Making the value of technology clear
New Product Development is More Complex
For manufacturers, delivering profitable products to the market has become significantly harder. Products are more complex than ever, requiring additional resources with expertise in new disciplines, driving up development costs, and putting profit margins at risk.
The Heightened Impact of External Pressures
Some of this complexity arises from external factors outside a manufacturer’s control. Customers are increasingly demanding, expecting innovative products more quickly than ever before. Competition is coming from all directions. Not only from traditional competitors, but also from new entrants. Our State of Product Development survey found that 56% of manufacturers face competition from adjacent industries, while 52% compete with low-cost or offshore manufacturers.1 Today’s supply chains add to the challenge. In fact, 74% of manufacturers in the survey identified supply chain disruptions or market volatility as a top challenge in product development.2 Beyond that, government and industry regulations are widespread, especially in High Tech and medical technology, demanding strict engineering and quality processes with thorough data collection and management.
Multi-CAD Environment Complicates Design Collaboration
Some of the complexity stems from internal issues. Remember when products were primarily mechanical?
Those days are gone. Now, mechanical, electrical, and software teams all need to work together – and be productive doing it. They must ensure that form, fit, and function all work in harmony while delivering their designs on the same development and launch timeline.
However, each design discipline uses different tools, with product data stored and managed separately or, in the worst case, only on an individual engineer's drives. Managing and accessing product data across multiple design systems, let alone file folders and shared drives, negatively impacts collaboration and reduces productivity.
Where to Start
For some manufacturers, establishing a digital thread may be viewed as out of reach when facing budget, resources, and time constraints. However, manufacturers can establish a digital thread despite these challenges.
Use 80-20 Rule
Applying the 80-20 rule helps focus on the most important and common use cases and workflows first. These deliver the most significant business value without getting bogged down with less common and more complicated edge cases. In short, keep it simple.
Keep Established Workflows
Established workflows need to continue, especially those supporting regulatory requirements, but avoid excessive customization whenever possible. Using out-of-the-box functionality saves implementation time and money, and reduces the need for dedicated IT resources.
Connect Existing Systems
There is no need to start from scratch. A practical approach is to connect existing CAD, PDM, and PLM investments and applications that are working well to create the product digital thread.
Take a Phased Approach
The best path is to take it one step at a time. Since data across systems is probably not perfectly aligned, a phased approach to PDM-PLM integration is preferred. Start with a small project, or assembly, to avoid a massive data cleanup upfront. Then add new projects and products as data cleansing progresses.
*This summary is an abbreviated version of the eBook and does not contain the full content. For the full research, please visit our sponsor, Propel (registration required).
If you have difficulty obtaining a copy of the research, please contact us.
[post_title] => Building the Digital Thread to Improve NPD Performance
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Can highly regulated pharmaceutical companies use AI effectively? What are companies doing to leverage AI without compromising their CGMP-validated processes? Please join this practical, real-world conversation on moving AI beyond pilots and into meaningful results.
Tech-Clarity’s Julie Fraser joins with Kate Porter, Director of Product Management and Research at POMS, along with a customer. Roland Esquivel, POMS VP of Sales and Marketing will moderate the discussion and lend his experience also. This diverse panel will discuss what is already working and where regulatory, quality, IT, and other questions and challenges lie.
The discussion will touch on these topics:
How do manufacturers integrate design data (PLM) with manufacturing data (MES)?
Tech-Clarity invites you to join a research study on PLM-MES Integration. Please take about 10 minutes to fill out our 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 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] => How are Manufacturing Leaders Integrating PLM and MES?
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How can a strong auditing program, as practiced in major automotive suppliers, improve? By going digital. Ease.io as been doing that for years, with a SaaS software platform for Layered Process Audits (LPAs), 5S, Safety Inspections, Gemba walks, Root Cause Analysis (RCA), and problem-solving. They recently added on-the-job (OTJ) training support to strengthen customers’ outcomes.
Many lean and operational excellence programs include regular audits. Audits are designed to improve quality, productivity, and safety, and nearly always do. However, using paper, spreadsheets, tribal knowledge, and legacy or homegrown software can create inefficiencies and missed opportunities. For example, if standard processes are not executed consistently or when follow-ups to issues are slow, manufacturing issues can arise and cause problems.
For over 10 years, EASE has been selling software to support these process audit activities. Customers report vastly increased audit completion rates, with up to a 90% reduction in leadtime for major audits. Allowing data to flow smoothly with less administrative burden can help LPA and other audit processes deliver their value with minimal non-value-added overhead.
Manufacturers adopting a digital platform may encounter early resistance from end-users to the change in how audits have been conducted in the past. EASE encourages customers to explore how new technologies enable them to rethink how they do things. Using integrated technology can also help identify all trends and breakdowns. It also helps to trace the root causes of problems and track whether actions have improved the situation.
One of the most significant benefits of a digital approach is the speed to identify and notify about non-conformances. Another is the ability to make audits more effective, consistent, and visible. The digital record also makes it easier to detect when corrective actions have not had the expected impact, to re-address needed issues, and truly close the loop to optimize outcomes.
EASE is available as a SaaS subscription. The base audit & inspection version of EASE supports mobile audit and inspection checklist authoring through both pulling existing checklists and creating new ones. It also incorporates automated scheduling, findings management, and real-time data and dashboards for clear visuals of audit results. Naturally, EASE must connect to the systems of record, including QMS, MES, and CMMS. EASE Connect also enables bulk data access for BI tools and dashboarding, while Insights is their own dashboard solution that delivers custom-built dashboards specific to individual customers.
The next Level of EASE subscription includes creation and management of action plans. Action plans support collaborative RCA and analysis to document and facilitate problem investigation and understanding. Here, the EASE solution enables customers to create a library of guided problem-solving processes, milestones, and tasks. Then, the customer sets an action plan based on findings, assigning owners and approvers to each task along with due dates. Finally, this enables monitoring progress and scheduling validation tasks for sustained corrective actions.
A new release from summer 2025 includes OTJ capabilities. In performing corrective actions, EASE saw a way to facilitate training. As operator errors and poor training are shared drivers of non-conformances across the customer base, this became a clear need. Customers generate training from existing documents and publish it as contextual training that is triggered from findings. It can accommodate individual or group training, quiz users, and require sign-offs after training, also checking whether it addressed the issue. With the current “gray tsunami” of knowledgeable workers retiring, this need is only increasing.
EASE reports that customers have achieved excellent results. These include a 20% decrease in the cost of poor quality, a 2% improvement in OEE, and a 67% decrease in time to close out findings. Better audits and process improvements lead to lower cost of poor quality, higher productivity, and improved labor efficiency.
EASE claims to have over 350 customers using EASE in more than 3,500 plants across 60 countries. Customers are in the automotive, aerospace and defense, furniture, and a range of both process and discrete manufacturing industries. It appears that in these companies, use is also growing, as EASE reports that the platform now supports over four million audits each year.
We look forward to following EASE’s continued progress and growth in the manufacturing markets. Clearly this company Is helping manufacturers rethink and improve their audit processes. Ironically, Julie Fraser met Ease.io at the Manufacturing Leadership Council’s Rethink 2025 event. Thank you, Josh Santo, John Fredrickson, and Andrea Walter, for the briefing!
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How are manufacturers approaching the AI opportunity?
We invite you to join our research study on the challenges, capabilities, and future plans manufacturers and supporting engineering (EPC) companies have for Artificial Intelligence (AI). Please take about 10 to 15 minutes to complete this short survey to share your perspective.
All individual responses will be kept confidential. Thank you for helping us understand and shape the future of AI.
As a thank you for your time, Tech-Clarity will share a copy of the final results with you.
[post_title] => AI Maturity in Manufacturing and EPC
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If you are in the semiconductor industry, do you have the right development and manufacturing solution to scale the business to meet growing demand?
The semiconductor industry is entering a period of rapid growth, driven by AI, electric vehicles, autonomous systems, industrial connectivity, and rising data demands. To capitalize on this opportunity, semiconductor companies must scale to meet growing demand.
Yet, nearly all semiconductor companies report challenges with New Product Introduction (NPI), often caused by disconnected processes, limited visibility, and tools that don’t scale. Top Performers are addressing these issues by investing in digitalization and adopting PLM platforms tailored for semiconductor development. What should semiconductor companies consider to select the right solution?
Based on a survey of 207 semiconductor and high-tech professionals, the Buyer’s Guide for Semiconductor Development: Ideation through Manufacturing outlines key buying criteria across four critical areas: software functionality, service and implementation support, vendor capabilities, and company-specific needs. Based on expert interviews and survey research, it’s designed to help semiconductor leaders evaluate solutions to invest in the tools that will support scalable, profitable growth.
Please enjoy the summary* below. For the full research, please visit our sponsor, Siemens (registration required).
To learn more about the business value of investing in development and manufacturing processes, read our survey-based research report, Three Ways Semiconductor Companies Can Prepare for Profitable Growth.
By diversifying into different industries and broadening their portfolios, semiconductor companies can adapt their existing expertise and innovations for new applications and high-growth areas that require specialized chips like AI, electric vehicles, and autonomous driving. Not only does this open new revenue streams, but it also reduces development costs and improves margins. It also helps offset demand shifts, such as slowing mobile phone sales.
However, managing multiple product lines adds complexity, necessitating efficient processes to encourage reuse and streamline development. Improving how they manage and integrate data can help.
To uncover what drives leading performance, Tech-Clarity surveyed 207 semiconductor and high-tech professionals and identified “Top Performers” as the top 25% that outperform their competitors in metrics that indicate business success. These metrics were:
*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 (registration required).
If you have difficulty obtaining a copy of the research, please contact us.
[post_title] => Semiconductor Buyer’s Guide: Ideation through Manufacturing
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How can design engineers balance conflicting time, cost, and quality goals?
As businesses and products grow in complexity, design engineers have much to consider to produce optimal product designs. This is particularly true for smaller and medium-sized businesses (SMBs) that struggle with the same challenges as their larger counterparts, but have fewer resources to address them. What are the most successful SMBs doing to manage this? This research explores this question.
Based on a survey of 230 respondents, this research study examines engineering practices and simulation use. It identifies how executives at SMBs (companies with revenues less than a billion US dollars) can realize higher development returns through simulation-driven design, which should lead to increased profitability.
Please enjoy the summary* below. For the full research, please visit our sponsor, Siemens (registration required).
Increasing Complexity
Engineers have much to consider to design products with the best chance of market success. Products must be high quality, economical, and fast to market. However, as business environments and products become more complex, old ways of working may no longer be enough. Engineers need better methods to navigate the complexity of their engineering and design decisions to meet their goals. This can be especially challenging for a resourced-constrained smaller or medium size business (SMB).
What SMB Top Performers Do
Despite this complexity, Top Performers have implemented practices that allow them to be 2.1 times more likely to have highly effective processes to understand trade-offs. To achieve this, they increase their use of simulation and invest in more software capabilities. Unlike their less successful competitors, they leverage simulation throughout the entire product development lifecycle, supporting a simulation-driven design approach. In fact, SMB Top Performers are 75% more likely than Others to use simulation at the concept phase, and they continue to use it from this early stage through testing.
This is such a powerful approach that 99% of SMBs using simulation to explore design ideas report finding value. They report benefits such as better products, greater productivity, faster innovation, and a higher return on their development investments.
The Right Solution
Part of successfully adopting this approach requires using the right solution and technology. Design engineers report that CAD/CAE integration and embedding simulation inside CAD are the most important solution qualities to support their use of simulation.
This Research Report
This report shares research findings that provide an in-depth look at why today's engineers at SMBs have so much more to consider than they did even just ten years ago. Design decisions are even more complicated, and simply relying on experience is no longer sufficient. The research reveals what the most successful SMBs do to address this, helping them to release more successful products and improve their profitability.
Significant Product Complexity
While business complexity has created a challenging environment, the products have also become more complex, creating even more difficulties for engineers. The graph shows the top sources of product complexity. Much of this complexity comes from increased requirements.
More Requirements
With increased regulations, engineers have more safety requirements to deal with. As we saw earlier, quality is critical to product success, and this is driving more quality requirements. Customers also expect high performance, which is vital for competitive differentiation. Yet, innovation requirements have increased the number of components and systems, creating more factors to consider and making it even harder for engineers to understand the impact of their decisions. They need better ways to understand this to optimize designs and avoid inadvertently introducing errors. They also need to validate and verify requirements.
Getting this insight as the engineer works on it is the most efficient use time, especially compared to waiting weeks or months for physical test results when the design details are no longer fresh in the engineer's memory. Not to mention, the later it is in the design process, the more a design has solidified, meaning changes will impact far more components. Any error or overlooked impact will result in errors that can increase costs, cause delays, and hurt quality.
More Configurations
Finally, as companies need to appeal to various market and customer needs, engineers must manage multiple product configurations. Each variant must also meet safety, quality, and performance requirements, adding further complexity while increasing the risk of missing the mark on critical product success factors.
Integrated Design and Analysis
Regardless of performance, design engineers agree on what helps them use simulation the most. Integrated simulation and design tools and simulation embedded inside CAD are the commonly identified features. These features make simulation more accessible to design engineers and provide a way to access the functionality without disrupting their workflow. Plus, engineers stay in a familiar environment.
Integrated Test and Simulation
Beyond making simulation easier to access, most design engineers also appreciate when simulation and test are integrated. As discussed previously, this can help reduce test time. At the same time, engineering teams can benefit from access to test results to improve future simulation models to catch problems caught during physical testing.
How can semiconductor companies establish a foundation to scale and profitably grow?
The semiconductor industry is projected to experience substantial growth over the next five years. How can semiconductor companies position themselves to take advantage of this growth and increase their profits? What challenges should they overcome to scale and grow the business?
Based on a survey of 207 semiconductor and high-tech professionals, this research study examines semiconductor companies' growth strategies. It identifies challenges related to new product introduction (NPI) challenges that hinder their progress. The research reveals best practices for overcoming these challenges and shares recommendations for establishing a foundation for scalable and profitable growth.
Please enjoy the summary* below. For the full research, please visit our sponsor, Siemens (registration required).
Expand Offerings
By venturing into new industries and broadening their portfolios, semiconductor companies can leverage their existing expertise and innovation investments while tailoring offerings for different use cases. For example, AI, electronic vehicles, and autonomous driving all require specialized chips. By adapting their offerings for these various applications, semiconductor companies can unlock new revenue opportunities. Additionally, reworking existing offerings for specific applications reduces development costs for adjacent offerings, thereby boosting profit margins. Moreover, diversification can help mitigate risks associated with fluctuating demand in specific segments, as experienced with mobile phones in the past. However, overseeing the development of various offerings introduces complexity that must be managed, especially to encourage and support reuse.
Accelerate Time to Market
The cyclical nature of the semiconductor industry means timing is crucial. Being the first to market allows a company to seize emerging trends and technological advancements ahead of competitors, thus gaining a competitive edge by capturing market share before rivals respond. This strategy also maximizes the revenue potential of new offerings before the next generation emerges. To achieve this goal, companies must find ways to improve process efficiency.
Innovation
Semiconductor companies face many opportunities for innovation, especially to meet the demanding requirements of AI applications. Those that can improve performance and reduce power consumption better than competitors should capture a substantial share of that market segment. Capabilities that foster collaboration and leverage existing expertise should help to generate new ideas and solutions to accelerate innovation.
Sustainability Strategy
Once semiconductor companies establish a foundation for growth, another important consideration that can provide a competitive advantage is sustainability. While only 15% of companies reported that sustainability is part of their growth plans, an impressive 97% of Top Performing semiconductor companies have implemented a sustainability strategy.
A well-defined sustainability strategy can give a semiconductor company a competitive edge. Many customers increasingly focus on producing energy-efficient, sustainable products with a reduced carbon footprint. Consequently, these customers are more likely to engage with semiconductor companies that prioritize sustainability. The graph shows the top actions taken by Top Performers to become more sustainable
Leverage the PLM Foundation for Sustainability
With the integration of data through a semiconductor PLM platform, companies can also utilize this information to support their sustainability initiatives. By employing digital technologies such as digital twins, simulations, analytics, and BOM roll-ups, companies can evaluate sustainability factors like the carbon footprint from the early stages. This helps understand the impact of different scenarios or options, enabling businesses to identify the best strategies for achieving their sustainability goals. The collaboration tools, supplier management capabilities, and integrated data provided by PLM can assist in capturing the necessary information for these assessments and enabling more informed decision-making.
*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 (registration required).
If you have difficulty obtaining a copy of the research, please contact us.
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How can automotive manufacturers improve engineering productivity?
It's an inspiring time for the automotive industry. Innovations through electrification, automation, and more are revolutionizing the industry like never before. Vehicles continue to be more comfortable, safer, and fuel-efficient, while new service offerings present further opportunities for innovation. All of this relies on the engineering team’s ability to deliver. Unfortunately, engineers regularly lose productivity to non-value-added tasks that not only rob them of their ability to innovate, but also threaten their company’s ability to compete, differentiate, and grow. Imagine the potential of identifying and removing the most common non-value-add activities engineers face and empowering them to focus on better vehicles, components, and systems.
This research examines how engineers spend their time, where they lose productivity, and the impact on the business. It then identifies solutions and approaches to reduce time wasters. Based on a survey of 228 manufacturers across industries, this report focuses on automotive companies and looks at the challenges and opportunities from their perspective.
Please enjoy the summary* below. For the full research, please visit our sponsor, Siemens (registration required).
This report is based off the research published in The Business Value of Reducing Engineering Time Wasters which takes a look across all industries.
For other industry-specific related research, read our Reducing Engineering Time Wasters reports for:
To received personalized recommendations for how your company could improve engineering productivity, take our 5-minute online assessment.
Engineers Impact Business Success
Automotive companies’ ability to deliver exceptional offerings is critical to success. Likewise, their engineers are crucial to ensuring vehicles, components, and systems have what it takes to succeed in the market. Therefore, empowering engineers is key to the successful execution of business strategies.
Too Many Time Wasters
Unfortunately, engineers report spending too much time on non-value-added work with too many interruptions, taking them away from critical innovation work. Furthermore, 97% of surveyed automotive companies say this loss in engineering productivity comes at a significant business cost due to missed deadlines, higher costs, and less innovation. To overcome productivity losses, one approach is to manage product data and processes better and make it accessible to those who need it, when they need it.
Reclaiming Wasted Time
This report identifies major engineering time wasters in the automotive industry. It explores how companies of all sizes reclaim lost time by examining the use and value of PLM (Product Lifecycle Management) solutions to centralize data, manage processes, and collaborate better. PLM users reported fewer changes due to outdated information and errors, significantly reducing non-value-added work and shortening development times. This report also examines how companies select and use PLM solutions, including cloud-based implementations.
Based on industry experience and research for this report, Tech-Clarity offers the following recommendations to automotive companies:
What would add value to a comprehensive, AI-based connected frontline worker (CFW) platform? Augmentir has added more customized and agentic AI, and we see considerable potential benefits. This company launched in 2019 with AR and deep machine learning-based AI, and has most recently added a no-code industrial AI agent studio. This is an addition to their GenAI-based Augie capabilities that span the platform.
Augmentir set out to address “the impact of a smaller, less skilled, and less experienced frontline workforce” and is making headway in building out the software and the customer base. The concept is to have a single pane of glass where a frontline worker gets everything they need, in context, and nothing more. The platform integrates with a wide variety of plant and enterprise software for training and daily work.
Augmentir’s platform includes what most CFWs do: content authoring and conversion for instructions, a skills matrix, knowledge management, and sharing. It delivers any needed training based on the user’s profile and previous experience with a particular task in the flow of work. It also includes data visualization and reporting, with Microsoft’s Power BI embedded in the Augmentir platform. The founders also have an augmented reality background, so leveraging that immersive approach is native.
Where it differs from other CFWs: Augmentir aims to close the loop with machine-learning style AI, analyzing data from the operation to identify top opportunities for continuous improvement. As work proceeds day-to-day, deficiencies or errors in performing tasks appear by task, machine, worker, or cohort of workers. They call these True Insights. These indicate not only where more training might be needed, but also where to focus efforts to improve process efficiency, instruction clarity, or safety.
In early 2025, Augmentir introduced its Industrial AI Agent Studio. This builds on the 2-year-old Augie GenAI in the platform. Augie already included Assistants for Work, Content, Data, and Extensibility, as well as APIs.
Now, it also includes agents for maintenance notifications and what they call True Productivity. True Productivity is a stacked ranking across people and work processes. Seeing the ranking from best to worst indicates where targeted training is needed – a job or area, a worker, or a work cohort. This augments the existing data flows for continuous improvement and targeting training.
Augmentir has very large customers, and this no-code approach to building agents enables them to craft agents specifically tailored to their needs. The company has published its Six Laws of AI Agents, and its Industrial AI Agent Studio helps companies build agents that adhere to those laws. Simply, they are:
These are sensible governance principles to safeguard their customers’ and workers’ integrity and safety.
So, if companies have MES, QMS, SCADA, and more, what added value can they gain from using Augmentir? Target markets include food & beverage, paper, CPG, building materials, chemicals, pharmaceuticals, and industrial equipment. These companies do not all have deep MES that supports workers well, and they have many other systems that contribute to the data for the frontline.
They have customers in over 70 countries, including many large global players. Some have measured value. The large print in the diagram below shows a pure benefit. The small print is fascinating—a comparison of this inherently AI-based approach vs. earlier generation or non-AI-based approaches to CFW. The delta is significant (250% to 400%) in every case.
One company reported that if every worker just followed standard work, the operation would be 31% more efficient. With employee turnover high, cutting onboarding time by 82% with this on-the-job training and support is also significant. Saving time on issue resolution is always valuable, as time is a resource that cannot be replaced.
To build out the model of what should be happening in a plant, Augmentir has always supported time-and-motion studies. Customers have now conducted over 5 million optimized time-and-motion studies on this platform.
It’s great to see Augmentir’s expansion to include every flavor of AI (ML, Gen, and Agents). We are particularly pleased to see their Six Laws of AI Agents as guidelines built into their new Industrial AI Agent Studio.
Those seeking a CFW to support a wide range of use cases would be well-served to review Augmentir and its platform. Beyond workforce instruction and training, this is natively designed to accelerate a range of continuous improvement efforts.
This company was founded by some of the most successful serial entrepreneurs in the industrial software market. The brand-name customers making enterprise decisions to buy and implement Augmentir speak well to the confidence they have in this company and its platform.
Thank you, Chris Kuntz, for briefing our Rick Franzosa and Julie Fraser. We look forward to following your progress in the market.
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Can today’s aerospace and defense (A&D) manufacturers meet the challenges of adopting Model-Based Enterprise approaches (a requirement for some new DoD projects), leveraging AI, while continuing to support legacy products that are over 50 years old? That is the challenge that iBase-t’s customers face every day. We recently sat down with iBase-t’s management team to receive a business update. Spoiler alert: MES is not dead; it is growing and thriving, and so is iBase-t.
Over the years, a typical trajectory of MES software vendors has emerged. They start small, tackling a specific manufacturing industry. They make a name for themselves, start to grow in stature and reputation. For some, this leads to acquisition by a much larger company, typically a software vendor in an adjacent market, where every effort is made to modify the industry-specific MES into a more generic offering. This generic offering is ‘good enough’ for some industries, but no longer excellent for any one sector.
iBase-t was tempted to branch out to ‘related’ regulated industries such as medical devices and pharma. However, in the last few years, they have doubled down on their commitment to A&D manufacturing, serving leading A&D manufacturers like Lockheed Martin, GE Aerospace, RTX - Pratt & Whitney and Collins Aerospace among others, with a strong focus on the Model-Based Enterprise (MBE), supplier quality management, and sustainment (MRO – Maintenance, Repair, and Overhaul). They have also made changes and additions to their management team. The result has been significant business growth for iBase-t in the aerospace and defense (A&D) industry.
From a product perspective, the cloud-enabled Solumina iSeries has become the company standard, not only with new customers but also with existing customer migrations from the Solumina G-series. Each functional domain in Solumina iSeries—manufacturing execution, MBE, EQMS, SQM, BIS, and AI services—runs as independently scalable services. Customers can deploy Solumina in Customer-managed clusters (on-prem or cloud). This architecture supports high availability (multi-node clusters, health checks, automatic failover), rolling upgrades, and the ability to scale specific services (e.g., BIS or shop-floor execution) independently as transaction volumes grow. Solumina’s Business Integration Services (BIS) provide a standardized integration layer for ERP, PLM, HR, and other enterprise systems. Solumina is designed for deployment in ITAR/EAR-regulated and CUI environments. We discussed the contents of the upcoming Solumina iSeries release, i130.
iBase-t has been evangelizing the Model-Based Enterprise (MBE) for nearly a decade, and not only is MBE becoming a reality, but it is also becoming a mandate from their customers’ customer, the US Department of Defense (DoD). MBE involves a high degree of integration and automation across the product lifecycle. The challenge of MBE for A&D is that an infinite number of connections are required to support a digital thread across the supply chain and lifecycle of complex A&D products. It is virtually impossible for a single technology platform to support such a diverse range of applications. In addition, these A&D corporations already have multiple ERP, PLM, and MES environments (see Tech-Clarity’s research report Adopting a Model-Based Enterprise (MBE) Strategy).
In iBase-t’s view, the key is not the adoption of a single platform. The key is Model-Based Continuity, the ability to integrate and share key product data across the supply chain from suppliers to manufacturers to MRO depots, maintaining a digital thread to share critical product, quality, and safety information. At the technical level, Solumina’s MBE capabilities are built around a persistent mapping of PLM objects, 3D models, and manufacturing data. During execution, operators observe synchronized “tri-lighting” between the 3D model, data collection, and instructions, and can identify discrepancies tied back to the correct geometry and characteristics. Those same identifiers flow into MRO and sustainment, maintaining a digital thread from design through production to maintenance. This approach turns the “Model-Based Enterprise” concept into a practical, traceable data spine across PLM, MES, EQMS, and MRO.
iBase-t is working with their corporate customers to define key data elements and schemas to support model-based continuity at the part unique identifier (UID) and Quality Information Framework Persistent Identifier (QPId) level, annotated with semantic Product Manufacturing Identifiers (PMI).
iBase-t is wrapping up the development of the Solumina iSeries release, i130, planned for general availability in spring 2026. The enhancements and new product features were decided based on customers’ requests. A new product targeted for i130 is Material Out Time Tracking (MOTT). This capability had been delivered as part of implementation services for a few customers in the past; however, as manufacturers increasingly move to composite materials, it has become a mandatory requirement. This new product is Web UI-based and tracks layup and cure trigger points, splitting and kit creation, and time calculation of MOTT materials in work orders. iBase-t also made enhancements to include part attributes for MOTT materials, authoring of MOTT materials into process plans, and integrating MOTT processes into work orders.
Also targeted for i130 are enhancements to process planning, quality management, MBE, shop floor execution, and work order management.
iBase-t continues to invest in key areas of business value, A&D digital innovation, global deployments at scale, and support of A&D manufacturing from design through sustainment. This is enabled by their commitment to open systems and global partnerships. As with virtually all software providers, they are also investing in AI, but specifically from an A&D industry viewpoint, offering ‘air-gapped’ inferencing models that have no external connections, to protect IP and provide a total focus driven by models based on customer and iBase-t expertise. Solumina AI uses a retrieval-augmented generation (RAG) pattern, where Solumina remains the system of record and AI models operate over vectorized embeddings of controlled content (knowledge center, release notes, SOPs, and customer documents). Customer deployments can run in network-isolated environments with no outbound connections to public LLM services, and all training/fine-tuning uses customer and iBase-t curated data only.
For operational visibility, they have trained an AI engine using their data schema and BI scripts, converting natural language queries into SQL queries to understand their data retrieval methods.
Current AI offerings (delivered, in Beta, or under development) include:
Future AI Offerings:
Julie Fraser and I thank the entire iBase-t management team that participated in this business update briefing: Naveen Singh Poonian, Scott Baril, Sebastian Grady, Tom Hennessey, Kathryn Hoffman, Sung Kim, and Chris Morris. We have been covering iBase-t for many years. The focus, commitment, and drive of this management team are evident, and their current growth rate and success serve as proof.
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We are researching MedTech processes for bringing new products and devices to market. The survey asks about top challenges and approaches to common processes based on your role. The survey also looks at the current status of digital health, use of AI, digital thread maturity, and agility to respond to respond to market disruption. We will also use the results to report on the state of the market in MedTech and identify best practices. The survey takes about 10 to 15 minutes.
If you work in the MedTech industry and are part of the R&D, engineering, manufacturing, quality, or compliance team, either in management or individual contributor, 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
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How can A&D manufacturers extend the value of PLM into manufacturing? Aerospace and Defense companies face growing backlogs that hamper revenue and profitability. This buyer’s guide outlines high-level requirements for A&D companies to select PLM solutions that accelerate production without compromising quality, efficiency, or compliance.
Please enjoy the summary* below. For the full research, please visit our sponsor, PTC (registration required).
This research is also available in French, German, and Italian.
Welcome to AI 2025 (pun intended)Tech-Clarity had the opportunity to attend Autodesk University, now known simply as AU, in Nashville this year. AU 2025 was a large event with a lot of fanfare and excitement from Autodesk customers, partners, employees, analysts, and press. It’s always a great time to reconnect with Autodesk executives and product leaders to understand their strategy and hear from their customers about how using Autodesk products is working for them.
Nashville is the home of country music, but this week AI was on the center stage, as it was at the prior AU. One thing was different. Last year the audience listened to news about AI. This year, many came to hear about it. Autodesk CTO Raji Arasu’s AI Keynote was packed and people were taking notes. Companies know they need to pay attention to understand AI opportunities.
We titled our recap of AU 2024 as Autodesk University 2024 Focuses on AI, Platform, Core Products, and more AI. In the spirit of all things AI, we asked ChatGPT to summarize our post from last year, and it said, “Autodesk is making AI a core platform capability to automate routine work, assist designers, and speed innovation through integrated, scalable tools.” AI was the key takeaway of the multiday conference, according to AI. There was more, as the title shares, but the emphasis was on Autodesk’s AI efforts.
Following that, it didn’t seem likely that AI could gain any more focus at AU than they did last year. But it did. Autodesk CEO Andrew Anagnost focused the opening keynote primarily on how AI is transforming what they do and how their customers will work with their solutions. It was more than talk or generalities. Andrew brought up product leaders from all three of the industries they serve and showed tangible, valuable ways AI can add value by solving real problems.
Autodesk is not just researching AI technology, they’re focused on how it improves the way their customers work. When Andrew was asked what sets the Autodesk agentic AI solution, Autodesk Assistant, apart from others he shared that they are not adding a generic tool, but instead adding a layer on top of their solutions that takes into account the context of what application the user is using and what they’re doing. Autodesk’s AI agents will be specific about what they can do, not just try to be able to do anything. We agree with the practical approach because it helps customers get moving with AI quickly without having to do all of the learning and experimentation themselves.
In parallel with the practical applications, Autodesk is investing heavily in core research. They created an AI Lab team with dedicated AI experts since 2018. Since then, Raji Arasu’s shares that they have developed patents and published over 90 AI research papers, and she claims that Autodesk is the leading publisher in AI for CAD, design, and geometry. From what we’ve seen, that is far more than others have invested.
This year, Autodesk shared a lot of AI progress. Last year we learned about Project Bernini, Autodesk’s R&D effort into AI for 3D shape creation. It turns out that Bernini was one of several foundation models that the Autodesk AI Lab was investing in. Autodesk is doing something unique. They are creating foundation models for 3D, as a corollary to the large language models (LLMs) that fuel AI giants like ChatGPT. Most of the research has gone into the detailed aspects of geometry like constraints and attachment points to precisely control models. The result is professional-grade foundation models for 2D and 3D. Their strategy is to have a hierarchy of AI models (see graphic) that starts with LLMs and concludes with industry specific models that understand 3D, CAD, physics, and event behavior. Following that will be customer specific models that can incorporate and apply detailed company knowledge.
They also shared significant advances with Autodesk Assistant. They learned from their early investments in AI assistants and now have a uniform assistant that is consistent across products. As mentioned earlier, one of the key benefits is that it maintains context. I expect we will see more from Autodesk from their agentic AI investments as they learn to leverage generally available LLMs and further exploit Autodesk’s foundation models.
Autodesk has been investing in generative design from the early days. Now, they are taking that to the next level with what they call “Neural CAD” leveraging “Neural Technology.” This is the applied result of Autodesk’s modeling expertise, early investments in generative design, and their recent research into 2D and 3D AI foundation models. Neural CAD models are different because the foundation models are trained on how people design, not from an LLM.
The result is the ability to leverage GenAI foundation models to generate editable CAD objects with sketch and text prompts, which Autodesk says is a world first. This may very well be true. As compared with typical generative design models, the result is not just a 3D shape but true CAD geometry, a “first class citizen.” Neural CAD not only generates a solution, it creates the history and sequence of Fusion commands needed to create it. This way it can be modified like any other CAD model. Neural CAD will be available in Fusion and support Fusion BOMs, parts, and assemblies.
Raji Arasu shared the significance of Neural CAD. She explained that we need to rethink CAD engines, and compared it to moving from combustion engines to electric drive. She says the future of CAD engines is dynamic, adaptive systems that create editable geometry. It’s a bold vision, and Autodesk has taken significant strides in that direction.
In addition to general AI announcements, we heard specific examples applicable to the manufacturing industry. Autodesk’s Executive Vice President for Design and Manufacturing, Jeff Kinder, explained Autodesk’s AI strategy for manufacturing. The first phase, he explained, is task automation. The second phase will be workflow automation, and the final phase is planned to be systems automation. Autodesk has big plans and a structured approach to introducing AI into their solutions.
Vice President of Design & Manufacturing Product Development Stephen Hooper shared specific examples of agentic AI capabilities focused on solving practical issues. The examples ranged from Fusion integration with Microsoft Office to address common, time-consuming challenges like preparing data for design reviews to more complex examples including applying a machining template to a new part.
Some of the other examples shared include bringing AI to Alias Form Explorer to aid with conceptual design by learning a brand’s “design language” and applying AI to aerodynamics. In Fusion, he mentioned exploring potential starting points for a design from prompts and creating CAD models you can work from, likely referencing Neural CAD. Other examples included auto dimensioning and tolerancing and automating 2D drawings with annotations. He also shared examples aimed at unifying factory and production solutions. The commonality in these examples is that they solve specific, practical challenges in design and manufacturing.
Autodesk has been making strides across all its primary industries, AECO (Architecture, Engineering, Construction, and Operations), Media & Entertainment, and Design & Manufacturing. At the core of each of these is the Autodesk Platform, and the expression of these is an “industry cloud.” As Andrew shared the industry clouds are a new codebase but the work with the tools of today and the tools of tomorrow. Further, he explained, that “AI is native to industry clouds.”
Autodesk is investing in all of their industry clouds. They shared developments that help cut time and effort in developing media content like movies and television shows. They also highlighted their continued move to bring project management and BIM into the Autodesk Construction Cloud to create the central repository for infrastructure projects.
The industry cloud for Design & Manufacturing is Fusion. Autodesk continues to invest in the Fusion platform to make it a more complete offering. We’ve been impressed over time with their vision for an end-to-end solution that embraces openness and the granular data approach. Over time, we expect to see Autodesk customers migrate from existing solutions like Inventor and Vault to Fusion.
One of the most interesting developments this year is the announcement about the Manufacturing Data Model. It’s the culmination of investments to bring product digital thread data from all of Autodesk’s products into a common ontology. It’s intended to be open and extensible so manufacturers can connect data from the many other applications with Autodesk-generated digital thread data. Autodesk announced that product lifecycle data is now part of Fusion, that all of the manufacturing data is available in the graph database, and that there are APIs for Fusion, Vault, and Inventor. Srinath Jonnalagadda, Vice President of Data Management for Design and Manufacturing, explained that Vault Data and Fusion are now available through Autodesk Platform Services in the form of Fusion granular data APIs. This is an important step toward a unified Fusion solution. This has big implications for Autodesk customers and partners, opening up integration and extensibility of Fusion applications.
Overall, data and integration were key focus points for design and manufacturing. They announced integration between Fusion and Vault, Autodesk’s proven product data management (PDM) solution, bringing Autodesk’s PLM (product lifecycle management) and PDM data together. This is an important step forward for Autodesk customers that want to have product development and other product-related processes integrated with their product design data prior to moving fully to Fusion. This connection will become increasingly seamless as Fusion matures.
Once again AU was home to the PLM Summit, bringing together customers of Autodesk’s PLM solutions. It was a collaborative session, as usual, with manufacturers and Autodesk partners sharing their successes and challenges with each other to help everyone improve. Our two key takeaways are represented in the following pictures.
1 – The way Autodesk customers use Fusion PLM is varied based on their needs. This is a testament to the flexibility of the solution. This panel hosted by Michael Vesperman included four companies and each shared the different ways they use Autodesk PLM solutions.
2 – Fusion PLM is not just for simple companies. Bridgestone explained how they support standardized processes across multiple, global locations. This chart helps describe the level of complexity of their PLM processes supported by Autodesk.
It will be interesting to watch as Fusion Manage supports more PDM capabilities and rivals the functionality in their mature PDM solution, Vault, in a more integral fashion with broader process management and enterprise PLM capabilities. Our eyes are on Autodesk to see their continued development and maturation in PLM.
Autodesk clearly believes the key to differentiation and success in the future is AI. Clearly, AI is upending a lot of existing solution paradigms. At the same time, however, they are working to make their core products and industry clouds better at meeting customers’ functional needs. Autodesk has a significant advantage in AI, particularly in the 3D AI space, as a result of their significant R&D investments. Time will tell how much that translates to future success, but if Autodesk is right, they are getting a significant head start on their competition that will be hard to match.
Thank you Autodesk for including us in AU 2025, it’s always a pleasure to learn about what’s happening in Autodesk and the Autodesk ecosystem.
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We had the opportunity to attend an AI-focused ChapsVision event in New York City. The meeting was a continuation of an annual event hosted by Sinequa by ChapsVision, one of the companies acquired to form ChapsVision, and was well attended. We were introduced to Sinequa and their AI-powered enterprise search a couple of years ago, and we’re excited to see the significant role they are playing within ChapsVision.
ChapsVision is an interesting solution provider. They are only 5 years old, but they have acquired 29 companies to form what they call “the trusted partner for your agentic AI journey.” We had the opportunity to hear Brian Kirk, GM North America, share their view on the future of the AI market. In his terms, “the future is agentic.” He shared some important ChapsVision beliefs (see image) to describe their vision.
The beliefs Brian shared align well with our view of AI, in particular, the importance of accessing company knowledge and the importance of vertical industry understanding. Our research and experience show the importance of verticalization for AI. In specific, our Making Manufacturing Analytics and AI Matter report shares that manufacturers want industry- and application-specific AI software. Solutions are more valuable when they understand the data, semantics, and the context, which is all very specific to industries. In Brian’s words, “The agentic AI war will be won in the (Industry Vertical) trenches.” We couldn’t agree more.
The event was grounded by one of the best AI keynotes I’ve seen. We heard from Raphaelle d'Ornano about Decoding Discontinuity. It was a high value, low pretense discussion where she introduced “Orchestration Economics” and the importance of orchestrators and agents to deliver AI value. She shared three laws of economic value in the Agentic Era:
It was a great construct to think about how agentic AI adds value and what determines solution provider worth.
The conference went beyond high-level positioning and pillars to share details about ChapsVision’s offerings. ChapsVision offers three primary solution platforms:
We learned more from an education presentation by CPO Jeff Evernham, who presented on the five eras of agentic AI revolution before sharing details about their products. Some of our key takeaways were that ChapsVision is packaging agents as tools, becoming an agentic platform, and verticalizing. Further, we heard that they are committed to being multi-model and allowing customers to choose their own LLMs. ChapsVision doesn’t want to create an LLM, he explained, they want their customers to get more value from them.
As mentioned earlier, ChapsVision believes in industry specialization. They focus on key verticals, including manufacturing, energy, life sciences, private equity, and legal businesses. There was fascinating information about ArgonOS for data ops and decision intelligence, and they shared examples for manufacturing, supply chain, and maintenance. We also heard about the value of their Systran solution for language translation. Another interesting proofpoint of their industry focus was the explanation of their AI Workplace solutions. Mingee Kim , Head of Customer Success, and Lead Presales Consultant Irene Margarit showed how they are building repeatable, verticalized solutions. The current focus has been on Legal and Financial Services verticals, but the approach is applicable to all of their verticals. We look forward to learning more as they extend Workplace solutions to manufacturing.
Based on our own industry specialization in the industrial industries, what caught our attention the most were the solutions specific to manufacturing. In particular, we were excited to get a refresher and learn more about Sinequa. We and saw a Sinequa demo focused on finding part information, including specs and drawings, which are typically spread out across multiple systems. The demo explained that companies need to find part data so they don’t recreate them, and went further into the negative financial impacts of the inability to retrieve part information.
They explained that Sinequa is a governed, secure, and trustable solution grounded in the content of the organization. He shared that Sinqua connects to a variety of enterprise systems, including PLM, ERP, SharePoint, and more, but only shares what the user is allowed to see based on underlying permissions.
Miles Yaeger demonstrated a Part360 Dashboard of the part, connected and contextualized, showing a comprehensive overview of part data. To demo showcased their understanding of engineering and manufacturing data, testaments to their vertical focus, including:
All of this data was traceable back to the engineering documentation sources, providing trust while also offering users the ability to drill down to the underlying data.
One of the highlights of the event was a presentation by global power technology leader Cummins Inc.. Cummins was one of a number of larger, world-class manufacturers in attendance including Boeing, Blue Origin, Northrop Grumman, and Exxon. We heard from Scott Beard, Engineering Efficiency Project Manager for Cummins, as he shared an informative and entertaining story of how Cummins chose to use Sinequa as their solution for their 13,000 engineers to look for specialized information.
Cummins ran one of the most metrics-driven proof of concepts (POC) I’ve heard of. First, they selected 200 testers from across their business units and functions and asked them 36 questions about their ability to find and leverage product information. They gained a baseline that showed a 19 out of 100 performance score and 4.4 out of 10 on a satisfaction scale. Further, they calculated an average of 5.3 hours per week of wasted time looking for data. That is a common scenario, according to our Effective Design Data Management and Collaboration research study, that shows engineers typically spend 19% of their time on non-value-added data management activities, which equates to about one day per week.
The scoring for the POC included objectives for productivity, lifting real-world decision-making performance, how desirable the solution is to users, whether it beats incumbent solutions like PLM, and whether it scales beyond tech search. In addition, he explained, it had some advanced features test. Multiple solutions were benchmarked and Scott says that Sinequa was the clear winner. The results included a 63% productivity gain, representing a 2.9 hours per week improvement. Sinequa also performed well on other metrics, including the performance and interest scores, leading to their selection as the solution of choice. It was a fascinating story set to a theme of The Wizard of Oz, of all things!
Scott reported that Cummins achieved a 15X ROI from their investment. This confirms what Jeff Evernham said earlier in the day, that although a recent MIT study shares that 95% of companies are missing agentic AI value, the other 5% are seeing significant value.
Going forward, Cummins explained that they see more value from Sinequa than just part search. As Scott explained, they now view Sinequa as their “Knowledge Hub for Enterprise AI.” They’re looking at 7 different design patterns and planning to implement virtual SMEs (subject matter experts), such as a geartrain expert. He also pointed out the value of capturing Cummins corporate IP (intellectual property) by interviewing retiring workers to build out a Cummins knowledge base.
ChapsVision has assembled a significant collection of AI capabilities delivering vertical solution value. We were impressed with our earlier conversations with Sinequa, and now we are more excited to hear about how ChapsVision will leverage and extend Sinequa’s capabilities for the manufacturing industries. We’re excited to see how the combined offerings help industrial companies get business value from AI and their digital threads, and look forward to watching the progress with workplaces to deliver more ready-to-use, verticalized solutions.
Thank you, Laurent Fanichet, for inviting us to the event and to Stacey Greene and the team for their organization that made it such a valuable time. Further thanks to Brian Kirk, Jeff Evernham, and Xavier Pornain for sharing your vision and progress in AI for the manufacturing industries. We look forward to staying in touch and learning more about your plans.
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We recently had the opportunity to reconnect with aPriori Technologies, a company we’ve known for years. It has been exciting to watch their solution evolve from a product cost management tool into a comprehensive digital manufacturing intelligence platform. Today, their platform enables improved decision-making across sales, design engineering, sourcing, sustainability, and manufacturing.
aPriori’s cloud-based platform simulates manufacturing processes to deliver real-time feedback on cost estimates, manufacturability, cost drivers, and environmental impact. They say their solution, with its process-specific, regionalized manufacturing models and enterprise data integration, helps companies win new business, improve margins, and avoid manufacturability issues.
The system models the entire production lifecycle, taking into consideration part geometry, material, manufacturing process, and regional cost data. Leveraging hundreds of manufacturing processes and tens of thousands of materials, teams can run what-if scenarios, compare alternatives, and make early, data-driven tradeoffs when changes matter most.
aPriori supports multiple product lifecycle stages, from quoting and cost engineering through design and sourcing. The platform provides:
According to aPriori, several factors differentiate their solution:
aPriori says its customers use the platform to solve three primary challenges:
Tech-Clarity’s State of Product Development: 7 Trends Shaping Product Innovation research highlights top challenges facing product development:
aPriori seems well-positioned to help manufacturers address all of these challenges.
In an era of volatile supply chains and unpredictable costs, predictability in cost and sourcing can be a competitive advantage. aPriori’s ability to model what-if manufacturing scenarios across regions could help. For example, comparing production in China, Vietnam, Germany, Brazil, Mexico, or the US extends its value beyond cost optimization to supply chain resilience and agility. The solution could help hedge against volatility and enable companies to plan and pivot with greater confidence.
aPriori describes their solution as having a “manufacturing engineer in your pocket,” with its ability to embed process expertise into the product development lifecycle. Not only can this help address bottlenecks in handoffs to manufacturing, but it can also help overcome knowledge loss due to the retirement of experienced engineers. Retirements are eroding decades of tacit knowledge, while many younger engineers have limited shop-floor experience. It can help identify manufacturability issues earlier, despite growing complexity, and automate many manual tasks required to support decision-making on cost, manufacturability, and sustainability.
As younger employees join the workforce, a cultural shift is taking shape. As digital natives, they are adept at using technology for decision-making, rather than relying on years of accumulated experience and rules of thumb. As product complexity increases, decisions will become increasingly difficult, so this new generation will likely accelerate the adoption of intelligent solutions like aPriori to support better-informed decisions.
Given current market trends, aPriori appears well-placed to support manufacturers amid economic change and workforce evolution. The company’s next wave of innovation will center on AI-driven guidance, from sourcing coaches to intelligent DFM assistants, which should build upon their existing capabilities. The appointment of a new CTO with deep AI expertise demonstrates a commitment to this strategy.
We look forward to seeing how these developments further expand aPriori’s role in shaping the future of embedding manufacturing intelligence into the product lifecycle.
Thanks to Rick Burke, Chris Jeznach, and Mark Rushton for briefing us.
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Is your manufacturing organization ready for AI? In this conversation David J. Jacq, CEO of Mapex, and Rick Franzosa, VP of Research for Manufacturing at Tech-Clarity, Inc., will bring their deep expertise in digital transformation, operations, and advanced manufacturing to explore the challenges and opportunities in smart manufacturing in the age of AI.
Listen as David asks Rick about what companies should prioritize in this fast-changing world. Rick shares his view of top challenges and the evolving perceptions of MES, including when it became more strategic. They also explore MES implementation wisdom, including common mistakes and what companies need to do before an implementation. Even implementation failures come into the session.
They wrap up with a discussion of how AI has changed the role of MES – shallow or deep? Is AI a differentiating factor for MES already? How to avoid false starts when adding AI to MES. And a look ahead at what industrial companies should be doing now to prepare their operations for unknown future realities. Watch the full video podcast here.
[post_title] => Inaugural Mapex International Smart Industry Podcast on Digital Manufacturing and AI
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Once you have machine monitoring, MES, and scheduling on a common plant data model, how can you improve the ability to automate and execute the work? MachineMetrics is doing that by adding Max AI. While the company started in machine performance monitoring, this year it has expanded to focus on plant performance and frontline worker performance (See previous insight). This comprehensive plant data set, which can also encompass multiple plants, serves as the foundation for Max AI.
Can you un-tribalize knowledge? This is how MachineMetrics aims to tackle the frontline skills shortage challenge. They are creating a fleet of agentic digital coworkers as part of the Max AI addition. These agents have full access to the unified data model, so they know what’s happening and when, as well as who needs what data at any given moment.
Like a good expert coworker, the human workers can engage with these digital coworkers using natural language prompts. The system can find data, provide quick responses to questions, adjust and direct, or even automate core production tasks. It can also create a software widget for ongoing use for issues that arise on a regular basis.
One example of a specific agent is for shift note handoffs. The agent can automate the basics, since it has access to the full record for that shift, and operators can augment that with their own annotations. Another is for Continuous Improvement (CI), and the analysis capabilities of Max AI can help everyone be involved. The CI agent can greatly speed up data analysis and understanding, allowing people to focus their efforts where they will make the most impact on performance.
Digital coworkers are great, and people also need all elements of their experience with MES to be intuitive and match their expectations and specific job needs. MachineMetrics claims all screens are now configurable. A widget framework for building the UI enables drag-and-drop and adjustable customization to tailor screens so they truly support users. With the LLM-based capabilities of Max AI, customers can add a summary to a dashboard.
They claim some of this can replace Power BI and other data analysis and visualization tools for customers. A significant upgrade in functional scope this year has enabled MachineMetrics to be a more comprehensive single platform for the frontline than it was before. MES, scheduling, analytics, and data from connected machines and other software feeds any needed data through this single interface.
Beyond the unified data model, the software now also features a Knowledge Hub, where manufacturers can upload and centralize any knowledge they need. MachineMetrics has already uploaded thousands of machine manuals. Customers can provide work instructions and SOPs, safety and hazardous materials information, and generate documents based on experienced and skilled workers’ knowledge on any topic.
Even snippets from these experts can contribute to success if they leave or retire. For example, each machine may have a different impact when it’s down. The experts know whether a 10-minute downtime is important to address or not; when a particular vibrational pattern is likely to lead to a downtime event, and more.
All of this expert knowledge in a central location can be beneficial, as anyone who searches for the data they need to act knows. The Knowledge Hub is also part of what agents use to make recommendations.
The Knowledge Hub augments the rich set of data models, time-series data stores, vector and graph databases, and APIs. All of this builds on the original connectivity through APIs that got MachineMetrics its start.
MachineMetrics plans to release new agents soon. The shift handoff agent is scheduled to launch in 2025. After that, in early 2026, an agent to automate scheduling is due. The goal is to change behavior for operators and other manufacturing and production support staff.
The team believes that frontline workers require an easy, intuitive user experience, with access to the real-time data they need. This encompasses not just the task at hand, but also improving it and solving problems.
An onboarding agent aims to improve the implementation and rollout of this young MES. The company has already structured itself for long-term customer relationships in a land-and-expand manner. They have three main teams: Commercial, Product, and Technical Solutions that provide implementation.
MachineMetrics is rapidly making the transition from its machine monitoring roots to MES and an industrial data management system. While some of the core MES functionality is just coming online, the data structures and AI approach seem solid. These, along with the existing customer relationships, will form the foundation for continued growth and delivering value to customers.
Discrete manufacturers seeking a rapid approach to get up and running, both to keep equipment humming and empower operators, may find MachineMetrics worth exploring. Current customers are the focus, and they have reason to celebrate the rapid expansion of functionality and AI capabilities.
Thank you, Graham Immerman and Rutherford Wilson, for updating Rick Franzosa and Julie Fraser on Max AI and MachineMetrics’ progress in the market.
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The market for MES in mid-sized manufacturers has traditionally been underserved. Major MES vendors find it unsuitable, as it conflicts with their direct sales model and complex product offerings. However, mid-sized manufacturers have the same MES needs as larger companies but lack the IT resources necessary to implement and support these systems.
ISE has provided MES and paperless shop floor solutions for forty years through its MV2 MES solution. It has been proven to excel at understanding and addressing the needs of mid-market discrete manufacturers. The combination of customer satisfaction, recommendations, and ISE’s longevity suggests it’s delivering to customer needs. Topping the list of differentiators is ease of use, which was evident in its demonstration.
The MV2 product's broad-based standard functionality encompasses essential MES features, including production scheduling and execution, quality management, inventory control, time and attendance, Kanban, machine integration, and real-time analytics. These tools are specifically designed to meet the needs of mid-sized discrete manufacturers, ensuring cost-effective implementation and operational efficiency. Its MV2 platform is positioned as a flexible and scalable solution for a diverse range of discrete manufacturing environments, with a breadth of functionality that matches and, in some cases, exceeds that of enterprise-scale MES solutions.
ISE has been providing solutions to discrete manufacturing industries, with a focus on the unique challenges faced by mid-sized manufacturers in this market. The company was established to complement and integrate with what is now the Infor XA ERP system. More recently, ISE added standard ERP integration to Microsoft Dynamics 365 Business Central. Through its open architecture and API integration toolkit, MV2 has been integrated with other ERP systems such as SAP and Epicor Kinetic.
I had the pleasure of attending ISE’s Manufacturing Optimization Forum event on September 25, 2025. The event included a panel session featuring four customers who shared their experiences and the benefits they realized from MV2. Some highlights I heard include:
The evolution of MV2 is centered around three main pillars: scalability, interoperability, and user-centric design. Scalability ensures the MV2 product can grow alongside its customers' operations, accommodating increasing complexity without compromising performance. Interoperability focuses on seamless integration with other enterprise software systems, ensuring adaptability to different technological ecosystems. Ultimately, the user-centric approach prioritizes intuitive interfaces and streamlined processes, enabling manufacturers to optimize efficiency with minimal training and support requirements. Together, these pillars form the foundation for ISE's vision of delivering its comprehensive MV2 MES solution tailored to the distinct needs of smaller manufacturers.
Thanks to Jay Gentle, Erin Bonde, Chris McLean, Dan Van Kempen, Jim Rozewicz, and Rick Reith at ISE for catching us up on the latest news.
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How can manufacturers develop a digital thread and unlock the business value necessary to stay competitive?
Today’s manufacturers operate in an environment defined by compressed timelines, increasing product complexity, and heightened customer expectations. Success depends on the ability to move quickly without sacrificing quality, compliance, or profitability. To achieve this, organizations must enable seamless collaboration and smarter decision-making across engineering, manufacturing, quality, and the supply chain.
True operational efficiency comes from connecting people and processes through a single, reliable source of product information. When every team works from accurate, up-to-date product information, organizations reduce errors, eliminate rework, and respond more effectively to change.
A product digital thread makes this possible. Enabled by product lifecycle management (PLM), the digital thread creates a continuous, end-to-end flow of product data across the organization and throughout the product lifecycle. This eBook explores what a PLM-enabled digital thread is, why it matters, and how manufacturers can build one to drive lasting business value.
Please enjoy the summary* below. For the full research, please visit our sponsor, Propel (registration required).
New Product Development is More Complex
For manufacturers, delivering profitable products to the market has become significantly harder. Products are more complex than ever, requiring additional resources with expertise in new disciplines, driving up development costs, and putting profit margins at risk.
The Heightened Impact of External Pressures
Some of this complexity arises from external factors outside a manufacturer’s control. Customers are increasingly demanding, expecting innovative products more quickly than ever before. Competition is coming from all directions. Not only from traditional competitors, but also from new entrants. Our State of Product Development survey found that 56% of manufacturers face competition from adjacent industries, while 52% compete with low-cost or offshore manufacturers.1 Today’s supply chains add to the challenge. In fact, 74% of manufacturers in the survey identified supply chain disruptions or market volatility as a top challenge in product development.2 Beyond that, government and industry regulations are widespread, especially in High Tech and medical technology, demanding strict engineering and quality processes with thorough data collection and management.
Multi-CAD Environment Complicates Design Collaboration
Some of the complexity stems from internal issues. Remember when products were primarily mechanical?
Those days are gone. Now, mechanical, electrical, and software teams all need to work together – and be productive doing it. They must ensure that form, fit, and function all work in harmony while delivering their designs on the same development and launch timeline.
However, each design discipline uses different tools, with product data stored and managed separately or, in the worst case, only on an individual engineer's drives. Managing and accessing product data across multiple design systems, let alone file folders and shared drives, negatively impacts collaboration and reduces productivity.
Where to Start
For some manufacturers, establishing a digital thread may be viewed as out of reach when facing budget, resources, and time constraints. However, manufacturers can establish a digital thread despite these challenges.
Use 80-20 Rule
Applying the 80-20 rule helps focus on the most important and common use cases and workflows first. These deliver the most significant business value without getting bogged down with less common and more complicated edge cases. In short, keep it simple.
Keep Established Workflows
Established workflows need to continue, especially those supporting regulatory requirements, but avoid excessive customization whenever possible. Using out-of-the-box functionality saves implementation time and money, and reduces the need for dedicated IT resources.
Connect Existing Systems
There is no need to start from scratch. A practical approach is to connect existing CAD, PDM, and PLM investments and applications that are working well to create the product digital thread.
Take a Phased Approach
The best path is to take it one step at a time. Since data across systems is probably not perfectly aligned, a phased approach to PDM-PLM integration is preferred. Start with a small project, or assembly, to avoid a massive data cleanup upfront. Then add new projects and products as data cleansing progresses.
*This summary is an abbreviated version of the eBook and does not contain the full content. For the full research, please visit our sponsor, Propel (registration required).
If you have difficulty obtaining a copy of the research, please contact us.
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How can manufacturers develop a digital thread and unlock the business value necessary to stay competitive? Today’s manufacturers operate in an environment defined by compressed timelines, increasing product complexity, and heightened customer expectations. Success depends on the ability to move quickly without sacrificing quality, compliance, or profitability. To achieve this, organizations must enable seamless collaboration…
Revisiting the future of PLM in Consumer Packaged Goods in the Age of AI In 2022 Tech-Clarity, Kalypso, and PepsiCo discussed the future of PLM in CPG based on a Tech-Clarity survey on the state of CPG PLM. So much has changed over the last several years. Even then, the majority of companies felt their…
Can highly regulated pharmaceutical companies use AI effectively? What are companies doing to leverage AI without compromising their CGMP-validated processes? Please join this practical, real-world conversation on moving AI beyond pilots and into meaningful results. Tech-Clarity’s Julie Fraser joins with Kate Porter, Director of Product Management and Research at POMS, along with a customer. Roland…
How do manufacturers integrate design data (PLM) with manufacturing data (MES)? Tech-Clarity invites you to join a research study on PLM-MES Integration. Please take about 10 minutes to fill out our survey. As a thank you, we will send you a copy of the report summarizing the findings. In addition, eligible respondents will be entered…
How can a strong auditing program, as practiced in major automotive suppliers, improve? By going digital. Ease.io as been doing that for years, with a SaaS software platform for Layered Process Audits (LPAs), 5S, Safety Inspections, Gemba walks, Root Cause Analysis (RCA), and problem-solving. They recently added on-the-job (OTJ) training support to strengthen customers’ outcomes….
How are manufacturers approaching the AI opportunity? We invite you to join our research study on the challenges, capabilities, and future plans manufacturers and supporting engineering (EPC) companies have for Artificial Intelligence (AI). Please take about 10 to 15 minutes to complete this short survey to share your perspective. All individual responses will be kept…
If you are in the semiconductor industry, do you have the right development and manufacturing solution to scale the business to meet growing demand? The semiconductor industry is entering a period of rapid growth, driven by AI, electric vehicles, autonomous systems, industrial connectivity, and rising data demands. To capitalize on this opportunity, semiconductor companies must…
How can design engineers balance conflicting time, cost, and quality goals? As businesses and products grow in complexity, design engineers have much to consider to produce optimal product designs. This is particularly true for smaller and medium-sized businesses (SMBs) that struggle with the same challenges as their larger counterparts, but have fewer resources to address…
How can semiconductor companies establish a foundation to scale and profitably grow? The semiconductor industry is projected to experience substantial growth over the next five years. How can semiconductor companies position themselves to take advantage of this growth and increase their profits? What challenges should they overcome to scale and grow the business? Based on…
How can automotive manufacturers improve engineering productivity? It’s an inspiring time for the automotive industry. Innovations through electrification, automation, and more are revolutionizing the industry like never before. Vehicles continue to be more comfortable, safer, and fuel-efficient, while new service offerings present further opportunities for innovation. All of this relies on the engineering team’s ability…
What would add value to a comprehensive, AI-based connected frontline worker (CFW) platform? Augmentir has added more customized and agentic AI, and we see considerable potential benefits. This company launched in 2019 with AR and deep machine learning-based AI, and has most recently added a no-code industrial AI agent studio. This is an addition to…
Can today’s aerospace and defense (A&D) manufacturers meet the challenges of adopting Model-Based Enterprise approaches (a requirement for some new DoD projects), leveraging AI, while continuing to support legacy products that are over 50 years old? That is the challenge that iBase-t’s customers face every day. We recently sat down with iBase-t’s management team to…
In MedTech, are your company’s development, manufacturing, quality, and compliance processes keeping pace with your peers? We are researching MedTech processes for bringing new products and devices to market. The survey asks about top challenges and approaches to common processes based on your role. The survey also looks at the current status of digital health,…
How can A&D manufacturers extend the value of PLM into manufacturing? Aerospace and Defense companies face growing backlogs that hamper revenue and profitability. This buyer’s guide outlines high-level requirements for A&D companies to select PLM solutions that accelerate production without compromising quality, efficiency, or compliance. Please enjoy the summary* below. For the full research, please visit…
Welcome to AI 2025 (pun intended) Tech-Clarity had the opportunity to attend Autodesk University, now known simply as AU, in Nashville this year. AU 2025 was a large event with a lot of fanfare and excitement from Autodesk customers, partners, employees, analysts, and press. It’s always a great time to reconnect with Autodesk executives and…
ChapsVision AI Summit 2025 We had the opportunity to attend an AI-focused ChapsVision event in New York City. The meeting was a continuation of an annual event hosted by Sinequa by ChapsVision, one of the companies acquired to form ChapsVision, and was well attended. We were introduced to Sinequa and their AI-powered enterprise search a…
We recently had the opportunity to reconnect with aPriori Technologies, a company we’ve known for years. It has been exciting to watch their solution evolve from a product cost management tool into a comprehensive digital manufacturing intelligence platform. Today, their platform enables improved decision-making across sales, design engineering, sourcing, sustainability, and manufacturing. Who They Are…
Is your manufacturing organization ready for AI? In this conversation David J. Jacq, CEO of Mapex, and Rick Franzosa, VP of Research for Manufacturing at Tech-Clarity, Inc., will bring their deep expertise in digital transformation, operations, and advanced manufacturing to explore the challenges and opportunities in smart manufacturing in the age of AI. Listen as…
Once you have machine monitoring, MES, and scheduling on a common plant data model, how can you improve the ability to automate and execute the work? MachineMetrics is doing that by adding Max AI. While the company started in machine performance monitoring, this year it has expanded to focus on plant performance and frontline worker…
The market for MES in mid-sized manufacturers has traditionally been underserved. Major MES vendors find it unsuitable, as it conflicts with their direct sales model and complex product offerings. However, mid-sized manufacturers have the same MES needs as larger companies but lack the IT resources necessary to implement and support these systems. Decades of Service…
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