Making the value of technology clear
Take Advantage of the PDM Backbone
PDM is a valuable asset for any company that designs and/or manufactures products. It creates a central repository of information and controls design data to improve efficiency, prevent errors, protect intellectual property, encourage reuse, and manage design complexity. Our research consistently confirms the value of PDM, finding that Top Performers use more structured, collaborative data management solutions.1 As a result, Top Performers spend 25% less time on nonproductive data management tasks.
Tap the Added Value of PLM
Getting design data under control in Engineering sets the stage for more significant business benefits. Manufacturers can get more from PDM by adding PLM capabilities on top of their existing infrastructure.
PLM extends the value of product data and PDM by expanding data and processes to a broader range of people outside of Engineering, effectively transforming PDM from a departmental efficiency tool to a cross-departmental product development and innovation platform. The value increases as the foundation of trusted product data moves from a departmental view toward an enterprise and supply chain perspective. PLM gets PDM data to more stakeholders to speed up product development, enhance innovation, increase product quality, improve sustainability, drive down cost, and enhance traceability and compliance. In fact, our research shows that Top Performers are 65% more likely to view PLM as a strategic platform that supports the enterprise as opposed to a tool that supports a single department.
Set Your Path to Value with PLM
Growing value to the enterprise level from a PDM foundation can seem overwhelming. Where should manufacturers start? First, it’s important to take an honest assessment of your current PDM and/or PLM maturity. You should understand how you collaborate with internal and external stakeholders and how you manage product-related processes. With solid PDM basics in place and knowledge of your current capabilities, you can set the course to leverage the foundation and adopt incremental improvements that provide new value along the way to more significant, strategic business value.
Respect the Basics
Now that we’ve discussed the business value of extending PDM with PLM, let’s discuss what that means from a practical perspective. First, it’s important to acknowledge the importance of PDM. PDM basics allow engineers and other designers to operate effectively despite increasingly complex product and product development environments.
Our PDM framework includes three key capabilities that should be in place in any PDM system. These include the ability to control, access, and share product data. We won’t go into detail on these in this eBook, but it’s important to understand the starting point:
Expand the Product Model
Most companies begin to use PDM to store CAD models and drawings. They create a central repository to help engineers and others access designs. This is high value, but PLM can incorporate much more than CAD and metadata.
PLM allows companies to develop a much more valuable digital product data repository. One of the highest value transitions companies can make, before adding any new data, is to make existing data trapped in drawings, scanned documents, or spreadsheets more usable. Information like bills of materials (BOMs) and tolerances that are embedded in drawings are much more valuable if they can be accessed and shared independently of the drawing or the need for a person to interpret the data.
PLM can go far beyond design data to support more departments. Downstream departments that create derivative information like product documentation or supplier data can store that information in context with the design in PLM. Managing these relationships makes it easier to find information and understand the impacts of a design change on this important information. This helps companies get more value from their existing PDM system by coordinating activities to improve quality and improve time to market.
Potential Focus Areas
Where should companies start getting more value by extending their product definition in PLM? One way is by managing CAD from design disciplines beyond mechanical design. They can incorporate electrical design, PCBs, wire harnesses, or even software revisions into their product model. Another way is to expand the view of products beyond design further into the product lifecycle, for example, managing manufacturing or services BOMs contextually with the engineering BOM.
Additionally, companies can enrich and extend product data to support new departments and processes alongside Engineering. For example, they can use PLM to manage quality processes and data alongside product definitions to track return merchandise authorizations (RMAs), nonconformance reports (NCRs), or design failure mode and effects analyses (DFMEAs) in context.
Sharing released product data downstream is valuable, but there is even more value available by implementing bi-directional collaboration. Manufacturers can not only share designs with downstream departments for their review but also capture valuable design feedback. Getting downstream perspectives, for example on manufacturability, helps validate designs and prevent errors. This can improve quality, reduce cost, and prevent design rework through better design decisions based on understanding downstream impacts. Storing the review feedback in PLM provides an audit trail and a knowledge base to learn from for similar products.
Release management can also enhance control by better managing the product lifecycle. PLM allows companies to move beyond managing CAD file versions to more comprehensive product revision releases that consolidate multiple updates. This sets the stage for PLM to manage engineering changes and roll them out across the company and supply chain. A well-defined, effective engineering change management process ensures a smooth rollout, prevents mistakes, and improves quality. This helps companies get more from their existing PDM system by improving product quality and speeding time to market.
Potential Focus Areas
Where should companies start getting more value through design review and release processes with PLM? One potential improvement is to adopt a formal “DFx” program that incorporates downstream input and feedback to “Design for” aspects like cost, sustainability, manufacturability, and more. This gives downstream resources advanced visibility to designs and design decisions so potential issues can be identified and mitigated upfront.
Another valuable option is to implement or enhance change management by taking a more comprehensive approach to where-used and impact analysis to manage downstream impacts. This can help determine the effects of an obsolete part or component, or a design change required to meet new regulatory requirements. PLM can help manage and coordinate changes to BOMs, sourced components, test specifications, product documentation, service procedures, marketing documentation, and more.
Cloud Momentum
Manufacturers are transitioning to cloud PLM at a rapid pace and are willing to replace their existing solutions to achieve cloud benefits. Companies are moving applications to the cloud to gain value from:
Can multi-tenant cloud-based low-code software run and optimize production operations? Apparently, some companies are using TilliT for just that. The combination of MES with quality, AI-powered scheduling, analytics, and edge technology serves not just mid-market but also smaller and larger companies. Partners and manufacturers are deploying the system themselves after just a few days of training.
With the June announcement of Australia-based TilliT being acquired by Nordics-based Roima Intelligence, we expect more manufacturers will start to consider this system. Roima has a broad global presence and is growing rapidly, raising its profile for both software and services. TilliT adds digital execution and production scheduling that can deliver value quickly.
TilliT’s functions include production scheduling, workflow for digital work orders, OEE and downtime recording, paperless quality, track and trace, a mini data historian, and knowledge management, including SOPs. In addition to these capabilities, it includes analytics for activities, process variables, and production performance.
The system is designed to orchestrate machines, people, processes, and materials, flowing seamlessly among the functions an operator, engineer, or supervisor needs to perform. While we list functional areas, they do not appear separate to the user.
TilliT was already a merger of two companies, one with MES or digital execution modules and another with AI-based production scheduling. Each of these is still available independently, but is integrated natively. Naturally, the systems also have integration with automation and equipment as well as ERP and SCP.
TilliT is event-driven and based on the concepts of assets, orders, and activities, which the system models and manages in workflows. In these workflows, the project team can work bottom-up to specify automated sets of activities and triggers where and when human interactions are needed. They can also specify expiration times for activities and escalation methods. Using events, triggers, and rules is typically an easy way to record tribal knowledge and create an accurate model of production.
Part of the magic of TilliT is that it is no- / low-code. It needs no customization. Low-code configuration of assets, materials, and attributes is with a proprietary manufacturing-specific form builder. TilliT uses drag-and-drop, not coding, to create forms and workflows.
TilliT is hosted on AWS, which has the inherent benefits of the cloud. These include speed to deploy and change, seamless upgrades, and professional-grade cybersecurity. The system connects to a virtual or physical low-code edge device to connect to automation or equipment. All of this fosters a fast time to benefit.
TilliT includes several types of standard reports: activity, production, dashboards, advanced analytics, and more. Reporting and visualization can serve operators and supervisors, plant managers, and others who need an overview of performance. Customers build dashboards with pre-built cubes. In addition to their own dashboarding, they also OEM AWS’ QuickSight BI tools.
TilliT can use an LLM to build forms and SOPs automatically. These are excellent uses of LLMs’ strength in processing language-based information.
Two new features of note also leverage AI.
Many Australian winemakers already use TilliT. Other Customers in beverages, food CPG, consumer durables, and industrial equipment are also using it successfully. Some large companies are also customers: Schneider Electric is using it to optimize injection molding operations, and Carlsberg is replacing a more traditional MES and IIoT platform with TilliT across its 80+ site global brewing footprint.
The acquisition of TilliT is part of a roll-up play for mature manufacturing solution provider Roima. A long-time implementation partner to AVEVA and others, Roima has been increasing their focus towards delivering their own software technologies to the global manufacturing industry after several acquisitions since 2016. The vision is to create a digital thread for visibility and control from product innovation (PLM) to integrated business planning (IBP) to customer management (CRM/CPQ) to digital commerce and product twin, to WMS and MES.
This is an ambitious vision, and achieving it will depend on how well Roima manages the many acquisitions and the increased focus on a unified multi-tenant platform for the industry. Another key factor will be how well its WIRE integration middleware can overcome gaps and create the data and information flows to connect this thread. Yet another open question is whether the private equity backers will have the patience to support everything needed to be profitable in the comparatively conservative and slower-moving manufacturing markets.
We appreciate the lightweight low-code approach, particularly for the consumer goods segments TilliT has traditionally served. We also like the bottom-up approach to pulling tribal knowledge to build the value map. TilliT has plans to expand AI across its functions.
We look forward to following the progress of TilliT and Roima’s software suite. Thank you, James Balzary, for introducing us to your modern approach to operations software.
[post_title] => TilliT Low-Code MES and APS Soon to Weave into Roima [post_excerpt] => [post_status] => publish [comment_status] => open [ping_status] => open [post_password] => [post_name] => low-code-mes-and-aps [to_ping] => [pinged] => [post_modified] => 2025-07-24 14:12:26 [post_modified_gmt] => 2025-07-24 18:12:26 [post_content_filtered] => [post_parent] => 0 [guid] => https://tech-clarity.com/?p=22386 [menu_order] => 0 [post_type] => post [post_mime_type] => [comment_count] => 0 [filter] => raw ) [3] => WP_Post Object ( [ID] => 22272 [post_author] => 2580 [post_date] => 2025-07-22 09:00:05 [post_date_gmt] => 2025-07-22 13:00:05 [post_content] => How can manufacturers digitally transform their factory lifecycle to get the most out of their plant investments? As highlighted in Tech-Clarity's eBook "Digitally Transforming the Factory Lifecycle," top manufacturers have adopted more digital tools and taken a collaborative, integrated approach across the factory lifecycle. This infographic explains how companies can digitally transform factory designs and manufacturing planning to plan, design, build, and operate their factories. When done effectively, manufacturers can increase design efficiency, reduce time to full production, improve quality, increase throughput, and accelerate innovation. Click here to view or download the full infographic. For more information about digital factory, please visit our sponsor, Autodesk.
[post_title] => Meet Your Factory Lifecycle Goals like a Top Performer
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Why hasn’t Augmented Reality (AR) had a greater uptake in the manufacturing market? Maybe it had not found the best use. Data Time Space believes they may have found the killer app for AR: speeding continuous improvement (CI) and freeing CI experts to do the analysis, not the data collection. This young company enables manufacturers to use AR in new ways on mobile devices to collect real-time data over time in 3D space.
Data Time Space has developed a collaborative AI-powered spatial computing platform that collects data within a 3D video scan from a mobile device. The platform’s data model and AI capture, store, normalize, and put in context spatial data. The platform is inherently designed to connect to the Internet of Things (IoT) and track human movement. All computing in the platform is 3D space-related.
The company is initially aiming it at measuring ergonomics and safety, conducting time-and-motion studies, and gauging how people and processes are performing. The common thread for these applications is that they need data with spatial context to succeed. Seeing how a worker performs in 3D space is one thing. Capturing all of the data in context, based on an actual video scan of any part of a factory, and normalizing it is what Data Time Space is offering. The resulting type of dataset is foundational for all of these issues. The accurate, unbiased, contextualized data set has been complicated for most CI teams to assemble until now.
Many AR applications’ hardware is based either on headgear or fixed cameras. Headgear can be uncomfortable, and cameras are inflexible and intrusive if always on in a work area. Data Time Space uses tablets or mobile phones. They point out that mobile devices can now be located within a few centimeters, with accurate LiDAR depth perception for precise signal localization in the camera. Signals are object locations or movement deltas. This spatial accuracy and the flexibility of using it where and only when needed could significantly improve AR's spatial-context data's accuracy, comfort, and privacy.
Rather than 2D maps and analysis tools, everything starts and stays in realistic 3D. End-to-end 3D eliminates transformations and the delays and possible errors they introduce. It also facilitates understanding and streamlines data gathering and analysis. The model trains on pointing the system at the user or process in question and pushing a button to record the data in 3D in real-time, time series views.
While the platform is the major differentiator, one of its capabilities that may raise questions is spatial AI. AI helps with input and output to the spatial data model. AI collects spatial and temporal data, understanding what is in front of the camera and localizing it in 3D space. AI also uses and analyzes data in the model. A large language model (LLM) enables queries and processes data stored in the model.
It is designed to make the experts’ lives easier. If these professionals have the data collected and analyzed in real time, they can focus on how to best solve the problem. Data Time Space points out that the AI in their platform helps to understand the world and probe the complex data set, but it is human-in-the-loop and data model-centric. So, it has a strong model with logic in the platform and a streamlined, researched human interface.
Another challenge with CI data collection and analysis is bias. Using the Data Time Space system quickly alleviates any concern about that, as everyone sees exactly what happened in the recorded data. The demo is compelling, showing it tracking movement for ergonomics and trace or spaghetti maps for movement and waste reduction.
Data Time Space has also created AI-based applications to run on the platform. The main applications are factory analytics for continuous improvement and remote assistance. Factory analytics could compare differences between shifts, workers, or facilities. The focus could be on eliminating wasted movement, ergonomics, or safety. Remote support enables experts to stay in one location and support production teams worldwide, as they see exactly what is happening. The visual nature of the system and its data capture means it can also support employee training and education.
Data Time Space says it has found a way to scale applications that conform to its spatial data model flatly. It expects to build more applications or tools that help users. The applications can play together, enabling a remote expert to perform CI analysis without being physically present, for example.
While Data Time Space is a young company, the founders have been focused on building and enhancing this technology for 12 years. Before this, they worked at PTC, building and honing the Vuforia AR/VR platform. Data Time Space now owns the IP behind this spatial platform, while PTC owns a share of the company. This is a complete spatial data collection and analysis platform, with AI incorporated to understand the spatial data better. The user interface also includes AI. With their deep background, they have quickly begun to offer proofs of concept (PoC) that hold promise.
CI data collection may well be the killer app for AR. Data Time Space’s next steps will be helping to prepare the data sets for uses beyond the CI team. Thank you, Valentin Heun, for taking the time to explain your approach and technology to us. We look forward to reconnecting and learning more as you make progress in the market.
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What makes warehouse management software (WMS) work for larger enterprises over the long haul?
As uncertain as the economy, supply chain, and labor market are, WMS capabilities and fit can determine long-term business success. Yet many systems are relatively rigid and require custom coding by the solution provider or a partner to make updates. Some are not ready to coordinate well with the advanced automation warehouses, distribution centers, and fulfillment facilities have. Some WMS companies treat the software sale as a one-time transaction, rather than the beginning of a long partnership.
Companies must choose their enterprise WMS wisely, knowing their business needs will change. The Tech-Clarity Warehouse Management Systems Buyer’s Guide breaks it down and includes images and quotes from two examples of fast-moving, fast-growing companies that are confident they have made a good decision.
Please enjoy the summary* below. For the full research, please visit our sponsor Infios (registration required).
Overcoming Labor Dependency
Most enterprises have been facing the challenge of worker shortages. Automation is one obvious response. In fast-moving warehouse environments, an array of modern automation can help even less experienced people be efficient and effective. Yet, in many cases, the mix of stock-keeping units (SKUs) through the warehouse has also increased, so the WMS must run at very high performance even when the product flow and processes change quickly.
Holistic
A warehouse or DC is all about moving and storing materials as efficiently, safely, and accurately as possible. This demands the WMS to deliver a detailed understanding of where everything is at every moment and who last touched it. Ideally, it helps ensure that associates and automated equipment perform every step of the process correctly.
Get WMS that will Fit
In today’s challenging supply chain environment, every company needs all the advantages they can get. Enterprises compete on best practices they have discovered. Choose a WMS designed to allow your team to mold the software to those, and keep up as you learn, grow, and change.
Optimize the Operation
Today’s enterprise needs more from WMS than repeatedly executing the same flows. The goal is to improve what you are doing, even as things change. The opportunity to optimize flows with simulation for the warehouse is enormous. Predictive analytics are not just a shiny buzzword but are available today in some WMS.
Future-ready
Looking ahead is the core to choosing the right WMS. Companies must prepare for what's next, whether it’s automating more, hiring new people, serving new markets, or just knowing something unforeseen will happen. Ensure your WMS is ready.
Total Cost of Ownership
Look for WMS with a good fit, self-configuration, robust integration, optimization, and a reliable software partner. All of the above adds up to a lower total cost of ownership (TCO) for an enterprise. It also points to the potential for better performance for the warehouse, the enterprise, and the entire supply chain not only initially, but into the unforeseeable future.
*This summary is an abbreviated version of the ebook and does not contain the full content. For the full report, please visit our sponsor Infios.
If you have difficulty obtaining a copy of the research, please contact us.
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What might batch industry manufacturers still desire in their enterprise-grade MES? Maybe simpler ways to configure, roll out, and manage the system. TrakSYS MES V13 features major upgrades in solution creation and templates. It also prepares for cloud deployment, expands containerization, includes MQTT, and maintains a clean user interface— the kind of UI that has made them a customer favorite for decades.
TrakSYS has been on the market since 2001 and has recently been growing revenue by 20% annually. Its workforce has increased by 30% each year over the past two years, following strategic investment from partner BVP Forge. With deployments in over 100 countries, this well-established company continues to grow rapidly in all regions, with accelerated growth in APAC. This extensive geographic reach is rare in the MES market for companies of Parsec's size, demonstrating the global acceptance of TrakSYS and the worldwide partner network it has built.
Its focus remains on leading manufacturers in food & beverage, pharmaceuticals, CPG, and chemicals, though it serves other industries as well.
Parsec’s growth reflects the rising demand for adaptable and scalable MES solutions across sectors. Powered by V13, TrakSYS is set to transform how manufacturers pursue operational efficiency, offering an unmatched blend of flexibility and advanced technology.
TrakSYS was originally built on the Microsoft .NET platform. Unlike its larger MES competitors, which have assembled solutions by acquiring multiple software offerings, all TrakSYS modules (27 and counting) are developed on the same platform and use the same database. This standardization has allowed Parsec to unify communication, integrations, and the user interface, facilitating a quick transition from on-premise or data center deployment to the cloud by leveraging capabilities such as .NET Core, SQL Server, HTML5, and other Microsoft Azure tools for migration.
Currently, only 15% of their client base remains on-premise, 30% are in a corporate data center, and 55% are deployed in the cloud. Additionally, 90% of these cloud deployments are single instance multi-site (SIMS). Parsec’s largest SIMS deployment is with a food and beverage manufacturer supporting 46 sites from a single instance.
TrakSYS features a core framework for solution creation, with pre-built Function Blocks for common MES functionalities like SPC. These can significantly reduce service time in project implementation. Users can quickly create new SPC overview pages using existing data definitions, aiming to expand function blocks across various MES use cases.
Users can combine tables, menus, pages, charts, and lists to create custom visual elements and interactive pages with buttons and web service calls. They can also modify screens by accessing the parts viewer, dragging components from the parts gallery, and configuring their properties.
Parsec Smart Devices are now in their third generation, with enhanced capabilities like responses and indicator lights. Parsec supports both OPC UA and MQTT, with the Sparkplug B protocol expected to be available by the end of 2025.
This new release includes updates to the workflow builder, including the ability to create nested workflows for complex automation processes. A newly released SVG editor allows customers to interact with shop floor schematics within TrakSYS, enabling features like clicking on parts or dynamic visual indicators.
TrakSYS Cloud will be a new SaaS offering, serving as a command layer that complements existing cloud deployments. It will help with backups and data transfer across TrakSYS instances. TrakSYS Cloud aims to enable quick startup, license visibility, simplified installations, and hosted non-production environments, with a planned release by the end of the year. The process includes creating templates for custom pages and managing their versions for deployment to different sites. A development area is designated for template creation, and a QC area connects to relevant data sources. The database transfer tool moves configurations between environments without affecting data. TrakSYS Cloud oversees the template management system, allowing categorized templates to be deployed to specific sites.
TrakSYS IQ Assistant, an AI chat feature being added to the product, utilizes Azure Open AI. This will let users ask questions about their TrakSYS data and get insights, charts, and tables in return. It will support multiple languages and allow actions like creating pie charts or adding trend lines. Discussions are ongoing about role-based access control to protect data security within the AI assistant. Regional data governance is also being explored, with a prototype currently in private-preview with select customers.
Thank you, Ryan McMartin, for the detailed briefing and demo for Tech-Clarity's Rick Franzosa and Julie Fraser.
[post_title] => Parsec Keeps Growing, TrakSYS Gets Easier to Fit & Scale [post_excerpt] => [post_status] => publish [comment_status] => open [ping_status] => open [post_password] => [post_name] => parsec-traksys [to_ping] => [pinged] => [post_modified] => 2025-09-15 22:26:58 [post_modified_gmt] => 2025-09-16 02:26:58 [post_content_filtered] => [post_parent] => 0 [guid] => https://tech-clarity.com/?p=22363 [menu_order] => 0 [post_type] => post [post_mime_type] => [comment_count] => 0 [filter] => raw ) [7] => WP_Post Object ( [ID] => 22184 [post_author] => 2574 [post_date] => 2025-07-02 01:08:48 [post_date_gmt] => 2025-07-02 05:08:48 [post_content] =>Where can you meet senior manufacturing leaders who are taking strides toward digital transformation and learning from one another? One venue is the Manufacturing Leadership Council’s (MLC) annual Rethink conference. In addition to a packed two-and-a-half-day conference, the program included an MLC member meeting and an awards banquet.
The MLC’s mission is “to help senior executives define and shape a better future for themselves, their organizations, and the industry at large by focusing on the intersection of critical business and technology issues that will drive growth today and in the future.” This conference, held at the JW Marriott beach resort on Marco Island, FL, on June 16-18, 2025, did that well.
Tech-Clarity’s Julie Fraser was on the Power Panel: Handicapping the Global Digital Transformation Race. David R. Brousell MLC’s founder, moderated the discussion. Other analysts were Bob Parker of IDC, Craig Resnick of ARC, and Matthew Littlefield of LNS. It was a lively discussion. Each firm’s research and perspectives led to some areas of agreement and also some divergent views. In aggregate, the research from these companies shows progress in digital transformation in the US and worldwide.
This conference was rich in examples of manufacturers undertaking aggressive digital transformation.
In addition to the highlights from manufacturers, experts took the stage as well. Gregory Daco, the Chief Economist of EY, kicked off the conference with a mixed bag of encouraging and discouraging news; uncertainty reigns. A managing director in software from Goldman Sachs, Jack Anstey, discussed How Wall Street Views Digital Transformation in Manufacturing. They love the high-margin recurring revenue of SaaS offerings but look for margin expansion.
The exhibit hall was rich with support. The solution providers supporting this event included
We also heard from some solution providers directly:
The awards gala was dazzling – hundreds of manufacturing professionals in evening gowns and tuxedos. Thank you to Julie’s long-time business friend, David R. Brousell, emcee Lauren Bisset, and the MLC team for the invitation and well-executed event. We hope to join you in Scottsdale, Arizona, June 21-24, 2026, for the next Rethink!
[post_title] => Powerful Connections and Realities at Rethink 2025 [post_excerpt] => [post_status] => publish [comment_status] => open [ping_status] => open [post_password] => [post_name] => mlc-rethink-2025 [to_ping] => [pinged] => [post_modified] => 2025-07-03 01:12:10 [post_modified_gmt] => 2025-07-03 05:12:10 [post_content_filtered] => [post_parent] => 0 [guid] => https://tech-clarity.com/?p=22184 [menu_order] => 0 [post_type] => post [post_mime_type] => [comment_count] => 0 [filter] => raw ) [8] => WP_Post Object ( [ID] => 22209 [post_author] => 2574 [post_date] => 2025-07-01 16:02:15 [post_date_gmt] => 2025-07-01 20:02:15 [post_content] =>How can innovative manufacturers improve their operational performance? With coherent, modern software to run the operation. First Resonance's ION Factory Operating System is designed for manufacturers in space tech, new energy, and other breakthrough products ready to craft fresh manufacturing processes. They have added agentic AI, an updated user experience (UX), and a partner marketplace to showcase ION partners.
First Resonance’s ION has long had AI functionality baked into it. With the introduction of ION Intelligence, they have added semi-autonomous workflow agents, which use the power of modern large language models (LLMs to leverage datasets from business operations. These new agents each specialize in specific tasks and domains. Beyond automating data search and retrieval, they keep production and its information flowing without manual handoffs. The initial agent focus is on improving the workflows for how customers have been using ION already.
ION Intelligence can also provide action recommendations. We saw these agents on screen. They showed the various alternative recommendation options and the system’s confidence levels for each recommendation. This can significantly speed up the time to decision and confident action in the plant. As with other LLM-based AI, First Resonance reports that ION Intelligence is improving rapidly, even before launch and wider customer use.
First Resonance integrates data into ION Intelligence using the new but increasingly widely accepted Model Context Protocol (MCP) standard. We see more and more companies using MCP for integration beyond their own set of agents. The benefit is that MCP standardizes communication with APIs and other data sources, becoming a common, consistent language for every AI tool, desktop app, and data source to connect intelligently.
User Experience (UX) is crucial to any manufacturing plant floor software. It must serve the operators, technicians, engineers, and supervisors without distracting them from the primary focus of making products. The new UI uses best practices such as choosing dark or light modes, voice capture, OCR, etc.
While the user interface (UI) is part of the upgrade for a clean look and the AI agents on screen, there’s more. The new ION experience also speeds load times for processes that need large quantities of data. Based on how customers have used ION over the past five years, First Resonance made UX changes to address some priority needs in real-world situations.
First Resonance has long had partners in PLM, PDM, Finance, Procurement, and more. It has pre-built integrations to 16 systems, and even more through their partnerships with integration platforms like Violet Labs. Their customers use integrations with many ERP systems and other common software platforms. They also have integration and services partners to support customers with developing integrations. The ION Marketplace initially helps ION customers find and connect with these companies. It may evolve for deeper interactions and become a place for customer- or partner-developed extensions in the future.
First Resonance’s target market is quality-critical manufacturers. To date, many have been forward-thinking companies emerging into production for breakthrough products. It is designed for companies that are willing to depart from traditional working methods. Because First Resonance ION includes MES and other capabilities, it might also overlap with existing ERP, SCM, and analytics systems. We suspect traditional A&D, energy, medical device, and robotics makers could learn from the First Resonance approach and streamline data flows beyond traditional operating siloes.
We are excited to hear how well First Resonance supports its customers in growing their businesses with sound factory information and guidance. The combination of new items in this release is ambitious, and we agree it’s an excellent fit for what manufacturers need today. Innovation-centered manufacturing companies can benefit from ION’s new AI, UX, and easy ecosystem access.
Thank you, Ron Close, for the advance materials and briefing for Julie Fraser and Rick Franzosa. The launch webinar was also very informative; thank you to Karan Talati, Allan Cutler, and Manav Sanghvi for explaining the news. We look forward to tracking First Resonance’s progress in the market.
[post_title] => First Resonance Updates ION with Agentic AI, UX Improvements, Marketplace [post_excerpt] => [post_status] => publish [comment_status] => open [ping_status] => open [post_password] => [post_name] => ion-ai-ux-marketplace [to_ping] => [pinged] => [post_modified] => 2025-07-03 16:06:41 [post_modified_gmt] => 2025-07-03 20:06:41 [post_content_filtered] => [post_parent] => 0 [guid] => https://tech-clarity.com/?p=22209 [menu_order] => 0 [post_type] => post [post_mime_type] => [comment_count] => 0 [filter] => raw ) [9] => WP_Post Object ( [ID] => 22145 [post_author] => 2572 [post_date] => 2025-06-26 10:07:58 [post_date_gmt] => 2025-06-26 14:07:58 [post_content] => What should Automotive companies consider when integrating ALM and PLM? In today’s automotive industry, software is a critical differentiator. From advanced driver assistance systems (ADAS) to infotainment and over-the-air updates, software brings much opportunity for innovation. As we move toward software-defined vehicles, it is critical that hardware and software development become as integrated as possible. Yet, software is often managed in Application Lifecycle Management (ALM), while hardware remains in Product Lifecycle Management (PLM). This separation creates silos that slow development, increase risk, and hinder innovation. As vehicles become more connected, autonomous, and software-driven, and regulatory requirements tighten, it’s more important than ever to integrate ALM and PLM to establish a digital thread across the entire vehicle lifecycle. In this 20-minute video, Michelle Boucher and PTC’s Meg Folcarelli discuss key considerations for Automotive companies to support the co-development of hardware and software. They cover:
Watch the conversation here (no registration required) and learn more about Automotive ALM and PLM.
For more insight on integrating ALM and PLM in any industry, refer to our buyer’s guide.
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Tech-Clarity’s Jim Brown had the opportunity to join Propulsion 2025, Propel Software’s sixth annual user event and its second in-person conference. It was exciting to share in the energy from the manufacturers using Propel as they gathered to learn from Propel leaders, partners, and especially their fellow customers.
Propel continues to grow in both core PLM capabilities and further their unique Product Value Management (PVM) strategy under the leadership of CEO Ross Meyercord. In addition, they continue to leverage the capabilities of the Salesforce platform to bring new value to their product and customers. This year, Propel announced significant plans for AI with a robust approach that would likely be out of reach for most vendors their size, but is accessible to Propel and their customers through their relationship with Salesforce.
Let’s step back to discuss Propel’s unique scope. Their PVM (Product Value Management) platform integrates elements of Product Lifecycle Management (#PLM), Quality Management Systems (#QMS), and Product Information Management (#PIM). We’ve written about PVM before and believe it’s a compelling approach for companies looking to better streamline product innovation, product development, and product commercialization. Salesforce also provides the ability for Propel to extend into the product service lifecycle.
Beyond the functional strategy, Propel is interesting because they inherit extremely robust and capable technology underpinnings from the Salesforce platform. Because of this, Propel was cloud-native from the start and can take advantage of Salesforce’s product roadmap investments. This showed up at Propulsion this year with the introduction of #AgenticAI.
To get people thinking strategically about AI, Propel brought in Ray Wang of Constellation Research, Inc. Ray gave an energetic, well-researched presentation on how companies can leverage AI to redefine and transform their businesses. He discussed examples where new technology opened the door for brand new business models, using CocaCola and Sam’s Club as examples. His talk set the bar for strategic changes similar to what Amazon did to redefine buying experiences as the internet and the world wide web gained traction. It was great food for thought.
Following Ray’s talk about strategic, fundamental shifts that are possible by rethinking business were other presentations about how AI can help with more everyday, practical tasks. There was an interesting contrast in the presentations. Because the audience at the event was not necessarily those who could fundamentally change the business, we were encouraged that the following use cases presented by both Salesforce and Propel were approachable, practical, and achievable for the participants.
VP Solutions Marketing Kevin Crothers and Senior Product Marketing Director Jill Mueller demonstrated some of Propel’s current AI offerings such as extracting engineering specs to create PIM data, reading through product documentation to create quiz questions for field service teams, and summarizing product thread information about a serialized asset to create a unified digital thread from engineering and service data. These may not reinvent the business, but they can certainly improve efficiency, quality, and time to market. Although these tactical changes taken individually may seem small, they are achievable, can add value quickly, and in aggregate they provide very strategic improvements.
Salesforce also presented practical examples, including asking questions in Spanish for AI to find and summarize information from English documentation, proactively noticing errors on an asset using real-time data, and providing a 24x7 agent on the website. These capabilities from Salesforce come from Agentforce, Salesforce’s AI platform. Propel is embedding Agentforce capabilities into their platform to create their AI offering, Propel One. Propel currently has two agents available on Salesforce App Exchange: Propel One for Product Engineering and Propel One for Product Information, and plans to ship a series of packaged agents with Propel One, and also allow customers to modify them or create their own to meet their unique needs.
Senior Product Manager Steve Toukmaji and Jill Mueller also presented on AI advancements. They shared Propel’s guiding principles that data is secure, humans stay in the loop, and they will focus on high value “jobs to be done.” They also went into detail about the Agenforce Trust Layer, a very mature approach to managing AI inherited from Agentforce. It’s another example of where Propel gains significant value from Salesforce R&D investments.
Propel is what we typically call a supply-chain-centric PLM solution as opposed to an engineering-centric PLM. In this way, it serves manufacturers in fast-moving markets who typically procure elements of their product, such as high-tech, electronics, MedTech, consumer goods, and other related industries. Deep CAD integration, in this form of PLM, is a lower priority. Having said that, Propel’s customers are asking for more and Propel is responding. Propel plans to release DesignHub this fall with integration to 10 mechanical and electrical CAD solutions.
Chief Product Officer Eric Schrader shared further product roadmap plans, including demonstrations by Kevin Crothers and Director of Technical Product Marketing Michael Prom. These included trace matrix enhancements, visual communication improvements, and component insights to evaluate BOMs with help from their partner SiliconExpert. One of the more interesting concepts highlights the comprehensive nature of the combined Propel Salesforce offering, connecting product and customer thread in a holistic “Product Graph” that extends fully into the service lifecycle. There is much more than we could cover here, but the key takeaway is that Propel continues to invest across their PVM footprint in addition to introducing a robust AI strategy.
There were a number of customer presentations, too many for us to detail here. We do want to mention one interesting trend we heard across the presentations. Several customers mentioned transitioning from Agile. As Oracle is no longer supporting Agile, many diehard companies are being forced to choose a new solution. Given the shared history and deep Agile expertise through Founder and Chairman Ray Hein and Eric Schrader, it’s no surprise they see Propel as a logical replacement option. Propel is not the only choice, nor the only vendor offering a safe haven to these manufacturers. But they are a logical consideration based on the history of the company, the PVM approach, the QMS capabilities built with traceability and compliance in mind, and the customer base.
Propel put on an energetic event and customers gained a lot from it. They highlighted key advancements in the product as well as their AI strategy. We look forward to following Propel’s progress and growth.
Thank you Propel for including me in your user event. It was educational and enjoyable. We really appreciate events like this where you can see the enthusiasm of the customers and see them eager to learn from their vendor and fellow customers.
Thanks to Erin Keefe and Tom Shoemaker for helping me attend the event, to Michelle Stone , Steve Toukmaji, and Jill Mueller for formal and informal product updates, and to Ross Meyercord and Eric Schrader for strategic updates. It was great to see other PLM friends as well including Ray Hein, Gregory Yow, Nate Brown, and Mike Prom, among others.
[post_title] => Propel Furthers Unique PVM Solution with Agentic AI [post_excerpt] => [post_status] => publish [comment_status] => open [ping_status] => open [post_password] => [post_name] => product-value-management [to_ping] => [pinged] => [post_modified] => 2025-07-03 16:03:55 [post_modified_gmt] => 2025-07-03 20:03:55 [post_content_filtered] => [post_parent] => 0 [guid] => https://tech-clarity.com/?p=22198 [menu_order] => 0 [post_type] => post [post_mime_type] => [comment_count] => 0 [filter] => raw ) [11] => WP_Post Object ( [ID] => 22177 [post_author] => 2 [post_date] => 2025-06-17 00:49:56 [post_date_gmt] => 2025-06-17 04:49:56 [post_content] =>We’ve been excited to get briefed by Makersite over the last two years, and we’ve been impressed. Now, we’re happy to share what we’ve learned. They’re a small company that has big customers, and they’re helping those companies make significant gains on compliance, cost, sustainability, and supply chain risk. Given the impact of today’s global political and economic disruption on manufacturers, these improvements are crucial to success.
Makersite is addressing the dirty little secret about sustainable design and DFX in general. Engineers simply don’t know the impact of their decisions on factors like carbon footprint. If manufacturers analyze the impacts after the fact, it’s often too late to impact their design decisions. Makersite plans to change this paradigm by providing engineers with trusted data in the context of their product digital twin during design. If engineers have the right information, they can make better decisions to improve the impacts of their decisions and speed up product development by preventing design rework to meet cost, compliance, sourcing, or sustainability issues. Further, having the right information – what Makersite calls “Product Lifecycle Intelligence” – speeds up time to market by automating product documentation like LCAs (lifecycle assessments), product compliance, and company Scope 3 reporting, among other use cases.
Makersite believes that delivering Product Lifecycle Intelligence will help manufacturers source smarter, design greener, collaborate faster, and build more resilient supply chains. We respect their approach, which addresses sustainability in the broader context of product profitability.
Our recent executive survey shows that manufacturers are facing increased calls for product transparency, for example, through product passports. It also shows that current economic conditions demand effective cost management and that trade wars and tariffs are making already challenging supply chains riskier. Makersite’s timing to offer Product Lifecycle Intelligence couldn’t be better.
Designing products like high tech electronics, which have complex supply chains, demands design tradeoffs that must be optimized early in design. Makersite’s Product Lifecycle Intelligence SaaS platform is based on a digital twin data model created from engineering BOMs. These data models, graph models that customers can refine, serve as both a visual representation of the product and the backbone on which to make LCA decisions.
From there, Makersite offers algorithms to help manufacturers map their digital twins to relevant data from a wide variety of sources. While they can use APIs to pull information from customers’ systems, such as ERP, PLM, or CAD, they also provide their own rich dataset. Makersite is not just an integration and calculation engine. They provide their own “decision-ready” data, including deep-tier supply chain information. They claim to provide the “world’s largest supply chain database” with over 150 external and verified databases.
Mapping this information to the digital twin creates the intelligence needed to make the right decisions and do so on a timely basis. We were assured that the data can be trusted because it is not a black box. Makersite offers a confidence score on their data and explained that transparency remains a cornerstone of Makersite’s approach. The platform allows customers to trace data sources, understand how the data impacts the modeling, and explore alternative options.
Without diving too deeply into the technology, 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.” AI is a natural fit to map data from a variety of sources and formats into something meaningful. Makersite explained that their automation and AI capabilities ensure the most accurate and granular mapping for supply chain models, while continuously learning from user-driven changes to validate and refine the product models and reflecting those updates in future runs.
We mentioned that Makersite has an impressive customer list, with many in consumer electronics. They also shared that they have success in the construction and chemicals industries. But they are applicable elsewhere, as well. Some notable customers they list on their website include Barco, Schaeffler, Cummings, 3M, and Lenovo. One customer, Microsoft, jointly shared a case study with Makersite. The results were impressive, including a 30% reduction in carbon footprint in design for the Surface Pro, in addition to making it more energy efficient in use. They also leveraged digital twins to source better materials. They then applied this approach to other Microsoft consumer products and their data centers.
Makersite is serving a valuable role at a time when manufacturers need it. Their ability to model product digital twins from customer data and enrich it with additional information from customer systems, third-party data, and Makersite’s own dataset makes it compelling. Their use of AI to contextually map data is a unique approach and solves quite a few of the challenges companies have faced in initiatives like LCA in the past. We’re excited to see their further success and adoption in the industry.
Thanks to Nicolás Artímez Wetz for the briefings and Vaqais Hussain for initiating the conversations.
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We are researching the state of product development, including the challenges, capabilities, and future plans manufacturers have. We are exploring questions such as, what challenges do you face? How well do hand-offs between engineering and manufacturing work? Can you find the skilled workers you need? Has AI had any impact? How are you using simulation, if at all? The survey takes about 10 to 15 minutes.
If you work in engineering, product development, manufacturing, or manage those teams, please take the survey to share your thoughts. As a thank you, we will send you a copy of the report summarizing the findings.
In addition, respondents will be entered into a drawing for one of 20 $25 Amazon gift cards.*
Individual responses will be kept confidential. Please feel free to forward this survey to others you feel have an opinion to share.
Thank you for your support, please check out our Active Research page for additional Tech-Clarity survey opportunities.
*See survey for eligibility rules
[post_title] => The State of Product Development Survey
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[post_content] => ComplianceQuest made some significant announcements at its recent ConQuest customer conference. The first was to identify the space where they play, as a platform and integrated software suite to clean up the messy “middle office.” They also continued to add more agentic AI to the platform to support the people using the applications sitting on it. The other key news came before the event: the release of SupplierQuest, which reaches beyond supplier quality to supplier management, onboarding, collaboration, and performance management.
ComplianceQuest has named its vision the Connected Middle Office Platform. Think about how ERP long ago integrated back-office data and workflows, such as HR, Finance, and Supply Chain, for greater efficiency. Since then, CRM has helped optimize the front office activities, including sales, marketing, and service. What’s in the middle? ComplianceQuest says core operations, and they are fragmented, manual, and lack a way to collaborate among functions.
With a proven platform and multi-module solutions for QMS, EHS, Supplier Management, and PLM, ComplianceQuest has a strong story for this middle office. Our research supports the ComplianceQuest view. In our Making Manufacturing Analytics and AI Matter survey, manufacturers’ top investment aim is gaining easy access to high-quality, timely, and complete plant data. Today, many struggle to share operations data, put it in context, and use it to make sound decisions. The ComplianceQuest platform, with its single database, enables collaboration and closed-loop processes across these disciplines and data sets. Consider how a DFMEA can be connected to complaints, CAPAs, and regulatory reports.
AI agents to assist humans in this middle office work are trained only on each customer’s data. ComplianceQuest has incorporated its years of experience in developing these agents. CQ AI agents focus on activities such as generating content, finding and connecting relevant data across the system, forecasting future outcomes, leveraging chat to intake information and create records , and communicating with suppliers through intelligent emails that turn into actionable records . This reduces redundant and duplicated data, enhances worker productivity, and augments data-driven decisions.
ComplianceQuest has been releasing AI since 2022, primarily as categorization and prioritization and retrieve and relate capabilities. Predictive AI is already embedded and is part of the quality maturity index, supplier performance, and safety incidents functionality. AI is a top priority on the CQ roadmap to improve efficiency and costs and mitigate risk with predictions. All of this is embedded in the platform, not separately licensed, as are the advanced analytics.
In March, ComplianceQuest introduced its SupplierQuality suite, expanding beyond supplier quality to encompass more aspects of supplier relationship management. Examples include:
This expansion leverages the collaborative platform and other solutions and adds capabilities to foster greater success with suppliers during a time of supply chain change.
ComplianceQuest has always leveraged the Salesforce platform for connectivity. As the company builds out its middle office platform and suite, we will continue to watch for new releases, additions, and improvements. Thank you, Nikki Willett, for catching us up on the latest news from ComplianceQuest.
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[post_content] => Last week, we had a chance to attend the Hitachi Digital Services Analyst & Advisor event in Dallas. The gathering offered compelling insights into the company’s strategic direction and differentiated capabilities. Through a series of thought-provoking presentations and fireside chats, one theme emerged with clarity and consistency: Hitachi Digital Services’ deep-rooted engineering expertise sets it apart. This foundational strength enables the company to understand its customers’ complex challenges and validate solutions across Hitachi’s diverse industrial ecosystem and co-innovate with clients to drive real business outcomes. Since we focus a lot on #digitaltransformation in product design, innovation, and manufacturing, we found several relevant takeaways worth highlighting.
Digital transformation in manufacturing is no longer optional but an urgent imperative. Industry leaders recognize that embracing this shift is essential to sustaining profitability by eliminating inefficiencies, unlocking new revenue streams, and navigating an increasingly complex environment shaped by product complexity, regulatory pressures, and geopolitics. A foundational step in this journey is establishing a robust digital thread by integrating data across diverse software systems and physical equipment. This is where Hitachi Digital Services stands out. Leveraging deep engineering expertise honed through collaboration with sister companies renowned for their manufacturing excellence, Hitachi Digital Services brings a practical understanding of product development processes. Coupled with their PLM and MES implementation capabilities, they are uniquely positioned to help manufacturers drive digital transformation across the value chain.
Manufacturers are increasingly prioritizing investments in AI and analytics. As highlighted in Tech-Clarity, Inc.'s research by Julie Fraser, Making Manufacturing Analytics and AI Matter, early adopters are already realizing significant gains, ranging from cost reduction and productivity improvements to enhanced quality, error-proofing, and better delivery performance. This space is a natural fit for Hitachi Digital Services, which showcased powerful use cases such as a rail application where edge computing in the train enables real-time data analysis from sensors monitoring track conditions and overhead power lines, directly enhancing safety outcomes. With modern factories generating massive volumes of data, Hitachi Digital Services is ideally positioned to help manufacturers harness this information to optimize energy usage, drive quality, and maximize overall equipment effectiveness.
The convergence of Information Technology and Operational Technology is a strategic enabler for manufacturers seeking to enhance operational efficiency, reduce costs, improve decision-making, and drive innovation. However, the path to IT-OT integration is often hindered by legitimate #cybersecurity concerns. Cyber threats, including data breaches, IP theft, ransomware, and operational disruptions, pose significant risks to brand reputation and profitability. To fully capitalize on the promise of IT-OT convergence, cybersecurity must be embedded as a core foundation. Hitachi Digital Services brings deep cybersecurity expertise through its partnership with Hitachi Cyber, offering tailored protection strategies and best-practice implementations. Their ability to integrate security seamlessly into digital transformation initiatives, along with providing managed security services, makes them a trusted partner in securing the connected factory of the future.
In conclusion, the Hitachi Digital Services Analyst event provided valuable insights into leveraging its deep engineering heritage and cross-division expertise to drive impactful digital transformation for its manufacturing customers. Their capabilities in enabling the digital thread, deploying AI and analytics, and securing IT-OT convergence align closely with the critical needs of manufacturers. We were especially impressed by their practical, field-tested approach and commitment to co-innovation. These strengths position Hitachi Digital Services as a trusted partner in helping manufacturers unlock efficiency, resilience, and innovation. We thank Patrick Corcoran and Mahesh Hanumanthu for the opportunity to participate in this event.
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“This is a strategic move,” explains Jim Brown, President and Founder of Tech-Clarity. “As manufacturers grapple with rapid change while combating global pressures, it's imperative that we offer a broad perspective on digital transformation for 2025 and beyond. More than ever, we have to help companies understand and achieve technology's value by aligning technology with processes and people. Rick brings great experience, credibility, and a global perspective. We are excited to have such a respected thought leader join our team to help further our mission of making the business value of technology clear.”Rick's research focus will include the role of MES/MOM amidst a sea of new technologies from both end-user and technology provider perspectives. This includes how technology improves the decision-making skills and competency of the manufacturing workforce. Please reach out to schedule a briefing or seek out Rick’s perspective as he begins his research agenda. Join our community to follow Rick's research. [post_title] => Tech-Clarity adds Digital Innovation and Manufacturing Analyst Rick Franzosa [post_excerpt] => [post_status] => publish [comment_status] => open [ping_status] => open [post_password] => [post_name] => rick-franzosa [to_ping] => [pinged] => [post_modified] => 2025-06-17 11:31:27 [post_modified_gmt] => 2025-06-17 15:31:27 [post_content_filtered] => [post_parent] => 0 [guid] => https://tech-clarity.com/?p=22042 [menu_order] => 0 [post_type] => post [post_mime_type] => [comment_count] => 0 [filter] => raw ) [17] => WP_Post Object ( [ID] => 22023 [post_author] => 2572 [post_date] => 2025-05-20 10:00:45 [post_date_gmt] => 2025-05-20 14:00:45 [post_content] =>
What should MedTech companies consider when integrating ALM and PLM?
Software has become a crucial component in MedTech products. However, with software development managed in Application Lifecycle Management (ALM) and hardware managed in Product Lifecycle Management (PLM), the separate systems can create silos within the product development team. With increasing regulatory pressures and trends such as personalized healthcare, it has become even more important to eliminate these silos and establish a digital thread across product development data.
In this 20-minute video, Michelle Boucher and PTC's Meg Folcarelli discuss key considerations for MedTech companies to support the co-development of hardware and software. They cover:
Can agentic AI improve how people work on the factory floor? Can it address the workforce crisis that has resulted in a plateau in productivity and increasing safety incidents? OpsMate AI believes they can do that quickly with their no-code agentic AI platform, which is purpose-built for the complexity and high-stakes environment of manufacturing operations. Their approach is to use generative and agentic AI to create hyper-intelligent digital teammates that assist the factory workforce.
Flexible Knowledge AssistantsAgents have access to more information than a person could. The agent-as-assistant concept makes sense, particularly in the unpredictable world of day-to-day operations. No matter the level of skill or experience an operator or associate has, they can ask the agent and get not only data but information, knowledge, and reasoning. This is quite different from traditional hard-coded, pre-defined applications and dashboards.
The great news about genAI is that it can train on a wide variety of data. So, it can naturally bridge the IT/OT divide. Whether the data is from IoT, MES, a machine, SharePoint, or a Teams transcript, OpsMate AI can ingest it, bring it together, put it in context, and make it available in any language.
As with all genAI, these agents work with natural language queries, not through coding or formal queries. They can answer questions, guide work, perform analysis, or take on routine tasks. This early-stage company created an advisory board based on the founders’ deep existing manufacturing relationships.
This advisory board has pointed to the common use cases that they would find valuable in their workflows. Since then, the OpsMate AI team has been busy building their minimum viable product to address those needs. Some of the initial use cases involve acting as a virtual technician to support less experienced maintenance staff with what is otherwise tribal knowledge. Another use case seeks to replicate the EH&S expertise of front-line managers with agents mentoring the workers.
OpsMate AI points to 3 layers of knowledge: general, industry-specific, and customer-proprietary knowledge. OpsMate AI builds in the first two, but the customer keeps control of their own data. They get an instance and decide who has access to address security and privacy concerns. The customer-specific knowledge repository can come from existing documents (SOPs, manuals, checklists, drawings, troubleshooting logs, corrective action reports, etc.) Experts can also train the model through an interview and knowledge capture process where their expertise becomes encoded in agents.
Once the agent is trained on the appropriate knowledge collection, it can effectively perform its tasks. The digital workforce will be available for in-line, real-time guidance for anyone in the plant. It could be as simple as “What does this PLC error code mean?” or as complex as “How can I maximize yield?” The system will also be transparent in showing the source(s) of data that lead to its recommendations.
OpsMate AI founders have developed a roadmap to an autonomous digital workforce, augmenting the people who remain at the center. They see robots doing physical work and OpsMate AI agents doing knowledge work. Over time, the agents can learn more and. progress from being virtual experts to analyzing data to executing tasks. At some point, they may fully automate tasks and become autonomous.
We see the possibility of this being a breakthrough for customers. Our research shows that GenAI can deliver significant benefits quickly. Systems that have been purpose-built as agents for factory floor issues, such as what OpsMate AI offers, are attractive because they have so much pre-built. Manufacturers seeking to shore up aspects of the operation that most need additional knowledge and expertise should take note.
OpsMate AI has a strong base of advisory board manufacturers driving its initial product development. These companies have their data in it and are testing and helping refine it. We expect to see a commercial release around the middle of this year.
Thank you, Howard Heppelmann and James Zhang, for briefing us on your vision and early customer direction. We appreciate the pragmatic approach to agentic AI for production.
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Rick Franzosa is the Vice President of Research for Manufacturing for Tech-Clarity. He covers MES/MOM and transitional technologies that support the evolution of manufacturing software technology into the age of Industry 4.0 and AI.
Rick has over 40 years of experience in manufacturing software technology, including 10 years as an analyst (Gartner) in addition to experience in design, manufacturing, and software engineering in aerospace and defense companies. He was involved in the early days of both PDM and shop floor solutions as they morphed into today’s PLM and MES solutions. He has a BS in Mechanical Engineering and an MBA from the University of Hartford.
Rick’s current areas of research include the role of MES/MOM in a sea of new technologies from both end-user and technology provider perspectives. This includes technology and how technology improves the decision-making skills and competency of the manufacturing workforce. He enjoys working with manufacturers and software solution providers on how to improve performance and create business value.
Rick enjoys spending time with his wife, children, and grandchildren. He also enjoys listening to and performing classic rock music and creating virtual models for train simulators.
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