Making the value of technology clear
For more information on the Industrial SOE and Nexus, you can read the full blog post on the Hexagon site. You can also download the full white paper directly from Hexagon.
[post_title] => Hexagon Delivers a New Digital Industrial Platform to Accelerate Innovation
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There is a lot of talk about the value of artificial intelligence in manufacturing, and rightfully so. Although AI isn't new, it appears to be reaching a tipping point where companies are more open to exploring its potential and AI techniques are more accessible than ever. Manufacturers are acting on the opportunity. Our recent Making Manufacturing Analytics and AI Matter research, for example, shows that 99% of manufacturers plan to invest in analytics and AI in the next year.
Further, AI has moved from the experimentation phase to the value phase. Our latest executive survey, Executive Strategies for Sustainable Business Success 2024, found that AI / ML (Machine Learning) was the most common application type providing value, with 88% of responding companies reporting they achieved business value from AI / ML. Further, these results can come quickly. The manufacturing analytics and AI study found that companies achieve fast ROI more frequently from advanced analytics / AI than from their other software investments. For example, the data shows that 73% of respondents received benefits from GenAI in less than a year.
Our research shows that AI / ML can drive value, and companies can achieve that value rapidly. But that value doesn't come from buying software. It comes from applying the right solution to solve a real problem. However, many manufacturers don't know where to start or how to implement these capabilities. To better understand how manufacturers can target and adopt AI, we sat down with two experienced consultants with real-world experience in helping manufacturers improve business value by adopting AI. We sat down with Kalypso: A Rockwell Automation Business's Senior Manager of Data Science and Digital Transformation, Chelsea Barnes and Master Data Consultant William Rosengarten to get their perspective. Let's see what they have to say!
Jim Brown
Kalypso works with a lot of manufacturing companies. What are manufacturers asking for related to their AI strategy and implementations?
Chelsea Barnes
At one end of the spectrum, some companies are enthusiastic but lack direction. They know they need to do something with AI because it's a buzzword or their CEO says it's important, but they don't know what that looks like. They need help setting a direction. On the other end of the spectrum, some clients have a very specific problem and sometimes possibly even a solution in mind, like needing a machine vision solution to detect malformities or burns on a chip because it's costing them $10 million in scrap every year. They need help validating and implementing a solution. But there's also a middle ground where companies have operational targets in mind and maybe some initial hypotheses about how to improve them but aren't sure where to start.
Regardless of what the starting point looks like, the core of the ask is the same. They want value delivered quickly, at scale, using the best advanced technologies available.
Jim Brown
I've known Kalypso for some time and I appreciate that you don't believe in technology for technology's sake but focus on adding business value. How do you get companies started or help them frame their problem?
Chelsea Barnes
We help them discover where their business problems really are and what technology solutions are best suited to those problems. Then, we bring those things together. When we talk to a company, they know their issues far better than we do. For example, the people operating a line will be able to specifically articulate what problems are happening and have a very good hypothesis as to why they’re happening. Then, we bring our business, operational, and technology expertise to those conversations so that they meet in the middle with solutions.
Jim Brown
One of the things I appreciate is that you're not just technologists. You are domain experts who understand operations and the manufacturing industry. For example, in the Consumer Packaged Goods (CPG) industry, when you mentioned "burns on a chip," I knew right away you meant potato chips and not microprocessors. Can you tell me a little bit about why it's important that advisors don’t just approach their clients with AI knowledge, but also bring relevant business expertise to the table?
William Rosengarten
We're not coming in cold because we have a depth of expertise in the industry. We already have a point of view on the end goal. If a client comes to us with a problem, we know what best-in-class in the CPG industry looks like, so we can help them create a plan to achieve it.
Chelsea Barnes
Exactly. We bring together a variety of expertise to make that happen. We're coming in with a really solid set of hypotheses around what the problems typically are. We're familiar with approaches to improve quality yields and deal with issues like variable material inputs that cause problems for food and beverage clients. The specifics come from the client and their own knowledge, but our experience helps us get to a diagnosis more quickly.
Jim Brown
In a recent cross-industry survey, we asked companies about their AI goals. The most common goals identified across industries were product and service innovation, product and service performance, and workforce efficiency. Those are essential in any industry. A survey specific to the manufacturing industry, however, clearly identified cost reduction as the most common investment driver. What are the CPG companies you're working with looking for?
Chelsea Barnes
We’re seeing the same thing on the ground. There are two macro trends that are really squeezing manufacturers right now. The first is inflation, which increases cost pressures. The other is workforce turnover, including a wave of seasoned specialists leaving the workforce, which puts a new sense of urgency on workforce efficiency. To meet those cost reduction and efficiency goals, the top AI use cases we hear are around quality control, process optimization and predictive and prescriptive maintenance.
William Rosengarten
We also see a common pain point in accessing the right data, especially when working with time series data. Five years ago or so, manufacturers felt they needed to capture everything from the plant floor, store it in the cloud, and historize it. So many manufacturers have created a giant haystack of all of their data, and they're struggling to find that needle that will drive specific use cases like the ones Chelsea is describing.
Jim Brown
With all of the potential projects you may identify with a client, how do you help them decide on what to focus on? Do you counsel them to focus on the most significant problems, or maybe try to have them find repeatable problems? Or is it purely the project with the largest ROI?
Chelsea Barnes
Manufacturers are absolutely looking at ROI. They need to understand how it will affect the process, the tangible value they will get from the initiative, and how they will measure achievement. It's critical that they know what their quantifiable goal is.
However, when it comes to investments in digital, sometimes the value isn’t as clear-cut as a 12-month payback. In some cases, companies are looking to stay ahead of the competition by operating on the bleeding edge of innovation. This might justify a more long-term investment approach to allow the transformation they’re looking for to take root.
Getting back to determining ROI, we’re big proponents of rapid use case identification and prioritization, where you quickly narrow down your short list of high-potential opportunities before investing too much time rigorously evaluating all options. To do this, you need a good value calculation framework, which we bring to all of our assessment projects. But,you also need the right technologists in the mix to help you quickly vet the solutions and estimate implementation complexity to understand the cost of an initiative.
Jim Brown
Generative AI is on most peoples' minds and has become popular in conversations because of OpenAI and ChatGPT. However, many other AI and machine learning (ML) techniques are available. AI can be applied at different levels, ranging from companies wanting to retrieve data more effectively to the other end of the spectrum where they are pursuing AI-driven autonomous, real-time decisions to drive equipment behavior on the floor. How do you help your clients decide what technologies to apply for a specific problem?
Chelsea Barnes
We always start by confirming the business needs and what's the problem to solve now. Even if they come to us with a very specific request, like "I need a machine vision solution," we will diagnose the issue together and then confirm that's the right solution. We don't tell our clients to “go GenAI” their business. That wouldn't be good business for them, and it's not good business for us. We have a collection of tools in our toolbox to bring to this equation depending on the problem to solve and the data they have to work with.
William Rosengarten
We make sure to map technologies to business needs. For AI technologies, we consider:
The choice will be different if they just need to organize and retrieve data quickly or if they're looking for insights from the data they have. For example, you shouldn't use copilots for autonomous control, but it's valuable when a human is in the loop for decision-making. These questions help drive considerations about the modeling and architecture that should come into play.
Chelsea Barnes
We always look at what kind of algorithmic approach will be best suited for the scenario. While generative AI is the topic of the day, there are plenty of cases where you should not be using it. For example, a GenAI model will not help make a prediction to autonomously control a production process - think predicting fill by monitoring time series data so you can adjust your filler dosage so it comes exactly at target. It's just not suited to do that. But if you are trying to process something like a year's worth of shift logs to find anomalous patterns in those free text shift logs, that's a situation very well-suited for a large language model.
Two other important decision criteria in regulated scenarios are the risk of error and whether the results are explainable. GenAI models, which are neural networks, are by nature black boxes where you don't know how it arrived at a decision, so having a human operator in the loop is critical to confirm the results.
Jim Brown
AI copilots are gaining a lot of traction to streamline and improve human workflow. When do you find those applicable for your clients?
Chelsea Barnes
A copilot makes sense when they are trying to augment what a human can do, to make them more efficient in a process, or help them with the decisions that they are making. A good example for an operator would be a troubleshooting copilot. For example, a line is down and a fault code comes up. Instead of looking that up manually, the copilot could take the operator through a decision-making process and walk them through the troubleshooting steps.
Copilots are attractive because the manufacturing industry has not fully rebounded following COVID, and many companies still have jobs left unfilled. Retaining institutional knowledge in manufacturing is even more of an imminent and challenging concern as a substantial percentage of the workforce nears retirement. Many companies would love to get to lights-out manufacturing, but that can be decades away. So the goal is to find the best way to augment and assist the workforce they have. Copilots can help make them more productive and efficient, and equip them with decades of institutional knowledge, even if they haven't worked on the line for 15 years.
William Rosengarten
Agree. Copilot assistants are an excellent solution for capturing and retaining institutional knowledge. For example, they are very good at taking notes. A technician running a troubleshooting process is trying to get the line back up and running and typically doesn't have time to document what they're doing. They are making decisions on what steps to take based on their experience. A copilot could take notes about the decisions they make and the impacts they have on the troubleshooting process. Doing this creates a feedback loop that typically only exists in free text or just in a technician's head and tribal knowledge today. In that way, copilots can help guide troubleshooting and feed information into a knowledge repository to assist in future troubleshooting efforts.
You've shared a lot of insights into how manufacturers can identify the right business opportunities and apply AI to solve them. Two of my key takeaways are that it's essential to have industry expertise to help diagnose the problem and that it's critical to have diverse technical knowledge to be able to apply the right AI capabilities to get the job done. This is an exciting topic, and we'll stay in touch about it.
To learn more from Kalypso, explore the Kalypso website, a Kalypso interview about operational CoPilots, or Kalypso Insights on GenAI.
Thank you to Kalypso, a Rockwell Automation Business, and Hadley Bauer for arranging the interview. We learned a lot from the discussion and know manufacturers will, too.
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How can recipe-based producers ensure cost-efficiency and consistent, high-quality products no matter where they are formulated or produced? By implementing enterprise recipe management (ERM) with a manufacturing execution system (MES) to deliver closed-loop data flows in and out of production. Leading Consumer Packaged Goods (CPG), specialty chemical, and other manufacturers are standardizing for ERM. Quite a bit of technology is required to digitalize this recipe transformation process (recipe management, PLM, digital twin. MES), but the value of eliminating inefficiencies, speeding product launches, and cross-disciplinary collaboration creates an excellent payback.
Please enjoy the summary* below. For the full research, please visit our sponsor Siemens (registration required).
Standardization is at the heart of enterprise success with recipe management. Companies must standardize many interdependent elements for ERM to work. One of the drivers behind the ISA88 standard, Dennis Brandl, explains that the ERM concept is “Standard product descriptions based on standard definitions of manufacturing operations, based on standard quality attribute definitions, based on standard process parameter and process report definitions.” This illustrates the many layers and facets to standardize for ERM.
ERM in Innovation
The NPDI process begins with innovation. This, in itself, is a multi-disciplinary effort, typically including:
It’s our goal as analysts to stay informed about the software solutions available on the market and how they drive business value for manufacturers. We do this by conducting surveys, speaking with manufacturers, attending events, and taking briefings from solution providers. One of the things that excites us the most is when we learn something new. Recently, we learned that we were underestimating Siemens Digital Industries Software Service Lifecycle Management (SLM) capabilities. We thought it was worth sharing.
One of the primary ways that Product Lifecycle Management (PLM) solutions can help add value in a device or equipment's service phase is by providing technical data downstream. Engineering data is highly valuable to service planners, techs, and other support personnel. We were aware of the capabilities Siemens delivers in this area based on prior conversations and briefings. Siemens understands how products are designed, made, and serviced.
Siemens Teamcenter is a proven PLM solution and Teamcenter SLM has matured nicely over the years. Siemens followed a natural expansion beyond engineering and manufacturing BOMs to support the service BOM. We’ve seen how Teamcenter manages multidisciplinary data across product lifecycles and know it can manage complex configurations for both standard and engineer-to-order (ETO) products. Teamcenter’s ability to manage variants, revisions, and changes for serialized assets creates valuable product intelligence for service-related processes.
Teamcenter’s capabilities in both PLM and SLM also create an opportunity for customers to close the loop from service back to engineering. An integrated view of service and product data allows engineers better visibility downstream so they can better design for service and quality based on real-world feedback.
We knew that Teamcenter SLM was a strong source of product- and service-related data and recognized that this underlying information is crucial to effective service. Our research shows the value of extending PLM data into the “in use” phase of the lifecycle. Integrating design and service information provides even greater business value, as shown in our recent infographic on integrated service. The combination of product and service intelligence to support service transformation is compelling. As our Extending Digital Thread to the Customer Experience research shares the value of integrating the digital thread across the development and service lifecycles allows companies to develop a more intimate understanding of the customer and product experience and create new value from increasing profitability with lower costs and higher revenue from spares, suppliers, upgrades, replacements, and cross-selling opportunities.
Teamcenter SLM goes further than providing existing data by expanding beyond engineering data to information like service procedures. Not only can the solution manage and share this information, their Tech Pubs/RapidAuthor solution leverages design models and data to develop accurate, 3D technical documentation including visual and animated service instructions.
Product Support Data Management (PSDM)One specific way Siemens drives value is with its Product Support Data Management (#PSDM) solution. PSDM is a standards-based approach to documenting product support requirements tied to specific asset configurations. The solution brings together product lifecycle and logistics support data, leveraging the standard GEIA-STD-0007. The standard originated from the military to support government contracts but is applicable across a variety of industries. Siemens adopted this standard and incorporated it in the Teamcenter data model to support maintenance and logistics related data imports and exports.
Beyond sharing information, our research points out the value of strong sales and service execution support. Manufacturing and service companies must improve and optimize the processes supporting sales and service operations to grow their aftermarket business. Sales and service transformation requires convergence across people, processes, data, and systems in the engineering and enterprise systems. Customer interactions, spare parts, service contracts, warranty entitlements, and other service complexities are managed in solutions like CRM, Field Service Management, Enterprise Asset Management (EAM), and even ERP. Siemens’ customers can integrate with these systems, but that doesn’t create a seamless process.
This is where our understanding of Siemens’ capabilities needed to expand. Siemens has been working to bridge the gap between design, service planning, and service execution. They now support the front office, sales teams, customer service, and even equipment operators through strong partnerships. This brings the value of SLM to the front office while keeping it tied into Teamcenter PLM to extend the value of rich digital data beyond the engineering population, in the right context, to enable execution processes. Siemens is not trying to recreate what field service and CRM solutions do, but instead make them more valuable by providing a more complete solution to their customers by connecting with them.
Siemens now supports cohesive processes and a consistent digital thread from engineering through field services and sales. Siemens was able to leverage their open approach and rich APIs in their platform to support a variety of partnerships. These partnerships include IBM Maximo Application Suite for EAM and Salesforce for CRM. In both of these cases, Siemens focuses on the product data and service knowledge, not the customer data and service execution. This is exciting news, and we were happy to hear that Teamcenter SLM for Salesforce has been available on the Salesforce AppExchange for about a year. The Salesforce partnership also opens up Teamcenter’s product and service data to Agentforce, Salesforce’s AI offering, unlocking significant new opportunities to leverage the cohesive digital thread.
Siemens demonstrated an example of how combining an integrated PLM-CRM environment with AI can streamline and improve customer experience, service performance, and profitability. They showed how Agentforce could be deployed on an equipment manufacturer’s website. A customer can visit the site and enter an asset name and serial number, which would be interpreted to create a draft case for review, combining details about the configuration, including product information like 3D visuals, service data like detailed service instructions, and business data such as warranty terms in one place. They went further to show how the combined, AI-enabled solution could identify additional opportunities like a service upgrade based on the combined data. It was a compelling view of an end-to-end integrated process.
Siemens has a lot to offer across the product and service lifecycle through the Siemens Xcelerator portfolio and their partnerships. Siemens’ value in SLM goes beyond product data and service knowledge to service execution, enabling an integrated digital thread. The offering extends on Siemens’ deep expertise and strength in PLM and their open platform approach. Together with their partners, they have developed a very compelling offering spanning the product and service lifecycle. If you haven’t seen it in a while, it’s worth a deeper look. We learned quite a bit.
Thank you for the briefing Kerri Doyle, Brian Johnsen, Dominique Gilles, Yishai Barak, and Rajvi Vaidya for taking the time for the briefings, we appreciate your time.
[post_title] => Siemens Support for SLM Exceeded Our Expectations [post_excerpt] => [post_status] => publish [comment_status] => open [ping_status] => open [post_password] => [post_name] => siemens-slm [to_ping] => [pinged] => [post_modified] => 2025-05-19 13:47:33 [post_modified_gmt] => 2025-05-19 17:47:33 [post_content_filtered] => [post_parent] => 0 [guid] => https://tech-clarity.com/?p=21842 [menu_order] => 0 [post_type] => post [post_mime_type] => [comment_count] => 0 [filter] => raw ) [5] => WP_Post Object ( [ID] => 21799 [post_author] => 2572 [post_date] => 2025-04-10 10:00:37 [post_date_gmt] => 2025-04-10 14:00:37 [post_content] =>We recently had a chance to catch up with Siemens Digital Industries Software and get an update on NX. We heard about recent developments, the latest strategic direction for #CAD, their efforts to support comprehensive workflows, and progress to enable a Model-Based Enterprise (MBE). Here are a few of the highlights.
The NX team at Siemens has been very busy. They are on a continuous release cycle, with major releases every December and June. The 4200 enhancements and 45 patents reflect the level of innovation investment over the last year. At the same time, they maintain high levels of quality, as 75% of NX customers are on a release from 2024 or 2023. They've also added over 1000 new customer logos, especially in the SMB space. Their cloud offering, NX X, largely drove this. Additionally, they continue to receive awards from peer review sites like G2 and Trust Radius.
On January 6, 2025, at the CES trade show in Las Vegas, Siemens introduced Designcenter.
Designcenter is a new brand that combines both CAD products, NX and Solid Edge, to provide a single scalable mechanical CAD offering. The intent is to offer comprehensive workflows that integrate with Teamcenter and Simcenter to provide a seamless experience across design, simulation, and manufacturing.
Both NX and Solid Edge are built on the same Parasolid kernel, making it easier to work with Solid Edge models in NX and vice versa. Over the last couple of years, Siemens has invested in evolving the user interfaces to give them a similar look and feel. This is intended to make it easier for users to switch between the products. Designcenter furthers this approach to bring the two brands closer.
To meet customer needs, whether they are a large, medium, or small enterprise, Designcenter offers four options:
Essentials is the rebranded product that was formerly Zel X. Standard, Advanced, and Premium are tiered offerings so that customers can match the package with their needs, yet have options to scale should their requirements evolve. All three include built-in data management.
Within these packages, NX offers extensive capabilities for typical parts, assemblies, and drawings. However, based on the business, organizational function, industry, project stage, and supply chain role, some users may need more specialized functionality that is available in an optional module. In the past, it was difficult to justify the investment in an optional module, especially if it was for functionality that wasn't needed regularly. Siemens has addressed this challenge with a value-based licensing model for NX. This concept has worked well in the Simulation/CAE space to give users access to specialized analyses when needed, so it makes sense to apply it to CAD as well.
NX's value-based licensing model uses tokens for optional modules, which allows customers to use and return tokens for different functionalities. The intent is to give customers a cost-effective, flexible way to access specialized functionality when needed. For example, an aerospace company may want to use the token pool to take advantage of optional modules to design machined aerospace components or composite parts.
Tech-Clarity’s research has found that while companies who have started the journey to adopt MBE report many benefits, including better traceability, greater agility, and faster time to market, there are still many barriers to adoption. Interestingly, people and culture challenges are even bigger obstacles than either technology or process challenges. To overcome this, the Product and Manufacturing Information (PMI) embedded in the CAD model needs to become more consumable to downstream departments. Siemens is looking to do this with NX Stage Models, which help manufacturing engineers decompose design models into manufacturing stages, and a new NX Inspector, which supports a new standard for model-based characteristics.
In 2024, the DMSC™, an ANSI Accredited Standards Developing Organization and an ISO A-Liaison, released the Model-Based Characteristics Standard (MBC). The new MBC standard defines a common approach for tagging and uniquely identifying product characteristics for quality processes in a way that makes the PMI both human-readable and machine-readable. This is a giant step toward making PMI more usable, which should encourage MBE initiatives. By supporting this new standard, NX Inspector provides a mechanism for automating inspection plans based on the PMI in the CAD model.
It is nice to see all the effort into making NX more accessible, especially to smaller and medium-sized enterprises. Solid Edge customers should also have an easier path to scale up to more advanced capabilities as they need them. The token-based licensing model is also a nice way to make advanced, productivity-improving capabilities available to more users.
What's really exciting to us is the MBE capabilities. The MBE capabilities should be particularly interesting to the Aerospace and Defense industry, which has shown the most interest in MBE.
The vision to move away from 2D drawings has been around for well over 20 years now. However, manufacturing still largely follows a 2D-driven process. Capabilities that leverage semantic PMI to automate downstream processes should go a long way to finally realize that vision. The functionality offered by NX Inspector that supports an opportunity to automate inspection plans based on the 3D model is a big step forward. It should save significant time and reduce the risk of errors introduced with traditional, manual processes that leverage a 2D drawing. This should lead to efficiency gains, which will be a huge motivator for adopting an MBE approach. We look forward to seeing how this evolves, especially as Siemens works towards integrating metrology within its digital industries.
Thank you George Rendell, Tod Parrella, Jennifer Piper, and Shaun Ennis for sharing this update.
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Where’s the best place to start in improving supply chain resilience? Our research shows it’s inside the company. Many companies will need to start new initiatives and make additional investments to ensure their data is flowing for supply chain internal value. Data sharing and collaboration are crucial within supply chain disciplines, but also throughout the company between supply chain and engineering, manufacturing engineering, and the plant floor.
Supply chain and business disruptions are normal and often have severe negative impacts. Some of those factors are outside a company’s control, so most companies are investing in supply chain resilience (70%). Supply chain leaders must invest to tackle the #1 supply chain issue for executives: risk. Most companies need to do more to manage effectively and stay agile in the face of uncertainty, volatility, and ever-changing issues.
So, what to do? Change suppliers? Move production? Pull more production in-house and away from external suppliers? Improve design for supply? All of those might have good results.
Supply Chain resilience inside is the key. Gaining visibility into the product development and manufacturing or “make” areas of the company are crucial differentiators for those companies doing well. How would it be to see changes to products or production capabilities within an hour? Companies that can do that are performing better. Our research suggests that strong data flows across the company are a foundation for supply chain resilience.
Yet for many companies, this will require setting up new initiatives and investing at an enterprise level. Some of the changes might be in employee metrics and incentives to foster collaboration. Another idea is to invest in software that might have agentic AI, improved integration, or multi-tier planning and response capabilities.
Read Julie Fraser’s article in the Global Links section of Supply Chain Management Review that went live in March 2025. The above is a summary, and the full PDF of the article is linked here.
Thanks to Richard Sherman and Supply Chain Management Review for the opportunity to share our views.
[post_title] => Starting Inside the Company: Supply Chain Resilience Requires New Initiatives and Investments
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How can companies be sure to gain value from manufacturing analytics and AI projects? It turns out benefits are ubiquitous for those who have invested. Yet the Top Performers in our new survey of over 400 manufacturing respondents indicates some key differences that improve their results.
Please enjoy the summary* below. For the full research, please visit our sponsor MESA International (registration required).
100% Gaining Benefits
Analytics projects are delivering significant benefits that support top drivers and help meet challenges. Every one of these respondents (100%) reports gaining benefits from analytics programs, which is excellent news for our industries.
Cost
Cost reduction is the #1 driver (p. 4) and actual benefit. Most companies know they could lower costs, but analytics and AI can help to pinpoint where to focus efforts. Predictive and preventive analytics can avoid an array of unnecessary expenses due to waste.
Efficiency & Quality
Efficiency contributes to cost and indicates people are effective, even in the face of a skilled workforce shortage. Error-proofing is necessary for less skilled staff, and even more experienced staff can benefit from error-proofing when change is rapid, the product mix through the plant is high, or specifications change frequently.
What Customers Need
On-time perfect orders make the company a reliable supplier, leading to revenue opportunities and being viewed as trustworthy in these uncertain times. Customers also seek quality; internally that lowers costs, increases revenue opportunity, improves supply chain resilience, and enhances sustainability.
And More
Other areas where respondents report significant benefits from analytics programs include:
Progress: Manufacturers are moving along the Smart Manufacturing path. Many have multiple projects underway or are already seeing results.
What does it take to digitalize assembly, an area where people must participate? Quite a lot, especially with complex configured products. Over time, MTEK Industry AB has consistently focused on developing software to support complex discrete assembly operations. They keep adding functionality in MOM and beyond to the MBrain digital production system. Integrating to equipment, informing people, and improving are core. They recently added a no-code IT integration platform, Mint, for enterprise data flow. They are delivering value in weeks.
For several years, MTEK has focused on creating no-code software that supports manufacturers with complex discrete manufacturing operations typically characterized by configured products, takted or untakted assembly flows, and a high reliance on manual operations. This market has largely been underserved, partly because it’s very complex and changes frequently, often for every configured product (“batch-size one”). Most of these companies have some equipment and automation, but many operations also rely on people since variation is generally the enemy of automation. MTEK started in 2002 as a manufacturing excellence consulting company, so its roots are deep in these industries.
Not constrained by what is traditionally included in MES, they have built many functions. MTEK calls it a Digital Production System. MBrain has core functions focused on delivering visibility, a large variety of “make” functions, and a suite of “improve” functions to make lean best practices digital. These functions support specific assembly challenges such as planning, andon, configuration QA, line tolerance, and user competences.
Traditionally, this scope might require four or five specialized systems besides MES. Because it’s no-code, once a customer gets proficient, they can – and typically do – expand it further to meet their specific needs.
Anyone who has worked in an assembly operation, particularly a takted one, knows how critical the processes are to keeping products flowing through the plant. MBrain includes a Process, or Method Builder, which is the core tool for constructing a digital replica of the logic of your production and the heart of why MBrain sets out to be a Digital Production System.
They encourage customers to start by mapping their current methods and keep working as they have. Given the no-code design, manufacturers can use the system to identify areas for improvement and then implement them. With more rigid system architectures, clients typically need to identify the “to-be” state before implementation. MTEK challenges that logic and way of working with a more flexible tool.
They can use or later add elements such as getting product data from PLM, reporting materials consumption to ERP, pushing a recipe to a machine, or printing a label. The great news is that customers can view the method or workflow in production, maintenance, quality, etc., at any level of granularity. This supports both skilled and less skilled workers at just the level they need.
While manufacturers are all different, the MTEK team realized these complex assembly operations have a typical profile. Whether the plant is part of a larger group or the company's entire operations, the facilities are typically 100-250 people, with around $100M in output (+/- 50%). They are often in smaller towns, which limits how large operations can be. Typically, even larger companies are also built by acquiring many smaller operations – meaning there are geographical and historical reasons for the size of a typical plant in discrete manufacturing and assembly. Focusing on this scope of operation, they knew a traditional multi-million dollar, multi-year MES implementation was not the best approach. MTEK’s no-code approach enables MBrain to be both affordable and configurable to the specific operating needs of each customer.
In November 2024, Microsoft’s Azure IoT Operations included MTEK as a launch partner. This is a prestigious position in the Microsoft Adaptive Cloud approach for cloud-to-edge connectivity in manufacturing. Using the Mint and MTalk elements of MTEK’s digital production system, data from equipment, sensors, IoT devices, and adjacent IT systems can feed not only MBrain but also cloud-based analytics. MTEK is strong on its own, but the Microsoft partnership illustrates the advanced nature of their software architecture to enable scaling.
The new addition to the MTEK product family is a no-code integration platform, Mint. This innovation from MTEK was originally designed to remove the barrier to growth created by the monetary cost and delay caused by traditional integration work.
As the platform has been deployed with clients, however, MTEK has noticed that the bulk of the value comes from creating a much more integrated systems architecture and event-driven IT/OT stack. Gaining data flows among IT systems can be a powerful and transformative step for manufacturers. The goal is not just to integrate IT systems but to integrate and automate business processes seamlessly. This patent-pending approach to integration can serve MBrain customers and others.
Much of the breadth of functionality has come from customer needs. Since 2020, MTEK has had success with complex discrete assembly customers, including many larger companies whose names you recognize. In one larger company, Accenture was the service partner and saw the value of MBrain. With this no-code approach, many customers report seeing results in a couple of weeks after starting to deploy MBrain. This is not possible with traditional full-code manufacturing software. With the improved IT integration of Mint, the value of these deployments could continue to grow even beyond the MBrain functional footprint.
No-code, but with built-out functionality out of the box as a starting point, is a strong starting point. Adding so much functionality beyond traditional MES allows MTEK to lay claim to a larger footprint and deliver more value. Integrating the lean tools natively gives continuous improvement a leg up. Integration to automation with MTalk supports automated aspects of the operation. Adding the Mint integration platform addresses one of the most troubling issues in manufacturing digital transformation, helping data flow among systems across the company.
We are convinced that assembly-based manufacturers should explore the MTEK Digital Production System. It seems to have both industry-specific functions that are ready to use and extreme configurability with the no-code approach. Thank you, Tord Johnson and Oscar Wallner, for updating me.
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How does expanding PLM beyond engineering to support the digital thread from requirements through commercialization help manufacturers launch quality products that meet regulatory demands and customer expectations? How can companies take an end-to-end PLM approach to drive better new product success?
Join the Meet the Pros: New Solutions & Expert Advice To Help You Achieve Product Success webinar to hear Jim Brown share Tech-Clarity’s research on how Top Performers get the most out of PLM to drive better, more profitable products. Jim will join Propel experts who will share their perspectives on a more valuable approach to PLM and directly share some announcements and answer questions about their offerings.
[post_title] => How an End-to-End PLM Approach Drives Product Success
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Why would batch process manufacturers want more than their MES to manage operations? To gain real-time intelligence with AI-based optimization at scale. Quartic.ai’s industrial DataOps platform is open to handle data from and serve intelligence to nearly any plant floor or enterprise application. It focuses on confident, timely decisions, complementing MES, LIMS, EAM, and other operations software that focuses on execution.
Manufacturers have long had challenges closing the gap between MES and ERP. This integration may suffer for various reasons, but Quartic plays a role in ensuring the data from all systems is available for operations intelligence. Like older automation or OT systems, some batch process MES and LIMS systems are not entirely open to sharing data. Quartic can again manage that data and put it into analytics data streams.
Quartic has spent years refining a comprehensive, enterprise-capable platform for digital DataOps, MLOps, and intelligence delivery for batch process industries. It has successes in pharmaceutical and life sciences, CPG, food and beverage, specialty chemicals, and metals and mining. This means they are ready to handle time-series and relational data, spectral data, real-time and historical data, and events. This company understands batch issues, phases, and challenges. The variability and interdependencies of these industries that make them difficult for others are the starting point for Quartic. The team has expertise in process manufacturing and reliability, data science and AI, and enterprise software development.
The Quartic Intelligent MOM Platform has three main components: DataOps, Intelligence, and Applications. This is an unusually broad set of capabilities for manufacturing operations, and Quartic is working to ensure that each component is complete.
Manufacturers have been discovering in their Smart Manufacturing and AI-based initiatives that getting data ready to use is more complicated than first meets the eye. Quartic’s platform includes OT and IT data connectors and ingestion from many sources, with the capability to put it all in context through a data fabric. This might also be called a unified namespace (UNS). The full stack DataOps with enriched data serves as the enterprise broker that can feed many analytical models, use cases, and feed source systems for wider distribution.
For Quartic, AI is a founding focus, not a new addition. Beyond DataOps, this platform includes MLOps and a full array of industrial AI capabilities. Whether you want an assistant for your people making decisions, a predictive engine to drive forward-looking insights and enable preparatory or preventive action, or a fully autonomous AI agent, these are proven in the platform. While agentic AI has become a hot buzzword in the last year, Quartic has taken this approach for about 5 years.
Quartic has also had applications available for batch manufacturers for years. The applications may be native or offered with a partner. There are dozens of them, mainly developed at a customer request and developed into a reusable application. Categories of applications include:
Quartic has been building this extensive and robust functionality for many years, working with customers to expand in areas of greatest need. Their goal is to create a flexible and robust system that’s also quick to deploy. Flexibility matters to fit companies at different stages of maturity in data integration. It also matters to keep up with future needs as they evolve.
Thanks, Rajiv Anand, Vinodh Rodrigues, and Bryan Pope, for getting us back up to speed! We look forward to following Quartic’s progress in the market.
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Why should Siemens Xcelerator customers embrace low-code capabilities from Mendix, a Siemens Digital Industries Software Business, to accelerate their #digitaltransformation journey?
In today’s fast-paced manufacturing environment, using data from various sources to make informed decisions is crucial for operating profitably. This dynamic data often resides in systems like ERP, CRM, PLM, and MES. However, most manufacturers lack the resources to rapidly develop and deploy software applications integrating data from these systems. IT teams struggle to meet these needs, often too company-specific for solution providers like Siemens to address directly.
Our research, Filling Digital Transformation Gaps with Applications, found that digital transformation increases the speed of change, demands more agility, and creates the need for more (and faster) software development. The survey also found, however, that manufacturers using low-code development are better able to fill their digital transformation gaps to meet their evolving needs.
To stay competitive, manufacturers must repurpose existing solutions, connect them, and augment them with new capabilities. Mendix, Siemens’ low-code platform, enables this adaptability. A recent Mendix briefing to Tech-Clarity analysts shed light on their strategy for helping manufacturers efficiently extend and enhance their core systems.
Mendix makes low-code available for any industry, but Siemens has invested in making Mendix more valuable for industrial customers using Siemens’ existing solutions. There are two ways Siemens customers can use Mendix to extend their product development and manufacturing data:
No briefing today is complete without the mention of #AI, and the Mendix team delivered. Developers can harness AI-assisted development in Mendix to streamline and enhance the application-building process. This capability provides real-time guidance, helping both novice and experienced users develop applications faster, with fewer errors, and accelerating their path to expertise. Beyond the AI capabilities of Mendix itself, Mendix’s low-code approach allows companies to embed new technology, including AI, into their existing solutions. Mendix also enables seamless integration of Generative AI, allowing manufacturers to build intelligent, AI-augmented applications that enhance decision-making, automate processes, and drive greater efficiency across their operations.
Mendix offers Siemens Xcelerator customers a new way to stay competitive by enabling rapid custom-application development, seamless data integration from core systems such as PLM, MES, ERP, QMS, and CRM, and leveraging AI. Whether building stand-alone custom Mendix applications or leveraging embedded low-code capabilities within Siemens solutions, Mendix offers manufacturers the potential to enhance agility and drive smarter decision-making. With Mendix, Siemens aims to help manufacturers gain the flexibility to adapt and extend their systems to meet rapidly changing business needs and unlock new efficiencies, ensuring they stay profitable in an increasingly digital and data-driven world. Thank you, UV Subba Rao, Gregory Rosa, and Shaun Ennis, for updating us on Mendix's latest developments.
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How do you accelerate the co-development of hardware and software for your products?
Today's products increasingly rely on software for innovation. Yet, development teams follow different processes and use separate tools to support the engineering and development of hardware and software. This creates silos within the development team that causes breakdowns in communication and hurts efficiency. Then, the inevitable errors and bottlenecks drive up costs.
By integrating Product Lifecycle Management (PLM) for hardware and Application Lifecycle Management (ALM) for software, engineering teams can overcome development silos. This will enable more efficient processes that accelerate the development of higher-quality products. This buyer's guide reveals buying criteria to select the right solutions for integrating PLM and ALM.
Please enjoy the summary* below. For the full research, please visit our sponsor PTC (registration required).
Growing Importance of Software
Software is increasingly becoming a major source of innovation, increasing its importance in product development. Yet, despite its criticality, many companies develop it separately from the hardware. As a result, companies experience many challenges that make the jobs of engineers much harder.
Business Cost
While engineers have done their best to develop processes to deal with the challenges associated with siloed development teams, the increasing complexity and growing requirements make it easy to overlook errors. These errors come at a business cost. Companies can avoid this cost by adapting their development processes to support a more integrated multi-disciplinary approach.
Conclusions and Next Steps
[post_title] => Improve Profitability and Customer Loyalty by Maintaining Accurate Service Records
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Can you reimagine how people interact with enterprise software? Epicor is doing just that. Their announcement of the Prism agentic AI network and orchestrator takes the next step in moving from a system of record to a system of action.
Prism includes conversational AI for employees to interact with ERP, and Epicor plans to integrate automation and causal AI in the future.
Kinetic, Epicor’s manufacturing-focused ERP, already has five types of Prism vertical AI agents. The graphic below illustrates and describes the specialized functions of these semiautonomous, pre-trained bots.
Prism's agents use LLM and RAG technologies. While LLMs and related generative AI excel at understanding unstructured data like documents, they struggle with structured data typically found in ERP systems. Prism's first innovation is its ability to navigate ERP structured data effectively.
These agents can answer questions and create filtered lists in multiple ways.
Most ERP users need only particular portions of the whole data set to perform their tasks, and Prism can streamline that.
Epicor has encoded the data structures of Kinetic ERP with typical fields and processes into Prism to pass context onto the LLM. In addition to the standard Epicor agents, they can include customer-specific questions and point to their customizations. So, it is both Kinetic-specific and customer-specific in function.
When crafting new approaches to support employees or automate processes, Prism will determine which agents are required and how many. Depending on the problem to solve or the process to improve, one or multiple agents may come into play.
Epicor says it has an unfair advantage since it has broad enterprise functionality that many customers have had in place for years. So, historical data about the business is already stored in a structured data ontology that fits their industry. This forms a quick-start basis for RAG to create an Epicor- and customer-specific context for the large language model (LLM). Newer customers are encouraged to import a rich data set during their implementation.
Epicor talks about three elements of this agentic AI.
One of the top issues or fears companies have with using AI is security, trust, and ensuring results are valid. For security, Prism agents act on behalf of each system user. That means the agents only have access to the portions of the system that the user has, based on existing security settings, through Kinetic.
On the LLM security front, Epicor follows standard guidance on OWASP’s 10 pillars for safety and security. They have separate data for each customer and do not pool data across customers. The LLM for Prism is always guided by the context of a specific customer. The business activity queries focus on the company’s data set, logic, and rules.
As in the graphic above, Epicor is creating an agent for their Enterprise Content Management or ECM. While digitalization has come a long way, companies still tend to do business with documents such as contracts, requirements, etc. Today's many supply chain challenges suggest that this could be a beneficial agent. Questions around supply contracts will likely proliferate as companies restructure their supply chains in the face of tariffs, regulations, and natural disasters.
Another in-development agent will support automating mass modifications. For example, if you have 100 lines in an order, and they all need to be modified, Prism will be able to do that for the user at their request.
Epicor has continued to innovate and include leading-edge technologies in their ERP platforms for years. This is an excellent milestone for Kinetic in manufacturing. We look forward to hearing more as Epicor keeps moving forward. Thank you, Steven Knuff, for keeping us updated, and to Arturo Buzzalino, Michael Atkisson, Ph.D., and Michael Muckala for creating a concise and informative briefing!
[post_title] => Epicor’s Next Step in Cognitive ERP: Prism GenAI Agent Network [post_excerpt] => [post_status] => publish [comment_status] => open [ping_status] => open [post_password] => [post_name] => genai-agent-network [to_ping] => [pinged] => [post_modified] => 2025-03-11 12:15:52 [post_modified_gmt] => 2025-03-11 16:15:52 [post_content_filtered] => [post_parent] => 0 [guid] => https://tech-clarity.com/?p=21675 [menu_order] => 0 [post_type] => post [post_mime_type] => [comment_count] => 0 [filter] => raw ) [16] => WP_Post Object ( [ID] => 21599 [post_author] => 2580 [post_date] => 2025-02-26 10:00:26 [post_date_gmt] => 2025-02-26 15:00:26 [post_content] => As highlighted in a Tech-Clarity report, “Drive Higher Profits with IoT Machine Monitoring and Optimization,” Top Performers are more likely to integrate machine data, enterprise systems, engineering data, and operational systems to drive higher profitability. This integration improves their ability to offer low-cost service, keep unplanned downtime low, troubleshoot machine problems quickly, provide data security, and ensure the customer’s machines perform with the highest quality. How can manufacturers win more service business with integrated service and ultimately improve profitability? This imaginary game gives manufacturers some ideas on excelling at offering integrated service. By improving the way they deliver service, companies can win more service contracts and increase revenue and profits from additional sources. Click here to download the full infographic. For more information about improving service performance and profitability, please visit our sponsor Siemens.
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How can manufacturers take advantage of the benefits of product customization without suffering from decreased profitability? We gathered responses from 234 companies involved with to-order products and interviewed two leading manufacturers to find out how to improve product configuration.
Read on or click here to download the full report with no registration required, courtesy of our sponsor, Siemens.
For more information about product configuration solutions please visit our sponsor Siemens.
To-Order Continues to Expand
Change Revenue Drives Customization
Given the growth of customization, we wanted to understand the primary reasons companies offer to-order products. The short answer is product sales. Three-quarters of companies say they sell to-order products to differentiate from their competition. In addition, two-thirds do it to command higher prices, perhaps because they can better meet customer needs. This driver grew from only 28% in the prior survey. It's important to note that these benefits are not exclusive, and many companies reported both as drivers.
Some Companies Don’t See Another Option
Historically, our surveys show that many companies feel they must configure products. It's just the nature of their industry. Typically, between one-third and one-half of companies in our studies report this. In this study, fewer companies selected that option. However, more than one-half say that they feel required to sell to-order products in order to sell their standard products. This may account for the discrepancy because selling configured products to maintain the ability to sell standard products wasn't an answer choice in the earlier studies. Protecting sales may be why they said it's just the nature of their industry. While an industry may not require all products to be custom, some customers may require suppliers to customize products to their needs to maintain their status as a preferred supplier.
Localizing Products is also a Common Driver
Configuring products to order can also help companies define a product once and tailor it to specific market needs for an order. Over one-third of respondents report selling to-order products to allow them to localize their offerings for different markets or geographies, similar to the earlier survey.
The Bar on Configuration is Higher
The challenges companies face designing, selling, and producing to-order products are significant, and the bar for selling and delivering them is higher than ever. How are companies responding?
Make Customization More Standard
To-order product strategies are shifting to help companies simultaneously increase customization and lower configured order complexity. They want to offer customization, but they want it to be less burdensome. Several of the most common strategies aim to meet customer needs in a more standard or configure-to-order (CTO) approach instead of an engineer-to-order (ETO) way to reduce cost, time, and the chance to introduce errors. Around three-quarters of companies are shifting products from ETO to more pre-engineered, modular CTO products. “Customers want it faster. CTO, taking out the ETO element, is helping us get there,” shares Geoff Leach of Ingersoll Rand. “Engineering is expensive, and CTO reduces our engineering hours,” he adds.
A related approach, reported by almost two-thirds of respondents, is to steer customers toward more standard products through guided selling. This allows companies to meet customized needs but do so with more standard configurations. Another approach that decreases complexity, reported by 42% of companies, is selling products that are ETO at the systems level but basing them on more standard / CTO components.
Maintain the Ability to Meet Customer Needs
Clearly, manufacturers are pursuing more than one of these approaches. They are complementary and signify a desire to minimize custom engineering if possible while recognizing the reality that meeting customer needs drives differentiation. It's important to note that this isn't counter to the goal of meeting customer needs, as over one-third are trying to increase customization. The idea is to pursue custom products with the ease of standard products.
We investigated a trend for manufacturers to move configuration closer to the customer, such as from sales engineers to salespeople or from distributors to customer self-service. We found a clear desire to shift configuration "left" toward the customer. Having customers configure their own orders may be an opportunity for them to better validate their orders if they have the means to do that effectively via self-service. It can also help save time and increase efficiency, increasing quote response times and freeing up engineering time for innovation, again, if it is enabled properly.
Inefficiency and Extra Engineering Work
In addition to selling challenges, let's review challenges related to engineering and manufacturing to-order products. The first category of challenges is the burden on engineering. The most common challenge, reported by about three-quarters of manufacturers, is too many "specials" that require extra engineering work. This is likely why companies are trying to shift from ETO to CTO. The next most common challenge, reported by about one-half of companies, is accommodating customer change requests. “Projects evolve over time. It’s not uncommon to deliver solutions that differ from the original plans ,” shares Stone Kane of Schumacher Elevator. Changes may occur because customer needs change or because of errors in validating orders or predicting performance. Either way, they are time-consuming and disruptive.
Over one-third of respondents also share that they struggle with long engineering lead times with too much manual effort, perhaps due to specials or lack of automation. These challenges impact engineering and delivery leadtimes, which are in the top three most common profitability drivers.
Predicting Performance and Cost
Predicting performance and cost are both issues reported by over 40% of responding companies. This is a big challenge considering that accurate quotes are the most common profitability driver and better meeting needs is the most common differentiator. Overall, to-order product challenges reflect both engineering workload and the inability to estimate cost and performance of to-order products, signifying that engineers are working hard to meet deadlines but can’t determine the engineering information needed to drive profitable business.
Additional Challenges
We discussed the most common challenges, but manufacturers reported many other challenges, including supply chain volatility, designing new configured products, providing complete / accurate information to manufacturing, coordinating with suppliers, increased software in products, and a lack of knowledgeable engineering resources. Designing and producing configured products is just hard.
Tech-Clarity’s Performance Banding Process
To understand best practices processes and technology, we use a benchmarking process we call "Performance Banding." We look at responding companies' performance against metrics that represent success in our research topic. We create an aggregate score across these metrics and identify (approximately) the top 25% as "Top Performers." We then look at what these leaders do differently from the poorer performing companies, the "Others.”
Benchmarking To-Order Top Performers
To understand which companies profit more effectively from their configured products, we benchmarked the financial performance for to-order products over the last 24 months as compared to their competitors for:
Top Performers Support To-Order with Configurators and PLM
Top Performers are also leveraging different technical enablers. The most common way Top Performers and Others take orders and develop quotes is by using a sales configurator. Sales configurators typically handle customer and order parameters very well. However, this is not the most differentiating approach among the Top Performers.
Top Performers are more than twice as likely to use PLM to develop quotes. This is likely due to the need to get designs right so they can use them for cost, leadtime, and performance predictions. “Having one source of truth to find information can save you a lot of money and opens up the door to hand off the data downstream in a consistent manner to improve leadtimes and efficiency,” explains Stone Kane of Schumacher Elevator. “Otherwise, a lot of money can be left on the table due to poor communication .”
Top Performers Use More Configuration Capabilities
Beyond sales configurators and PLM, companies use other technologies like technical configurators. Researchers found that the most differentiating use of configurators was not which type of configurator they used but what capabilities they used it for.
The most common uses, pricing and quotation, are important but not very differentiating. What sets Top Performers apart is using more advanced configurator capabilities, including technical drawing creation, CAE automation, 3D visualization, and engineering calculations. These capabilities go beyond simple options and variants to leverage an engineering product model. More importantly, these specialized capabilities help support the top to-order differentiators and profitability drivers, including estimating costs, leadtimes, and performance and driving speed, efficiency, and quality. ”We added a considerable amount of automation to create models and documentation,” explains Goff Leach of Ingersoll Rand. “We were able to reduce engineering hours by about 75% because we have CAD automation that works hand-in-hand with our PLM.”
The Basics Aren’t Enough for Higher Business Value
The Top Performers and Others commonly use standard capabilities like capturing order specs. They are essential features and add value but do not necessarily lead to better business performance. We believe that configuration maturity has increased and these features have become basic requirements to compete, but driving a competitive advantage now requires more advanced capabilities.
Value Achieved from Product Configurators
While Top Performers are gaining more significant competitive advantages through their best practices, both Top Performers and Others reported valuable business benefits. Companies don't have to be Top Performers to overcome to-order challenges and improve to-order execution.
The benefits reported from using configurators are clear, and range across different facets of to-order business, including:
Configuration Strategies are Evolving
Respondents report adopting new strategies that reduce to-order complexity to address these challenges, including shifting from ETO to CTO products and guided selling. We don't believe these approaches are to reduce the ability to meet customer needs, but to address them more efficiently. As evidence, we still see that quite a few companies are increasing the level of customization in their products.
Configuration Strategies are Maturing
To support these strategies, companies are adopting best practice order processes, engineering techniques, and technical enablers. These practices include shifting configuration toward the customer and adopting proven engineering practices like modular design. Best practices also extend to technology, including configurators and PLM, and more advanced configuration capabilities that rely on an engineering product model. These best practices help manufacturers more rapidly and predictably deliver customized products that meet customer needs with less of the engineering burden typically associated with ETO.
Configurators Drive Business Value
The final conclusion from the survey is that configurator technology leads to significant benefits for both Top Performers and Others. “Our configurator enables us to estimate projects with increased speed and accuracy,” explains Stone Kane of Schumacher Elevator. “That increases our opportunities to secure more jobs .” These benefits add strategic competitive advantages because they align well with the to-order differentiators and profitability drivers. “Our configurator led to lower cost and leadtime savings, it’s working,” concluded Geoff Leach from Ingersoll Rand. Although Top Performers report more significant advantages, companies from all performance levels report achieving benefits from product configurators.
Click here for the full report with no registration required, courtesy of our sponsor, Siemens.
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Do disconnected project management approaches hinder project execution, risk management, and decision-making, ultimately setting project leaders up for failure due to a lack of project intelligence?
Engineering programs, including new product development projects, are becoming more complex. Companies delivering these programs often struggle to complete them on time and within budget. However, companies can improve their project management and execution processes to better meet deadlines and manage budgets.
This eBook explores five data-driven, deliverables-based approaches manufacturers can adopt to better hit their complex engineering project targets by leveraging project intelligence. It incorporates practical insights from L&T Energy Hydrocarbon to provide a real-world perspective on improving project performance.
Please enjoy the summary* below. For the full research, please visit our sponsor, Dassault Systèmes (registration required).
Complex Engineering Project Status Quo isn’t Enough
Companies executing complex engineering programs often struggle to complete their projects on time and within budget. New product development programs are frequently delivered late, compromising profitability, competitive positioning, and brand. Tech-Clarity's research indicates that manufacturers miss product development deadlines and budgets over one-half the time. This unacceptable performance raises the question, do disconnected project management approaches hinder project execution, risk management, and decision-making, ultimately setting project leaders up for failure?
Transform Engineering Project Management
In this eBook, we explore five data-driven, deliverables-based approaches that manufacturers can adopt to better hit their complex engineering project targets. We also incorporate practical insights from L&T Energy Hydrocarbon to provide real-world perspective on improving project performance.
How can you accelerate battery design innovation?
The incredible demand for batteries has created significant opportunities for battery manufacturers at both existing businesses and new market entrants. However, current battery limitations can create an adoption barrier for more sustainable energy solutions. Companies with the right capabilities to rapidly innovate to overcome these battery limitations will be well-positioned to capture market share and establish themselves as a leader in the battery space.
This eBook examines how battery manufacturers can accelerate their innovation practices. It explains how to foster a culture of innovation, supported by digitalization, to advance R&D and engineering efforts for the next generation of batteries, and shares best practices for R&D and product development teams.
Please enjoy the summary* below. For the full research, please visit our sponsor Siemens (registration required)
Energy Storage and EVs
Renewable energy sources such as wind and solar are not constant, so batteries are critical to support their adoption. With projections for renewable capacity to grow by almost 2,400 GW, an 85% acceleration over the previous five years, the demand for batteries will continue to spike. Further, the electric vehicles market is booming, setting new sales records every year, with 2023 approaching $40 billion, nearly double 2022 sales. This demand should continue, as by 2040, EVs are expected to represent 70% of all passenger vehicles. With EV's reliance on batteries as a power source, this could create incredible opportunities for those already in the battery industry, as well as those looking to enter it.
Battery Innovation
Much of this growth has been made possible by the impressive advancements in battery technology. For example, battery energy density, the amount of energy batteries can store, is now nearly three times what it was in 2010. Further, prices have dropped 87%. However, more work is needed to encourage wide-scale adoption. EVs must increase their range, charge faster, and cost less, while ensuring safety and long operational life. Achieving this requires companies to develop new material chemistries and innovate around battery cell and pack designs.
Batteries Impact Profitability
To accomplish this innovation, companies should invest in their R&D and development processes. Those investments will pay off as batteries contribute more than a quarter of an EV's weight, account for 35% to 50% of the vehicle's cost, and significantly impact performance and profitability. With the right capabilities and technology solutions, manufacturers can accelerate innovation to enjoy a competitive advantage by getting to market faster and capturing market share. By acting quickly, they can even offset costs by taking advantage of government incentives and new funding to support green alternatives.
Engineering Challenges
Despite the advancements in battery technology, batteries must perform beyond what is capable today to realize the market potential. Overcoming these challenges requires analysis and trade-offs across multiple domains spanning chemistry, structural engineering, heat transfer, electrical engineering, electronics, software engineering, and more. Manufacturers will be better positioned to manage this with the right technology solutions.
Overcome Adoption Barriers
EVs must charge faster and increase their range to overcome major adoption barriers. They also need to use more environmentally friendly materials and cost-effective production methods. The chemistry, cell, and pack design directly impact this. Manufacturers must create an environment to facilitate innovation, enabling R&D and engineering to develop new solutions to these challenges.
Battery Safety
As battery technology has advanced, batteries can store more energy. However, this also means that heat can build up. If the heat is not managed properly, a dangerous situation called thermal runaway can occur, which can lead to fires. It is so challenging to manage, even experienced manufacturers struggle. In 2013, this problem led to Boeing needing to ground 50 787 Dreamliners. It is also why Panasonic, Sony, and Lenovo had to recall their laptops due to the potential fire hazard.
A Battery Management System (BMS) provides the intelligence to monitor the battery and make adjustments to manage thermal conditions and optimize performance, but it adds additional design complexity. The right development tools can provide needed insights to manage thermal runaway and the complexity of the BMS.
Slow Development Cycles
Batteries are so complicated, it is difficult to predict their internal reactions, particularly around safety and aging. This leads to a trial-and-error approach that depends on physical testing. However, physical testing takes time and slows development efforts. Plus, it is often hard to obtain needed insights to understand the root cause of failures to make improvements. Development teams need better insights into real-world performance during development to accelerate innovation.
*This summary is an abbreviated version of the ebook and does not contain the full content. For the full research, please visit our sponsor Siemens.
If you have difficulty obtaining a copy of the research, please contact us.
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How are manufacturers doing at global Digital Transformation? What could accelerate progress? These are a few of the questions that Julie Fraser and other experienced industry analysts will discuss on a panel on June 17th. The session is part of the Manufacturing Leadership Council’s (MLC) Rethink Conference on Marco Island, Florida, and supports its theme, Accelerating Digital Transformation in Manufacturing.
This “Power Panel” will leverage research from four analyst firms to explore the current state of Digital Transformation in manufacturing. Joining the panel are Tech-Clarity’s Julie Fraser, Bob Parker of IDC, Craig Resnick of ARC, and Matt Littlefield of LNS Research. The panel moderator is David Brousell, Director and Founder of the MLC, which is part of the National Association of Manufacturers.
The panel will also address “handicapping” questions such as: What is the picture for global digital transformation in manufacturing? Are some regions ahead of others? If so, why? And questions about the path forward such as: What’s needed to move to the next stages of maturity? Are we ready to move from Industry 4.0 to Industry 5.0? These four long-time manufacturing software industry analysts will each contribute their opinions and share insights.
This panel is just a part of this dynamic in-person conference. The program includes a wide array of presentations, panel discussions, case studies, and networking sessions on culture, leadership, process, and technology. Topics range from what to expect in the economy to emotional intelligence for a thriving culture to defining the human-machine relationship to digital transformation for small and medium manufacturers. There is also an exhibit hall for sponsors to show their offerings on Tuesday and Wednesday.
Rethink runs from Sunday evening June 15th happy hour and then has three days of content. It ends with the black-tie Manufacturing Leadership Awards gala on Wednesday evening June 18th. The many categories of awards showcase what leading manufacturers are doing in AI Vision and Strategy, Business Model Transformation, Collaborative Ecosystems, Digital Supply Chains, Engineering, Production, and Integration Technology, Operational Excellence, Sustainability and the Circular Economy, and Transformational Business Cultures. There are also individual awards.
The MLC website describes the event: “Rethink examines today’s digital factory as it intersects with technology, organizations, and leadership. Come away with a better understanding of the smart factory and what’s needed to compete, succeed, and thrive in the connected future.” Last year’s event was compelling, and the topic for this year is global digital transformation. If you are attending, please let Julie know to set up some time to get face-to-face.
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How are manufacturers doing at global Digital Transformation? What could accelerate progress? These are a few of the questions that Julie Fraser and other experienced industry analysts will discuss on a panel on June 17th. The session is part of the Manufacturing Leadership Council’s (MLC) Rethink Conference on Marco Island, Florida, and supports its theme,…
Jim Brown’s recently published eBook research, The Business Value of An Industrial System of Engagement Platform, introduced the concept of the Industrial System of Engagement (SOE). Following the eBook, he penned a guest post for Hexagon summarizing the eBook and how the Industrial SOE provides a platform to extend the value of current enterprise systems…
The Value of AI in Manufacturing is Accelerating There is a lot of talk about the value of artificial intelligence in manufacturing, and rightfully so. Although AI isn’t new, it appears to be reaching a tipping point where companies are more open to exploring its potential and AI techniques are more accessible than ever. Manufacturers…
How can recipe-based producers ensure cost-efficiency and consistent, high-quality products no matter where they are formulated or produced? By implementing enterprise recipe management (ERM) with a manufacturing execution system (MES) to deliver closed-loop data flows in and out of production. Leading Consumer Packaged Goods (CPG), specialty chemical, and other manufacturers are standardizing for ERM. Quite…
Siemens Support for Service Exceeded Our Expectations It’s our goal as analysts to stay informed about the software solutions available on the market and how they drive business value for manufacturers. We do this by conducting surveys, speaking with manufacturers, attending events, and taking briefings from solution providers. One of the things…
We recently had a chance to catch up with Siemens Digital Industries Software and get an update on NX. We heard about recent developments, the latest strategic direction for #CAD, their efforts to support comprehensive workflows, and progress to enable a Model-Based Enterprise (MBE). Here are a few of the highlights. NX Innovation and Customer…
Where’s the best place to start in improving supply chain resilience? Our research shows it’s inside the company. Many companies will need to start new initiatives and make additional investments to ensure their data is flowing for supply chain internal value. Data sharing and collaboration are crucial within supply chain disciplines, but also throughout the…
How can companies be sure to gain value from manufacturing analytics and AI projects? It turns out benefits are ubiquitous for those who have invested. Yet the Top Performers in our new survey of over 400 manufacturing respondents indicates some key differences that improve their results. Please enjoy the summary* below. For the full research,…
What does it take to digitalize assembly, an area where people must participate? Quite a lot, especially with complex configured products. Over time, MTEK Industry AB has consistently focused on developing software to support complex discrete assembly operations. They keep adding functionality in MOM and beyond to the MBrain digital production system. Integrating to equipment,…
How does expanding PLM beyond engineering to support the digital thread from requirements through commercialization help manufacturers launch quality products that meet regulatory demands and customer expectations? How can companies take an end-to-end PLM approach to drive better new product success? Join the Meet the Pros: New Solutions & Expert Advice To Help You Achieve…
Why would batch process manufacturers want more than their MES to manage operations? To gain real-time intelligence with AI-based optimization at scale. Quartic.ai’s industrial DataOps platform is open to handle data from and serve intelligence to nearly any plant floor or enterprise application. It focuses on confident, timely decisions, complementing MES, LIMS, EAM, and other…
The Case for Boosting Digital Transformation with Low-Code Why should Siemens Xcelerator customers embrace low-code capabilities from Mendix, a Siemens Digital Industries Software Business, to accelerate their #digitaltransformation journey? In today’s fast-paced manufacturing environment, using data from various sources to make informed decisions is crucial for operating profitably. This dynamic data often resides in systems…
Facing Skill Shortages, Intense Competition, and Supply Chain Chaos? Are You Ready to Adapt? In today’s volatile market, operational agility is no longer a luxury—it’s a necessity to future-proof your manufacturing operations. Our own researcher Julie Fraser joined a panel of experts from electronics, powder coatings, and building products for an interactive webinar on…
How do you accelerate the co-development of hardware and software for your products? Today’s products increasingly rely on software for innovation. Yet, development teams follow different processes and use separate tools to support the engineering and development of hardware and software. This creates silos within the development team that causes breakdowns in communication and hurts…
As highlighted in a Tech-Clarity eBook, “Extending the Digital Thread to the Customer Experience,” manufacturers who maintain accurate, up-to-date, and trusted digital thread data with complete service and maintenance records offer superior customer experience. Additionally, these manufacturers reduce service costs and increase customer confidence by avoiding multiple service visits and fixing issues perfectly the first…
Can you reimagine how people interact with enterprise software? Epicor is doing just that. Their announcement of the Prism agentic AI network and orchestrator takes the next step in moving from a system of record to a system of action. Prism includes conversational AI for employees to interact with ERP, and Epicor plans to integrate…
As highlighted in a Tech-Clarity report, “Drive Higher Profits with IoT Machine Monitoring and Optimization,” Top Performers are more likely to integrate machine data, enterprise systems, engineering data, and operational systems to drive higher profitability. This integration improves their ability to offer low-cost service, keep unplanned downtime low, troubleshoot machine problems quickly, provide data security,…
How can manufacturers take advantage of the benefits of product customization without suffering from decreased profitability? We gathered responses from 234 companies involved with to-order products and interviewed two leading manufacturers to find out how to improve product configuration. Read on or click here to download the full report with no registration required, courtesy of…
Do disconnected project management approaches hinder project execution, risk management, and decision-making, ultimately setting project leaders up for failure due to a lack of project intelligence? Engineering programs, including new product development projects, are becoming more complex. Companies delivering these programs often struggle to complete them on time and within budget. However, companies can improve…
How can you accelerate battery design innovation? The incredible demand for batteries has created significant opportunities for battery manufacturers at both existing businesses and new market entrants. However, current battery limitations can create an adoption barrier for more sustainable energy solutions. Companies with the right capabilities to rapidly innovate to overcome these battery limitations will…
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