Making the value of technology clear
Physical AI
One of the more unique elements of the event was the seamless inclusion of robotics, or Physical AI, in the AI story. Most AI strategies live fully in the digital realm. Instead, IFS included the physical world in a cohesive strategy. AI obviously plays a big role in robotics, including vision systems and autonomous decision-making, but it’s not often that you see a continuum of action across digital and physical worlds. It’s part of what IFS talks about as the new workforce, which includes human, industrial, and digital as one system. We were treated to some use cases, for example, involving Boston Dynamics’ Spot robot that highlighted how robotic workers can extend human capabilities by working in environments that are harsh or unattractive to human workers.
The Demo that Pulled it Together
It’s uncommon to see a demo that effectively pulls together all the various elements to present an integrated view of the future way of working. IFS pulled this off. They showed an example that connected the AI vision from predicting a maintenance issue, triaging it, optimizing a service schedule, readying materials, servicing the equipment, reporting on the event, and learning from it for future corrective action.
The demo showed the interaction across an integrated workflow focused on maintaining uptime in a factory. It included traditional workers but also a digital colleague, the Digital Triage Worker (a purpose-built AI agent). It was a good view showing how agents can play different roles, at the right time, with the right information. To further the point, they extended the demo to have Spot the robot dog respond to an issue to gather more information before involving a human, saving precious service personnel a trip by gathering more information in advance without the need for human intervention. The scenario is realistic and achievable, and it's clear how Industrial X would support it.
The Demo that Pulled it Together
It’s uncommon to see a demo that effectively pulls together all the various elements to present an integrated view of the future way of working. IFS pulled this off. They showed an example that connected the AI vision from predicting a maintenance issue, triaging it, optimizing a service schedule, readying materials, servicing the equipment, reporting on the event, and learning from it for future corrective action.
The demo showed the interaction across an integrated workflow focused on maintaining uptime in a factory. It included traditional workers but also a digital colleague, the Digital Triage Worker (a purpose-built AI agent). It was a good view showing how agents can play different roles, at the right time, with the right information. To further the point, they extended the demo to have Spot the robot dog respond to an issue to gather more information before involving a human, saving precious service personnel a trip by gathering more information in advance without the need for human intervention. The scenario is realistic and achievable, and it's clear how Industrial X would support it.
Helping Make it Happen
Finding the path to value for industrial companies takes guidance. We heard from leaders at both PWC and Accenture sharing their advice. In addition to partners, however, IFS acquired an experienced AI services team. The team, Nexus Black, works directly onsite with customers to help them achieve value. Their approach is to put AI engineers in the field to deliver AI use cases. As CEO, Kriti Sharma explained, they like to get their hands dirty to implement technology to solve real problems.
Nexus Black also espouses a vertically focused approach. The message was “AI for the industries we rely on, built with the workers who run them.’ In addition to an example at a distiller, they shared solutions including Nexus Black for Aerospace to review FAA Airworthiness Directives to determine necessary actions and Nexus Black for Energy to help identify which field assets should be fixed first due to their higher risk of wildfire.
So Much More
There was so much more to this event that we don’t have time to share, including insightful presentations by MIT CISR and IFS customers Noble and Kodiak Gas Services.
Our Take
IFS shared significant progress toward their Industrial AI vision to add new value in addition to their ERP, enterprise asset management (EAM), supply chain, and field service solutions. It was great to see how mobilized they are, how broad their view is, how practical their approach is, and the world-class partnerships they’ve developed to make it happen. There was excitement in the air as IFS showed how Industrial X pulls it all together. We’re looking forward to following progress and speaking with customers who are walking down this path.
Thank You
Thank you, Mark Frampton, for including us, and congratulations to the IFS team and their partners for an impressive event – complete with a billboard! We look forward to following your progress.
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How can A&D manufacturers extend the value of PLM into manufacturing? Aerospace and Defense companies face growing backlogs that hamper revenue and profitability. This buyer’s guide outlines high-level requirements for A&D companies to select PLM solutions that accelerate production without compromising quality, efficiency, or compliance.
Please enjoy the summary* below. For the full research, please visit our sponsor, PTC (registration required).
This research is also available in French, German, and Italian.
Welcome to AI 2025 (pun intended)Tech-Clarity had the opportunity to attend Autodesk University, now known simply as AU, in Nashville this year. AU 2025 was a large event with a lot of fanfare and excitement from Autodesk customers, partners, employees, analysts, and press. It’s always a great time to reconnect with Autodesk executives and product leaders to understand their strategy and hear from their customers about how using Autodesk products is working for them.
Nashville is the home of country music, but this week AI was on the center stage, as it was at the prior AU. One thing was different. Last year the audience listened to news about AI. This year, many came to hear about it. Autodesk CTO Raji Arasu’s AI Keynote was packed and people were taking notes. Companies know they need to pay attention to understand AI opportunities.
We titled our recap of AU 2024 as Autodesk University 2024 Focuses on AI, Platform, Core Products, and more AI. In the spirit of all things AI, we asked ChatGPT to summarize our post from last year, and it said, “Autodesk is making AI a core platform capability to automate routine work, assist designers, and speed innovation through integrated, scalable tools.” AI was the key takeaway of the multiday conference, according to AI. There was more, as the title shares, but the emphasis was on Autodesk’s AI efforts.
Following that, it didn’t seem likely that AI could gain any more focus at AU than they did last year. But it did. Autodesk CEO Andrew Anagnost focused the opening keynote primarily on how AI is transforming what they do and how their customers will work with their solutions. It was more than talk or generalities. Andrew brought up product leaders from all three of the industries they serve and showed tangible, valuable ways AI can add value by solving real problems.
Autodesk is not just researching AI technology, they’re focused on how it improves the way their customers work. When Andrew was asked what sets the Autodesk agentic AI solution, Autodesk Assistant, apart from others he shared that they are not adding a generic tool, but instead adding a layer on top of their solutions that takes into account the context of what application the user is using and what they’re doing. Autodesk’s AI agents will be specific about what they can do, not just try to be able to do anything. We agree with the practical approach because it helps customers get moving with AI quickly without having to do all of the learning and experimentation themselves.
In parallel with the practical applications, Autodesk is investing heavily in core research. They created an AI Lab team with dedicated AI experts since 2018. Since then, Raji Arasu’s shares that they have developed patents and published over 90 AI research papers, and she claims that Autodesk is the leading publisher in AI for CAD, design, and geometry. From what we’ve seen, that is far more than others have invested.
This year, Autodesk shared a lot of AI progress. Last year we learned about Project Bernini, Autodesk’s R&D effort into AI for 3D shape creation. It turns out that Bernini was one of several foundation models that the Autodesk AI Lab was investing in. Autodesk is doing something unique. They are creating foundation models for 3D, as a corollary to the large language models (LLMs) that fuel AI giants like ChatGPT. Most of the research has gone into the detailed aspects of geometry like constraints and attachment points to precisely control models. The result is professional-grade foundation models for 2D and 3D. Their strategy is to have a hierarchy of AI models (see graphic) that starts with LLMs and concludes with industry specific models that understand 3D, CAD, physics, and event behavior. Following that will be customer specific models that can incorporate and apply detailed company knowledge.
They also shared significant advances with Autodesk Assistant. They learned from their early investments in AI assistants and now have a uniform assistant that is consistent across products. As mentioned earlier, one of the key benefits is that it maintains context. I expect we will see more from Autodesk from their agentic AI investments as they learn to leverage generally available LLMs and further exploit Autodesk’s foundation models.
Autodesk has been investing in generative design from the early days. Now, they are taking that to the next level with what they call “Neural CAD” leveraging “Neural Technology.” This is the applied result of Autodesk’s modeling expertise, early investments in generative design, and their recent research into 2D and 3D AI foundation models. Neural CAD models are different because the foundation models are trained on how people design, not from an LLM.
The result is the ability to leverage GenAI foundation models to generate editable CAD objects with sketch and text prompts, which Autodesk says is a world first. This may very well be true. As compared with typical generative design models, the result is not just a 3D shape but true CAD geometry, a “first class citizen.” Neural CAD not only generates a solution, it creates the history and sequence of Fusion commands needed to create it. This way it can be modified like any other CAD model. Neural CAD will be available in Fusion and support Fusion BOMs, parts, and assemblies.
Raji Arasu shared the significance of Neural CAD. She explained that we need to rethink CAD engines, and compared it to moving from combustion engines to electric drive. She says the future of CAD engines is dynamic, adaptive systems that create editable geometry. It’s a bold vision, and Autodesk has taken significant strides in that direction.
In addition to general AI announcements, we heard specific examples applicable to the manufacturing industry. Autodesk’s Executive Vice President for Design and Manufacturing, Jeff Kinder, explained Autodesk’s AI strategy for manufacturing. The first phase, he explained, is task automation. The second phase will be workflow automation, and the final phase is planned to be systems automation. Autodesk has big plans and a structured approach to introducing AI into their solutions.
Vice President of Design & Manufacturing Product Development Stephen Hooper shared specific examples of agentic AI capabilities focused on solving practical issues. The examples ranged from Fusion integration with Microsoft Office to address common, time-consuming challenges like preparing data for design reviews to more complex examples including applying a machining template to a new part.
Some of the other examples shared include bringing AI to Alias Form Explorer to aid with conceptual design by learning a brand’s “design language” and applying AI to aerodynamics. In Fusion, he mentioned exploring potential starting points for a design from prompts and creating CAD models you can work from, likely referencing Neural CAD. Other examples included auto dimensioning and tolerancing and automating 2D drawings with annotations. He also shared examples aimed at unifying factory and production solutions. The commonality in these examples is that they solve specific, practical challenges in design and manufacturing.
Autodesk has been making strides across all its primary industries, AECO (Architecture, Engineering, Construction, and Operations), Media & Entertainment, and Design & Manufacturing. At the core of each of these is the Autodesk Platform, and the expression of these is an “industry cloud.” As Andrew shared the industry clouds are a new codebase but the work with the tools of today and the tools of tomorrow. Further, he explained, that “AI is native to industry clouds.”
Autodesk is investing in all of their industry clouds. They shared developments that help cut time and effort in developing media content like movies and television shows. They also highlighted their continued move to bring project management and BIM into the Autodesk Construction Cloud to create the central repository for infrastructure projects.
The industry cloud for Design & Manufacturing is Fusion. Autodesk continues to invest in the Fusion platform to make it a more complete offering. We’ve been impressed over time with their vision for an end-to-end solution that embraces openness and the granular data approach. Over time, we expect to see Autodesk customers migrate from existing solutions like Inventor and Vault to Fusion.
One of the most interesting developments this year is the announcement about the Manufacturing Data Model. It’s the culmination of investments to bring product digital thread data from all of Autodesk’s products into a common ontology. It’s intended to be open and extensible so manufacturers can connect data from the many other applications with Autodesk-generated digital thread data. Autodesk announced that product lifecycle data is now part of Fusion, that all of the manufacturing data is available in the graph database, and that there are APIs for Fusion, Vault, and Inventor. Srinath Jonnalagadda, Vice President of Data Management for Design and Manufacturing, explained that Vault Data and Fusion are now available through Autodesk Platform Services in the form of Fusion granular data APIs. This is an important step toward a unified Fusion solution. This has big implications for Autodesk customers and partners, opening up integration and extensibility of Fusion applications.
Overall, data and integration were key focus points for design and manufacturing. They announced integration between Fusion and Vault, Autodesk’s proven product data management (PDM) solution, bringing Autodesk’s PLM (product lifecycle management) and PDM data together. This is an important step forward for Autodesk customers that want to have product development and other product-related processes integrated with their product design data prior to moving fully to Fusion. This connection will become increasingly seamless as Fusion matures.
Once again AU was home to the PLM Summit, bringing together customers of Autodesk’s PLM solutions. It was a collaborative session, as usual, with manufacturers and Autodesk partners sharing their successes and challenges with each other to help everyone improve. Our two key takeaways are represented in the following pictures.
1 – The way Autodesk customers use Fusion PLM is varied based on their needs. This is a testament to the flexibility of the solution. This panel hosted by Michael Vesperman included four companies and each shared the different ways they use Autodesk PLM solutions.
2 – Fusion PLM is not just for simple companies. Bridgestone explained how they support standardized processes across multiple, global locations. This chart helps describe the level of complexity of their PLM processes supported by Autodesk.
It will be interesting to watch as Fusion Manage supports more PDM capabilities and rivals the functionality in their mature PDM solution, Vault, in a more integral fashion with broader process management and enterprise PLM capabilities. Our eyes are on Autodesk to see their continued development and maturation in PLM.
Autodesk clearly believes the key to differentiation and success in the future is AI. Clearly, AI is upending a lot of existing solution paradigms. At the same time, however, they are working to make their core products and industry clouds better at meeting customers’ functional needs. Autodesk has a significant advantage in AI, particularly in the 3D AI space, as a result of their significant R&D investments. Time will tell how much that translates to future success, but if Autodesk is right, they are getting a significant head start on their competition that will be hard to match.
Thank you Autodesk for including us in AU 2025, it’s always a pleasure to learn about what’s happening in Autodesk and the Autodesk ecosystem.
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We had the opportunity to attend an AI-focused ChapsVision event in New York City. The meeting was a continuation of an annual event hosted by Sinequa by ChapsVision, one of the companies acquired to form ChapsVision, and was well attended. We were introduced to Sinequa and their AI-powered enterprise search a couple of years ago, and we’re excited to see the significant role they are playing within ChapsVision.
ChapsVision is an interesting solution provider. They are only 5 years old, but they have acquired 29 companies to form what they call “the trusted partner for your agentic AI journey.” We had the opportunity to hear Brian Kirk, GM North America, share their view on the future of the AI market. In his terms, “the future is agentic.” He shared some important ChapsVision beliefs (see image) to describe their vision.
The beliefs Brian shared align well with our view of AI, in particular, the importance of accessing company knowledge and the importance of vertical industry understanding. Our research and experience show the importance of verticalization for AI. In specific, our Making Manufacturing Analytics and AI Matter report shares that manufacturers want industry- and application-specific AI software. Solutions are more valuable when they understand the data, semantics, and the context, which is all very specific to industries. In Brian’s words, “The agentic AI war will be won in the (Industry Vertical) trenches.” We couldn’t agree more.
The event was grounded by one of the best AI keynotes I’ve seen. We heard from Raphaelle d'Ornano about Decoding Discontinuity. It was a high value, low pretense discussion where she introduced “Orchestration Economics” and the importance of orchestrators and agents to deliver AI value. She shared three laws of economic value in the Agentic Era:
It was a great construct to think about how agentic AI adds value and what determines solution provider worth.
The conference went beyond high-level positioning and pillars to share details about ChapsVision’s offerings. ChapsVision offers three primary solution platforms:
We learned more from an education presentation by CPO Jeff Evernham, who presented on the five eras of agentic AI revolution before sharing details about their products. Some of our key takeaways were that ChapsVision is packaging agents as tools, becoming an agentic platform, and verticalizing. Further, we heard that they are committed to being multi-model and allowing customers to choose their own LLMs. ChapsVision doesn’t want to create an LLM, he explained, they want their customers to get more value from them.
As mentioned earlier, ChapsVision believes in industry specialization. They focus on key verticals, including manufacturing, energy, life sciences, private equity, and legal businesses. There was fascinating information about ArgonOS for data ops and decision intelligence, and they shared examples for manufacturing, supply chain, and maintenance. We also heard about the value of their Systran solution for language translation. Another interesting proofpoint of their industry focus was the explanation of their AI Workplace solutions. Mingee Kim , Head of Customer Success, and Lead Presales Consultant Irene Margarit showed how they are building repeatable, verticalized solutions. The current focus has been on Legal and Financial Services verticals, but the approach is applicable to all of their verticals. We look forward to learning more as they extend Workplace solutions to manufacturing.
Based on our own industry specialization in the industrial industries, what caught our attention the most were the solutions specific to manufacturing. In particular, we were excited to get a refresher and learn more about Sinequa. We and saw a Sinequa demo focused on finding part information, including specs and drawings, which are typically spread out across multiple systems. The demo explained that companies need to find part data so they don’t recreate them, and went further into the negative financial impacts of the inability to retrieve part information.
They explained that Sinequa is a governed, secure, and trustable solution grounded in the content of the organization. He shared that Sinqua connects to a variety of enterprise systems, including PLM, ERP, SharePoint, and more, but only shares what the user is allowed to see based on underlying permissions.
Miles Yaeger demonstrated a Part360 Dashboard of the part, connected and contextualized, showing a comprehensive overview of part data. To demo showcased their understanding of engineering and manufacturing data, testaments to their vertical focus, including:
All of this data was traceable back to the engineering documentation sources, providing trust while also offering users the ability to drill down to the underlying data.
One of the highlights of the event was a presentation by global power technology leader Cummins Inc.. Cummins was one of a number of larger, world-class manufacturers in attendance including Boeing, Blue Origin, Northrop Grumman, and Exxon. We heard from Scott Beard, Engineering Efficiency Project Manager for Cummins, as he shared an informative and entertaining story of how Cummins chose to use Sinequa as their solution for their 13,000 engineers to look for specialized information.
Cummins ran one of the most metrics-driven proof of concepts (POC) I’ve heard of. First, they selected 200 testers from across their business units and functions and asked them 36 questions about their ability to find and leverage product information. They gained a baseline that showed a 19 out of 100 performance score and 4.4 out of 10 on a satisfaction scale. Further, they calculated an average of 5.3 hours per week of wasted time looking for data. That is a common scenario, according to our Effective Design Data Management and Collaboration research study, that shows engineers typically spend 19% of their time on non-value-added data management activities, which equates to about one day per week.
The scoring for the POC included objectives for productivity, lifting real-world decision-making performance, how desirable the solution is to users, whether it beats incumbent solutions like PLM, and whether it scales beyond tech search. In addition, he explained, it had some advanced features test. Multiple solutions were benchmarked and Scott says that Sinequa was the clear winner. The results included a 63% productivity gain, representing a 2.9 hours per week improvement. Sinequa also performed well on other metrics, including the performance and interest scores, leading to their selection as the solution of choice. It was a fascinating story set to a theme of The Wizard of Oz, of all things!
Scott reported that Cummins achieved a 15X ROI from their investment. This confirms what Jeff Evernham said earlier in the day, that although a recent MIT study shares that 95% of companies are missing agentic AI value, the other 5% are seeing significant value.
Going forward, Cummins explained that they see more value from Sinequa than just part search. As Scott explained, they now view Sinequa as their “Knowledge Hub for Enterprise AI.” They’re looking at 7 different design patterns and planning to implement virtual SMEs (subject matter experts), such as a geartrain expert. He also pointed out the value of capturing Cummins corporate IP (intellectual property) by interviewing retiring workers to build out a Cummins knowledge base.
ChapsVision has assembled a significant collection of AI capabilities delivering vertical solution value. We were impressed with our earlier conversations with Sinequa, and now we are more excited to hear about how ChapsVision will leverage and extend Sinequa’s capabilities for the manufacturing industries. We’re excited to see how the combined offerings help industrial companies get business value from AI and their digital threads, and look forward to watching the progress with workplaces to deliver more ready-to-use, verticalized solutions.
Thank you, Laurent Fanichet, for inviting us to the event and to Stacey Greene and the team for their organization that made it such a valuable time. Further thanks to Brian Kirk, Jeff Evernham, and Xavier Pornain for sharing your vision and progress in AI for the manufacturing industries. We look forward to staying in touch and learning more about your plans.
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We recently had the opportunity to reconnect with aPriori Technologies, a company we’ve known for years. It has been exciting to watch their solution evolve from a product cost management tool into a comprehensive digital manufacturing intelligence platform. Today, their platform enables improved decision-making across sales, design engineering, sourcing, sustainability, and manufacturing.
aPriori’s cloud-based platform simulates manufacturing processes to deliver real-time feedback on cost estimates, manufacturability, cost drivers, and environmental impact. They say their solution, with its process-specific, regionalized manufacturing models and enterprise data integration, helps companies win new business, improve margins, and avoid manufacturability issues.
The system models the entire production lifecycle, taking into consideration part geometry, material, manufacturing process, and regional cost data. Leveraging hundreds of manufacturing processes and tens of thousands of materials, teams can run what-if scenarios, compare alternatives, and make early, data-driven tradeoffs when changes matter most.
aPriori supports multiple product lifecycle stages, from quoting and cost engineering through design and sourcing. The platform provides:
According to aPriori, several factors differentiate their solution:
aPriori says its customers use the platform to solve three primary challenges:
Tech-Clarity’s State of Product Development: 7 Trends Shaping Product Innovation research highlights top challenges facing product development:
aPriori seems well-positioned to help manufacturers address all of these challenges.
In an era of volatile supply chains and unpredictable costs, predictability in cost and sourcing can be a competitive advantage. aPriori’s ability to model what-if manufacturing scenarios across regions could help. For example, comparing production in China, Vietnam, Germany, Brazil, Mexico, or the US extends its value beyond cost optimization to supply chain resilience and agility. The solution could help hedge against volatility and enable companies to plan and pivot with greater confidence.
aPriori describes their solution as having a “manufacturing engineer in your pocket,” with its ability to embed process expertise into the product development lifecycle. Not only can this help address bottlenecks in handoffs to manufacturing, but it can also help overcome knowledge loss due to the retirement of experienced engineers. Retirements are eroding decades of tacit knowledge, while many younger engineers have limited shop-floor experience. It can help identify manufacturability issues earlier, despite growing complexity, and automate many manual tasks required to support decision-making on cost, manufacturability, and sustainability.
As younger employees join the workforce, a cultural shift is taking shape. As digital natives, they are adept at using technology for decision-making, rather than relying on years of accumulated experience and rules of thumb. As product complexity increases, decisions will become increasingly difficult, so this new generation will likely accelerate the adoption of intelligent solutions like aPriori to support better-informed decisions.
Given current market trends, aPriori appears well-placed to support manufacturers amid economic change and workforce evolution. The company’s next wave of innovation will center on AI-driven guidance, from sourcing coaches to intelligent DFM assistants, which should build upon their existing capabilities. The appointment of a new CTO with deep AI expertise demonstrates a commitment to this strategy.
We look forward to seeing how these developments further expand aPriori’s role in shaping the future of embedding manufacturing intelligence into the product lifecycle.
Thanks to Rick Burke, Chris Jeznach, and Mark Rushton for briefing us.
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Is your manufacturing organization ready for AI? In this conversation David J. Jacq, CEO of Mapex, and Rick Franzosa, VP of Research for Manufacturing at Tech-Clarity, Inc., will bring their deep expertise in digital transformation, operations, and advanced manufacturing to explore the challenges and opportunities in smart manufacturing in the age of AI.
Listen as David asks Rick about what companies should prioritize in this fast-changing world. Rick shares his view of top challenges and the evolving perceptions of MES, including when it became more strategic. They also explore MES implementation wisdom, including common mistakes and what companies need to do before an implementation. Even implementation failures come into the session.
They wrap up with a discussion of how AI has changed the role of MES – shallow or deep? Is AI a differentiating factor for MES already? How to avoid false starts when adding AI to MES. And a look ahead at what industrial companies should be doing now to prepare their operations for unknown future realities. Watch the full video podcast here.
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Once you have machine monitoring, MES, and scheduling on a common plant data model, how can you improve the ability to automate and execute the work? MachineMetrics is doing that by adding Max AI. While the company started in machine performance monitoring, this year it has expanded to focus on plant performance and frontline worker performance (See previous insight). This comprehensive plant data set, which can also encompass multiple plants, serves as the foundation for Max AI.
Can you un-tribalize knowledge? This is how MachineMetrics aims to tackle the frontline skills shortage challenge. They are creating a fleet of agentic digital coworkers as part of the Max AI addition. These agents have full access to the unified data model, so they know what’s happening and when, as well as who needs what data at any given moment.
Like a good expert coworker, the human workers can engage with these digital coworkers using natural language prompts. The system can find data, provide quick responses to questions, adjust and direct, or even automate core production tasks. It can also create a software widget for ongoing use for issues that arise on a regular basis.
One example of a specific agent is for shift note handoffs. The agent can automate the basics, since it has access to the full record for that shift, and operators can augment that with their own annotations. Another is for Continuous Improvement (CI), and the analysis capabilities of Max AI can help everyone be involved. The CI agent can greatly speed up data analysis and understanding, allowing people to focus their efforts where they will make the most impact on performance.
Digital coworkers are great, and people also need all elements of their experience with MES to be intuitive and match their expectations and specific job needs. MachineMetrics claims all screens are now configurable. A widget framework for building the UI enables drag-and-drop and adjustable customization to tailor screens so they truly support users. With the LLM-based capabilities of Max AI, customers can add a summary to a dashboard.
They claim some of this can replace Power BI and other data analysis and visualization tools for customers. A significant upgrade in functional scope this year has enabled MachineMetrics to be a more comprehensive single platform for the frontline than it was before. MES, scheduling, analytics, and data from connected machines and other software feeds any needed data through this single interface.
Beyond the unified data model, the software now also features a Knowledge Hub, where manufacturers can upload and centralize any knowledge they need. MachineMetrics has already uploaded thousands of machine manuals. Customers can provide work instructions and SOPs, safety and hazardous materials information, and generate documents based on experienced and skilled workers’ knowledge on any topic.
Even snippets from these experts can contribute to success if they leave or retire. For example, each machine may have a different impact when it’s down. The experts know whether a 10-minute downtime is important to address or not; when a particular vibrational pattern is likely to lead to a downtime event, and more.
All of this expert knowledge in a central location can be beneficial, as anyone who searches for the data they need to act knows. The Knowledge Hub is also part of what agents use to make recommendations.
The Knowledge Hub augments the rich set of data models, time-series data stores, vector and graph databases, and APIs. All of this builds on the original connectivity through APIs that got MachineMetrics its start.
MachineMetrics plans to release new agents soon. The shift handoff agent is scheduled to launch in 2025. After that, in early 2026, an agent to automate scheduling is due. The goal is to change behavior for operators and other manufacturing and production support staff.
The team believes that frontline workers require an easy, intuitive user experience, with access to the real-time data they need. This encompasses not just the task at hand, but also improving it and solving problems.
An onboarding agent aims to improve the implementation and rollout of this young MES. The company has already structured itself for long-term customer relationships in a land-and-expand manner. They have three main teams: Commercial, Product, and Technical Solutions that provide implementation.
MachineMetrics is rapidly making the transition from its machine monitoring roots to MES and an industrial data management system. While some of the core MES functionality is just coming online, the data structures and AI approach seem solid. These, along with the existing customer relationships, will form the foundation for continued growth and delivering value to customers.
Discrete manufacturers seeking a rapid approach to get up and running, both to keep equipment humming and empower operators, may find MachineMetrics worth exploring. Current customers are the focus, and they have reason to celebrate the rapid expansion of functionality and AI capabilities.
Thank you, Graham Immerman and Rutherford Wilson, for updating Rick Franzosa and Julie Fraser on Max AI and MachineMetrics’ progress in the market.
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The market for MES in mid-sized manufacturers has traditionally been underserved. Major MES vendors find it unsuitable, as it conflicts with their direct sales model and complex product offerings. However, mid-sized manufacturers have the same MES needs as larger companies but lack the IT resources necessary to implement and support these systems.
ISE has provided MES and paperless shop floor solutions for forty years through its MV2 MES solution. It has been proven to excel at understanding and addressing the needs of mid-market discrete manufacturers. The combination of customer satisfaction, recommendations, and ISE’s longevity suggests it’s delivering to customer needs. Topping the list of differentiators is ease of use, which was evident in its demonstration.
The MV2 product's broad-based standard functionality encompasses essential MES features, including production scheduling and execution, quality management, inventory control, time and attendance, Kanban, machine integration, and real-time analytics. These tools are specifically designed to meet the needs of mid-sized discrete manufacturers, ensuring cost-effective implementation and operational efficiency. Its MV2 platform is positioned as a flexible and scalable solution for a diverse range of discrete manufacturing environments, with a breadth of functionality that matches and, in some cases, exceeds that of enterprise-scale MES solutions.
ISE has been providing solutions to discrete manufacturing industries, with a focus on the unique challenges faced by mid-sized manufacturers in this market. The company was established to complement and integrate with what is now the Infor XA ERP system. More recently, ISE added standard ERP integration to Microsoft Dynamics 365 Business Central. Through its open architecture and API integration toolkit, MV2 has been integrated with other ERP systems such as SAP and Epicor Kinetic.
I had the pleasure of attending ISE’s Manufacturing Optimization Forum event on September 25, 2025. The event included a panel session featuring four customers who shared their experiences and the benefits they realized from MV2. Some highlights I heard include:
The evolution of MV2 is centered around three main pillars: scalability, interoperability, and user-centric design. Scalability ensures the MV2 product can grow alongside its customers' operations, accommodating increasing complexity without compromising performance. Interoperability focuses on seamless integration with other enterprise software systems, ensuring adaptability to different technological ecosystems. Ultimately, the user-centric approach prioritizes intuitive interfaces and streamlined processes, enabling manufacturers to optimize efficiency with minimal training and support requirements. Together, these pillars form the foundation for ISE's vision of delivering its comprehensive MV2 MES solution tailored to the distinct needs of smaller manufacturers.
Thanks to Jay Gentle, Erin Bonde, Chris McLean, Dan Van Kempen, Jim Rozewicz, and Rick Reith at ISE for catching us up on the latest news.
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Developing 3D engineering applications is seldom fast or easy. One way to ease development is to use commercially available software components. Software components are developer toolkits that provide standard application capabilities such as modeling, visualization, and data exchange. By using components, development resources can focus on the secret sauce that makes their application more valuable, different, or better at solving end-user challenges.
Today, it seems every application must include AI. Not only does AI enable unique, modern, and highly useful capabilities for companies doing internal software development, but it has also become table stakes for new applications entering the market. AI-enabled functionality will differ across applications. However, software components could provide some fundamental frameworks for AI development. Enter HOOPS AI.
Recently, Jonathan Girroir from Tech Soft 3D gave us a preview of HOOPS AI, a framework to help developers jumpstart their AI development. HOOPS AI is a set of tools built on top of HOOPS Exchange that support machine learning and AI research and development. Rich data sources are at the heart of machine learning. As a long-term provider of data interoperability tools, Tech Soft 3D toolkits process a robust set of translators that can provide rich engineering data to feed machine learning models. HOOPS AI can be used to clean, segment, and organize CAD data. This framework has the potential to quickly allow developers to build their own unique ML workflows.
Unlike typical text-based datasets for LLMs, HOOPS AI works with 3D engineering data such as surface geometry (B-rep), topology, metadata, and assembly relationships. HOOPS AI converts this data into machine-learning formats and connects to popular ML libraries. The machine learning framework includes additional tools for recording, storing, and optimizing workflows.
It should be noted that users of HOOPS AI will need to have their own training data. Tech Soft 3D provides an application development framework for connecting this data to ML algorithms. Tech Soft 3D does not have direct access to this data.
While the toolkit applies across a wide variety of applications and industries, Tech Soft 3D is currently focusing on early adopters in manufacturing, including internal development teams and commercial software vendors.
Large independent software vendors likely have the data and resources to accomplish what Tech Soft 3D provides on their own; however, small to medium-sized companies with limited resources and data may find HOOPS AI to be a way to jumpstart their AI-enabled application development. Of course, it takes time for applications to leverage new toolkits and incorporate software components into their offerings. It will be fascinating to see the AI-enabled applications and corresponding capabilities that emerge leveraging HOOPS AI.
Thank you, Charrise Dalton, Fiona Minchella, and Jonathan for the briefing on HOOPS AI!
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Once you have a digital twin of a plant that includes predictive and prescriptive AI capabilities, how could you improve it? TwinThread is doing that by adding genAI and agentic AI in Advisor to help accelerate model interpretation, as well as a new solution aptly named Perfect Centerline to help manage and stabilize the process effectively. These can both improve time-to-value for customers.
The TwinThread Industrial AI platform is founded on pulling in operational data to build a comprehensive digital twin. To optimize performance, this PaaS includes predictive AI (as machine learning), and to take it a step further and close the loop, it also includes prescriptive AI. In addition to the core platform, TwinThread offers
You can find the basics about TwinThread and its offerings in our Insight posted in May 2025.
What’s new in this release is TwinThread Advisor, which includes both GenAI and Agentic AI capability. These language-based AI capabilities are context-aware, helping people interact with and interpret the results from the numbers-based predictive and prescriptive models in the core platform. This, in turn, can accelerate time to value, as anyone who has used AI to leverage a vast pile of information has experienced.
Incorporating the Advisor on top of TwinThread’s rich data set in a complete digital twin live model of a production site offers many potential benefits. Supporting understanding is a foundation. Process engineers, operators, and digital teams, as well as data scientists, can all benefit. It enables Q&A in their native language, performs global search, and builds analytical content. The agents in Advisor can integrate with various systems, and each has a specific persona that works in conjunction with other agents in a context-aware manner.
Advisor can use the TwinThread knowledge base of that customer’s operation, plus, if desired, the Internet. The customer has complete control of where the system pulls data. Using the corporation’s own data only is an option that can actually reduce spending on the cloud hosting service. It has complete security, is designed for privacy, and does not entail the sharing of one customer’s data with others. Advisor is currently available as a preview, an optional capability.
Engineers in process industries are almost certainly familiar with centerlining to minimize variability from run to run. Most companies do it, but this new pre-built TwinThread Perfect Centerline solution automates managing the centerlining process and improves it with each run. These advanced capabilities are now fully launched to make it simpler to derive and manage centerlines with confidence.
The customer can set up to five target KPIs. The system will then monitor and automatically optimize to those objectives. The system enables overriding automated setpoints and displays the centerline, current readings, and statistics about the run in real-time. To avoid users being overwhelmed, the system can also simply display centerline deviations. TwinThread projects a 50-75% increase in process stability, which can yield significant benefits.
TwinThread expects this to be a point of entry for most customers, as a baseline. The process model first derives a centerline, which process engineers can modify, if desired. Once the data is all connected in the platform, centerline analysis can be performed in minutes, indicating how much the process might improve. If the financial impact per percentage of each objective KPI is established upfront, the system can deliver a value-based view of optimizing to the centerline. TwinThread states that if the company has a historian, this transition from legacy to advanced approach may even occur within a week.
Once implemented, TwinThread’s proven predictive and prescriptive industrial AI and digital twin platform has a comprehensive and up-to-date data set. This is a rich starting point for genAI and agentic AI to deliver value. It is also a perfect home for advanced centerlining capabilities.
Thank you, Elise Loffredo, Jason Dietrich, and Andrew Waycott, for briefing Rick Franzosa and Julie Fraser.
on these new capabilities. We are excited to see both the added AI and the streamlined centerlining. We look forward to learning what you add next.
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Product innovation today is driven by an orchestration of mechanical, electrical, and software components working together as a system. So why hasn’t ECAD data management gotten the attention compared to MCAD data management with PDM? ECAD designs have gone uncontrolled in most engineering organizations for too long. It’s time for manufacturers to modernize how they manage EDA designs. This eBook shares five key ways that companies can bring ECAD into the product digital thread.
Please enjoy the summary* below. For the full research, please visit our sponsor, Accelerated Designs (registration required).
The Status Quo
Why are design cycles slow? Today’s status quo is disconnected processes and disconnected systems. Engineers too frequently make decisions without accurate, timely information. Further, lack of integrated design processes, ECAD systems, and electronic design data leads to suboptimal decisions that require costly and time-consuming rework. Finally, lack of ECAD interoperability limits design reuse and hampers sharing and collaboration across design teams and the supply chain.
It’s Time to Change
Companies must increase the efficiency of their engineers and engineering teams. They can’t do it without integrating electronic design data into the full product digital thread. First, engineers must have data readily available to make design decisions to get products right the first time. They should have accurate, up-to-date information in a common environment and centralized ECAD data management across teams, locations, and systems to enable collaboration and reuse. As a principal engineer for an aerospace company explains, “Ideally, I want one master system that talks to all ECAD systems, manages the whole ecosystem, and talks to multiple toolsets and PLMs.”
Electronic designs should be centrally managed across ECAD solutions. Then, the design data should be made available in a broader product context to facilitate collaboration across design disciplines and enable other applications, including AI. Unfortunately, there are no ECAD and PLM solutions available as part of a single platform. The best path to include ECAD in the digital thread in the foreseeable future is to consolidate ECAD design data, manage data from different design disciplines separately, and then create a holistic view at the product level in PLM.
Improving the Status Quo
So how can companies get the most out of ECAD and PLM investments to drive time to market? We propose five ways companies can improve processes and we’ll review each of these in detail:
*This summary is an abbreviated version of the ebook and does not contain the full content. For the full report, please visit our sponsor Accelerated Designs.
If you have difficulty obtaining a copy of the research, please contact us.
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How can manufacturers manage their complex design data, including both MCAD and ECAD files, to streamline design, eliminate wasted time looking for design data, and improve collaboration? Jim Brown will moderate a webinar, From Chaos to Collaboration: Solving the Design Data Dilemma, to hear from Propel and their partners on how they can seamlessly manage CAD data of different formats.
Learn from:
How far can large enterprises go in consolidating their software stack from IIoT and data foundations to apps? Could a startup EV OEM run its business on just two pieces of software? Fuuz, by MFGx, aims to answer that second question with a yes, using just Fuuz and Oracle NetSuite. We recently had an update briefing with Fuuz, and their focus has changed since our last conversation. They don’t fit neatly into a category and are calling what they do an industrial intelligence platform. Fuuz includes IIoT, a comprehensive data stack, no-code/low-code rapid application development tools, and MES, WMS, APS, Quality, CMMS, as well as Production and Process Monitoring solutions.
Fuuz initially aimed its platform and applications offering at small and medium businesses (SMBs) who wanted to extend their ERP to the shop floor. It still serves many SMBs with greenfield operations; however, they discovered that manufacturers with over $300 million in revenue are more mature. Their experience typically means they have a deeper understanding of why the combination of IIoT, a modern full-stack data management platform, and Purdue Model or ISA95 Level 3 applications can deliver immediate and lasting benefits.
The Fuuz platform is within multi-tenant SaaS, where each customer has their own segmented resources and database. This delivers cloud benefits while also accommodating highly regulated industries. Its Gateway enables edge connection for devices such as industrial controls, printers, scales, and CMMs, as well as edge computing. The Fuuz team claims to be winning against OT software players, IT data platform players, and manufacturing IT application powerhouses.
Many larger companies have been attempting to build out their architecture for years. One Fuuz customer is planning to replace 12 different software products with one. Some of the recent customer announcements include large and startup companies in pet food (planning to replace eight applications), EVs, auto parts, aviation, and life sciences.
Fuuz offers consolidation at many levels.
Tailor UIs, Workflows, and Apps – The Fuuz Platform offers tools, including a responsive UI builder, a workflow builder, and its own apps, as well as low-code app-building capabilities. A key differentiator is that they built these tools to address the scaling challenges of low-code/no-code solutions, implementing them as an SI before shifting entirely to a software company.
Unified Data Ontology – Each customer can select whether to use ISA95 or another structure for their enterprise data. The ontology is based on a no-code MongoDB and can span the entire enterprise.
AI Tools – The Fuuz team has decided not to build AI into the system. Customers can use its GraphQL APIs, data flows, and native MCP cloud or edge capabilities to integrate with any LLMs or AI tools they choose.
High-Value Starting Points – Since the platform encompasses everything an app needs, companies can start with their high-priority applications and obtain the entire infrastructure required for them to function. This is in stark contrast to many enterprise infrastructure approaches, which can require years before the high-value applications work well.
Fuuz’s strategy to sell and implement through partners remains strong. The ecosystem is growing, and partners have substantial control over the IP they put out on the Fuuz marketplace.
Partners have traditionally included ERP companies and their service partners. These include NetSuite, PwC, CohnReznick, Strategic Information Group, Western Computer, and Guidepath Consulting. Other partners are from IIoT, OT, or PLM backgrounds, such as Razorleaf Corporation, Castor Engineering, and newly-launched Abelara. Some have used the Fuuz platform to build their own MES, WMS, and EDI applications. Recently, Fanuc Robotics America Inc has started building Fuuz into its products to enable remote monitoring and predictive maintenance.
Fuuz’s partners often have deep and lasting relationships with customers. The partner vetting process is rigorous, ensuring that partner solution architects can help companies envision their future digital support. The goal is to grow with customers through these strong partner relationships.
Fuuz built to a vision a decade ago that few others had. At the core of the platform are flexible, modern, scalable technologies. They continue to improve, but are not flitting from one new technology trend to the next. They are building out from the original vision in a versionless approach, where upgrades never entail downtime, and customer implementations continue to support evolution.
Thank you, Craig Scott, Brad Hafer, and Steve Modrall, for briefing us on the state of the Fuuz and where Fuuz is igniting value.
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How mature is your digital approach to manufacturing operations? What are the next steps you need to take on your journey to improve digital support for the plant floor and support teams? Take our Manufacturing Operations Digital Maturity Assessment to find out.
The assessment leverages our research to assess your company’s digitalization progress in manufacturing operations. What could you do to build out from where you have already invested and succeeded? How are Top Performers doing things differently?
The assessment asks about your top objectives and challenges. It also considers five pillars of manufacturing operations digital maturity.
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How can manufacturers gain the data connectivity, analytics, and AI capabilities necessary to make informed decisions with confidence? Arch Systems was founded to provide that, and it continues to accelerate customer success via, in their words, “Intelligent Actions for Manufacturing”. Arch customers are finding that building their own AI infrastructure does not make sense when they can purchase this pre-built architecture, which includes connectors, dashboarding, a knowledge graph for context, analytics, and AI capabilities. We are excited about the progress they have made since our first briefing in the summer of 2024.
Why is managing CAD/CAM important to job shops? How does poor manufacturing data management negatively impact job shop performance and profits? More importantly, what lessons learned can they from managing product data to add control without burdening already busy resources?
Read the full report here, or download the full PDF, courtesy of Siemens.
You can also learn more from Siemens Digital Industries Software here. Thank you Siemens for supporting our work and helping us educate manufacturers on the value of improving CAD/CAM data management.
Design and production data are intrinsically related, and the data created by the shop should be managed in the context of the customer's CAD design. Unfortunately, job shops don't always manage this data and data relationships well because they don't want to be slowed down by cumbersome enterprise data management solutions. Some may not even have IT departments or the resources to implement formal data management solutions like PDM or PLM. This leads to disconnected data that is difficult to find and nearly impossible to manage as changes are received.
Unmanaged CAD/CAM Costs the Shop
Despite the challenges adopting data management, unmanaged CAD/CAM data causes frustration and reduces productivity. Our research on non-value-added time shares that technical resources only spend about one-half of their time on technical work on a typical project. They spend 19% of their time, about one day a week, on non-value-added (NVA) data management tasks such as finding information, working with the wrong data, recreating data, translating formats, and other common time wasters.1
Unmanaged CAD/CAM hurts a job shop's performance and profitability in four ways:
Four Ways to Improve
Now that we've discussed how poor data management negatively impacts job shops let's talk about how to fix it. Few job shop leaders would disagree that they should have effective control over their data, be able to reuse existing work, and capture learnings to continuously improve and avoid repeating mistakes. These factors are important to running an efficient, profitable shop. However, they can't always achieve the control they need over their CAD/CAM data because of the potential overhead of data management solutions.
In this eBook, we'll discuss four ways to increase profitability, performance, and customer satisfaction with minimal disruption and investment by taking a lightweight data management approach that manages CAD and related data like NC programs and CAM project files so they can:
Control, Access, and Share Data
Tech-Clarity's research shows that engineering and manufacturing teams must put three key capabilities in place to reduce wasted, non-value-added work. These basic capabilities include effectively controlling, accessing, and sharing information (see details on page "Control, Access, and Share with PDM").
PDM is a critical enabler to centralize and manage CAD/CAM and other related manufacturing data. It's proven to enable job shops to control, access, and share information effectively. PDM centralizes data to limit the time wasted on non-value-added tasks. It ensures that data stays current and updated, so there is no need to question or recreate work. It also allows other technical resources to have easier access to needed CAD/CAM or other data in a self-service model so they don't have to interrupt others who have their own jobs to do. Lastly, without PDM, CAM and other manufacturing data are not managed, and it is likely recreated every time a job comes in, instead of reusing NC data when the CAD is the same revision or using the manufacturing as a starting point if only minor details have changed.
Beyond internal value, customers may be more inclined to work with tech-savvy partners who can deliver more data and data-driven insights. Customers may value this information to train their own AI or improve their design process. On the other hand, job shops that are not leveraging AI have the potential to be perceived as outdated or less capable. Beyond AI, better managing CAD/CAM data sets the foundation for broader PLM capabilities. Our data shows that PDM / PLM improves operational control leading to reduced cost and higher profitability. However, the most important step is to get started by controlling, accessing, and sharing CAD/CAM data with PDM.
Build a Foundation for Future Value
Effective CAD/CAM data management is a core capability that drives agility and operational excellence internally. It also sets the stage for higher value and positive customer perception. One thing is sure, if AI does become a necessity, companies will want to have their CAD and manufacturing data in order so that AI can learn from it and leverage it. And if customers begin demanding PDM or PLM capabilities, you will have the foundation in place to support them. Mature CAD/CAM data management prepares the shop to answer questions about what they're doing about AI and PLM – internally and externally.
Put the Basics in Place
Job shops need to control, access, and share their CAD/CAM and related manufacturing data in context in order to drive agility and operational excellence, but it does not have to be burdensome and require deep IT resources. Job shops that are concerned about the overhead of managing CAD/CAM should consider a lightweight data management solution that keeps all data in a centralized location, contextualizes it, protects it, and allows people to securely collaborate on it. Today, job shops can implement basic PDM capabilities by taking a simpler, less burdensome approach. At the same time, they can leverage a solution that can scale with their needs over time.
Leverage the Cloud
Job shops looking for a lightweight PDM data management solution to manage CAD/CAM data should look to the cloud. Cloud solutions are more accessible than ever. They offer a low-cost option and reduce adoption barriers for small job shops. The path doesn't have to be burdensome. An overwhelming 78% of respondents who characterized their PLM implementation as 'Easy' had deployed a cloud-based solution compared to only 15% with onsite implementations.
Additionally, cloud solutions make it easier to securely collaborate with customers, both internally and externally. In fact, our research shows that the cloud provides value in several different ways:
What do pharmaceutical manufacturers most need? To be compliant with government regulations without wasting time and effort. Leucine set out to deliver that with its SaaS software suite, which includes hubs for Manufacturing, Quality, and, most recently, Laboratory, with Intelligence (including AI) serving it all. The company launched in 2019 and joined forces with Ecolab to sell in the US and Europe in 2022. In late 2023, Ecolab, an established cleaning and contamination control provider in the pharmaceutical industry with a digital arm, invested in Leucine.
Leucine is determined to stay focused on manufacturing and quality software for pharmaceutical companies. They aim to help customers achieve two goals:
That may sound straightforward. Yet, as they point out, most processes are inherently multidisciplinary, so the Leucine suite includes functions for manufacturing, quality, and quality lab activities. On top of the specific functional hubs sits the Intelligence Hub for dashboards, natural language Q&A based on GenAI, alerts, and insights. The Cortex Command Center includes GenAI for goal-based analytics and is typically used by process engineers, production supervisors and operational excellence professionals. Beneath is a common platform to ensure data is shared and in full context across all disciplines.
In the Manufacturing Hub, where our briefing focused, Leucine’s MES includes:
Any of these elements can be the starting point for a customer, and as they add functional elements, they seamlessly integrate with in-context data from the ontology. They assert that both data and context are dynamic and took on the challenge of that path to evolution and growth, as well as openness and connectivity.
The MES is designed for operators to have minimal friction. It includes hands-free voice-guided workflows, IoT-connected Andon light integration for process-ready signals, and proactive voice and visual alerts. This includes task timers and audible and visible escalation cues.
SaaS, with 99.9% uptime plus ongoing upgrades and security capabilities that the cloud offers, is just the start for agility and data flow. This platform has SOC 2 Type 2 Certified data security for privacy, availability, and security. Both Leucine and Ecolab have specialized expertise in FDA-ready audits, software validation and 21 CFR Part 11.
The platform streamlines connecting to varied data sources. It includes enterprise connectors with OPC-UA and SQL, APIs to QMS and LIMS, RFC and FTP to level 2 systems such as data historians, and OPC-UA or MQTT for analytical balances, plus the native logbooks in the system.
From the company’s inception, they developed a Leucine Ontology or single data model for the enterprise. This ontology has a semantic understanding of the objects involved in pharma manufacturing and quality. Increasingly, we find that an ontology is crucial to effective deployment, integration, and long-term value from manufacturing software.
One thing that stood out to us is the company’s inclusion of an FDA Tracker. This tool keeps up to date with the definition of compliance as it changes. Of course, the analytics intelligence hub and Cortex also support continuous improvement and agility.
Leucine is a US registered company and has already signed up a strong customer base worldwide since its inception six years ago. It claims over 350 GMP sites and nearly 50 pharmaceutical enterprise customers.
Ecolab brings 30 years of digital innovation, 1,400 employees in the digital group, 100,000 connected sites across 40 industries, 1,000,000 connected devices, and over 120 billion data points go through the Ecolab cloud platform every year.
We think Leucine was brilliant to bring in a strong partner as its channel to market. Ecolab has been making its mark in pharmaceuticals for years, and the decontamination and cleaning processes are a clear part of compliant manufacturing and quality.
Thank you to Leucine co-founder and CEO Vivek Gera, plus Ecolab’s Michael Cates, William Goodman, and Sarah Otterstetter for briefing Rick Franzosa and Julie Fraser on this modern approach to pharmaceutical industry production, quality, and compliance.
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How can small and mid-size businesses (SMBs) - specifically manufacturers and warehouses - gain an operations digital thread that connects from materials movement through production and quality? Ideally, with well-integrated, easy-to-deploy software. Alpiconn has been delivering a combination of MES, WMS, CMMS, QMS, and traceability for the past decade. By focusing on practicality, flexibility, and depth, this company has served various companies from its headquarters in Athens, Greece. They are now ready to expand into the rest of Europe.
Alpiconn enables a digital thread from production to quality to traceability, including the warehouse. The company started in 2008 and was rebranded as Alpiconn in 2014, so it has been building for over a decade. The Plantecho digital operations management system has four major components: MES, WMS, QC, and CMMS. From the beginning, the company’s vision was based on ISA-95. It includes all major elements of the Purdue Model’s Level 3 for seamless data flows. This end-to-end functionality is designed for small and midsize manufacturers to adopt as a digital transformation accelerator.
Plantecho is also hardware and ERP agnostic, aiming to integrate with existing systems. Minimizing the integration headache can accelerate value and enable smaller companies to adopt such a comprehensive software suite. Plantecho is designed to be a unified system that orchestrates all operations. They believe their job is to connect people, data, and processes in a way that turns complexity into clarity and sound decisions. The company has won numerous Greek awards for software, IoT, science in business, and services over the years, and seems to have loyal customers.
To serve small and mid-size businesses (SMBs), Alpiconn says this system can go live in weeks, not quarters or years. They have also focused on making the system easy to use.
Alpiconn’s focus on smaller companies began to make advanced industrial technologies accessible and practical for companies that may not have the IT capacity or know-how to implement them on their own. By doing so, Alpiconn helps modernize and elevate SME operations by applying best practices and proven tools..
They have deployed Plantecho across a wide range of industries, including chemicals, aluminum and other construction materials, as well as food and beverages of many types. The foundational structures can work with any industry due to the ISA-95 basis.
However, Plantecho can serve larger multi-plant enterprises. Its multi-tenant (not cloud) approach can support multiple plants and partners. Customers have started small and grown, some using the system for over 10 years. Customer growth has also driven new capabilities and functionality in the software.
Another key factor is that Alpiconn is a turnkey provider. They enter into long-term partnerships with customers, delivering software, services, and hardware. They tend to become trusted advisors, helping customers model and optimize their business, both initially and as things change. Alpiconn also provides ongoing consulting and support services tailored to each customer’s evolving operational needs.
In addition to the broad Plantecho digital operations management system, Alpiconn offers Flexus, a low-code, event-based data and process automation platform. Customers can use Flexus to build custom workflows and business logic to respond to situations. This flexible tool has been used to develop:
Through technology partners, Alpiconn also offers connected industrial labelling from Loftware and AR-powered indoor navigation and guidance from Insider Navigation. Alpiconn is also an Independent Software Vendor (ISV) and Machine Vision partner of Zebra Technologies, integrating industrial-grade scanning, coding, labeling, and vision solutions into its systems for full visibility and control across shop floor operations. Alpiconn also has integration partners for more complex or specialized projects, such as robotic warehouses.
The software is already available in English, and the company plans to expand more aggressively into the rest of Europe soon. Alpiconn regularly introduces major releases of Plantecho that include improvements and new features. It is exploring working with companies; this is logical, given its background in chemicals and regulated food and beverage.
AI is on the roadmap, but Alpiconn management is being conservative, ensuring they introduce AI solutions that address specific customer problems. SMBs typically balk at new technology that does not deliver clear business value.
Thank you, Theodore Papadopoulos and Athina Fysekidou, for explaining Alpiconn’s business and offerings to us. We look forward to following your expansion into the rest of Europe.
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Is your approach to product development helping you compete or holding you back?
Product development teams today face growing pressure to respond to market volatility, supply chain disruptions, and global competition. At the same time, they’re dealing with long-standing challenges like manual inefficiencies, poor collaboration, disconnected systems, and workforce shortages. These issues cause costly delays, especially during engineering-to-manufacturing hand-offs. In the past, these inefficiencies were tolerated. But today’s market demands more agility, and the tools to support it are finally available. Advances in digitalization, simulation, and AI are helping teams move faster and smarter. These issues are shaping product development.
The research identifies seven trends that are reshaping product development as companies confront this new reality. Top Performers are leading the way in adapting to these trends to gain a competitive edge. What are they doing differently, and what can others learn?
This study is based on a survey of 233 professionals involved in product development across design, engineering, and manufacturing roles. The findings reveal the biggest challenges teams face today, the trends driving change, and the strategic moves Top Performers are making to reduce risk, accelerate innovation, and deliver better products faster.
Please enjoy the summary* below. For the full research, please visit our sponsor Dassault Systèmes SOLIDWORKS (registration required).
State of Product Development
Product development continues to face an era defined by growing complexity, shrinking margins for error, and accelerating market demands. This is exacerbated as companies are under increasingly pressure not only from traditional competitors, but also from nimble startups, vertically integrated disruptors, and offshore manufacturers. At the same time, they must manage ongoing supply chain volatility, shifting customer expectations, and a growing need for smarter, more connected products.
To remain competitive, companies must design faster, adapt to change more easily, and eliminate costly inefficiencies. Yet many are still burdened by manual tasks, disconnected tools, and poor collaboration, especially between engineering and manufacturing. These challenges are not just frustrating for employees; they lead to missed deadlines, higher costs, and compromised quality. In fact, 78% of respondents report increased manufacturing and development costs due to product development challenges.
Key Findings
Top Performers, companies that consistently meet or exceed product development goals, are leading the way. Compared to Others, they are:
Expand simulation usage: Use simulation earlier and across more roles to cut physical prototyping, identify issues sooner, and improve design quality. Seventy-four percent of Top Performers use simulation to optimize performance.
How can MedTech companies accelerate digital transformation?
On Thursday, October 23 at 10:00 am EDT, please join Tech-Clarity’s Michelle Boucher for an interactive discussion with Christian Vogelei of Fresenius Medical Care and René Zoelfl of PTC as they discuss this.
They will explore topics like:
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IFS made a big splash with their vision for the future of industrial AI at their recent Industrial X Unleashed event in New York City. We were excited to join the event to learn how IFS plans to expand their already rich solution portfolio for manufacturing and industrial environments. The event was packed with vision, full of new announcements, and helped showcase what the industrial AI future holds.
The event was the debut of IFS’ AI strategy which includes TheLoops, an agentic platform for mission-critical operations, acquired earlier this year. But it was much more. IFS shared a comprehensive view of the value that their IFS.ai platform, services, and partners can deliver to their customers. The event and the IFS strategy transcend software to Physical AI, incorporating the world of robotics. IFS generated quite a bit of excitement with the event, and went beyond the normal hype to deliver a view into the future where digital solutions, AI agents, and people work together to drive industrial improvements.
IFS CEO Mark Moffat kicked things off and shared some key tenets of IFS’ AI approach. One of the key points we took away is that Industrial X is for mission-critical applications where you can’t afford to get it wrong. IFS drew a significant distinction for what’s required in these settings compared to back-office applications. This is what they call Applied AI, which is in context, in reality, auditable, domain aware, works offline, mindful of safety, and keeps people safe doing actual work. The goal, he explains, is to “10x” the workforce with robotics, addressing the industrial skills gap and elevating the humans in the process.
IFS SVP Technology Vaibs Kumar further shared the strategy for industrial settings, sharing three critical challenges that we also find in our research; workforce issues, unplanned outages, and changing supply chains. The solution, he explained, is the autonomous enterprise with agents you can trust. The vision he shared is for digital workers that act on the same data as people, dynamically reason and plan, and bring humans into the loop. This will be delivered through digital worker templates that will come “out of the box’ with an associated outcome and leverage over 50 agentic skills that are already available today. He spearheaded a comprehensive demo to showcase the approach, which we’ll share more about later.
Context Matters
Another area we found alignment with IFS’ messaging was around vertical industry context. AI is a class of very valuable tools. But AI doesn’t create value. The application of AI to solve real-world problems (or gain real-world advantages) in a specific data and industry context is where business value comes to life. Our research and experience show the importance of verticalization for AI. In specific, our Making Manufacturing Analytics and AI Matter report shares that manufacturers want industry- and application-specific AI software.
Mark Moffat echoed the importance to their strategy and the value of taking advantage of decades of operational data and thousands of pretrained workflows for their six core industries. Anthropic Applied AI Lead Garvan Doyle explained this was a key reason for the newly announced partnership with IFS. They wanted a way to leverage their secure, enterprise-grade AI capabilities in factories and plants by tapping into IFS’ expertise for AI for the real world. In his words, it’s easy to simulate digital industries because the data is available, but the real world requires a lot of different data. This is where industry context is critical for manufacturing and industrial industries.
Partners, Partners, Partners!
The event showcased a number of important partners and included announcements about new relationships with:
Physical AI
One of the more unique elements of the event was the seamless inclusion of robotics, or Physical AI, in the AI story. Most AI strategies live fully in the digital realm. Instead, IFS included the physical world in a cohesive strategy. AI obviously plays a big role in robotics, including vision systems and autonomous decision-making, but it’s not often that you see a continuum of action across digital and physical worlds. It’s part of what IFS talks about as the new workforce, which includes human, industrial, and digital as one system. We were treated to some use cases, for example, involving Boston Dynamics’ Spot robot that highlighted how robotic workers can extend human capabilities by working in environments that are harsh or unattractive to human workers.
The Demo that Pulled it Together
It’s uncommon to see a demo that effectively pulls together all the various elements to present an integrated view of the future way of working. IFS pulled this off. They showed an example that connected the AI vision from predicting a maintenance issue, triaging it, optimizing a service schedule, readying materials, servicing the equipment, reporting on the event, and learning from it for future corrective action.
The demo showed the interaction across an integrated workflow focused on maintaining uptime in a factory. It included traditional workers but also a digital colleague, the Digital Triage Worker (a purpose-built AI agent). It was a good view showing how agents can play different roles, at the right time, with the right information. To further the point, they extended the demo to have Spot the robot dog respond to an issue to gather more information before involving a human, saving precious service personnel a trip by gathering more information in advance without the need for human intervention. The scenario is realistic and achievable, and it's clear how Industrial X would support it.
The Demo that Pulled it Together
It’s uncommon to see a demo that effectively pulls together all the various elements to present an integrated view of the future way of working. IFS pulled this off. They showed an example that connected the AI vision from predicting a maintenance issue, triaging it, optimizing a service schedule, readying materials, servicing the equipment, reporting on the event, and learning from it for future corrective action.
The demo showed the interaction across an integrated workflow focused on maintaining uptime in a factory. It included traditional workers but also a digital colleague, the Digital Triage Worker (a purpose-built AI agent). It was a good view showing how agents can play different roles, at the right time, with the right information. To further the point, they extended the demo to have Spot the robot dog respond to an issue to gather more information before involving a human, saving precious service personnel a trip by gathering more information in advance without the need for human intervention. The scenario is realistic and achievable, and it's clear how Industrial X would support it.
Helping Make it Happen
Finding the path to value for industrial companies takes guidance. We heard from leaders at both PWC and Accenture sharing their advice. In addition to partners, however, IFS acquired an experienced AI services team. The team, Nexus Black, works directly onsite with customers to help them achieve value. Their approach is to put AI engineers in the field to deliver AI use cases. As CEO, Kriti Sharma explained, they like to get their hands dirty to implement technology to solve real problems.
Nexus Black also espouses a vertically focused approach. The message was “AI for the industries we rely on, built with the workers who run them.’ In addition to an example at a distiller, they shared solutions including Nexus Black for Aerospace to review FAA Airworthiness Directives to determine necessary actions and Nexus Black for Energy to help identify which field assets should be fixed first due to their higher risk of wildfire.
So Much More
There was so much more to this event that we don’t have time to share, including insightful presentations by MIT CISR and IFS customers Noble and Kodiak Gas Services.
Our Take
IFS shared significant progress toward their Industrial AI vision to add new value in addition to their ERP, enterprise asset management (EAM), supply chain, and field service solutions. It was great to see how mobilized they are, how broad their view is, how practical their approach is, and the world-class partnerships they’ve developed to make it happen. There was excitement in the air as IFS showed how Industrial X pulls it all together. We’re looking forward to following progress and speaking with customers who are walking down this path.
Thank You
Thank you, Mark Frampton, for including us, and congratulations to the IFS team and their partners for an impressive event – complete with a billboard! We look forward to following your progress.
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