Making the value of technology clear
Product development and manufacturing are evolving rapidly. Organizations are facing increasing product complexity, workforce challenges, rising customer expectations, and growing pressure to improve speed, quality, and innovation. At the same time, emerging technologies such as AI are creating new opportunities, and new questions.
Tech-Clarity is conducting research to better understand the current practices, challenges, and future of product development and manufacturing. We are exploring questions such as:
Qualified respondents will also be entered into a drawing for one of twenty $25 Amazon gift cards.*`
Individual responses will be kept confidential and will only be reported in aggregate. Please feel free to share this survey with colleagues who have experience in product development or manufacturing.
Thank you for your support. Pease check out our Active Research page for additional Tech-Clarity research opportunities.
*See survey for eligibility requirements.
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Media, PA, USA, July 15, 2026 – Results are streaming in: interest in Manufacturing Operations Software (MOS) is very high. Tech-Clarity, Inc. has reached the maximum of six sponsors for the 2026 research program on The Business Value and Evolution of Manufacturing Operations Software in the Age of AI, as Siemens Digital Industries Software is also sponsoring. The other five sponsors are: Critical Manufacturing, Infor, ISE, Parsec Automation LLC, and SAS, demonstrating broad industry support and momentum.
The survey is open for response until July 26, click here to share your perspective.
Responses are coming in from manufacturers across process, batch, discrete, and hybrid production segments and regions worldwide.
Share your experience now. Individual responses are kept confidential. As a thank-you for participating, Tech-Clarity will send the report to all who respond.
This research initiative focuses on a few key topics that are pivotal for manufacturers’ success:
We have been impressed with Makersite and their focus on Product Lifecycle Intelligence (PLI), as we shared in our earlier briefing insight. Makersite is a small company that has big customers, and they’re helping those companies make significant gains on compliance, cost, sustainability, and supply chain risk. Their #PLI approach connects product data across design, supply chain, compliance, cost, and sustainability to create digital models that help manufacturers make better product decisions throughout the lifecycle. It’s a valuable offering to help companies get the data they need for designers to make better, more holistic decisions around components. The company offers more than applications, they provide data from a variety of sources – both internal and external - including over 150 Makersite gathered database.
Now, Makersite focuses on helping manufacturers “source smarter, design greener, and collaborate faster” by leveraging their AI-powered PLI for sustainable product and supply chain decisions at scale. There’s a lot to that, so let’s unpack what it means. Makersite had already gone beyond focusing solely on carbon content to help manufacturers design for sustainability. This is critical to long-term business success, as show in our Executive Strategies for Long-Term Business Success research series. Makersite is continuing down this path and extending their scope, leveraging their structured product model (aka digital twin) capabilities to collect and associate data with other critical impacts of design decisions including cost. Recently, they also announced they have acquired SiGREEN from Siemens Digital Industries Software and rebranded the platform as Mattermaps, strengthening their product data collection and exchange capabilities.
The big news is that Makersite is adding a “should cost” capability. It’s a new feature to evaluate costs at the portfolio level based on the digital twin, for example accounting for which components are going up in price. The solution is intended to help with scenario and what-if planning to help optimize decisions amid market disruption and uncertainty, which our study on long-term success show has been growing steadily over the last 5 years.
The solution complements others on the market because it more focused on purchased components, not for custom components that have to be evaluated at the 3D / CAD level detail. This makes the Makersite should cost approach complementary to feature-centric costing capability covered by other companies that address Product Cost Management (#PCM), such as aPriori or Facton. The Makersite methodology takes advantage of their strength in understanding the supply chain, and focuses across the product portfolio as opposed to a particular part or part family. We see the value of these solutions as additive, acting on different kinds of parts and at different levels of granularity. Makersite has added a very valuable capability to help manufacturers make better decisions that directly impact competitiveness and profitability.
In addition to the should cost, and supporting it, are now agentic AI capabilities. AI is not new to Makersite. AI has been an important part of their solution for some time. As we said before, it’s important to understand the role artificial intelligence (#AI) and machine learning (#ML) play in Makersite’s ability to create a contextually rich digital thread mapping. Makersite refers to their platform capabilities as “AI powered Product Lifecycle Intelligence.” These capabilities were more embedded in the solution and behind the scenes. Now that they are expanding the use of AI Agents. The strength leverages their structured product model and offers mapping agents, modeling agents, and reporting agents. Makersite explains that the results, unlike some AI solutions, are grounded in data and traceable. The approach leverages AI but keeps a human in the loop for verification. It’s a practical approach that leverages their past AI experience to improve the value they deliver.
One example of how the agents can add value is through a new offering, ChemAI. We were able to see a demo of how it works. ChemAI generates a product model for a chemical when suitable chemical data is limited or can’t be found in existing datasets. It uses available chemical information and AI to infer likely synthesis pathways and raw materials. This provides a foundation to determine sustainability, cost, and compliance where rich chemical datasets are unavailable. Then, the user can review and validate the results, and select the most appropriate synthesis pathway for that chemical. From my understanding, ChemAI helps fill chemical data gaps by inferring a recipe, raw materials, and associated impacts (see demo image).
Makersite ensures they are not just turning to AI and trusting answers. The solution creates a model by working upstream and connecting with existing data where available. It generates a variety of suggestions for how it was produced for the user to pick from. As a byproduct, the process develops a validated model that can be reused for future scenarios.
ChemAI is just one example of Maketsite’s plans, there will be others released, with plastics and metals mentioned as applications we could expect them to deliver at some point in the near future.
One of the things that has always impressed us is the Makersite customer list. They shared two published case studies during the briefing, each showing impressive results, including:
Makersite is expanding on already strong capabilities in their PLI offering. The two case studies reflect Makersite’s strong capabilities in sustainability, and we look forward to seeing more success stories around cost and supply chain risk as they expand their offering and use of AI. We are excited to following their continued success.
Thank you Nicolás Artímez Wetz and Kerrie Kennedy for your time and for sharing your solution plans and customer successes.
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Don’t miss this recorded event replay on achieving product data-driven digital maturity in the age of AI!
Digital maturity is no longer just about adopting new tools. For design and manufacturing organizations, it is about building the strategy, culture, and connected data foundation needed to make better decisions across the product lifecycle — and to prepare for the next wave of AI-enabled transformation.
Watch Jim Brown, President of Tech-Clarity, and Bassanio Peters, Senior Market Development Manager of Design & Manufacturing at Autodesk, on a LinkedIn Live conversation on what it takes to become a more data-driven organization. They discuss how manufacturers can evaluate where they are today, identify the organizational and process gaps holding them back, and create a practical roadmap for advancing digital maturity.
This lively discussion explores why product data is becoming more critical as a business asset, how AI is raising the stakes for data quality and accessibility, and why connected, contextualized information is essential for faster, more confident decision-making. Jim and Bassanio also discuss how leading organizations are improving visibility and alignment across teams as they modernize the way product information moves through the enterprise.
Attendees will come away with a higher-level understanding of how to:
How are manufacturers managing their manufacturing operations?
Tech-Clarity invites you to participate in a research study on “level 3” manufacturing operations software for production facilities. What business value are companies gaining from software at this level? How is MES/MOM/MOS evolving? What impact is AI having on this landscape?
We will also use the results to report on best practices to maximize business value from MOS and AI. Please take 10-15 minutes to fill out this short survey. As a thank you, we will send you a copy of the report summarizing the findings.
In addition, eligible respondents will be entered into a drawing for one of twenty $25 Amazon gift cards. See the survey for eligibility details.
Take the survey now to share your perspective!
Please feel free to forward this survey to others you feel have an opinion about US-based fabs to share. Individual responses will be kept confidential.
Thank you for your support. Please check out our Active Research page for additional Tech-Clarity survey opportunities.
[post_title] => What is the State of Manufacturing Operations Software (MOS) in the Age of AI?
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How can manufacturers gain confidence in moving to autonomous operations? Combining the agility of SaaS with the reliability of edge at the plantwide level is an emerging approach for MES. This eBook walks through why MES is core to moving from automation to autonomy. It discusses the challenges of gaining the resilience between the plant floor and SaaS applications. It also explains edge-to-cloud architectures and their benefits.
Please enjoy an overview of our findings, below. For the full research, please visit our sponsor, Rockwell Automation.
Beyond normal data access, autonomous operations need resilience. 24/7 autonomous operations need seamless, low-latency data access and data management across IT and OT. Still, most companies, even Top Performers, rate their capabilities for the seamless movement of operational data from collection to analysis between ‘Not at all’ and ‘OK’. AI intensifies the pressure to improve industrial data movement and governance. Our research shows that the #1 issue for AI success is a lack of data readiness.
What happens when industry veterans join in commercializing an incubator-developed CPG MES? We sat down with the management team at O3sigma to learn about their history and plans for this enticing new offering in the CPG MES arena.
The origins of O3sigma trace back to Obeikan Investment Group. This Saudi-based enterprise failed to realize the promise of MES in its packaging plants, with less-than-successful implementations of a solutions from a couple of well-known MES vendors. They turned to their internal ‘incubator’, Obeikan Digital Solutions, and formed a team of manufacturing engineering experts to build their own MES for high-volume CPG production using lean concepts.
The result was a solution that delivered respectable, repeatable savings for the Obeikan factories. They claim customers consistently achieve an average improvement of 15 points in Overall Equipment Effectiveness (OEE), with an annualized return on investment of over 500 percent. Obeikan decided that this offering was a great candidate for commercialization. They assembled a management team, consisting mostly of executives who had worked together at Infora decade earlier, including two who were lured out of retirement. The commercial entity, O3sigma, was born.
The system is defined by an architecture consisting of five planes, not the Purdue model, but a set of planes that build from physical shop-floor actions to intelligence. The goal is to go beyond simple connectivity.
The first plane is the physical plane, the actual shop floor where people and machines work together to produce a product. The second plane is the Industrial DataOps plane, where data from every action on the shop floor is captured with fidelity and genealogy. Next is the control plane, where industrial data points are converted into virtual objects to build a generalized model-based on a global ontology.
This becomes the framework for the process plane, the home for apps, and the orchestration of workflows that deliver value to the workforce. The fifth plane is the intelligence layer, where O3sigma’s Industrial Composite & Foundation Models enable high-end simulation and analytics.
This freemium, open-source system includes 256 PLC drivers, digital twin libraries, a shared ontology, semantic contracts, governance, and interoperability. This architecture enables the implementation of agents, workflows, and composable solutions. By using a global ontology and virtual objects, the system aims to create a generalized model that ensures broad portability across various industrial applications.
The key is not in the five planes; it lies in the depth of the connection among them. In the view of O3sigma, smart manufacturing projects fail because implementors pay insufficient attention to this vertical connectivity.
This inventive industrial plane model appears highly adaptable and flexible, capable of handling high-speed, high-volume manufacturing processes. In the real world, a single PLC transaction could affect the operator's co-pilot, the process engineer, the production manager, the quality engineer, and the routing, all in real time.
Coordinating tens of thousands of transactions from the plant is a daunting task. They have adopted an approach that defines a set of ontologies. This approach allows different constituencies to make decisions and take specific actions based on how the data or event affects their individual responsibilities. In effect, this takes the lofty and sometimes contentious ‘one version of the truth’ aspirations and turns them into actionable insights by interpreting the data in the context in which it is being evaluated.
O3sigma CEO Tarik Taman stated in this briefing that “predictive maintenance is the substrate of AI.” This has also been reflected in our own research, “Making Manufacturing Analytics and AI Matter.” In this research, the manufacturing technologies with the highest adoption rates in GenAI were APM/CMMS and IIoT Platforms (where the most common industrial use cases are in predictive maintenance).
With a nod to Meta’s Chief AI Scientist, Yann LeCun, who was quoted as saying, “Building agentic systems on LLMs is a recipe for disaster,” O3sigma’s approach to adopting AI in manufacturing is to limit the role of LLMs to interpretation of natural language processing and to focus its AI efforts on physics, numerical methods, and neurosymbolic AI, supported by their graph neural network (GNN), O3-GNN. O3 reports that their O3-GNN recently achieved the best published accuracy on NASA’s C-MAPSS turbofan degradation benchmark — considered by some as the gold standard for predictive maintenance evaluation since 2008.
O3-GNN is built on RelationalAI’s GNN framework, which runs natively on Snowflake. This approach eliminates the need for separate ML infrastructure.
O3sigma is very optimistic about their ambitious goal of joining the race to create the world's first industrial foundation models built from primitives. They are racing with other companies to see who can generalize instructions for controlling a machine. In the world of physics, this race is about understanding and generalizing processes, the behavior of components down to the physics level, and the relationships between them.
This means you need generalized knowledge of everything at the atomic level, compose that knowledge to answer questions, and determine the next best action by persona. This is a gargantuan task. It requires the ability to parse 10 million transactions per second and tell what's happening from quality, maintenance, and production perspectives. Only then can the workforce be informed about what everybody should do before, during, and after.
It all starts with their original ontology, a lean taxonomy. They are digitally mapping all of their corporate and parent companies' assets, one SOP at a time. This task will take a long time, but they believe they are building great assets.
O3sigma provides the MES core “forever free.” Access to more powerful analytics and AI tools is a premium feature that customers can add to their implementation when they are ready. As O3sigma continues to add manufacturing sites to its customer base, it is working to encourage freemium customers to contribute their libraries and hierarchies to the O3sigma community library, making the community asset more valuable to both existing and new customers as the customer base grows.
Thank you, Tarik Taman, Matt Grabow, Marius Blass, Fouad TOUMERT, and John Bermudez, for the detailed briefing for Rick Franzosa and Julie Fraser. We thoroughly enjoyed learning about your company and look forward to our next update as O3sigma expands into the North American market.
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Propulsion 2026 was in Denver this year, and I was happy to attend. My engineering degree is from the University of Colorado, so a visit to the state brought back fond memories. More importantly, I have been following Propel Software for a while and was eager to hear more about their vision and product strategy, especially regarding AI, and to engage with customers and partners to get their perspectives as well. I did not attend last year's conference; however, Jim Brown attended and provided his Propulsion 2025 Insight. From my learnings at the conference, it is clear that Propel has made tremendous progress over the past year.
Propel is in a unique position because it can leverage the robustness and technology advancements of the Salesforce platform. This means they can adopt new features quickly, which is particularly helpful in the age of AI. I was as impressed by their product delivery and roadmap as I was by their customers’ enthusiasm.
Ross Meyercord, Propel CEO, opened the conference by examining the three major forces reshaping manufacturing: economic uncertainty, geopolitical disruption, and the rapid emergence of AI-driven technologies.
From an economic perspective, manufacturers continue to face pressure from rising raw material and labor costs, creating a greater need for operational agility and efficiency. Geopolitics adds further complexity, influencing sourcing strategies, manufacturing footprints, and access to critical materials.
The centerpiece of his presentation was the emergence of Model Context Protocol (MCP), an open framework designed to connect data and actions across enterprise applications. Rather than relying on predefined integrations or requiring users to navigate multiple systems, MCP enables AI agents to discover information across applications and execute tasks through natural language interactions. Among other benefits, this has significant implications for enhancing the user experience.
What logically followed was the announcement of MCP support (to be released in mid-2026), positioning the platform within the emerging AI ecosystem. The vision is one in which users can access product and business information through AI assistants, regardless of the application they are working in. Using natural language prompts, users can continue to perform detailed tasks directly within Propel, such as impact analysis and quality incident management, or access Propel via an AI platform to analyze alternatives, identify suppliers, assess lead times, and even generate business documents such as RFQs. There is no need to log in to Propel or any other application.
Propel's architecture centers on business processes that connect underlying enterprise data, with an AI agent layer sitting above those processes and the user interface serving as the access point. This architecture opens Propel capabilities and data to the enterprise.
Eric Schrader, Chief Product Officer, provided his insights on the industry's transition from AI experimentation to operational deployment. While manufacturers have embraced AI initiatives, Eric said adoption remains fragmented across organizations and workflows. This correlates well with our research, which shows that many are gaining value from AI, but only a small percentage of leaders have made significant progress scaling beyond early initiatives. Propel is embedding AI directly into product development and lifecycle processes, enabling organizations to move from isolated use cases toward enterprise-scale productivity gains.
Demonstrations showed how users can interact with product information through Propel One Assistant using natural language, reducing reliance on traditional navigation and search methods.
Looking ahead, Propel plans to expand its AI capabilities by incorporating both structured and unstructured data sources, broadening the range of potential AI-driven use cases. The product roadmap includes a Manufacturing Hub to connect Propel workflows with ERP systems, support for configurable products, and enhanced management of digital products and software-based offerings.
The company's AI strategy is anchored by Propel One, a multi-layered architecture that supports embedded, composable, and networked AI capabilities. A notable capability is the use of AI to monitor policy compliance and standard operating procedures. Demonstrations showed how organizations can automatically evaluate whether processes are being followed and generate reports to identify compliance gaps.
Composable AI enables customers to build and extend AI-driven workflows without extensive development effort. Using tools such as Skills, Agent Builder, and Prompt Builder, users can create custom agents and process flows with templates and guided development. These agents can be invoked from within Propel or external collaboration environments such as Slack, extending AI-assisted workflows beyond the core application.
Propel's vision includes Networked AI, which enables AI interactions across enterprise systems. Users will be able to access Propel data from external AI clients or leverage information from other enterprise applications through Propel agents. Demonstrations illustrated how users could work within external AI environments to identify components, compare specifications, evaluate alternatives, search for suppliers, and even extend searches to external sources when internal options are unavailable.
Propel also highlighted the progress of DesignHub, introduced six months ago, which is designed to improve collaboration and ensure that design data remains enriched and connected as it moves through downstream business processes. Tech-Clarity’s recently published research Building the Digital Thread to Improve NPD Performance covers the business benefits of connected data.
There was an interesting discussion on the future of PLM between Ross Meyercord and Kevin Prendeville, Principal, Supply Chain & Network Operations, Deloitte. The discussion highlighted the growing complexity manufacturers face as they balance traditional business challenges with rapid technological change. A recurring theme was the continued evolution of the digital thread. What began primarily as engineering-focused Product Data Management (PDM) capabilities has expanded into a broader enterprise framework that connects engineering, manufacturing, quality, service, software, and customer-facing systems. As products increasingly incorporate software, connected services, and mobile applications, the scope and importance of product lifecycle data continue to grow.
The conversation also reflected the industry's shift in perspective on AI. Kevin stated that manufacturers are moving beyond viewing AI as an interesting technology experiment and are increasingly focused on achieving measurable business value. Realizing these benefits requires a clear understanding of business processes and a disciplined approach to identifying where automation and intelligence can create the greatest impact.
Data quality was cited as one of the most important prerequisites for AI success. They noted that AI's effectiveness is directly tied to the quality, consistency, and governance of underlying product and operational data. Organizations that have not yet established trusted, connected data foundations may struggle to achieve meaningful AI outcomes.
Kevin emphasized that technology initiatives must remain focused on business outcomes rather than technical experimentation. Questions of ownership and governance remain challenging, particularly for the digital thread, which often spans multiple functions and stakeholders. Successful transformation efforts require clear objectives, strong governance, and broad organizational alignment.
Zachary Macht of KPMG explored the growing challenge of balancing AI adoption with regulatory compliance in highly regulated industries such as life sciences. Zachary emphasized that while organizations are eager to capitalize on AI's productivity and decision-support capabilities, they must do so within a framework of governance, oversight, and accountability.
A central theme was that AI cannot be treated as an autonomous decision-maker in regulated processes. Regulatory agencies, including the FDA, continue to hold organizations accountable for outcomes regardless of whether decisions were influenced by AI. As a result, companies cannot rely on explanations such as "the AI did not identify the requirement." Human review, approval, and accountability remain essential components of compliant processes.
However, Zachary also challenged the common assumption that simply placing a human "in the loop" is sufficient. Effective oversight requires knowledgeable reviewers who can critically evaluate AI-generated outputs and add meaningful judgment to the process. Human participation must function as a true control rather than a procedural checkbox.
As regulators begin evaluating AI-enabled processes, organizations should expect increased scrutiny around governance, decision-making, and traceability. Inspectors are increasingly interested in understanding how AI is used, what controls are in place, how outputs are validated, and how organizations document and manage AI-related risks.
The session concluded with a clear message: AI adoption is accelerating, and organizations cannot afford to ignore its potential.
The conference offered plenty of opportunities to hear directly from Propel customers. Gregory Yow and Angela Moliterno shared Zoetis' 5-year product lifecycle management journey, highlighting both the unique challenges of medical device development within a pharmaceutical organization and the value of connecting product data across the enterprise.
Zoetis, known for animal health products ranging from pet medicines to advanced vaccination technologies, described how adopting formal design control processes required a significant cultural shift. While design control is fundamental in regulated product development environments, it was not traditionally part of the pharmaceutical mindset, creating organizational challenges as the company expanded its medical device and equipment development capabilities.
The company began its PLM journey with a focus on engineering, integrating product development processes with CAD tools such as SolidWorks and subsequently extending connectivity to SAP. Over time, however, the scope expanded well beyond engineering. Zoetis described how its PLM initiative evolved into a cross-functional platform that connects engineering, manufacturing, service, and commercial to improve information flow across departments and created new opportunities to streamline customer-facing activities. This step-by-step approach, starting small and leveraging more of Propel’s capabilities over time, was a common theme across customer presentations.
Propel is moving beyond being just a cloud-native PLM provider and positioning itself as an AI-enabled product operations platform. The announcement of MCP support is particularly significant because it shifts the conversation from using AI within PLM to accessing PLM data and processes from wherever work is being performed. This approach could help break down traditional application silos and make product information available to a much broader audience. Combined with Propel One’s embedded, composable, and network AI capabilities, Propel appears focused on delivering practical business value rather than AI as a standalone feature. We are excited to see what comes next.
Thank you, Erin Keefe, for including me in the user conference. And Ross Meyercord, Eric Schrader, and Dario Ambrosini, for the strategic company and product updates.
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OEMs spend significant time and effort pulling together data to meet Integrated Product Support (IPS) requirements. Creating and aggregating product sustainment data is inefficient due to disconnected data and processes. How can PLM help OEMs and suppliers develop and capture high quality IPS data to meet standards like SAE GEIA-0007C without creating redundancy and excess cost? How does this approach support a closed loop between engineering and sustainment? We explore three levels of Product Support Data Management (PSDM) maturity ranging from manual approaches to an integral approach leveraging the PLM data model.
Please enjoy an overview of our findings, below. For the full research, please visit our sponsor, Siemens.
The Critical Role of IPS in Sustainment
The best designed equipment doesn’t fulfill mission objectives if it’s not operational. Mission readiness relies on well-maintained equipment. That may be obvious. But keeping aircraft flying or vehicles rolling can’t come at any cost. Operators must be able to sustain their fleets with an optimal balance of cost and risk.
Service data from the OEM is essential to maintaining this balance. IPS (Integrated Product Support) is essential for providing operators with the information they need to sustain equipment across both military and commercial fleets. For defense contractors, of course, IPS is a mandatory, contractual obligation.
PSDM: Improving IPS with PLM
Today, OEMs and their suppliers are meeting IPS demands through brute force and significant manual effort. Today’s processes are inefficient and costly. This is true for initial IPS database development and delivery, but even more so in the field as equipment is updated through ECOs and MRO activities.
PLM-enabled IPS, or PSDM (Product Support Data Management), provides the support data management processes required for the generation and storage of the data required for standards such as SAE GEIA-0007C.
PLM offers the opportunity to streamline IPS development by connecting data across the digital thread from design through service and improve processes with AI. But managing product support data in a PLM context can do more than just increase efficiency. It can improve sustainment processes and data.
PSDM Maturity
PSDM can bring engineering and logistics data – and engineers – closer together to better design for sustainment and close the loop on service issues. For commercial operations, it can also help drive service profitability.
We see three levels of increasing maturity and value available from PSDM:
Drive Higher Strategic Value
Striving for Integral PSDM
Achieving the highest level of maturity could be viewed as a journey starting with added efficiency using PLM and then better managing configuration and changes to extend the value. Then, OEMs could strive to create a collaborative, closed-loop environment to further extend the value. There may be opportunities, however, for a program to leap multiple levels of maturity at one time without passing through lower levels of maturity. In the end, collaborative, closed loop PSDM is what will differentiate a winning OEM from a compliant one. It moves the conversation from simply delivering a logistics product database to creating a comprehensive digital twin from engineering to service. This value is achievable with PLM and promises significant new value ranging from efficiency to increased service revenue, where applicable.
*This summary is an abbreviated version of the ebook and does not contain the full content. For the full research, please visit our sponsor, Siemens.
If you have difficulty obtaining a copy of the research, please contact us.
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Rick Franzosa and I recently had the opportunity to attend the Proficy Accelerate User Conference. This customer conference, already planned for the Proficy® from Velotic™ community, was the first since the formation of Velotic. Less than two months into being a new company, the event showcased not only the latest from Proficy but also the Kepware® from Velotic™ and ThingWorx® from Velotic™ suites in demo booths. Implementation and reseller partners were also there in force.
Brian Shepherd, Velotic Software CEO, kicked off the event by explaining their clarity of purpose: helping producers “do the right thing right.” Their tag line is “Build Brighter.” He pointed out that it has 1,200 employees across 27 countries, over 150 partners, and $350M in revenue. Making a fresh start across all three product suites is crucial, as both GE Vernova and PTC had other main focus areas.
So far, investor TPG appears to be ready to invest aggressively in Velotic’s people, processes, and technology. The technology investments are aimed at improving each product and integrating them effectively to make them easier to use and deploy. They will also be infusing AI throughout the products for both augmented and autonomous operations. He hinted at an interest in growing the functional footprint to serve other problems as well.
Several customer speakers from the Food industry highlighted their successes using Proficy Smart Factory Cloud MES.
Perhaps the biggest news at the event was the unveiling of Proficy Data Hub, due out in full production release in calendar Q3 of 2026. Data Hub is the foundation for industrial DataOps and data fabric. While the Proficy Operations Hub delivers the user experience and visualization, the Data Hub creates a unified namespace (UNS) around an asset model. The Data Hub composable framework is intended to help customers achieve faster time-to-value and quicker decision-making.
Brian Johnson, product manager for both Data Hub and Historian, points out that this overarching data model or fabric spanning all Proficy products is a long-anticipated advancement. Each Proficy product (Historian, SCADA, MES) has its own data model, and the Data Hub pulls them all into a single model. This enables users to better leverage all of them without needing to know where the data resides.
Since the Velotic Proficy portfolio includes a Historian, SCADA, and MES, and they integrate with ERP and other systems, there is already context and governance to a certain extent. The Data Hub becomes a single, back-end source to integrate data from across the plant and the enterprise. Companies can connect just once and reuse those. MCP helps to abstract the user experience from source understanding. We expect the ThingWorx ThingModel to play a role in the Data Hub as well, but that’s still in the works.
Historian is the default time-series data storage system for the portfolio. This year, they are also launching a Hyper Scale Cloud Historian that runs natively on AWS. This scalable microservices historian will enable one billion samples per minute. This is three orders of magnitude faster than the current version, and will be useful in industries such as the electric grid. AI is also coming into Historian for natural language queries.
Velotic is weaving AI into the Proficy Smart Factory MES and the entire Plant Apps suite. What we saw in action was the natural language interface. The MES context helps deliver good data for AI to leverage. The demonstration also showed how the new Data Hub can pull data from many sources to answer complex questions through that natural language interface.
There is also a major industry-specific release in the works for discrete assembly, Proficy for Assembly Operations. Until now, the automotive industry has primarily used level 2 Proficy products. This MES is being co-developed with major automotive customers to meet their specific requirements. In an automotive environment, MES does not actually ‘execute’. Execution occurs at the SCADA or PLC level. For this reason, level 2 and level 3 systems are tightly linked to perform their functions. Proficy for Assembly Operations is designed to address the high-speed, complex-model-mix discrete market, using a model-driven approach to configuration, execution, and data collection, with a cloud-native, 100% web-based UI.
Velotic demonstrated chat-based capabilities with Proficy Smart Factory MES, and have a series of other AI enhancements, including a GraphQL API that enables AI through Model Context Protocol (MCP), a natural language chat interface just like the frontier models, multi-tenant, multi-user Role Based Access Control (RBAC). Velotic is also planning to include conversation history and Retrieval-Augmented Generation (RAG) stores to house additional searchable context. These capabilities are targeted for release in August 2026.
Velotic is coming together in a way that matches its name – with velocity and agentic capabilities across the portfolio. Velotic branding and websites should be ready by the end of the year. Seeing some of the people from each of the three product camps and previously two companies come together at this event was fun. We are reassured that the new company, with its sole focus on these industrial software products, will continue to innovate and invest, both in the near term and in the future.
We appreciate the opportunity Velotic provided to host Julie Fraser and Rick Franzosa at this event. Thanks to Carver Conway for arranging our meetings and keeping us analysts on track. Special thanks to Proficy VP of Operations Jeff Bartoletti and CMO Nicole Rowe for your openness. It was a delight to spend time in person with Stephen Pavlosky, Prasad Pai, Brian Johnson, and Joe Gerstl from the product management team, as well as Phaedra Martin from industry marketing.
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We recently had a briefing with Lytica, which has long brought unprecedented visibility to electronic component sourcing for its customers. The big news is that they have developed AI agents to leverage the data about purchase prices and transactions from their growing customer base. Sourcing is challenging, but having actual data from across the industry is Lytica’s competitive advantage, which they offer in a win-win approach with customers.
Materials sourcing is always about making tradeoffs. Wise procurement professionals know this, but don’t always have all of the data they need to see where they might not be getting the best deal, even if it’s the lowest price. Lytica is proclaiming itself the first agentic AI system to balance procurement cost, supply risk, design decisions, and scale needs. Procurement costs are the company’s original focus, and that continues to expand.
With the speed of change in this industry and geopolitical tensions, the way manufacturers procure for each of their production sites is evolving. Parts shortages due to the huge boom in AI and data center needs are only one angle; every industry sector and chip class has its own tensions. Finding the balance, Lytica argues, is all about having the data available to see what’s happening around the industry at any given time.
SupplyLens™ Pro is Lytica’s electronic component market intelligence platform, built on real customer transactional data and increasingly delivered through AI-powered workflows. Its ML engine learns from real customer transactions involving electronic components, helping buyers negotiate more effectively and review supplier proposals with confidence.
How? The platform anonymizes, secures, and aggregates customer data from each OEM or EMS, creating a current electronic component purchasing data set that we have not seen elsewhere. Lytica calls this a digital twin of the electronics marketplace. Using anonymized market intelligence on what components are being bought, from which supplier channels, and at what prices, Lytica offers benchmarking for OEMs and EMS providers. If you know Lytica, this network effect — or give-to-get data platform — is not new.
Customers are global brands you’d recognize. They showed dozens of the biggest names in electronics, telecommunications, medical devices, industrial equipment, defense, and household hard goods. Using data from those sources, they claim they can typically achieve 10% savings for a new customer. So, customers get big enough benefits to justify trusting Lytica with their component procurement data.
Each of the four solutions is composed of multiple modules that the company has built over time. Negotiator is the data for procurement professionals. They can use Validator for quotes, Mitigator to review risks and options, and Accelerator for design-to-source collaboration, which is often the starting point for sourcing challenges.
What’s new is the set of AI Agents Lytica has designed to leverage their industrywide dataset further. The important point is not simply that Lytica has added agents; it is that those agents operate on a proprietary, continuously refreshed market intelligence foundation. The first agent, Neo, was announced on April 30, 2026.
Neo is an agent designed to support procurement professionals in their negotiations. It guides a buyer through four phases:
The other agents were in a demo but have not yet been announced. They include
LISA – Lytica Intelligent Sourcing Agent – is the starting point for a procurement professional’s day. This personal analyst prioritizes what the person needs to do and explains why it is prioritized, with coordination across the other dedicated specialized AI agents.
DESI – The design agent supports product design and development engineers by flagging potential risks and high-impact components. It also recommends ways to mitigate them while maintaining design integrity.
RICK – The risk agent is focused on risk mitigation throughout the process.
So, the Lytica balancing act is still gaining strength. Adding agents and building out more strength in the core solution sets as a result of that and other enhancements.
Lytica has built strong trust and eye-popping testimonials from major customers. One reports 10-20% savings, for example. Trust building began long ago, when the company was founded by Ken Bradley, who last served as CPO at Nortel for 3 decades in the 70s-2000s. Since then, the company has collected data in a private way that still serves the entire community. With such strong customer logos, prospects start to relax and believe the savings and opportunities are worth any potential risk from sharing the data. Trust is not just a sales enabler for Lytica; it is part of the product architecture. The more customers contribute anonymized transactional data, the stronger the market intelligence becomes for the entire community.
We have rarely seen customers willing to share their data with a software provider, but in Lytica’s case, it appears to be working for everyone. Data that enables benchmarking, along with internal and supplier collaboration, can make a huge difference in margins for many companies in these markets. At Tech-Clarity, we cover the product from concept and design through the entire lifecycle into manufacturing, and Lytica’s vision sees some of the same needs we have identified for better supplier and internal collaboration.
Electronic component sourcing is becoming a category of its own. No wonder; supply chain volatility has been an ongoing reality since the dawn of the electronics market and semiconductor chips. As more products use electronic components, the need for and value of sourcing these effectively increase. Having collected data on its SaaS platform for years, Lytica is well-positioned to grow.
We are grateful to Gerry Abbey for arranging the briefing, and to Lytica CEO Martin Sendyk and VP Product Shawn Bradley for briefing us on the vision and solutions. We look forward to following Lytica’s progress in the market!
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How far have manufacturers come in connecting the product digital thread from design through manufacturing? How mature is the integration of data in the two primary systems supporting engineering and production? We interviewed over 200 large manufacturers to answer those questions and many more.
Please enjoy an overview of our findings, below. For the full research, please visit our sponsor, Kalypso.
Investing in PLM-MES Integration
Today’s manufacturers need to rapidly bring high-quality products to market despite rising product complexity. One way they can do this is by improving the quality and timeliness of their digital thread data and enabling better collaboration between engineering and manufacturing. The challenges and impacts of a disconnected product digital thread caused by poor PLM – MES integration hampers that ability.
Mixed Messages on the Status Quo
Our survey of over 200 complex, discrete manufacturers with revenues greater than $500 million that have implemented both Product Lifecycle Management (PLM) and Manufacturing Execution Systems (MES) systems shows relatively low integration maturity. Only about one in five companies in our study demonstrates truly mature PLM-MES integration across the areas assessed. Very few have adopted the advanced practices needed to fully connect the digital thread from engineering through manufacturing.
The results, however, suggest that “average” respondents have adopted more advanced processes than one might expect. We believe this is because the survey participants reflect larger, more advanced companies based on the targeted industries, company size, and level of system adoption. Based on our experience, this audience is more likely to have adopted advanced practices than the average manufacturer.
Clear Opportunity for Improvement
Despite the somewhat optimistic state of the average respondents, the survey points to clear room for improvement. Manufacturers that have integrated MES are achieving the product quality and time-to-market advantages they seek, among other valuable benefits. PLM – MES integration yields benefits even when companies don’t achieve the highest level of maturity. But manufacturers still have a long way to go to create a closed-loop, model-based digital thread between engineering and manufacturing.
Room for Improvement
Despite sharing optimistic levels of maturity and integration across the different aspects of PLM – MES integration, manufacturers reported relatively low maturity in the one process examined in more detail, engineering change management. Although most companies say they have at least somewhat integrated engineering and manufacturing data, over three-quarters still need to run reports or manually look up information for change impact analysis.
This example shows that the vast majority of manufacturers can continue to improve value by adopting more mature practices. PLM – MES data integration should continue to be a high-priority investment for manufacturers.
Further, this report focused on complex, discrete manufacturers with over $500 million in annual revenue. This sample likely represents an advanced set of manufacturers, and smaller companies likely have less mature practices. These companies can follow the examples and best practices adopted by these larger manufacturers.
*This summary is an abbreviated version of the ebook and does not contain the full content. For the full research, please visit our sponsor, Kalypso.
If you have difficulty obtaining a copy of the research, please contact us.
[post_title] => Integrating PLM with MES
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How strong is your foundation for Industrial DataOps, Smart Manufacturing, and plant-level AI? Are you considering an AI stack, or separate layers from leading proven providers?
See the replay of this webinar to get new ideas about how to evaluate and choose the right industrial connectivity approach for long-term success.
AI is moving fast, and yet deciding on the best approach for connectivity needs careful consideration. To ensure IT and OT data are connected, extracted, normalized and secured, manufacturers need to make crucial architectural decisions. While companies may choose differently, it’s important to understand the tradeoffs that are often overlooked at this level.
Connectivity is the starting point for data quality, governance, and trust. Those, in turn, can enable agility to change, resilience for unexpected challenges, and data that’s always ready for the next decision. We will discuss the pros and cons of an all-in-one industrial stack vs. a best-of-breed connectivity approach.
Check out Tech-Clarity’s Julie Fraser and Velotic Kepware’s Emily Griffin in this discussion packed with important topics and pragmatic insights. Julie shares some of Tech-Clarity’s new Industrial Connectivity Buyer’s Guide and Emily shared about her years working with customers and what the move into Velotic might mean. Don’t leave your data connectivity to chance - make the right decision for your business. Enjoy this webinar discussion!
[post_title] => Webinar: The Connectivity Choice: All-in-One Stacks vs. Best-of-Breed Platforms
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We recently spoke with Athena Technology Solutions and its partner, LLM at SCALE.AI, about the launch of a new offering: FabOrchestrator.AI, their agentic AI foundry for manufacturing. This multi-faceted platform streamlines everyday tasks across the plant.
As the name suggests, this new offering is focused on orchestrating agents for a semiconductor fab or other manufacturing facility. Athena, as a major implementation partner for leading discrete and mixed-mode MES solutions, understands the current realities in manufacturing. They see that every plant is being asked to do more with less, struggling to keep up with manual reporting and repetitive tasks, and seeking to make sense of data across plant and enterprise systems with different data models. The focus for FabOrchestrator.AI is to transform manufacturing data into intelligent decisions, enabling streamlined processes for implementing MES and running the plant(s).
Athena calls FabOrchestrator.AI a foundry because it blends an array of existing systems into a cohesive operations intelligence platform. The platform has a five-layer AI stack, spanning data connectivity, data access, intelligence, and the user experience, with feedback loops to users and systems at each layer. It incorporates security and governance at all layers and can output in interactive dashboards, text-based summaries, visualizations, or actionable insights.
The system’s four major capabilities are:
Together, these are designed to reduce the effort needed for MES implementation, rollout, and support. FabOrchestrator.AI is also intended to reduce routine support tickets coming into engineering and deliver stronger, more complete, and real-time intelligence to support manufacturers’ decision-making.
Athena’s long history working as a partner with both Siemens Opcenter and Critical Manufacturing MES means they see what manufacturers are doing. They know where the weaknesses and challenges lie, and have released FabOrchestrator.AI to supplement these environments. They have customers in the Americas, APAC, and EMEA, primarily making semiconductors, electronics, medical devices, solar panels, and clean energy, including batteries.
LLM at Scale AI is an enterprise-grade agentic AI platform specializing in factory automation, multi-agent orchestration, and large language models (LLMs). Their customers include JTC, CBRE, JLL, Cushman & Wakefield, Johnson Controls, and the State of California. Starting from this proven platform, Athena adds its deep industry knowledge to create a central intelligence platform for manufacturing.
This is a notable addition to the crowded AI-for-MES market. With Athena’s strong understanding of leading comprehensive MES systems, we expect customers will be guided to get their data ready to leverage their data more effectively. Athena has a good reputation with its partners, which speaks well.
This seems a good use of agentic AI. The combination of speeding up workflows, unifying systems, and automating decisions has a good chance of delivering strong value quickly. Athena has published a straightforward pricing approach. As a seasoned system integrator, we know they will support customers through the entire project lifecycle, and those implementations should run shorter when leveraging FabOrchestrator.AI.
Thank you to industry luminary and long-time friend Maryanne Steidinger for introducing us and setting up the briefing. Thank you, Senthil Ranganathan, founder and CEO of Athena, and Jothi Periasamy, Chief Agentic AI Architect at LLM at Scale AI, for explaining the offering you have launched. We look forward to following its progress in the market.
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My colleague Rick Franzosa and I attended the Infor Analyst Innovation Summit, and it was aptly named. This company, formed from over 60 acquisitions, has had to innovate to come together to tell a strong story across its product lines and industries. The corporate vision is “The Agentic Enterprise,” and I can see where the industry-specific companies at the core form a solid starting point. We learned too much to express here, so I’ll focus on the vision, product status, and what’s new for both discrete and process industries.
Infor envisions taking its customers from embedded intelligence to autonomous orchestration of industry-specific agents and employees. What CEO Kevin Samuelson articulated (having been in the post for nearly 10 years and previously serving as the company's CFO) showed how the company’s rich history has led to this point.
Starting with acquiring software companies that had an industry-specific focus, moving into building out the industry CloudSuites was the older history. Since 2018, Infor has been building process mining, robotic process automation (RPA), and AI. Then last year, they announced the Velocity Suite, combining all of those to help diagnose, automate, and optimize business processes using generative AI.
That foundation has enabled them to build orchestrated industry-specific agents. This would lead to semi-autonomous “headless” UX with human-in-the-loop (HITL). Fully autonomous orchestration, driven by outcomes and rich context, can eventually lead to what they call 'governed velocity' for the business. Their vision includes all four delivered in parallel for software that is precise, cohesive, agile, and trustworthy.
The story is not just one of software, but also of a customer journey that can match each customer’s pace and needs. We see this as a crucial element to customers’ ability to execute the vision. Infor has also been building out its approach and ecosystem to support the customer journey.
Infor has long been building out industry-specific enterprise software suites. These are now available as CloudSuites that leverage the extensive Infor Cloud platform. Beyond ERP, these suites may include PLM, configure, price quote (CPQ), supply chain, MES, WMS, and performance management, all tailored for that specific industry’s needs. Each CloudSuite includes Industry Process Catalogs (IPCs) to provide strong operating models for implementation, ongoing continuous improvement, and AI context.
Infor’s cloud platform rests on AWS and inherits all of those cybersecurity and infrastructure benefits. Beyond SaaS hosting, the platform includes many data-fabric components, including streaming and a knowledge graph, which deliver important benefits for a rich manufacturing context and understanding.
Layered on top of that is a decision-intelligence platform with industry-specific LLMs, plus agentic identity and semantics. The agentic automation layer draws on industry-specific knowledge of Infor applications and teams. An adaptive enterprise experience tops off the suite's usability. LLMs for natural language interaction are part of this, but they’re also designed to be actionable for each user by role and exception.
Infor focuses on eight industries, five of which are in manufacturing. These include automotive, A&D, and Industrial on the discrete side, and food & beverage and fashion & apparel, plus distribution. Infor does not stop there, but focuses on specific micro-verticals within each overall industry as well.
Infor’s CloudSuite food & beverage includes far more than traditional ERP. CloudSuite Food & Beverage also has:
We heard from Fernando Cardoso, Finance Director and Functional IT director at animal nutrition company Nutreco, about their journey to reaching 3000 users (1/4 of the employees) across their 130 factories and 60 divisions in 35 countries. They started on-prem with M3 in 2010, moved to single-tenant CloudSuite in 2020, and went live with multi-tenant CloudSuite for Food & Beverage in 2023. By 2024, they had over 350 streaming pipelines with over 40 million events per day. RPA on SharePoint came into play that year as well. Last year, they started Process Mining and were an early adopter of GenAI. Nutreco went live with agents early this year for inventory and lot summaries, and they’re adopting agents for price simulation, order exceptions, and replacing older workflows.
Infor CloudSuite Fashion has similarly compelling capabilities and customer wins across design, supply chain, and production, including scheduling. Luxury, sports, and many other fashion brands had cited benefits.
Infor has over 4,000 discrete industry customers, with over 1,000 on the cloud. Its CloudSuites for Industrial Manufacturing, Automotive, A&D, and Engineering & Construction go well beyond core ERP has over 4000 discrete industry customers, with over 1000 on the cloud. Beyond core ERP, the offerings include
We heard from customers Jennifer Terry of Xpress Boats, Bipin Jayaraj of Benchmark Electronics, and Zoaib S. of Amada America on a panel. Koch and Zahid Group also presented their successes. Having once been a Baan employee, it’s amazing to see the growth in capabilities and customers under Infor’s brand.
We feel the Infor Suites, from micro-vertical application suites to a full data stack, to every flavor of AI, is a solid foundation for in-context action by people and agents. The IPCs seem to be the best of industry templates and best practices, without being too rigid.
Its customer journey, adoption tracking, and next value recommendations are what customers need to move into the agentic future Infor envisions. We also love the baked-in industry benchmarking of what’s in use for the industry CloudSuites.
Infor has come a long way, and we’re particularly excited to see that Infor MES will finalize its migration to the cloud platform this year. We have seen the value of multi-tenant cloud solutions, and Infor’s complete stack magnifies it.
We appreciate the invitation from Jennifer Marzolf and the attention from the entire analyst relations team: Eileen Koach, Cindy Duco, and Krista Maddox. Shout out for the one-on-one time with Matt Barber 👀, Chris Gibson, and Stephen Burden from the Infor MES team. Also Vishal Minocha, VP Product Management, Andrew Kinder and Andrew Dalziel, VPs Industry and Solution Strategy, and Ole Rasmussen, SVP Product Management.
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Tech-Clarity is pleased to announce that we are expanding our research team and coverage to include the full range of supply chain functions that sit under the Chief Supply Chain Officer including advanced planning and scheduling, visibility, risk, procurement, and order management. With an extensive background as a Gartner analyst, supply chain vendor executive, and practitioner, Amber Salley joins Tech-Clarity as Vice President of Research for Supply Chain. Amber brings 25 years of experience spanning supply chain planning, S&OP/IBP, inventory management, materials planning and service parts planning with a strong focus on connecting technology decisions to financial outcomes. Please visit Amber's Bio Page for more on her background.
Extending our coverage to supply chain comes at a strategic time. Planning, procurement, and execution decisions are converging as AI and agentic systems move from pilot projects into core decision-making, while continued disruption from tariffs, geopolitical risk, and shifting sourcing strategies keeps risk and resilience on the boardroom agenda. Software vendors are moving down-market, giving midsize organizations access to planning, visibility, risk, and procurement technology once reserved for the enterprise — but often without the internal expertise to evaluate it. Stakeholders, from the Chief Supply Chain Officer to the CFO to private equity operating partners, are recognizing the need for independent, vendor-neutral guidance that separates real capability from hype and ties supply chain investment to business value including cash, margin, and service outcomes. Leading vendors across planning, visibility, risk, and procurement are advancing their offerings to meet this moment, while new entrants continue to emerge to fill the gaps legacy suites leave behind. Supply chain is shifting away from siloed, function-by-function decisions toward integrated, outcome-driven strategy — and buyers need a trusted, independent source to help them navigate it.
“The time is right to extend our coverage to supply chain,” explains Jim Brown, President and Founder of Tech-Clarity. “Supply chain management decisions are increasingly interconnected to Tech-Clarity’s core coverage areas, and portfolio owners, operating executives, and PE operating partners alike are looking for independent guidance on the value of technology across the full supply chain. Amber is uniquely qualified to offer that perspective, with a rare combination of practitioner, consulting, analyst, and vendor-executive experience. We're excited about how her depth and credibility will help further our mission of making the business value of technology clear.”
"I'm excited to be joining Tech-Clarity because their independent, vendor-neutral approach to research is exactly the perspective supply chain leaders need right now,” shares Amber Salley. “Tech-Clarity's reputation in manufacturing and product development creates a natural extension into supply chain, and I'm looking forward to helping buyers cut through vendor noise and make decisions that actually move their business forward."
Amber's research focus will include supply chain planning (demand, supply, inventory, and S&OP/IBP), supply chain visibility, supply chain risk and resilience, procurement (including direct spend), and order management.
Please follow Tech-Clarity on LinkedIn and join our mailing list to read Amber’s research. For more information or to schedule a briefing please feel free to contact us.
[post_title] => Tech-Clarity Expands Coverage to Supply Chain with Veteran Analyst Amber Salley
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Tech-Clarity is pleased to announce that we are expanding our research team and coverage to include the full range of supply chain functions that sit under the Chief Supply Chain Officer including advanced planning and scheduling, visibility, risk, procurement, and order management. With an extensive background as a Gartner analyst, supply chain vendor executive, and…
Amber Salley is the Vice President of Research for Supply Chain at Tech-Clarity, covering supply chain management, including advanced planning and scheduling, S&OP/IBP, supply chain visibility, risk and resilience, procurement, and order management. Amber has over 25 years of experience in supply chain. She has worked as a practitioner with the IBM Integrated Supply Chain,…
Does it feel like your investments in machines, automation, and production systems are not delivering their full potential? You can’t make high-volume manufacturing simpler. Yet you can shorten the time between plant activity, actionable insights, and measurable business value. In short, you can maximize your tech spend. Listen in as an industry analyst and operational…
How are product development and manufacturing changing? Product development and manufacturing are evolving rapidly. Organizations are facing increasing product complexity, workforce challenges, rising customer expectations, and growing pressure to improve speed, quality, and innovation. At the same time, emerging technologies such as AI are creating new opportunities, and new questions. Tech-Clarity is conducting research to…
Tech-Clarity’s survey reaches the maximum of six sponsors and is actively collecting manufacturers’ responses Media, PA, USA, July 15, 2026 – Results are streaming in: interest in Manufacturing Operations Software (MOS) is very high. Tech-Clarity, Inc. has reached the maximum of six sponsors for the 2026 research program on The Business Value and Evolution of Manufacturing…
Makersite Delivers Product Lifecycle Intelligence (PLI) We have been impressed with Makersite and their focus on Product Lifecycle Intelligence (PLI), as we shared in our earlier briefing insight. Makersite is a small company that has big customers, and they’re helping those companies make significant gains on compliance, cost, sustainability, and supply chain risk. Their #PLI…
Don’t miss this recorded event replay on achieving product data-driven digital maturity in the age of AI! Digital maturity is no longer just about adopting new tools. For design and manufacturing organizations, it is about building the strategy, culture, and connected data foundation needed to make better decisions across the product lifecycle — and to…
MESA and Tech-Clarity Open Survey on The Business Value and Evolution of MES and AI Knowledge Sharing Opportunity: Complete a survey to get the results and learn from other manufacturers Phoenix, AZ, and Media, PA, USA, June 23, 2026 – The Manufacturing Enterprise Solutions Association International (MESA) and Tech-Clarity, Inc. are inviting responses to a…
How are manufacturers managing their manufacturing operations? Tech-Clarity invites you to participate in a research study on “level 3” manufacturing operations software for production facilities. What business value are companies gaining from software at this level? How is MES/MOM/MOS evolving? What impact is AI having on this landscape? We will also use the results to…
How can manufacturers gain confidence in moving to autonomous operations? Combining the agility of SaaS with the reliability of edge at the plantwide level is an emerging approach for MES. This eBook walks through why MES is core to moving from automation to autonomy. It discusses the challenges of gaining the resilience between the plant…
What happens when industry veterans join in commercializing an incubator-developed CPG MES? We sat down with the management team at O3sigma to learn about their history and plans for this enticing new offering in the CPG MES arena. Born in CPG The origins of O3sigma trace back to Obeikan Investment Group. This Saudi-based enterprise failed…
Propulsion 2026 was in Denver this year, and I was happy to attend. My engineering degree is from the University of Colorado, so a visit to the state brought back fond memories. More importantly, I have been following Propel Software for a while and was eager to hear more about their vision and product strategy,…
OEMs spend significant time and effort pulling together data to meet Integrated Product Support (IPS) requirements. Creating and aggregating product sustainment data is inefficient due to disconnected data and processes. How can PLM help OEMs and suppliers develop and capture high quality IPS data to meet standards like SAE GEIA-0007C without creating redundancy and excess…
Rick Franzosa and I recently had the opportunity to attend the Proficy Accelerate User Conference. This customer conference, already planned for the Proficy® from Velotic™ community, was the first since the formation of Velotic. Less than two months into being a new company, the event showcased not only the latest from Proficy but also the…
We recently had a briefing with Lytica, which has long brought unprecedented visibility to electronic component sourcing for its customers. The big news is that they have developed AI agents to leverage the data about purchase prices and transactions from their growing customer base. Sourcing is challenging, but having actual data from across the industry…
Five sponsors support research into the value and evolution of commercial plant-level software, including AI Phoenix, AZ, and Media, PA, USA, May 29, 2026 – The Manufacturing Enterprise Solutions Association International (MESA) and Tech-Clarity, Inc. have added sponsors to the 2026 research program. The two additions joining to support The Business Value and Evolution of…
How far have manufacturers come in connecting the product digital thread from design through manufacturing? How mature is the integration of data in the two primary systems supporting engineering and production? We interviewed over 200 large manufacturers to answer those questions and many more. Please enjoy an overview of our findings, below. For the full…
How strong is your foundation for Industrial DataOps, Smart Manufacturing, and plant-level AI? Are you considering an AI stack, or separate layers from leading proven providers? See the replay of this webinar to get new ideas about how to evaluate and choose the right industrial connectivity approach for long-term success. AI is moving fast, and…
We recently spoke with Athena Technology Solutions and its partner, LLM at SCALE.AI, about the launch of a new offering: FabOrchestrator.AI, their agentic AI foundry for manufacturing. This multi-faceted platform streamlines everyday tasks across the plant. Why FabOrchestrator.AI? As the name suggests, this new offering is focused on orchestrating agents for a semiconductor fab or…
My colleague Rick Franzosa and I attended the Infor Analyst Innovation Summit, and it was aptly named. This company, formed from over 60 acquisitions, has had to innovate to come together to tell a strong story across its product lines and industries. The corporate vision is “The Agentic Enterprise,” and I can see where the…
