3] => WP_Post Object ( [ID] => 21935 [post_author] => 2574 [post_date] => 2025-05-29 07:00:58 [post_date_gmt] => 2025-05-29 11:00:58 [post_content] => Is AI just a shiny object, or is AI delivering value in manufacturing? Our latest infographic exposes five practices that our research shows Top Performers are using to ensure value for their company. Walk through this infographic to learn a few things you can do to move AI from being a distraction or shiny object to delivering significant value, often in a very short time. Due to the interest in AI and its ability to deliver value quickly, many manufacturers have already invested. That means everyone else needs to move quickly. This infographic shares ways to move with greater confidence that you’ll end up with strong business value from leveraging AI with complex manufacturing data sets. Yes, every company will need to invest to get the value - at least time and energy to plan for projects and experiment. Yet some of the other practices are things any company can do for greater success without buying new software. Others might require new technology, depending on your current solution set and data stack. Walk through the infographic - and up the ladder - to achieve the value your company deserves from its AI efforts. Learn a few tips to get a positive impact quickly for your company! This is based on the recent research Making Manufacturing Analytics and AI Matter Tech-Clarity conducted with thanks to MESA International for partnering with us. Special thank you also to the program's sponsors, Aegis Software, Arch Systems, Epicor, and GE Vernova. It was a collaborative effort, and we appreciate everyone's inputs.
[post_title] => Five Practices to Ensure Value from AI in Manufacturing
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[post_content] => Last week, we had a chance to attend the Hitachi Digital Services Analyst & Advisor event in Dallas. The gathering offered compelling insights into the company’s strategic direction and differentiated capabilities. Through a series of thought-provoking presentations and fireside chats, one theme emerged with clarity and consistency: Hitachi Digital Services’ deep-rooted engineering expertise sets it apart. This foundational strength enables the company to understand its customers’ complex challenges and validate solutions across Hitachi’s diverse industrial ecosystem and co-innovate with clients to drive real business outcomes. Since we focus a lot on #digitaltransformation in product design, innovation, and manufacturing, we found several relevant takeaways worth highlighting.
Digital transformation in manufacturing is no longer optional but an urgent imperative. Industry leaders recognize that embracing this shift is essential to sustaining profitability by eliminating inefficiencies, unlocking new revenue streams, and navigating an increasingly complex environment shaped by product complexity, regulatory pressures, and geopolitics. A foundational step in this journey is establishing a robust digital thread by integrating data across diverse software systems and physical equipment. This is where Hitachi Digital Services stands out. Leveraging deep engineering expertise honed through collaboration with sister companies renowned for their manufacturing excellence, Hitachi Digital Services brings a practical understanding of product development processes. Coupled with their PLM and MES implementation capabilities, they are uniquely positioned to help manufacturers drive digital transformation across the value chain.
Manufacturers are increasingly prioritizing investments in AI and analytics. As highlighted in Tech-Clarity, Inc.'s research by Julie Fraser, Making Manufacturing Analytics and AI Matter, early adopters are already realizing significant gains, ranging from cost reduction and productivity improvements to enhanced quality, error-proofing, and better delivery performance. This space is a natural fit for Hitachi Digital Services, which showcased powerful use cases such as a rail application where edge computing in the train enables real-time data analysis from sensors monitoring track conditions and overhead power lines, directly enhancing safety outcomes. With modern factories generating massive volumes of data, Hitachi Digital Services is ideally positioned to help manufacturers harness this information to optimize energy usage, drive quality, and maximize overall equipment effectiveness.
The convergence of Information Technology and Operational Technology is a strategic enabler for manufacturers seeking to enhance operational efficiency, reduce costs, improve decision-making, and drive innovation. However, the path to IT-OT integration is often hindered by legitimate #cybersecurity concerns. Cyber threats, including data breaches, IP theft, ransomware, and operational disruptions, pose significant risks to brand reputation and profitability. To fully capitalize on the promise of IT-OT convergence, cybersecurity must be embedded as a core foundation. Hitachi Digital Services brings deep cybersecurity expertise through its partnership with Hitachi Cyber, offering tailored protection strategies and best-practice implementations. Their ability to integrate security seamlessly into digital transformation initiatives, along with providing managed security services, makes them a trusted partner in securing the connected factory of the future.
In conclusion, the Hitachi Digital Services Analyst event provided valuable insights into leveraging its deep engineering heritage and cross-division expertise to drive impactful digital transformation for its manufacturing customers. Their capabilities in enabling the digital thread, deploying AI and analytics, and securing IT-OT convergence align closely with the critical needs of manufacturers. We were especially impressed by their practical, field-tested approach and commitment to co-innovation. These strengths position Hitachi Digital Services as a trusted partner in helping manufacturers unlock efficiency, resilience, and innovation. We thank Patrick Corcoran and Mahesh Hanumanthu for the opportunity to participate in this event.
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“This is a strategic move,” explains Jim Brown, President and Founder of Tech-Clarity. “As manufacturers grapple with rapid change while combating global pressures, it's imperative that we offer a broad perspective on digital transformation for 2025 and beyond. More than ever, we have to help companies understand and achieve technology's value by aligning technology with processes and people. Rick brings great experience, credibility, and a global perspective. We are excited to have such a respected thought leader join our team to help further our mission of making the business value of technology clear.”Rick's research focus will include the role of MES/MOM amidst a sea of new technologies from both end-user and technology provider perspectives. This includes how technology improves the decision-making skills and competency of the manufacturing workforce. Please reach out to schedule a briefing or seek out Rick’s perspective as he begins his research agenda. Join our community to follow Rick's research. [post_title] => Tech-Clarity adds Digital Innovation and Manufacturing Analyst Rick Franzosa [post_excerpt] => [post_status] => publish [comment_status] => open [ping_status] => open [post_password] => [post_name] => rick-franzosa [to_ping] => [pinged] => [post_modified] => 2025-06-17 11:31:27 [post_modified_gmt] => 2025-06-17 15:31:27 [post_content_filtered] => [post_parent] => 0 [guid] => https://tech-clarity.com/?p=22042 [menu_order] => 0 [post_type] => post [post_mime_type] => [comment_count] => 0 [filter] => raw ) [6] => WP_Post Object ( [ID] => 22023 [post_author] => 2572 [post_date] => 2025-05-20 10:00:45 [post_date_gmt] => 2025-05-20 14:00:45 [post_content] =>
What should MedTech companies consider when integrating ALM and PLM?
Software has become a crucial component in MedTech products. However, with software development managed in Application Lifecycle Management (ALM) and hardware managed in Product Lifecycle Management (PLM), the separate systems can create silos within the product development team. With increasing regulatory pressures and trends such as personalized healthcare, it has become even more important to eliminate these silos and establish a digital thread across product development data.
In this 20-minute video, Michelle Boucher and PTC's Meg Folcarelli discuss key considerations for MedTech companies to support the co-development of hardware and software. They cover:
Can agentic AI improve how people work on the factory floor? Can it address the workforce crisis that has resulted in a plateau in productivity and increasing safety incidents? OpsMate AI believes they can do that quickly with their no-code agentic AI platform, which is purpose-built for the complexity and high-stakes environment of manufacturing operations. Their approach is to use generative and agentic AI to create hyper-intelligent digital teammates that assist the factory workforce.
Flexible Knowledge AssistantsAgents have access to more information than a person could. The agent-as-assistant concept makes sense, particularly in the unpredictable world of day-to-day operations. No matter the level of skill or experience an operator or associate has, they can ask the agent and get not only data but information, knowledge, and reasoning. This is quite different from traditional hard-coded, pre-defined applications and dashboards.
The great news about genAI is that it can train on a wide variety of data. So, it can naturally bridge the IT/OT divide. Whether the data is from IoT, MES, a machine, SharePoint, or a Teams transcript, OpsMate AI can ingest it, bring it together, put it in context, and make it available in any language.
As with all genAI, these agents work with natural language queries, not through coding or formal queries. They can answer questions, guide work, perform analysis, or take on routine tasks. This early-stage company created an advisory board based on the founders’ deep existing manufacturing relationships.
This advisory board has pointed to the common use cases that they would find valuable in their workflows. Since then, the OpsMate AI team has been busy building their minimum viable product to address those needs. Some of the initial use cases involve acting as a virtual technician to support less experienced maintenance staff with what is otherwise tribal knowledge. Another use case seeks to replicate the EH&S expertise of front-line managers with agents mentoring the workers.
OpsMate AI points to 3 layers of knowledge: general, industry-specific, and customer-proprietary knowledge. OpsMate AI builds in the first two, but the customer keeps control of their own data. They get an instance and decide who has access to address security and privacy concerns. The customer-specific knowledge repository can come from existing documents (SOPs, manuals, checklists, drawings, troubleshooting logs, corrective action reports, etc.) Experts can also train the model through an interview and knowledge capture process where their expertise becomes encoded in agents.
Once the agent is trained on the appropriate knowledge collection, it can effectively perform its tasks. The digital workforce will be available for in-line, real-time guidance for anyone in the plant. It could be as simple as “What does this PLC error code mean?” or as complex as “How can I maximize yield?” The system will also be transparent in showing the source(s) of data that lead to its recommendations.
OpsMate AI founders have developed a roadmap to an autonomous digital workforce, augmenting the people who remain at the center. They see robots doing physical work and OpsMate AI agents doing knowledge work. Over time, the agents can learn more and. progress from being virtual experts to analyzing data to executing tasks. At some point, they may fully automate tasks and become autonomous.
We see the possibility of this being a breakthrough for customers. Our research shows that GenAI can deliver significant benefits quickly. Systems that have been purpose-built as agents for factory floor issues, such as what OpsMate AI offers, are attractive because they have so much pre-built. Manufacturers seeking to shore up aspects of the operation that most need additional knowledge and expertise should take note.
OpsMate AI has a strong base of advisory board manufacturers driving its initial product development. These companies have their data in it and are testing and helping refine it. We expect to see a commercial release around the middle of this year.
Thank you, Howard Heppelmann and James Zhang, for briefing us on your vision and early customer direction. We appreciate the pragmatic approach to agentic AI for production.
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Rick Franzosa is the Vice President of Research for Manufacturing for Tech-Clarity. He covers MES/MOM and transitional technologies that support the evolution of manufacturing software technology into the age of Industry 4.0 and AI.
Rick has over 40 years of experience in manufacturing software technology, including 10 years as an analyst (Gartner) in addition to experience in design, manufacturing, and software engineering in aerospace and defense companies. He was involved in the early days of both PDM and shop floor solutions as they morphed into today’s PLM and MES solutions. He has a BS in Mechanical Engineering and an MBA from the University of Hartford.
Rick’s current areas of research include the role of MES/MOM in a sea of new technologies from both end-user and technology provider perspectives. This includes technology and how technology improves the decision-making skills and competency of the manufacturing workforce. He enjoys working with manufacturers and software solution providers on how to improve performance and create business value.
Rick enjoys spending time with his wife, children, and grandchildren. He also enjoys listening to and performing classic rock music and creating virtual models for train simulators.
Can you gain operational insights from AI without data scientists or programming? Can you do it at scale in a highly automated environment? TwinThread would say you can. Working to solve this challenge, the company is growing rapidly deploying its SaaS Industrial Digital Twin AI Platform. In November, they were 322 on the Deloitte Fast 500 Technology company list.
TwinThread offers a cloud-native SaaS AI platform based on a digital twin with pre-built applications. They claim these solutions connect rapidly to existing systems, often deliver insights within hours, and solve complex industrial problems within days. The improvement-focused offerings are designed for mid-size and large companies in two primary markets:
One pet food maker rolling out TwinThread globally reports that they are saving $1.3M per year based on a subscription cost of $108K/year by using the predictive quality module. Across five plants and 18 lines, the system recommends real-time changes in processes and ingredients. This enables the company to optimize the parameters that matter most to the quality of their products: moisture, fat, protein, and product density.
With a combination of current, in-context data and applications that support the most common issues facing these industries, the TwinThread customer base is growing. Some are rolling out and gaining ROI on problems they failed to address previously. Value can expand over time as the platform gains more data and customers add more use cases or applications, or moves to greater autonomy. The pet food company reports never again having a bad batch, and the Cpk or process quality index improved over 40%.
The founding vision is to offer a virtual operations center where people can monitor plants and lines worldwide. This enables customer companies to more fully leverage the scarce employees who have the most domain expertise. The pet food company achieved those results in the face of employee attrition, with operator experience dropping from 10 to 2 years.
As data comes in, it can support the continuous improvement cycle by monitoring, alerting to issues, recommending improvements, learning through machine learning, and solving problems. This virtual operations center can be a foundation for improving operational and business performance.
TwinThread was founded in 2017 to provide industrial data management and prebuilt solutions at the IT/OT intersection. They recently added the Enterprise Data Factory to deliver pre-curated big data sets for industrial or enterprise business intelligence. At this stage, the company has over a million process or operational digital twins in customer use. Each of those includes actual and historical plant data, not just a simulation.
Partnerships have been a hallmark of TwinThread’s strategy from its early days. The company now has over 400 channel partners, including GE and AVEVA. It has Transformation, System Integrator, Technology, and OEM partners. The latest announcement was with AWS for native integration to IoT SiteWise.
The Predictive Operations Platform is a SaaS Industrial Digital Twin AI Platform. It includes out-of-the-box connectors and agents or dockerized containers at the edge. It feeds that diverse data into a process digital twin at the core. On top of the twin, a thread engine layers additional context and visualization. The platform also includes logic streaming, the ability to build out if-then-else logic for event detection, and capabilities for data curation, cleaning, and orchestration of behaviors. The behaviors can be fully automated or deliver prompts for people.
TwinThread sees that instability and variability are underlying problems for process and hybrid industries. As long-running processes often have inertia, optimizing them can be a challenge. So, the system focuses on helping companies identify the drivers of instability and move to a more consistent approach to running their production. The applications focus on both process-centric use cases and asset-centric use cases.
The users are operators, engineers, and digital teams. TwinThread calls the engineers and operations subject matter experts developers since they use TwinThread to mine data and solve problems without writing code. The operators are the customers of what they develop. Data scientists also use the platform to feed their tools, such as Jupyter Notebooks, to configure machine learning and data science workflows.
Enterprise-scale customers have typically failed more than once to scale with other approaches. Normally, TwinThread starts by defining success criteria for a three-month pilot on a single line and a few use cases. Their preferred approach is to mentor a process engineer to create the pilot themselves. By then, the customer understands how quickly and easily they can model their process in the digital twin and are typically in production and seeing benefits.
TwinThread’s comprehensive approach addresses people issues by enabling the process engineers to develop what they need and then having expert operations staff available in the virtual operations center to support the less experienced team members. The system maps processes in the digital twins, delivering inherent context to the data.
TwinThread was founded by three successful pioneers in process and hybrid production software. They met at Mountain Systems, which became GE’s Proficy MES and Historian. The co-founders are Erik Udstuen, the CEO, Tom Nettell, the CTO (those two co-founded Mountain Systems in 1996), and Andrew Waycott, the President. The team includes long-time industry mavens Brandon Ekberg, one of the first MES experts, running Product, and Sheila Kester, ex-GE, Emerson, and Intellution, running operations. Jason Dietrich, as CRO, is a recent hire with deep experience in manufacturing software.
Thank you, Sheila Kester, for setting up the briefing. Thanks also to Andrew Waycott, Marc Albu, Elise Loffredo, Jason Dietrich, and Brandon Ekberg for your time and insights in the briefing. We look forward to following TwinThread’s progress and continued profitable growth for the company and its customers.
[post_title] => TwinThread’s Industrial AI Platform and Apps Seeing Quick Results and Rapid Growth [post_excerpt] => [post_status] => publish [comment_status] => open [ping_status] => open [post_password] => [post_name] => saas-industrial-digital-twin-ai-platform [to_ping] => [pinged] => [post_modified] => 2025-06-05 10:09:08 [post_modified_gmt] => 2025-06-05 14:09:08 [post_content_filtered] => [post_parent] => 0 [guid] => https://tech-clarity.com/?p=22071 [menu_order] => 0 [post_type] => post [post_mime_type] => [comment_count] => 0 [filter] => raw ) [10] => WP_Post Object ( [ID] => 21955 [post_author] => 2 [post_date] => 2025-05-14 11:56:27 [post_date_gmt] => 2025-05-14 15:56:27 [post_content] =>We had the chance to attend the Aras Community Event, ACE 2025, to hear strategy and progress updates from Aras Corporation, their customers, and their partners. It was exciting because it was the 25th Anniversary of ACE and the first time in memory that we’ve seen a keynote speech from a balcony ballroom seat! It was a unique venue for a unique company in the PLM space.
Our key takeaways are that Aras is successfully delivering across the elements of what makes them unique, what’s known as “The Aras Effect.” Beyond those core fundamentals, Aras showcased strong customer success stories and progress on delivering new AI capabilities that will become increasingly important to both customers and the market in the future.
We’ve always seen Aras as a disruptor in the PLM space and believe that manufacturers looking for PLM – or to augment their current PLM solution – should understand what makes them different. This difference got a name and a more formal definition last year, “the Aras Effect,” what I previously called the “Aras difference” in a 2023 post.
But it’s more than a name on a marketing slide, it’s a way to better encapsulate the unique value proposition that Aras brings to the market and their customers. We won’t share it all here, but there are two things that are important to understand:
What we saw at ACE this year was Aras staying the course on these core differentiators and making progress to deliver them more fully to their customers. We’ll share a couple of specific examples, specifically the new product Aras InnovatorEdge and the continued growth in the Build with Aras.
One of the biggest value drivers of any PLM system is creating a cohesive, integrated digital thread of product data. That data extends beyond engineering, spanning the enterprise and the supply chain. Manufacturers have stopped looking for a magic PLM system that contains all possible digital thread data. Instead, the industry has matured to recognize that digital thread data will reside across multiple enterprise systems. InnovatorEdge is designed to address that reality.
We like how Aras CTO Rob McAveney introduced InnovatorEdge by asking how customers can extend the reach of their digital thread beyond the walls of their company into the supply chain / value chain, into more phases of product lifecycle, and to more disciplines. This closely reflects the value we see Top Performing manufacturers seek in what we call the Four Dimensions of PLM Expansion. We also recognize the need for integration to support this. In fact, our recent renditions of the framework include an integration layer around the edge.
Rob explains that to accomplish this, you need the Aras Effect. And in particular, you need the new Aras InnovatorEdge low-code API management framework.
Aras InnovatorEdge is intended to help manufacturers develop connected intelligence and has four primary elements:
This is just a short introduction to InnovatorEdge. The offering creates a home for some existing capabilities and incorporates new features. Together, it creates a framework for Aras to continue to deliver additional capabilities to meet customer needs beyond what’s included in Aras Innovator.
Aras also shared updates that show they are progressing their ability to enable customers to be agile. Agility is arguably more important to manufacturers today than it has been in years. Further, no one company’s PLM needs are exactly the same and creating a competitive advantage often means doing something differently than others do it. At the same time, it’s best to start with best practices.
One of the most unique aspects of Aras Innovator is that it was built with customization in mind. In recent years, we’ve seen Aras double down on this differentiator while also making it easier for their customers to customize their PLM solution and maintain it over time. In addition, they’ve invested in their marketplace to help customers and partners deliver custom-built capabilities to others. “Build with Aras” encapsulates this.
Aras customers shared some significant successes in this area. One of the areas that’s been personally interesting to us is the ability for customers with formula-based products to customize Innovator to meet their needs. Aras now supports food and beverage companies, including Red Bull and some breweries. Aras’ core capabilities can also be expanded by a solution in their marketplace, Fulvisol Food & Beverage, designed to meet the unique PLM needs of the food and beverage industry. This has been traditionally difficult for PLM companies built for bill-of-material oriented, discrete manufacturing environments. We’re keeping a close eye on this space
We’ve seen Aras make interesting advances in this area. For example, they continue to enhance their low-code capabilities and their low-code CAD connectors. One of the interesting advances also relates to Aras’ move to a SaaS offering. Aras has invested heavily in a DevOps environment for themselves and their customers to develop, deploy, and maintain their custom and marketplace applications. They introduced a low-code DevOps that will be coming soon in a beta that is designed to make it easier for non-developers who don’t know Git and related processes to manage the process.
One of the very unique ways that Innovator’s “Build With” adaptability comes to market is through their “Powered By” strategy. Aras has made their Innovator platform available for partners to use as the basis for their own solutions. Examples of commercial offerings built on top of Aras Innovator include:
Beyond these, Aras and Sandvik jointly announced a new partnership to use Aras Innovator as the foundation for MasterCAM CAM data management and to further connect the product digital thread. In a similar way, Steepgraph is offering Scale B, a solution leveraging Aras Innovator designed for small to medium sized manufacturers. Each of these solutions fills an important niche in the industry and leverages rich capabilities already built into Innovator.
No software conference is complete without an update on what the vendor is doing about AI. As experienced analysts, we could be jaded about that. After all, AI is not brand new and we’ve been using it in engineering and manufacturing for decades. However, we do believe there is a fundamental shift that software vendors must explore for their customers. Our recent research shows three important things:
Given that, our viewpoint is similar to the stance we took on the cloud transition. Even if a customer wasn’t interested in transitioning to the cloud, we believed it was important for their vendor to have a cloud strategy to remain viable in the future. Today, if a vendor doesn’t have an AI strategy and isn’t making progress, their customers should be concerned. From what we can tell, Aras customers should be satisfied with what they see.
The theme of ACE 2025, Connected Intelligence, focused on the AI imperative. Aras CEO Roque Martin kicked off the conversation about AI early. There was also a fascinating panel discussing the topic. Rob McAveney shared more details about the strategy breaking it into three phases:
Tangible examples of Aras’ investment are the new Aras AI Assistant, a chatbot currently in beta, created to help users access information in Aras Innovator. This capability will extend to custom capabilities and integrations in the future to support Build By. They also announced Aras AI-assisted Search, available later this year, that provides rich information including 3D data, images, and voice search results filtered on their semantic meaning. Aras is also working on CoPilot and AgenticAI technology, and we expect to see announcements from them in these areas soon.
While we’re speaking about AI, it’s a good time to mention the unique relationship Aras has with Microsoft. On one hand, Aras is a partner that strongly leverages Microsoft technologies. This has significant implications for Aras in AI because of Microsoft’s investments in Microsoft Azure AI tools. In addition, Microsoft is a customer that uses Aras Innovator to help them develop and manage their hardware products, including Surface, Xbox, and quantum products. They also use it for Azure servers and racks. They’re a significant customer, with over 800 users per day on average, including many outside of engineering, including sourcing, legal, and product management.
With that said, Microsoft’s presentation on what they’ve done for themselves with AI on top or Aras Innovator is one of the most compelling examples we’ve seen of a manufacturer using AI to extend the value of PLM. The Microsoft team shared three compelling examples:
These are all practical examples where Microsoft used AI, which they view as “just a tool,” for practical purposes and meaningfully improved efficiency.
There is too much to include from the conference, but some other takeaways we want to mention include:
Aras Innovator is a very different style of solution than most enterprise systems, and any other PLM system, due to its adaptability. While that’s not the right solution for every business, the Aras Effect offers an alternative that some manufacturers find compelling. Aras is doubling down on their differentiators, maturing the existing solution, helping further connect digital threads across the value chain with InnovatorEdge, and investing in new areas like requirements management and AI. The conference showed continued progress against the Aras strategy.
Thank you Roque Martin, Rob McAveney, and Igal Kaptsan for the business and product updates and to Josh Epstein , Bruce Bookbinder , Jason Kasper , Kylie Ochab, and others for informal conversations and help along the way.
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Jim Brown is moderating an AMC Bridge Expert Panel discussion on AI progress in industry. Has AI delivered on the hype? Are manufacturing and AEC companies adopting AI-driven use cases that drive real business value? Have we moved beyond the proof-of-concept phase in AI adoption?
The panel will discuss these questions and more to shed light on the progress made since our prior panel on the topic. Jim will share recent research and insights on AI progress, adoption, and value while leading a lively conversation with panelists who are actively helping move the needle on AI value. Jim will be joined by:
We are researching capital-intensive projects to understand best practices for managing them. The survey explores topics like common causes of delays and cost overruns, the best strategies to manage capital intensive projects, and the impact of improved visibility to project data. The survey takes about 10-15 minutes.
If you are involved in planning, designing, or delivering large-scale projects like:
In addition, respondents will be entered into a drawing for one of four $25 Amazon gift cards.*
Individual responses will be kept confidential. Please feel free to forward this survey to others you feel have an opinion to share.
Thank you for your support, please check out our Active Research page for additional Tech-Clarity survey opportunities.
*See survey for eligibility rules
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[post_content] => We were recently alerted to a company we weren’t covering because a long-time business friend took a key position there. After a quick review of what they do, Julie Fraser and Jim Brown decided to team up on the briefing because we could already see a combination of QMS and PLM capabilities. We learned that there was even more there than we initially thought, with some capabilities that you might expect from ERP and MES systems.
Yet, Enlil is not trying to be all those things to all people. Instead, they aim to be an integrated solution that supports the core needs of a specific audience, the early and mid-stage medical device ecosystem of product developers and contract manufacturers. Their sweet spot, by design, is supporting the varied and nuanced needs of medical device companies as they scale and seek regulatory clearance or approval. They’ve also discovered that contract manufacturers in the life sciences ecosystem find value in what they do. Enlil’s mission is to “empower MedTech product innovators to bring life-changing products to market faster, and with unwavering regulatory confidence.”
Let’s step back to talk about product specialization. We talk about specialized solutions because nobody likes to be called a niche offering. They feel “niche” limits their potential. Regardless of what you call it, solutions built for a specific customer profile can be immensely valuable because they just fit the customers’ needs. Enlil, Inc. positions itself as a vertical SaaS solution for regulated products. In this case, it’s intrinsic to where Enlil came from and their mission. We feel it’s led to a unique offering with an excellent fit for smaller teams, whether they are startups or focused teams in larger enterprises, focused on developing pre-clinical, class 2 and 3 medical devices.
The history is important. Enlil was not created as a startup software company looking for a problem to solve. Instead, Enlil is a purpose-built solution that grew out of an innovation hub that is part of the Shifamed Group. In this scenario, their first customers were their sister companies in the innovation accelerator. This not only helped create focus, but also helped ensure that user needs were met across a variety of similar startup medical device businesses. The result is that they are unique because of where they come from, who they serve, and what they do. Now, they have assembled an impressive team of industry veterans from the ERP, PLM, and MES industries to help grow the business.
The best way to answer what Enlil is, is by who they support. It’s not a cookie-cutter product that was designed to meet the top ten features of PLM, QMS, MES, or another software category. What we saw was an end-to-end, collaborative product development and innovation system that supports digital thread traceability for medical devices. That’s hard to fit on a marketing slide, but the customer profile focus is the value. What they’ve done is identify the essential needs of their sister companies to use as guiding requirements for the solution.
That leads to an interesting solution that handles QMS functions like traceability and documentation. They can document test results, but they’re not trying to be a LIMS. They manage the digital thread across both hardware and software components, but they aren’t a full PLM system. They manage materials and clinical supply, but they don’t want to become an ERP system. That makes Enlil hard to classify. Are they a PLM with some MES capabilities, a QMS with some PLM functionality? But trying to classify them misses the point. Enlil is a specialized solution tailored to meet the product innovation and product development needs of early-stage medical device developers and their ecosystem.
As mentioned, Enlil is a specialized offering. The customers that they see as the best bit for their capabilities are these profiles in the medical device ecosystem:
They have specialized capabilities that make these companies a good fit, including an interesting two-tier structure that allows innovation hubs, parent companies, or OEMs to share information and collaborate with related entities like incubated startups, product teams, or contract manufacturers. They’re also planning to release an AI chatbot, “Lilly,” to summarize information and help reduce the significant amount of manual work companies spend on regulatory documentation.
There is room for growth beyond these targets, for example, smaller development teams in larger enterprises, but the market they target today is a rich and exciting one. And, one that should appreciate Enlil’s focus.
This writeup doesn’t share a lot of details about what Enlil does. Unapologetically, that is because their feature list isn’t what’s important, it’s their specialization on a target profile. Medical device companies looking for a single solution to meet their basic product development and traceability needs should take a look at Enlil. Startups in the medical device industry, who are looking to develop their products and get regulatory approval from the FDA should definitely see how Enlil could help them. It’s rare to find such a tailored solution for a specific set of customers to solve a specific collection of problems. Enlil may not stop there, but they are positioned well to get a strong foothold in early medical device development.
Thank you Nader Fathi, Andrew Robbie , Christine Pearsall for introducing us to Enlil and your unique offering. We are excited to learn more and hear from the companies using your solution.
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How are manufacturers doing at global Digital Transformation? What could accelerate progress? These are a few of the questions that Julie Fraser and other experienced industry analysts will discuss on a panel on June 17th. The session is part of the Manufacturing Leadership Council’s (MLC) Rethink Conference on Marco Island, Florida, and supports its theme, Accelerating Digital Transformation in Manufacturing.
This “Power Panel” will leverage research from four analyst firms to explore the current state of Digital Transformation in manufacturing. Joining the panel are Tech-Clarity’s Julie Fraser, Bob Parker of IDC, Craig Resnick of ARC, and Matt Littlefield of LNS Research. The panel moderator is David Brousell, Director and Founder of the MLC, which is part of the National Association of Manufacturers.
The panel will also address “handicapping” questions such as: What is the picture for global digital transformation in manufacturing? Are some regions ahead of others? If so, why? And questions about the path forward such as: What’s needed to move to the next stages of maturity? Are we ready to move from Industry 4.0 to Industry 5.0? These four long-time manufacturing software industry analysts will each contribute their opinions and share insights.
This panel is just a part of this dynamic in-person conference. The program includes a wide array of presentations, panel discussions, case studies, and networking sessions on culture, leadership, process, and technology. Topics range from what to expect in the economy to emotional intelligence for a thriving culture to defining the human-machine relationship to digital transformation for small and medium manufacturers. There is also an exhibit hall for sponsors to show their offerings on Tuesday and Wednesday.
Rethink runs from Sunday evening June 15th happy hour and then has three days of content. It ends with the black-tie Manufacturing Leadership Awards gala on Wednesday evening June 18th. The many categories of awards showcase what leading manufacturers are doing in AI Vision and Strategy, Business Model Transformation, Collaborative Ecosystems, Digital Supply Chains, Engineering, Production, and Integration Technology, Operational Excellence, Sustainability and the Circular Economy, and Transformational Business Cultures. There are also individual awards.
The MLC website describes the event: “Rethink examines today’s digital factory as it intersects with technology, organizations, and leadership. Come away with a better understanding of the smart factory and what’s needed to compete, succeed, and thrive in the connected future.” Last year’s event was compelling, and the topic for this year is global digital transformation. If you are attending, please let Julie know to set up some time to get face-to-face.
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[post_content] => Jim Brown's recently published eBook research, The Business Value of An Industrial System of Engagement Platform, introduced the concept of the Industrial System of Engagement (SOE). Following the eBook, he penned a guest post for Hexagon summarizing the eBook and how the Industrial SOE provides a platform to extend the value of current enterprise systems by providing enhanced connectivity and collaboration.
For more information on the Industrial SOE and Nexus, you can read the full blog post on the Hexagon site. You can also download the full white paper directly from Hexagon.
[post_title] => Hexagon Delivers a New Digital Industrial Platform to Accelerate Innovation
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There is a lot of talk about the value of artificial intelligence in manufacturing, and rightfully so. Although AI isn't new, it appears to be reaching a tipping point where companies are more open to exploring its potential and AI techniques are more accessible than ever. Manufacturers are acting on the opportunity. Our recent Making Manufacturing Analytics and AI Matter research, for example, shows that 99% of manufacturers plan to invest in analytics and AI in the next year.
Further, AI has moved from the experimentation phase to the value phase. Our latest executive survey, Executive Strategies for Sustainable Business Success 2024, found that AI / ML (Machine Learning) was the most common application type providing value, with 88% of responding companies reporting they achieved business value from AI / ML. Further, these results can come quickly. The manufacturing analytics and AI study found that companies achieve fast ROI more frequently from advanced analytics / AI than from their other software investments. For example, the data shows that 73% of respondents received benefits from GenAI in less than a year.
Our research shows that AI / ML can drive value, and companies can achieve that value rapidly. But that value doesn't come from buying software. It comes from applying the right solution to solve a real problem. However, many manufacturers don't know where to start or how to implement these capabilities. To better understand how manufacturers can target and adopt AI, we sat down with two experienced consultants with real-world experience in helping manufacturers improve business value by adopting AI. We sat down with Kalypso: A Rockwell Automation Business's Senior Manager of Data Science and Digital Transformation, Chelsea Barnes and Master Data Consultant William Rosengarten to get their perspective. Let's see what they have to say!
Jim Brown
Kalypso works with a lot of manufacturing companies. What are manufacturers asking for related to their AI strategy and implementations?
Chelsea Barnes
At one end of the spectrum, some companies are enthusiastic but lack direction. They know they need to do something with AI because it's a buzzword or their CEO says it's important, but they don't know what that looks like. They need help setting a direction. On the other end of the spectrum, some clients have a very specific problem and sometimes possibly even a solution in mind, like needing a machine vision solution to detect malformities or burns on a chip because it's costing them $10 million in scrap every year. They need help validating and implementing a solution. But there's also a middle ground where companies have operational targets in mind and maybe some initial hypotheses about how to improve them but aren't sure where to start.
Regardless of what the starting point looks like, the core of the ask is the same. They want value delivered quickly, at scale, using the best advanced technologies available.
Jim Brown
I've known Kalypso for some time and I appreciate that you don't believe in technology for technology's sake but focus on adding business value. How do you get companies started or help them frame their problem?
Chelsea Barnes
We help them discover where their business problems really are and what technology solutions are best suited to those problems. Then, we bring those things together. When we talk to a company, they know their issues far better than we do. For example, the people operating a line will be able to specifically articulate what problems are happening and have a very good hypothesis as to why they’re happening. Then, we bring our business, operational, and technology expertise to those conversations so that they meet in the middle with solutions.
Jim Brown
One of the things I appreciate is that you're not just technologists. You are domain experts who understand operations and the manufacturing industry. For example, in the Consumer Packaged Goods (CPG) industry, when you mentioned "burns on a chip," I knew right away you meant potato chips and not microprocessors. Can you tell me a little bit about why it's important that advisors don’t just approach their clients with AI knowledge, but also bring relevant business expertise to the table?
William Rosengarten
We're not coming in cold because we have a depth of expertise in the industry. We already have a point of view on the end goal. If a client comes to us with a problem, we know what best-in-class in the CPG industry looks like, so we can help them create a plan to achieve it.
Chelsea Barnes
Exactly. We bring together a variety of expertise to make that happen. We're coming in with a really solid set of hypotheses around what the problems typically are. We're familiar with approaches to improve quality yields and deal with issues like variable material inputs that cause problems for food and beverage clients. The specifics come from the client and their own knowledge, but our experience helps us get to a diagnosis more quickly.
Jim Brown
In a recent cross-industry survey, we asked companies about their AI goals. The most common goals identified across industries were product and service innovation, product and service performance, and workforce efficiency. Those are essential in any industry. A survey specific to the manufacturing industry, however, clearly identified cost reduction as the most common investment driver. What are the CPG companies you're working with looking for?
Chelsea Barnes
We’re seeing the same thing on the ground. There are two macro trends that are really squeezing manufacturers right now. The first is inflation, which increases cost pressures. The other is workforce turnover, including a wave of seasoned specialists leaving the workforce, which puts a new sense of urgency on workforce efficiency. To meet those cost reduction and efficiency goals, the top AI use cases we hear are around quality control, process optimization and predictive and prescriptive maintenance.
William Rosengarten
We also see a common pain point in accessing the right data, especially when working with time series data. Five years ago or so, manufacturers felt they needed to capture everything from the plant floor, store it in the cloud, and historize it. So many manufacturers have created a giant haystack of all of their data, and they're struggling to find that needle that will drive specific use cases like the ones Chelsea is describing.
Jim Brown
With all of the potential projects you may identify with a client, how do you help them decide on what to focus on? Do you counsel them to focus on the most significant problems, or maybe try to have them find repeatable problems? Or is it purely the project with the largest ROI?
Chelsea Barnes
Manufacturers are absolutely looking at ROI. They need to understand how it will affect the process, the tangible value they will get from the initiative, and how they will measure achievement. It's critical that they know what their quantifiable goal is.
However, when it comes to investments in digital, sometimes the value isn’t as clear-cut as a 12-month payback. In some cases, companies are looking to stay ahead of the competition by operating on the bleeding edge of innovation. This might justify a more long-term investment approach to allow the transformation they’re looking for to take root.
Getting back to determining ROI, we’re big proponents of rapid use case identification and prioritization, where you quickly narrow down your short list of high-potential opportunities before investing too much time rigorously evaluating all options. To do this, you need a good value calculation framework, which we bring to all of our assessment projects. But,you also need the right technologists in the mix to help you quickly vet the solutions and estimate implementation complexity to understand the cost of an initiative.
Jim Brown
Generative AI is on most peoples' minds and has become popular in conversations because of OpenAI and ChatGPT. However, many other AI and machine learning (ML) techniques are available. AI can be applied at different levels, ranging from companies wanting to retrieve data more effectively to the other end of the spectrum where they are pursuing AI-driven autonomous, real-time decisions to drive equipment behavior on the floor. How do you help your clients decide what technologies to apply for a specific problem?
Chelsea Barnes
We always start by confirming the business needs and what's the problem to solve now. Even if they come to us with a very specific request, like "I need a machine vision solution," we will diagnose the issue together and then confirm that's the right solution. We don't tell our clients to “go GenAI” their business. That wouldn't be good business for them, and it's not good business for us. We have a collection of tools in our toolbox to bring to this equation depending on the problem to solve and the data they have to work with.
William Rosengarten
We make sure to map technologies to business needs. For AI technologies, we consider:
The choice will be different if they just need to organize and retrieve data quickly or if they're looking for insights from the data they have. For example, you shouldn't use copilots for autonomous control, but it's valuable when a human is in the loop for decision-making. These questions help drive considerations about the modeling and architecture that should come into play.
Chelsea Barnes
We always look at what kind of algorithmic approach will be best suited for the scenario. While generative AI is the topic of the day, there are plenty of cases where you should not be using it. For example, a GenAI model will not help make a prediction to autonomously control a production process - think predicting fill by monitoring time series data so you can adjust your filler dosage so it comes exactly at target. It's just not suited to do that. But if you are trying to process something like a year's worth of shift logs to find anomalous patterns in those free text shift logs, that's a situation very well-suited for a large language model.
Two other important decision criteria in regulated scenarios are the risk of error and whether the results are explainable. GenAI models, which are neural networks, are by nature black boxes where you don't know how it arrived at a decision, so having a human operator in the loop is critical to confirm the results.
Jim Brown
AI copilots are gaining a lot of traction to streamline and improve human workflow. When do you find those applicable for your clients?
Chelsea Barnes
A copilot makes sense when they are trying to augment what a human can do, to make them more efficient in a process, or help them with the decisions that they are making. A good example for an operator would be a troubleshooting copilot. For example, a line is down and a fault code comes up. Instead of looking that up manually, the copilot could take the operator through a decision-making process and walk them through the troubleshooting steps.
Copilots are attractive because the manufacturing industry has not fully rebounded following COVID, and many companies still have jobs left unfilled. Retaining institutional knowledge in manufacturing is even more of an imminent and challenging concern as a substantial percentage of the workforce nears retirement. Many companies would love to get to lights-out manufacturing, but that can be decades away. So the goal is to find the best way to augment and assist the workforce they have. Copilots can help make them more productive and efficient, and equip them with decades of institutional knowledge, even if they haven't worked on the line for 15 years.
William Rosengarten
Agree. Copilot assistants are an excellent solution for capturing and retaining institutional knowledge. For example, they are very good at taking notes. A technician running a troubleshooting process is trying to get the line back up and running and typically doesn't have time to document what they're doing. They are making decisions on what steps to take based on their experience. A copilot could take notes about the decisions they make and the impacts they have on the troubleshooting process. Doing this creates a feedback loop that typically only exists in free text or just in a technician's head and tribal knowledge today. In that way, copilots can help guide troubleshooting and feed information into a knowledge repository to assist in future troubleshooting efforts.
You've shared a lot of insights into how manufacturers can identify the right business opportunities and apply AI to solve them. Two of my key takeaways are that it's essential to have industry expertise to help diagnose the problem and that it's critical to have diverse technical knowledge to be able to apply the right AI capabilities to get the job done. This is an exciting topic, and we'll stay in touch about it.
To learn more from Kalypso, explore the Kalypso website, a Kalypso interview about operational CoPilots, or Kalypso Insights on GenAI.
Thank you to Kalypso, a Rockwell Automation Business, and Hadley Bauer for arranging the interview. We learned a lot from the discussion and know manufacturers will, too.
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How do leading companies drive sustainable business success?
Tech-Clarity is conducting our 7th annual study on the challenges, strategies, and plans companies have to develop their products, services, supply chain, workforce, and business for long-term business success.
Please complete this questionnaire, and we’ll send you a copy of the final report as a thank you. The survey should take no more than ten minutes to complete.
Individual responses will be kept confidential. Please feel free to forward this survey to others you feel have an opinion to share.
Thank you for your support, please check out our Active Research page for additional Tech-Clarity survey opportunities.
[post_title] => Strategies for Long-Term Success 2025+
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How can recipe-based producers ensure cost-efficiency and consistent, high-quality products no matter where they are formulated or produced? By implementing enterprise recipe management (ERM) with a manufacturing execution system (MES) to deliver closed-loop data flows in and out of production. Leading Consumer Packaged Goods (CPG), specialty chemical, and other manufacturers are standardizing for ERM. Quite a bit of technology is required to digitalize this recipe transformation process (recipe management, PLM, digital twin. MES), but the value of eliminating inefficiencies, speeding product launches, and cross-disciplinary collaboration creates an excellent payback.
Please enjoy the summary* below. For the full research, please visit our sponsor Siemens (registration required).
Standardization is at the heart of enterprise success with recipe management. Companies must standardize many interdependent elements for ERM to work. One of the drivers behind the ISA88 standard, Dennis Brandl, explains that the ERM concept is “Standard product descriptions based on standard definitions of manufacturing operations, based on standard quality attribute definitions, based on standard process parameter and process report definitions.” This illustrates the many layers and facets to standardize for ERM.
ERM in Innovation
The NPDI process begins with innovation. This, in itself, is a multi-disciplinary effort, typically including:
It’s our goal as analysts to stay informed about the software solutions available on the market and how they drive business value for manufacturers. We do this by conducting surveys, speaking with manufacturers, attending events, and taking briefings from solution providers. One of the things that excites us the most is when we learn something new. Recently, we learned that we were underestimating Siemens Digital Industries Software Service Lifecycle Management (SLM) capabilities. We thought it was worth sharing.
One of the primary ways that Product Lifecycle Management (PLM) solutions can help add value in a device or equipment's service phase is by providing technical data downstream. Engineering data is highly valuable to service planners, techs, and other support personnel. We were aware of the capabilities Siemens delivers in this area based on prior conversations and briefings. Siemens understands how products are designed, made, and serviced.
Siemens Teamcenter is a proven PLM solution and Teamcenter SLM has matured nicely over the years. Siemens followed a natural expansion beyond engineering and manufacturing BOMs to support the service BOM. We’ve seen how Teamcenter manages multidisciplinary data across product lifecycles and know it can manage complex configurations for both standard and engineer-to-order (ETO) products. Teamcenter’s ability to manage variants, revisions, and changes for serialized assets creates valuable product intelligence for service-related processes.
Teamcenter’s capabilities in both PLM and SLM also create an opportunity for customers to close the loop from service back to engineering. An integrated view of service and product data allows engineers better visibility downstream so they can better design for service and quality based on real-world feedback.
We knew that Teamcenter SLM was a strong source of product- and service-related data and recognized that this underlying information is crucial to effective service. Our research shows the value of extending PLM data into the “in use” phase of the lifecycle. Integrating design and service information provides even greater business value, as shown in our recent infographic on integrated service. The combination of product and service intelligence to support service transformation is compelling. As our Extending Digital Thread to the Customer Experience research shares the value of integrating the digital thread across the development and service lifecycles allows companies to develop a more intimate understanding of the customer and product experience and create new value from increasing profitability with lower costs and higher revenue from spares, suppliers, upgrades, replacements, and cross-selling opportunities.
Teamcenter SLM goes further than providing existing data by expanding beyond engineering data to information like service procedures. Not only can the solution manage and share this information, their Tech Pubs/RapidAuthor solution leverages design models and data to develop accurate, 3D technical documentation including visual and animated service instructions.
Product Support Data Management (PSDM)One specific way Siemens drives value is with its Product Support Data Management (#PSDM) solution. PSDM is a standards-based approach to documenting product support requirements tied to specific asset configurations. The solution brings together product lifecycle and logistics support data, leveraging the standard GEIA-STD-0007. The standard originated from the military to support government contracts but is applicable across a variety of industries. Siemens adopted this standard and incorporated it in the Teamcenter data model to support maintenance and logistics related data imports and exports.
Beyond sharing information, our research points out the value of strong sales and service execution support. Manufacturing and service companies must improve and optimize the processes supporting sales and service operations to grow their aftermarket business. Sales and service transformation requires convergence across people, processes, data, and systems in the engineering and enterprise systems. Customer interactions, spare parts, service contracts, warranty entitlements, and other service complexities are managed in solutions like CRM, Field Service Management, Enterprise Asset Management (EAM), and even ERP. Siemens’ customers can integrate with these systems, but that doesn’t create a seamless process.
This is where our understanding of Siemens’ capabilities needed to expand. Siemens has been working to bridge the gap between design, service planning, and service execution. They now support the front office, sales teams, customer service, and even equipment operators through strong partnerships. This brings the value of SLM to the front office while keeping it tied into Teamcenter PLM to extend the value of rich digital data beyond the engineering population, in the right context, to enable execution processes. Siemens is not trying to recreate what field service and CRM solutions do, but instead make them more valuable by providing a more complete solution to their customers by connecting with them.
Siemens now supports cohesive processes and a consistent digital thread from engineering through field services and sales. Siemens was able to leverage their open approach and rich APIs in their platform to support a variety of partnerships. These partnerships include IBM Maximo Application Suite for EAM and Salesforce for CRM. In both of these cases, Siemens focuses on the product data and service knowledge, not the customer data and service execution. This is exciting news, and we were happy to hear that Teamcenter SLM for Salesforce has been available on the Salesforce AppExchange for about a year. The Salesforce partnership also opens up Teamcenter’s product and service data to Agentforce, Salesforce’s AI offering, unlocking significant new opportunities to leverage the cohesive digital thread.
Siemens demonstrated an example of how combining an integrated PLM-CRM environment with AI can streamline and improve customer experience, service performance, and profitability. They showed how Agentforce could be deployed on an equipment manufacturer’s website. A customer can visit the site and enter an asset name and serial number, which would be interpreted to create a draft case for review, combining details about the configuration, including product information like 3D visuals, service data like detailed service instructions, and business data such as warranty terms in one place. They went further to show how the combined, AI-enabled solution could identify additional opportunities like a service upgrade based on the combined data. It was a compelling view of an end-to-end integrated process.
Siemens has a lot to offer across the product and service lifecycle through the Siemens Xcelerator portfolio and their partnerships. Siemens’ value in SLM goes beyond product data and service knowledge to service execution, enabling an integrated digital thread. The offering extends on Siemens’ deep expertise and strength in PLM and their open platform approach. Together with their partners, they have developed a very compelling offering spanning the product and service lifecycle. If you haven’t seen it in a while, it’s worth a deeper look. We learned quite a bit.
Thank you for the briefing Kerri Doyle, Brian Johnsen, Dominique Gilles, Yishai Barak, and Rajvi Vaidya for taking the time for the briefings, we appreciate your time.
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Watch the conversation here (no registration required) and learn more about Automotive ALM and PLM.
For more insight on integrating ALM and PLM in any industry, refer to our buyer’s guide.
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