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Initial deployment with Hyperion’s engineering team in Barcelona has laid the foundation for a unified engineering data platform across global sitesCHICAGO -- <a href="https://caddi.com/en-us/company/?ref=US-PR-2026-Q4-Hyperion-BW" rel="noopener noreferrer" target="_blank">CADDi</a>, the global developer of a manufacturing-exclusive AI data platform, announced a new technology collaboration with <a href="https://www.hyperionmt.com/en/" rel="noopener noreferrer" target="_blank">Hyperion Materials &amp; Technologies</a>, a leading worldwide provider of advanced materials and precision tooling solutions. The partnership has successfully accelerated Hyperion's quotation workflows by integrating engineering data with supply chain intelligence. Serving global industries across automotive, aerospace, mining, energy and industrial manufacturing, Hyperion specializes in hard and super-hard materials such as tungsten carbide and diamond-based products used in demanding industrial applications.<h2>Data Defragmentation with a "System of Insight"</h2>Hyperion’s engineers handle a high volume of customer requests for quotation (RFQs) that require reviewing technical drawings, evaluating manufacturing feasibility and referencing historical pricing data. Through CADDi's high-touch, consultative implementation process, Hyperion’s Barcelona-based engineering team was able to access and distill engineering data needed to support daily operations and decisions. This established a blueprint for faster RFQ response times and laid the groundwork for expansion across more use cases and manufacturing sites.Moving beyond basic document storage, Hyperion introduced CADDi Drawer to its Barcelona team to centralize engineering data and eliminate manual data silos. “Manufacturers generate enormous volumes of engineering data, but much of it remains difficult to access during day-to-day decision making. By structuring this information and making it searchable, Hyperion’s teams can unlock the value of their historical data and respond to customer opportunities more quickly,” said Anita Shao, customer success manager at CADDi.<h2>Accelerating Decision Velocity and Quotation Win Rates</h2>According to Hyperion CIO Steve Peale, “In highly competitive industrial markets, faster quotation response times directly improve win rates. When we explored CADDi Drawer fit, we were confident that it could help us align our engineering-to-manufacturing process and enable us to quote new parts faster. Early usage results indicate that it’s helping us do just that.”Today, with CADDi Drawer, engineers are able to assess a customer’s drawing and instantly identify Hyperion’s operations data that offers similar manufacturing processes and related cost history. This empowers the quotation team to prepare accurate quotes faster and spend more time making strategic pricing decisions, rather than performing low-value manual searches through disconnected internal archives. Eduard Forns, a business insights specialist at Hyperion in Barcelona, said, "I used to look in this folder, then Teamcenter. Now I know everything is in one place — really quickly and very trustable. I review the drawing, run a similarity search, verify correctness, and I know if I can apply an existing manufacturing and estimation model."<h2>Building the Foundation for Global Data Intelligence</h2>The Barcelona deployment is the first phase of a broader initiative to structure engineering and supply chain data across Hyperion's global operations. The company intends to extend the platform to additional teams and sites over time.In 2026, companies like Hyperion are shifting focus to reduce the time between identifying a question and executing the answer. CADDi’s <a href="https://caddi.com/en-us/resources/white-paper/decision-velocity-unlocking-faster-smarter-decisions-in-manufacturing/?ref=US-PR-2026-Q4-Hyperion-BW" rel="noopener noreferrer" target="_blank">new white paper</a> explains why improving decision velocity can drive significant productivity gains without large capital investments, while also improving the quality of decisions. CADDi will continue expanding its platform to help manufacturers strengthen operational performance and efficiency in engineering and sourcing processes, and build the intelligence foundation needed for long-term competitiveness. More case studies are available on CADDi’s website.<h3>About Hyperion Materials &amp; Technologies</h3>Hyperion Materials &amp; Technologies, headquartered in Worthington, Ohio, USA, is a global leader in hard and super-hard materials with more than 70 years of experience developing cemented carbide, diamond and cubic boron nitride technologies. Hyperion specializes in premium base materials, toolmaker components, engineered products, and process tools and solutions for the most demanding applications. With more than 2,000 employees worldwide, Hyperion has its production footprint in North and South America, Europe and Asia, and sales in more than 70 countries. To learn more, visit <a href="https://www.hyperionmt.com/en/" rel="noopener noreferrer" target="_blank">HyperionMT.com</a> or check out Hyperion’s profiles on <a href="https://www.linkedin.com/company/hyperion-materials-and-technologies/" rel="noopener noreferrer" target="_blank">LinkedIn</a> or <a href="https://www.youtube.com/channel/UCJ32n-KcXOjoymhb-D9nVvA" rel="noopener noreferrer" target="_blank">YouTube</a>.<h3>About CADDi</h3>CADDi is a global manufacturing technology company driven by the vision to accelerate physical innovation tenfold. Headquartered in Chicago and Tokyo, the company was founded in 2017 by industry veterans Yushiro Kato and Aki Kobashi, formerly of McKinsey and Apple. CADDi currently serves manufacturing businesses in more than 20 countries, including many of the largest publicly traded companies in Japan and the United States. CADDi’s manufacturing AI data platform analyzes, structures, contextualizes and connects data that has historically been fragmented across the manufacturing value chain. With a proprietary AI model, CADDi offers domain-specific products supporting every phase of the manufacturing value chain, enhancing decision-making and execution to drive business-critical impact. To learn more, visit <a href="https://caddi.com/en-us/company/?ref=US-PR-2026-Q4-Hyperion-BW" rel="noopener noreferrer" target="_blank">caddi.com</a> or follow the conversation on <a href="https://www.linkedin.com/company/caddiinc/" rel="noopener noreferrer" target="_blank">LinkedIn</a>.Media ContactMilbien MedinaSenior Manager, Brand MarketingCADDi<a href="mailto:milbien_medina@caddi.com" rel="noopener noreferrer">milbien_medina@caddi.com</a>‍<img src="https://cdn.prod.website-files.com/66a0057652cd363ed0446b3e/6a958e1d8416cb2270c59411_CADDi%20US_Press%20Release_Hyperion_GROUP%20PHOTO.png" alt="">Shown standing (from left) are CADDi Director of Sales Marcin Zmarzly, Hyperion Business Insights Specialist Eduard Forns, CADDi VP of Solutions Eddie Takeda, and Hyperion Business Application Analyst Iker Ramos. Taken during a recent site visit at Hyperion in Barcelona, Spain. (CADDi photo)

Hyperion Materials & Technologies Partners With CADDi To Reduce Custom Component Quotation Turnaround And Boost Decision Velocity

Originally featured in: <a href="https://bi-journal.com/nvenia-caddi-ai-digitize-750k-engineering-drawings/" target="_blank">https://bi-journal.com/nvenia-caddi-ai-digitize-750k-engineering-drawings/</a>nVenia, a Duravant company, adopts CADDi’s AI data platform to unlock the full value of decades of engineering, procurement, and aftermarket data through a shared intelligence layerCADDi, the global technology company behind the AI data platform for manufacturers, today announced a partnership with nVenia, a Duravant company that designs and builds engineered-to-order (ETO) and configured-to-order (CTO) <a href="https://bi-journal.com/ruizhi-machinery-unveils-sustainable-packaging-at-scie-2025/">packaging machinery</a>. nVenia delivers packaging equipment solutions for primary packaging, secondary packaging, and end-of-line processes through its well known Arpac, Hamer, and Fischbein brands.CADDi supports nVenia across the part lifecycle: helping engineers find parts to reuse with CADDi Drawer and its bulk similarity application, quoting parts to suppliers with CADDi Quote and recommending aftermarket parts with its aftermarket part recommender application. nVenia recently deployed CADDi AI to identify existing parts across thousands of drawings in real time to enable engineers with actionable insight to create new designs, quote new fabricated parts faster and streamline customer response time for spare parts.nVenia builds highly configured machinery, with 30,000 to 45,000 configuration options on CTO units and fully custom ETO builds. Over decades, that work produced hundreds of thousands of design drawings and a rich, deep component catalog. As the business scaled through growth and acquisition, <a href="https://bi-journal.com/nvenia-ips-packaging-automation-as-se-us-partner/">nVenia</a> recognized a strategic opportunity: by connecting its vast engineering history into a unified intelligence layer, engineers could leverage the full power of that catalog to design smarter, source faster, and deliver greater value to customers.<h2>Engineers leverage the full catalog to design smarter and faster</h2>Engineers search by text, metadata, or shape to identify and reuse existing components, streamlining new designs while reducing the time and cost of new stocking, ERP setup, quality inspection, and procurement steps. CADDi runs alongside nVenia’s Epicor ERP and SolidWorks PDM environment, extending and amplifying the value of those systems rather than replacing them.An early demonstration using CADDi Drawer and its bulk similarity application on 3,000 randomly selected parts validated the depth of nVenia’s catalog and confirmed the software could process real drawings accurately at scale. “CADDi has approximately 750,000 drawings of ours right now, and 3,000 were from the initial demo, which was really interesting,” said Ben Snyder, vice president of lifecycle services at nVenia. “One notable example involved a right-angle bracket used to support sensors. Within that 3,000-part sampling, we identified three nearly identical versions, differing only by minor variations in hole placement. This highlighted a clear opportunity for part consolidation and smarter reuse and it’s exactly the kind of insight that makes our engineers more effective across the entire catalog.”<h2>Service teams deliver faster, more complete responses to spare-part requests</h2>nVenia’s extensive installed base represents a significant and growing source of recurring aftermarket revenue. With CADDi, nVenia is now activating that potential at the moment a customer calls, turning deep product knowledge into real-time customer value. Snyder added, “By leveraging the CADDi aftermarket solution, we are enhancing both customer satisfaction and revenue performance. This tool enables our customer service representatives to guide customers toward complete component solutions, ensuring they receive everything needed rather than ordering individual parts.”CADDi’s aftermarket recommendation application, now in production with active users, draws on nVenia’s bill-of-materials structure and historical sales records to tie each recommendation to the specific machine a customer owns. When a representative takes a spare-part order, it returns the related replacements that machine will likely need next, so the representative can offer to add them to the same order, along with a rationale to share on the call. The team holds a strict quality bar, recommending fewer parts rather than risk recommending a wrong one.According to Kaimook Chirapathama, director of U.S. customer success and sales engineer at <a href="https://bi-journal.com/caddi-tadano-advance-global-tech-ma-synergies/">CADDi</a>, “nVenia has built an extraordinary knowledge base through decades of engineering excellence and strategic growth — hundreds of thousands of drawings and a rich order history that reflects the full breadth of their installed base. CADDi AI connects and activates that knowledge so an engineer can instantly leverage what already exists when designing something new, and a service representative can proactively recommend the right parts while the customer is still on the phone.”In 2026, companies like nVenia are shifting focus to reduce the time between identifying a question and executing the answer. CADDi’s new white paper explains why improving decision velocity can drive significant productivity gains without large capital investments, while also improving the quality of decisions. CADDi will continue expanding its platform to help manufacturers strengthen operational performance, simplify engineering and sourcing processes, and build the intelligence foundation needed for long-term competitiveness. More case studies and insights are available at <a href="/case-studies">us.caddi.com</a>.

nVenia Uses CADDi AI to Digitize 750K Engineering Drawings

Originally featured in: <a href="https://www.tipranks.com/news/private-companies/caddi-sharpens-industrial-ai-strategy-with-executive-forums-and-new-manufacturing-outlook-data">https://www.tipranks.com/news/private-companies/caddi-sharpens-industrial-ai-strategy-with-executive-forums-and-new-manufacturing-outlook-data</a><a href="https://www.tipranks.com/private-companies/caddi">CADDi</a> is a manufacturing technology company focused on digitizing and optimizing industrial operations, and this weekly recap reviews notable developments in its positioning around AI and automation. Over the past week, the company used a series of communications to emphasize how industrial AI can help manufacturers address mounting labor, <a href="https://www.tipranks.com/compare-stocks/stocks-hurt-by-trump-tariffs">tariff</a>, and data-management pressures.CADDi highlighted its participation in The Millennium Alliance’s Industrial Automation and Transformation Assembly in Houston, where a U.S. customer success and pre-sales leader will moderate a closed-door roundtable on enterprise AI. The session targets engineering, procurement, and operations leaders across discrete manufacturing sectors, including heavy industrial fabrication, oil and gas equipment, energy services, and commercial vehicles.The company also promoted findings from its 2026 Manufacturing Outlook Study, underscoring the operational challenges facing its target customers. According to the study, 79% of surveyed manufacturers view skilled labor shortages as their most significant external challenge, with 40% seeing direct impact on design and engineering teams and 66% citing tariff volatility as having a significant business impact.In addition, 56% of respondents identify poor cross-departmental collaboration as the main hurdle in managing parts data, pointing to persistent information silos in industrial organizations. CADDi positions AI not as a standalone tool but as an operating discipline embedded in workflows, aiming to tackle these issues by improving data visibility, collaboration, and decision-making in capital-intensive environments.Separately, CADDi announced plans to co-host its next CADDi Connect AUTOMATE 2026 forum in Chicago alongside Sara Zimmerman, PMP, and Sumitomo Drive Technologies USA. The peer-to-peer event for manufacturing and engineering executives will center on the keynote “From AI Curiosity to Operational Impact,” emphasizing problem-first automation strategies and clearer ROI from equipment and software investments.The company also amplified commentary from its VP of Marketing on manufacturing’s growing knowledge crisis as retirements accelerate, highlighting AI-powered systems as a means to capture and share institutional know-how. While CADDi has not disclosed new financial metrics or specific contract wins, its focus on thought-leadership, executive engagement, and workflow-embedded AI may strengthen its brand and support future adoption of its data and automation platform.Taken together, the week’s developments show CADDi reinforcing its role at the intersection of industrial AI, labor constraints, and supply chain complexity while deepening ties with senior decision-makers in key manufacturing segments. These efforts position the company to benefit from ongoing digitization trends even though concrete financial impacts remain to be seen.

CADDi Sharpens Industrial AI Strategy With Executive Forums and New Manufacturing Outlook Data

<h5>Originally featured in <a href="https://www.thefabricator-digital.com/thefabricator/library/page/202608/46/" rel="noopener noreferrer" target="_blank">The Fabricator</a> magazine</h5>Growth is good for a metal fabricating company, but it’s not necessarily easy. The increased revenue is the headline, but the real story is how the shop can stay on top of the additional work while still meeting quality and on-time delivery goals.Amerequip, Kiel, Wis., is a 106-year-old company that started out making farm implements, later added other products (such as the Snow Bird, a 1920s and ’30s vehicle with snow tracks, several of which were used in Admiral Richard Byrd’s expeditions to the South Pole), and finally found its way to becoming a contract manufacturer for major OEMs, beginning with John Deere in the 1980s. The latter move has proven a smart one, as major OEMs have increasingly sought to outsource metal fabricating services and focus more on final assembly."John Deere is a great example of a customer we like," said Peter Stilp, Amerequip’s business development manager. "We started out small in their ag division, and then we got into their turf division. Now, we’re doing all of that, and we’re into their construction division."Amerequip, a shortened version of American Equipment, now is a supply chain partner for not only Deere but also Caterpillar, the McNeilus Refuse (a business unit of Oshkosh Corp.), and other large manufacturing companies with global distribution for their products. This customer roster has grown with Amerequip’s commitment to support its customers along every step in the process—from design for manufacturing services to shipping. It’s one of the reasons the company has recently automated a paint line, purchased a 650-ton press brake, and added 32,000 sq. ft. of manufacturing space to accommodate a new large fabrication project. Amerequip is more than willing to make the proper investments to grow with its customers. (For the record, the metal fabricator is No. 28 on The Fabricator’s FAB 40 list. It reported $75 million in revenue for 2025 and projects $86 million this year.)But success brings complexities. One of the major challenges that Amerequip has had to wrestle with is the burgeoning number of part files—about 67,000 active files with drawings, according to Vice President of Engineering Tim Dorn. He’s seen the part files grow—along with frustration in the engineering department—over his 16 years with the company."Let’s say when we’re designing a product and there are some parts that are similar to what we’ve used in the past. At that point, we would have to use the memory of engineers to help with the manual searching to try and find that file," he said. "And frankly, that didn’t happen that often because it was such a challenging process."<h2>THE NEED FOR SPEED</h2>That arduous task motivated Dorn to look at some sort of solution that could help his engineering colleagues. He also recognized that such a tool could help Amerequip be more responsive to its customers."In engineering, these programs typically run an average of 18 to 24 months from cradle to grave—and the grave is really a handoff to operations for production," Dorn said. "When you’re talking about that long of a time period, anything that we can do to shrink that up gets us into revenue generation that much faster."Dorn added that Amerequip had a good file organization system, complete with a consistent naming structure, but it still involved manually searching for files, which could be very time-consuming if someone didn’t remember names or dates. More likely than not, any discussion about an older part began with "Do you remember that?" because someone needed a vague starting point from which to begin the file search.The company has plenty of long-term employees in its roster of approximately 300, including three project engineers with almost 85 years of combined experience. Even with all of that institutional knowledge, the metal fabricator still had lots of room for improvement when trying to find digital files in a timely manner."We’re talking about a digital environment, but yet you’re still actually combing as if you’re searching through a sock drawer," Dorn said.In the past, the engineering team took on continuous improvement projects, such as fastener standardization, but the manual task of looking at all the fasteners specified in design files and then deciding which was correct and how to designate it was daunting. Dorn said while this approach delivered suitable results, it might take 18 months to get there.That’s why any digital tool that could help with file organization and searching made sense. It would be able to shave minutes—and even hours in some cases—from certain projects. Dorn and his team, however, didn’t really know what they were looking for.<h2>FOUND AT FABTECH</h2>Attending a FABTECH in Chicago, an Amerequip project engineer stopped by a small booth to learn more about how the company’s software platform could help with organizing and locating information from a vast amount of data. CADDi Drawer looked intriguing.CADDi was founded in 2017 by two young men who understood the need to capture the intuition and wisdom of experienced engineers and craftsmen before they left an organization or were no longer part of a supply chain. During his time with McKinsey &amp; Co., CADDi’s co-founder and CEO Yushiro Kato worked with numerous manufacturers and helped the firm expand its IoT and AI practice. Aki Kobashi, also a co-founder and the CTO, has a background in building data processing systems in coordination with NASA and JAXA, the Japanese Aerospace Exploration Agency, and later spent four years working as an engineer at Apple. As the company’s website states, the duo built CADDi because they believe manufacturing cannot move forward if its expertise remains underutilized and scattered.CADDi Drawer is designed to help engineers get their parts repository in order. After drawings and other documents are uploaded into the centralized system, all of the files are searchable via keywords, which can be something as basic as material, size, designer’s name, and part name. The software’s proprietary technology also is able to discern each part’s shape, enabling a search for similar designs across all of the drawings.The following week, the engineer held a debriefing of what he’d seen at the trade show. Learning about CADDi for the first time, the engineering team began thinking what might be possible.It took a couple of years to find out, but in fall 2025, Amerequip took the step to digitize its parts library.<h2>THE DIGITAL DOMAIN</h2>Like other metal fabricating companies, Amerequip has experienced some painful digital migrations. Dorn wanted to avoid that if possible.<img src="https://cdn.prod.website-files.com/66a0057652cd363ed0446b3e/6a7df2ee3185e412c64af091_047.png" alt="__wf_reserved_inherit" height="auto" width="auto">With CADDi Drawer, Amerequip personnel can conduct a keyword search of drawings, CAD files, and other documents.<img src="https://cdn.prod.website-files.com/66a0057652cd363ed0446b3e/6a7df30b5121ae9788da3ce4_047-01.png" alt="__wf_reserved_inherit" height="auto" width="auto">CADDi Drawer can analyze different shapes that might be found in documents and return them in search queries.Part of the plan to minimize obstacles was to appoint an engineer who could become the in-house expert for using CADDi and help set up the software. Stella Rezabek, a mechanical designer in the engineering department, got the call."She was the tip of the spear in working with CADDi. She gets everybody here at Amerequip engineering in line, asked the right questions, and really coordinated things," Dorn said.She also was responsible for uploading the files, working closely with CADDi project managers. Dorn recalled that most of the files didn’t require alterations. Bulk uploads of the drawings and other documents made the process a task that a small team could handle.By January 2026, engineers started dabbling with the new platform. The results caught the attention of people using the search capabilities."I envisioned this would be exactly where it’s driving—speeding up the search for information," Dorn said. "But now I shouldn’t even be saying that, because I didn’t think it was going to be this much information that we were going to be able to pull up."Dorn recalled a recent product development project involving a lawn care application where a project engineer wanted to look for a similar assembly from a few years ago. In a matter of seconds, he pulled up the file, extracted the components of the assembly he wanted to use in the new product design, and was able to quickly determine the cost for those parts.CADDi Drawer also had been useful in avoiding the creation of duplicate part numbers. Say an engineer reviews a customer’s bill of materials and sees a component with a generic description and no drawing reference or additional identification. Before the implementation of the digital repository, the engineer likely would have created a new part number and issued a new purchase order, simply because it was difficult to locate a past reference to the component. CADDi’s keyword search and flexible matching logic can identify that component in a previously used part drawing, even if it has a different description than what was included on that original bill of material.Dorn said this opened the door to parts knowledge that the engineering staff never had before. They can see all parts ordered by Amerequip, not just the ones that the engineering staff had a role in purchasing.Project engineers aren’t the only ones finding CADDi Drawer useful. Dorn said the quality team was averaging about 250 to 300 searches per week by early summer. That could be just the beginning."When you look at making a decision on whether something needs to be changed or whether an action is going to affect something, it’s all about do you have the right data in front of you. In the past, you would have to go over to this area for finances, another area for the actual print and technical data, and other areas for quality reports, scrap warranty, customer issues, logistics, and all sorts of other stuff," Dorn said. "Now, potentially, we’re going to be able to pull this into one location where we can find it in one quick search."This digital domain has become the organizational brain that is able to remember things that escape veteran employees or that newer employees never knew. It’s proving more effective than a collection of networked drives and smart file organization ever could.<h2>GOOD FOR ALL CUSTOMERS</h2>Amerequip will be looking to add more part numbers to CADDi Drawer as the company continues its growth path. Management is projecting 14% growth in revenues this year. Some think that might be a bit conservative."We are optimistic," Stilp said. "There has been a lot of movement with potential projects and opportunities here in the last several months."Coming out of the pandemic, investments among Amerequip’s customers slowed down. Considering that some of these projects take two to four years to come to fruition, a lull in quoting activity leads to slowdowns later in the calendar."But those things are starting to fill in now," said Jon Karls, who also works in business development at Amerequip. "Things we’ve been working on for a bit are really starting to fill in now."When the deals are done, Amerequip will be looking to get the assemblies designed, validated, and approved for handoff to the shop floor as quickly as possible. The new digital file repository will help to speed up that process."We need to look at every way that we can improve how fast we can bring our products to our customers," Dorn said. "And my customer is not only our OEM partners. My customers are also the folks out on the production floor. We have to do it faster, and we have to do it better."

The Unexpected Impact of a Data Search Tool at Amerequip

As products increasingly house more data, component manufacturers say their clients want more traceable, validated information, according to Patrick Harrigan, vice president of sales at CADDi U.S. (Booth 135454). The company uses AI to digitize engineering and procurement data, turning drawings into a connected, searchable asset. Part proliferation and the cost of managing thousands of variants influence these digital solutions.CADDi’s 2026 Manufacturing Outlook Study found the average employee spends about one hour a day searching for parts data. And 56% of manufacturers said poor cross-departmental collaboration is their biggest challenge with the data.“A modern fastener, seal or pump manufacturer often runs tens of thousands of SKUs with small dimensional differences,” Harrigan says. “Without a way to instantly identify which variants already exist, engineering teams design new ones and procurement orders new ones.” Supply chain and trade volatility are additional hurdles. “For a components manufacturer sourcing materials, tooling and subassemblies from a global base, that volatility translates directly into sourcing decisions that have to move faster and rest on better data than they did three years ago,” Harrigan says.To this end, the company will demonstrate CADDi Drawer, a platform to help manufacturers aggregate, analyze and extract data; and CADDi Quote, which leverages AI to make quoting processes easier. CADDi Quote launched in the U.S. in 2025 and makes its IMTS debut this year. During the past two years, Harrigan says enterprise leaders and engineers have sought more understanding about how AI fits into the data and decision environment. “The conversation has shifted from ‘what value does AI bring?’ to ‘how do we democratize the data we already have to maximize the AI technology we are already investing in?’Excerpt from article, "COMPONENTS / CLEANING / ENVIRONMENTAL PAVILION: EFFICIENCY, SUSTAINABILITY AND DIGITIZATION", originally published in Manufacturing Engineering &amp; Technology Magazine<a href="https://digitaleditions.walsworth.com/publication/?i=867025&p=64&view=issueViewer" target="_blank"> https://digitaleditions.walsworth.com/publication/?i=867025&amp;p=64&amp;view=issueViewer</a>‍

Democratizing Data with AI

<h5>Originally posted in <a href="https://digitaleditions.walsworth.com/publication/?i=867025&p=50&view=issueViewer" target="_blank">Manufacturing Engineering &amp; Technology</a> magazine</h5>Engineering teams in discrete manufacturing routinely search for parts, drawings and specifications that already exist somewhere in the organization. When that data is locked in legacy systems, stored in disconnected folders or tied to individual institutional knowledge, the cost shows up in reordered parts, repeated design work, slower quotes and procurement decisions made without full information. The gap is access and structure, not volumeAnd yet, access alone does not capture the value. In my work helping manufacturers adopt and scale a data platform, the pattern I see most often is this: Structuring the data is the easy half. The harder half is changing how teams actually work—whether engineers trust the system enough to search it first, whether procurement builds it into how they buy, whether leadership treats the rollout as a change program rather than a software install. The manufacturers that get real returns are the ones that manage change deliberately.Here are five areas where structured engineering data produces measurable operational change:<h2>1. Reduce Search Time and Connect Drawings to the Full Record</h2>Engineers at Amerequip, a manufacturer with more than 100 years of accumulated engineering data, previously navigated disconnected systems to locate drawings, assemblies and cross-referenced part numbers. After structuring that data through CADDi Drawer, a single search returns drawings alongside associated assemblies, specifications and cost data. For engineering leaders managing large catalogs across long product histories, that consolidation changes how quickly teams can act on new requests for quote (RFQs).Sumitomo Drive Technologies applied CADDi’s AI data platform to 60 years of engineering records. Search time for parts and drawings decreased by up to 90%. That figure is consistent with what I observe across customer onboarding: The time engineers spend on retrieval, locating the right drawing in the right version from the right system, is substantial and largely invisible until it is measured.For engineering and quality teams that spend significant portions of their day locating reference documents, that reduction compounds across every project, every RFQ and every corrective action review. The engineers do not workless. They work on problems that require their judgment rather than on retrieval tasks that require only their persistence.<h2>2. Enable Procurement to Consolidate Suppliers</h2>Discrete manufacturers accumulate excess part numbers over years of design cycles, acquisitions and supplier changes. When engineers cannot locate an existing drawing quickly, they create a new one. In the deployments I work with, this is one of the first conditions manufacturers identify once their drawing archives become searchable: part numbers they did not know they already had.Structuring drawing archives so that similar parts are identifiable before a purchase order is placed allows engineering and procurement teams to consolidate part numbers at the source. Fewer active part numbers reduce supplier relationships, lower inventory carrying costs and simplify incoming inspection requirements downstream. When drawings are organized and cross-referenced, procurement teams identify parts with similar geometries or specifications that are currently purchased from different suppliers.Consolidating those orders reduces administrative cost, creates volume leverage with fewer suppliers and simplifies incoming inspection. Tadano Ltd. deployed CADDi’s AI data platform across operations in Europe and the Americas to achieve global parts integration and procurement optimization following merger and acquisition activity that integrated multiple group entities. The platform gave procurement teams visibility into what existed across the combined organization before placing new orders. For engineering leaders managing post-acquisition integration, that structure determines how quickly the combined organization can operate as one.<h2>3. Preserve Institutional Knowledge to Boost Onboarding, Upskilling and Enablement</h2>When senior engineers retire or change roles, accumulated design knowledge does not transfer automatically. The institutional memory held by a 30-year veteran—which drawings solved which problems, which suppliers delivered which tolerances, which assemblies failed under which conditions—exists in that person’s experience, not in any system the next engineer can access on day one.This is one of the most consistent conditions our Customer Success team encounters across deployments. Organizations that have operated for decades carry enormous engineering knowledge that is functionally inaccessible to anyone who was not present when the decisions were made. Amerequip’s deployment of CADDi Drawer addresses this directly. The single-search system connects drawings, assemblies and specifications built over more than a century of production, allowing new engineers to query what was previously designed and see how similar problems were solved.In the case of Nichirin Tennessee, Sales Manager Takehiro Ishimoto shared, “Our goal wasn’t just to implement new software—it was to eliminate barriers and operate as one unified team. CADDi is a tool, but the real success comes from our team’s commitment to make knowledge accessible and use it to optimize our workflow.”Both cases demonstrate that knowledge does not transfer through conversation. It becomes actionable through structured, searchable records.<h2>4. Use Retrievability as a VA/VE Foundation</h2>Value Analysis and Value Engineering (VA/VE) initiatives require engineers to compare current designs against alternatives. An engineer by training, I know that without a searchable record of past designs, that comparison is manual and incomplete.“Engineering knowledge is the foundation of effective VA/VE work,” CADDi CEO Yushiro Kato told Design World in April 2026. When drawings are structured and searchable, engineers retrieve prior iterations of a component, identify where material or complexity was added without corresponding performance gain and surface candidates for redesign. The data required for VA/VE work already exists in most organizations that have operated for decades. The barrier is retrievability, not volume.<h2>5. Build Data Reliability for AI-Driven Decisions</h2>AI tools in manufacturing require structured, accessible data to return useful output. Organizations that deploy AI applications without first structuring their underlying engineering data get inconsistent results. The AI tool surfaces what the data contains.The engineering information already in a manufacturer’s possession—drawings, specifications, procurement records and quality history—represents the primary input for any meaningful AI application in a design or production environment. CADDi structures that data so the AI tool presents it to engineers and procurement leaders in a form they can act on. The engineer reads the output, applies context and makes the decision. The system provides the reach that no individual engineer has across decades of records.The common thread across each of these applications is that the underlying data already exists. Manufacturers in discrete production have accumulated decades of engineering knowledge in drawings, specifications and procurement records. The operational question is whether that knowledge is accessible to the people who need it, at the moment they need it. The manufacturers that move first on structuring their existing engineering data establish an operational advantage that compounds. The ones that wait continue to pay for knowledge they already own.<h2>About the Author</h2>Yongli Deng is the Vice President of Customer Success at CADDi, an international technology company offering an AI-powered data intelligence platform for design, engineering and manufacturing. Deng partners with discrete manufacturers to drive successful adoption of CADDi’s platform, leading change management and value realization from deployment through scaled use. Before CADDi, Deng was a Principal at a global management consultancy, advising manufacturers on growth strategy and large-scale transformation. She holds a master’s degree in electrical engineering from Fudan University and an MBA from Northwestern Kellogg School of Management.CADDi is an international technology company that develops AI-powered data intelligence platforms for manufacturing, engineering and design. CADDi brings AI technology expertise to help manufacturers preserve decades of engineering and design knowledge. To learn more, visit <a href="http://us.caddi.com/company" target="_blank">us.caddi.com/company</a>.‍ Sources¹ CADDi. "Amerequip Transforms 100 Years of Engineering Data into Actionable Intelligence with CADDi AI Data Platform." CADDi News, 2026. <a href="http://us.caddi.com/resources/news/amerequip-transforms-100-years-of-engineering-data" target="_blank">us.caddi.com/resources/news/amerequip-transforms-100-years-of-engineering-data</a>² CADDi. "Sumitomo Drive Technologies and CADDi Transform Six Decades of Engineering Data into an AI-powered Intelligence Hub, Cutting Search Time by up to 90%." CADDi News, 2026. <a href="http://us.caddi.com/resources/news/sumitomo-drive-technologies-caddi-transform-six-decades-engineering-data" target="_blank">us.caddi.com/resources/news/sumitomo-drive-technologies-caddi-transform-six-decades-engineering-data</a>³ CADDi. "Manufacturing Leaders CADDi Inc. and Tadano Ltd. Advance Global Technology Utilization to Accelerate M&amp;A-Driven Group Synergies." CADDi News, 2026. <a href="http://us.caddi.com/resources/news/manufacturing-leaders-caddi-tadano-advance-global-technology" target="_blank">us.caddi.com/resources/news/manufacturing-leaders-caddi-tadano-advance-global-technology</a>⁴ CADDi. "Democratizing knowledge to empower independence with Nichirin Tennessee." CADDi Case Study, 2026.<a href="https://us.caddi.com/case-studies/nichirin" target="_blank"> https://us.caddi.com/case-studies/nichirin</a>⁵ Design World. "Is Engineering Knowledge the Key to Driving More Effective VA/VE Initiatives in American Manufacturing?" Q&amp;A with CADDi CEO Yushiro Kato, April 2026. <a href="http://designworld.com/caddi-ceo-yushiro-kato-qa-april-2026" target="_blank">designworld.com/caddi-ceo-yushiro-kato-qa-april-2026</a>

Five Ways Discrete Manufacturers Reframe Existing Engineering Data to Reduce Costs and Accelerate Decisions

<img src="https://cdn.prod.website-files.com/66a0057652cd363ed0446b3e/6a75e844bf9d7f5fa22cdf95_CADDi-Q-15701-Philip-Carpenito-DW-Voices-Featured-Graphic.webp" alt="__wf_reserved_inherit" width="1500px">By Sana Kazilbash | August 5, 2026Sponsored by <a href="/company" rel="noopener noreferrer">CADDi</a>. In this Voices article, Design World spoke with Philip Carpenito, Strategic Advisor at CADDi and the former Chief Procurement Officer at L3Harris Commercial Aviation, where he shares a procurement leader’s perspective on reducing costs and establishing effective cross-functional collaboration between engineering, procurement and manufacturing.Design World: Why is it increasingly important for procurement and engineering teams to work together earlier in the product development process?Philip Carpenito: This discussion is near and dear to my heart because I’ve lived with it my entire career in every company and industry that I have worked in. Collaboration has always been essential, especially in the design stage. What’s changed over the last several years is that this collaboration has become imperative.Before, procurement and engineering would perform their specific functions in silos. Today, companies need a more holistic approach. All functions including procurement, engineering, manufacturing, quality and supply chain need to work collaboratively in order to achieve success. Today’s companies face volatile markets due to geopolitical risk, long lead times, tariffs and increasing product complexity. This all has a significant impact on costs, manufacturability, delivery and customer satisfaction. Most product costs are determined right at the design stage of their lifecycle, which means that’s the best time to go after cost reduction. Design for manufacturability means that products can be built effectively, efficiently and at the right cost.When teams work together, institutional knowledge is preserved. Both engineers and procurement know what they’ve done in the past, and can work together to make the best decisions.Then there’s the newest change, AI technology to help with designing products. AI tools work best when they operate collaboratively with procurement, engineering and manufacturing to break down silos and bring a better product to market.All together, this demonstrates how procurement has evolved from buying what engineering tells them to buy, into a strategic contributor to product development. Organizations understand the immense value that procurement brings to the table, especially around lower total product costs, reduced supply chain risk and faster product launches.In the old days, people would come up with ideas, engineers would design a product, procurement would source materials and manufacturing would produce it. These are no longer sequential functions in my eyes. Instead, they form a chain all the way through, ensuring you get the best design, the best components that can be sourced, the best manufacturability — and at the end of the day, the best product that will drive revenue and margin growth within the business.What should manufacturers consider when deciding whether to make a part internally or source it externally, including the cost impact of that decision early in development?In my experience, make-buy decisions centered around direct product cost. A product is going to cost this amount internally manufactured, or that amount if externally manufactured.This decision has become much more significant, and much more strategic today. Companies are dealing with rising labor costs, supply chain disruptions, tariffs and capacity constraints. Today companies need to understand this decision from a holistic view and determine how this is going to affect the entire business.Factors that play into today’s make-buy decision include total cost of ownership, not just the product cost itself. Companies are evaluating:<ul><li>What is the total cost of this product?</li><li>Is this a core competency for us?</li><li>Is this something that you want to make core in your business?</li><li>Does it give us a competitive advantage over our competitors?</li></ul>A lot of companies miss that it’s not just the product cost. Now they understand it’s the cost of the equipment, manufacturing space, technology changes, supply chain labor and inventory requirements. When you decide to make a component or product, you need to be all-in on more than just the price of that one product. You have to take into account capacity utilization, supply chain expertise, equipment needs and capabilities, and skilled labor requirements to produce this product. There is also the supply chain risk to consider. Making a product internally means taking on the burden of finding and sourcing all materials, and performing manufacturing to produce the product. If you look at outsourcing, each stage of design, procurement and production, it frees up your internal resources to work on more strategic initiatives.At the end of the day, it’s a strategic decision that balances cost, capacity, risk, quality, IP and long-term competencies.When does it make sense to choose a custom part over a standard one, and what trade-offs should teams weigh in terms of performance, cost, availability and product lifecycle considerations?Choosing between a custom and commercial part is beyond important, because this affects everything in the lifecycle of the product. Custom parts can certainly deliver superior performance but can also result in greater cost, supply chain complexity and lifecycle risk.The decision is a strategic business decision, not just, “What about this one part?” Considerations for making a standard part include lower engineering effort, lower tooling investment, an established supply base and greater understanding of quality and reliability of a component. With a custom part, you’re starting from ground zero.Standard components are available immediately, so you don’t have to wait around or try to find a supplier. With a commodity or standard part, you don’t have to worry about inventory complexity, such as carrying inventory or having safety stock. Standard parts have a much longer product lifecycle versus a new custom part. Maintenance and replacement are also easier.There are advantages to using a custom part. Custom parts can drive IP in your product, making it harder for people to replicate. The performance should be better than a standard part. It differentiates your product from competitors. The downside is that lead times are usually longer with a limited supply base for that specific custom part.Bottomline for custom parts is, will customers pay for the additional performance? Procurement can weigh in, and help manufacturing engineers validate product development decisions:<ul><li>Is it going to differentiate my product significantly from the competition?</li><li>Are long-term economics feasible?</li><li>What are the financial, supply continuity and product differentiation benefits that justify a custom part?</li></ul>What lessons have manufacturers learned about maintaining supply continuity over the past few years?This has really changed. Prior to 2020, most organizations, and most procurement organizations, were all centered around cost and efficiency. Companies relied on just-in-time inventory, global sourcing, and drove initiatives to save time and money on inventory. But when COVID hit, all those processes exposed us to vulnerabilities when faced with global disruption.In today’s world, we have faced COVID, environmental disruptions and geopolitical issues. Companies now understand that risk and resiliency need to be balanced. Successful companies try not to have single-source suppliers, because that creates a larger amount of risk.Another difference over the last five years is visibility. Visibility was communicating with your suppliers and having them provide the updates on the market. Today, it’s very different. Not only do good companies have visibility into the company producing their product, but they also go down a layer to the tier two and tier three suppliers. They understand what those suppliers are doing to get the product to the tier one supplier to meet your needs.Raw material availability is another issue today. Companies need to monitor such items as oil, plastics and precious metals to ensure availability.Transportation is also vital. Before, companies could put products on a boat, or fly it over. Now that transportation network is very different and has greater pain points than in the past.Another consideration is strategic inventory. For example, memory chips and other chips used for data centers are becoming extremely critical. Companies have to be more aware of what your strategic inventory needs are.Supplier relationships matter more than the transaction. When you’re working within all these constraints, there’s nothing better than picking up the phone and talking to someone you trust, who you know will tell you the good, the bad and the indifferent. Creating trusted partnerships with your critical supply base is imperative.Business continuity and cross-functional planning all play a role. Companies that have spent money implementing software around supply diversification, digital visibility, strategic inventory and relationships are all in a much stronger place.How can better visibility into past designs, supplier history and part performance help manufacturers make stronger procurement decisions?This is the reason the CADDi AI data platform exists. Better visibility into historical engineering, procurement and operational data enables faster, lower-risk, more cost-effective sourcing decisions. Rather than treating each product as a new effort, CADDi software can help companies leverage institutional knowledge, avoid repeating mistakes and capitalize on proven solutions. It also improves the collaboration across functions, especially engineering, procurement, quality and manufacturing, because they all use the same data.The benefits include greater standardization across product lines. This helps avoid creating unnecessary new custom parts, reduces engineering effort, and it enables companies to leverage volume across different products. In most cases, there are lower qualification costs and testing costs because companies know what the product is, and how it performs. From a new product standpoint, this means shorter development cycles.CADDi software lets companies use their approved suppliers and components. This allows companies to increase their leverage and spend with their approved suppliers. From a procurement standpoint, this enables companies to understand delivery performance of particular materials:<ul><li>What are the quality results?</li><li>What’s the capacity and scalability of that part or one product?</li></ul>Warranty and field performance allows both the sourcing team and the engineering team to understand failure rates, warranty claims, failure modes and environmental performance. Supply chain risk — sole-source dependencies, suppliers with recurring delivery issues, geopolitical or geographical concentration risks — all can be better understood by having this information.I always return to AI because it’s the biggest initiative today. Modern analytics platforms become significantly more valuable when they can access integrated historical data. With CADDi, teams have every piece of information on every drawing, all the way down to the piece parts. They have CAD information, spec information, purchasing history, supplier report card information, quality, service, and pricing. Companies can find alternate suppliers, more standardized components and potential risks. All that can be done because you have a tool with all that information.To effectively capture and connect engineering, procurement, quality and other functions, data is a significant competitive advantage. Better visibility transforms engineering procurement from a reactive function to a data-driven function. Before, we used to ask a lot of different questions: who can supply this part, what’s the price. Now, we are moving to more valuable questions:<ul><li>What are the costs associated with the component?</li><li>What is the lifecycle?</li><li>Have we designed something similar?</li><li>How has this part performed not only today but in the future?</li><li>What risks have we encountered previously?</li></ul>By using historical knowledge and data to inform current decisions, we reduce cost, improve quality, shorten development time, strengthen supply continuity and build a more resilient and efficient product lifecycle. It’s the old adage: good data in, good data out. If you don’t have good data in, then you’re going to make the wrong decisions, and some of them are going to be very financially costly. CADDi turns decades of drawings, costs and quality records into a searchable intelligence system engineers use for that critical step: finding proven parts early and acting on them before release. Read the whitepaper <a href="/resources/whitepaper/value-analysis-value-engineering-for-manufacturing?ref=US-PR-2026-Q3-WTWH-DesignWorld-Voices6-SC" rel="noopener noreferrer">Value Analysis &amp; Value Engineering for Manufacturing: The Challenges and Solutions of Implementation</a>. <a href="/company#founders" rel="noopener noreferrer">Yushiro Kato</a>, co-founder and CEO at CADDi offers a related perspective in a recent Design World interview.CADDi is an international technology company that develops AI-powered data intelligence platforms for manufacturing, engineering and design. Headquartered in Tokyo and Chicago, the company was founded in 2017 by industry veterans Yushiro Kato and Aki Kobashi, formerly of McKinsey and Apple. CADDi brings AI technology expertise to help manufacturers preserve decades of engineering and design knowledge. Recognized globally for innovation, CADDi was listed in Fast Company’s Most Innovative Companies and received the SaaS Award for Best Business Intelligence and Engineering Management Software. To learn more, visit <a href="/company" rel="noopener noreferrer">us.caddi.com/company</a>.

VOICES: Philip Carpenito, Strategic Advisor at CADDi - Design World

<img src="https://cdn.prod.website-files.com/66a0057652cd363ed0446b3e/6a515cf5d9c70acacc56b714_CADDi-Q-15071-Stephan-Britz-DW-Voices-Featured-Graphic.webp" alt="__wf_reserved_inherit">Sponsored by <a href="https://us.caddi.com/company" rel="noopener noreferrer" target="_blank">CADDi</a>. In this VOICES interview, Design World spoke with Stephan Britz, Director of Engineering at <a href="https://us.sumitomodrive.com/en-us/facility/sumitomo-machinery-corporation-america-chesapeake-va?http%3A%2F%2Fus_caddi_com%2Fcase-studies%2Fsumitomo%3Fref=US-PR-2026-Q2-Sumitomo-BW" rel="noopener noreferrer" target="_blank">Sumitomo Machinery Corporation of America</a>, about turning six decades of design data into actionable intelligence with the help of CADDi, enabling engineers to locate relevant designs in minutes, apply prior work and existing designs to new orders, and shorten delivery timelines.Design World: Let’s start off with a little bit about yourself and your role at Sumitomo Drive Technologies.Stephan Britz: Sure. I’m the Director of Engineering at Sumitomo Machinery Corporation of America. We’re the U.S. subsidiary of Sumitomo’s power transmission business in Japan and operate under the Sumitomo Drive Technologies brand. We’re based in Chesapeake, Virginia, and serve as the Western Hemisphere headquarters, supporting facilities from Canada to Latin America. We also have engineering teams in Glendale Heights, Illinois; Verona, Virginia, in the Shenandoah Valley; and Lufkin, Texas, where our aftermarket facility is located. In the Western Hemisphere we have 3 manufacturer headquarters and 13 assembly facilities.I’m responsible for the U.S. Operations Engineering division, which includes front-end engineering and application engineering for selections, pricing, order processing and other technical administration. Our team also handles design engineering, including drawings, bills of materials, technical information, product IDs and manufacturing drawings. In addition, we support research and development, as well as aftermarket engineering for customer repairs, service needs and other technical support. That’s engineering in a nutshell.Sumitomo produces engineered-to-order drive systems for industrial applications with strict performance and delivery requirements. How does your engineering team support order-specific design, validation and release? Does access to prior drawings or manufacturing records affect your ability to meet those requirements?We manufacture industrial gearboxes, motors and power transmission equipment. One challenge with our data is that much of our growth has come through acquisitions. As Sumitomo expands into new industries or product areas, we often bring in companies with their own databases, part-number formats, pricing systems and drawing systems.Over time, that has created a complex data environment with information spread across multiple systems.Our drawings are stored across Lotus Notes, Inventor Vault and SharePoint, while our ERP, MRP and order-processing workflows rely on several connected platforms, including Salesforce. Engineers often spend significant time moving between these systems to process, verify and connect information.When an engineer receives an order, they need to determine whether we have sold a similar product before and whether components from a previous order can be reused. Before CADDi, that required familiarity with the ERP system, Lotus Notes, SharePoint, Vault and 3D design software, along with the ability to move between those systems to validate components. CADDi has helped bring that information together in one place.You’ve accumulated roughly 60,000 engineering drawings and documents across six decades, spread across multiple systems and formats. Before implementing CADDi, how did engineers access this information, and where were the biggest bottlenecks in their day-to-day design and research work?Over decades of growth, broader engineering responsibilities and continued investment, we built a substantial library of prior designs, revision history, documentation and application knowledge. The challenge was that this information was spread across many systems, making it time-consuming to search and difficult to use efficiently. That need to reduce search time is what led us to CADDi.Before CADDi, our engineering documents and drawings were stored primarily in Lotus Notes and Vault. Both systems functioned largely as document lists, so engineers had to search by numbers and file names rather than visually browsing drawings or components.An engineer would typically start in the ERP system to find bill-of-material information, then cross-reference that part number in Lotus Notes or Vault. From there, they had to locate and open the related drawing just to understand what the component was — for example, whether it was a shaft, bearing or another part.That process required significant data mining. Engineers had to work through long lists of part numbers, search separate systems and manually connect the pieces. Finding the right information was often tedious, time-consuming and more complicated than it needed to be.At the point of decision, what did the system need to do with your data, and what showed it was ready to deploy out of the box?The most powerful change was bringing drawings from multiple databases into one centralized location, creating a single, accessible repository of engineering data.That became CADDi’s first major advantage for us: one database and one source of truth. Instead of working through lists of numbers, engineers can now visually review drawings and documents, read them directly, and compare information side by side much faster than they could in our previous systems.During implementation and early use, what indicators confirmed the system was meeting your selection criteria? What gave you confidence it would continue to perform beyond initial deployment?Within the first few weeks, as we began uploading data and connecting it to ERP drawings, we could already see the impact. Tasks that once took half a day — four hours or more — could now be completed in minutes when searching for similar information.We have many long-tenured employees who know our systems extremely well, so we used a simple internal test. A new employee would search for a component in CADDi, while an experienced employee used the traditional systems and processes they knew best. Across multiple tests, the new employee consistently found the right information faster. In some cases, the experienced employees said they would not have found certain information at all resulting in the creation of a new part or product.We studied our initial deployment with CADDi’s team and found that a drawing research process that had once been difficult had become much more seamless. Our senior design checker reported a 90% reduction in the time required to find data, making it possible to locate critical information in minutes instead of hours. That created significant time savings and freed engineers to focus on higher-value work.The key takeaway was that fast access to a broader range of information gives engineers better options. Instead of simply finding data, they can evaluate multiple drawings or references and choose the best path forward.Engineering teams often rely on exact part numbers or prior knowledge to locate designs. How has the ability to search your historical archive using shape and feature similarity changed how Sumitomo’s engineers find and evaluate existing designs? How did that affect decisions around reuse versus new design?The similarity search feature is one of the capabilities we value most, and we are still finding new ways to use it. In the past, nearly every search depended on a part number in a list. It was difficult to picture what a part looked like, let alone find a similar component. Because the part numbers did not always have a logical relationship to one another, two nearly identical shafts could exist in the system without an engineer being able to locate both.With CADDi, engineers can visually review drawings as they move through the archive. The similarity feature adds another layer of value. After locating one component, we can search for similar parts and quickly identify closely matched drawings — sometimes 10 or 20 options. That gives engineers enough context to compare references and decide which design is the best starting point.Beyond finding similar past orders, CADDi helps uncover enough relevant references that engineers can choose a design requiring less modification before moving forward.That capability has helped us save weeks of design work and significantly reduce customer lead times. More importantly, the time engineers previously spent searching for information can now be redirected toward actual engineering work, such as evaluating options, refining designs and solving customer-specific application challenges. <a href="https://us.sumitomodrive.com/en-us/facility/sumitomo-machinery-corporation-america-chesapeake-va?ref=US-PR-2026-Q3-WTWH-DesignWorld-Voices6-SC" rel="noopener noreferrer" target="_blank">Sumitomo Drive Technologies</a> has been delivering power transmission and control solutions across a wide range of industries for over 130 years. As an engineer-to-order manufacturer, the company manages decades of engineering data across PLM, ERP, CAD and shared systems. That volume made it difficult for engineers to locate and apply prior designs. Through its partnership with CADDi, Sumitomo reduced the time required to find engineering data by up to 90 percent. In one case, the team identified a closely matched historical design and reclassified a complex order into a streamlined execution, saving weeks of engineering effort. Read the full case study, <a href="https://us.caddi.com/case-studies/sumitomo-machinery-america?ref=US-PR-2026-Q3-WTWH-DesignWorld-Voices6-SC" rel="noopener noreferrer" target="_blank">Transforming Searching into Solving with Sumitomo Drive Technologies,</a> and explore how manufacturers are applying engineering data to improve decision making.<a href="https://us.caddi.com/company?ref=US-PR-2026-Q3-WTWH-DesignWorld-Voices6-SC" rel="noopener noreferrer" target="_blank">CADDi</a> is a global technology company that develops manufacturing-exclusive data intelligence platforms. Headquartered in Tokyo and Chicago, the company was founded in 2017 by industry veterans Yushiro Kato and Aki Kobashi, formerly of McKinsey and Apple. CADDi brings Japanese design technology expertise to help manufacturers preserve decades of engineering knowledge. Recognized globally for innovation, CADDi was listed in Fast Company’s Most Innovative Companies and received the SaaS Award for Best Business Intelligence and Engineering Management Software. Visit <a href="https://us.caddi.com/company" rel="noopener noreferrer" target="_blank">us.caddi.com/company</a>.

Manufacturing Voices: Stephan Britz, Director of Engineering, Sumitomo Machinery Corporation of America

<h6>nVenia, a Duravant company, adopts CADDi's AI data platform to unlock the full value of decades of engineering, procurement, and aftermarket data through a shared intelligence layer</h6>‍CHICAGO, Ill. — CADDi, the global technology company behind the AI data platform for manufacturers, today announced a partnership with nVenia, a Duravant company that designs and builds engineered-to-order (ETO) and configured-to-order (CTO) packaging machinery. nVenia delivers packaging equipment solutions for primary packaging, secondary packaging, and end-of-line processes through its well known Arpac, Hamer, and Fischbein brands.CADDi supports nVenia across the part lifecycle: helping engineers find parts to reuse with CADDi Drawer and its bulk similarity application, quoting parts to suppliers with CADDi Quote and recommending aftermarket parts with its aftermarket part recommender application. nVenia recently deployed CADDi AI to identify existing parts across thousands of drawings in real time to enable engineers with actionable insight to create new designs, quote new fabricated parts faster and streamline customer response time for spare parts.nVenia builds highly configured machinery, with 30,000 to 45,000 configuration options on CTO units and fully custom ETO builds. Over decades, that work produced hundreds of thousands of design drawings and a rich, deep component catalog. As the business scaled through growth and acquisition, nVenia recognized a strategic opportunity: by connecting its vast engineering history into a unified intelligence layer, engineers could leverage the full power of that catalog to design smarter, source faster, and deliver greater value to customers.<h2>Engineers leverage the full catalog to design smarter and faster</h2>Engineers search by text, metadata, or shape to identify and reuse existing components, streamlining new designs while reducing the time and cost of new stocking, ERP setup, quality inspection, and procurement steps. CADDi runs alongside nVenia's Epicor ERP and SolidWorks PDM environment, extending and amplifying the value of those systems rather than replacing them.An early demonstration using CADDi Drawer and its bulk similarity application on 3,000 randomly selected parts validated the depth of nVenia's catalog and confirmed the software could process real drawings accurately at scale. "CADDi has approximately 750,000 drawings of ours right now, and 3,000 were from the initial demo, which was really interesting," said Ben Snyder, vice president of lifecycle services at nVenia. "One notable example involved a right-angle bracket used to support sensors. Within that 3,000-part sampling, we identified three nearly identical versions, differing only by minor variations in hole placement. This highlighted a clear opportunity for part consolidation and smarter reuse and it's exactly the kind of insight that makes our engineers more effective across the entire catalog."<h2>Service teams deliver faster, more complete responses to spare-part requests</h2>nVenia's extensive installed base represents a significant and growing source of recurring aftermarket revenue. With CADDi, nVenia is now activating that potential at the moment a customer calls, turning deep product knowledge into real-time customer value. Snyder added, "By leveraging the CADDi aftermarket solution, we are enhancing both customer satisfaction and revenue performance. This tool enables our customer service representatives to guide customers toward complete component solutions, ensuring they receive everything needed rather than ordering individual parts."CADDi's aftermarket recommendation application, now in production with active users, draws on nVenia's bill-of-materials structure and historical sales records to tie each recommendation to the specific machine a customer owns. When a representative takes a spare-part order, it returns the related replacements that machine will likely need next, so the representative can offer to add them to the same order, along with a rationale to share on the call. The team holds a strict quality bar, recommending fewer parts rather than risk recommending a wrong one.According to Kaimook Chirapathama, director of U.S. customer success and sales engineer at CADDi, "nVenia has built an extraordinary knowledge base through decades of engineering excellence and strategic growth — hundreds of thousands of drawings and a rich order history that reflects the full breadth of their installed base. CADDi AI connects and activates that knowledge so an engineer can instantly leverage what already exists when designing something new, and a service representative can proactively recommend the right parts while the customer is still on the phone."In 2026, companies like nVenia are shifting focus to reduce the time between identifying a question and executing the answer. CADDi's <a href="https://us.caddi.com/resources/whitepaper/decision-velocity-unlocking-faster-smarter-decisions-in-manufacturing?ref=US-PR-2026-Q3-nVenia-BW" rel="noopener noreferrer" target="_blank">new white paper</a> explains why improving decision velocity can drive significant productivity gains without large capital investments, while also improving the quality of decisions. CADDi will continue expanding its platform to help manufacturers strengthen operational performance, simplify engineering and sourcing processes, and build the intelligence foundation needed for long-term competitiveness. More case studies and insights are available at <a href="https://us.caddi.com/" rel="noopener noreferrer" target="_blank">us.caddi.com</a>.<h2>About CADDi</h2>CADDi is an AI-powered data platform that makes design and supply chain data accessible and actionable for manufacturing teams. Headquartered in Tokyo and Chicago, the company was founded in 2017 by industry veterans Yushiro Kato and Aki Kobashi, formerly of McKinsey and Apple. Its flagship product, CADDi Drawer, uses advanced AI to centralize and analyze unstructured design and production data, helping manufacturers improve efficiency, reduce redundancies, and unlock innovation. Recognized globally for innovation, CADDi was listed in Fast Company's Most Innovative Companies and won the SaaS Award for Best Business Intelligence and Engineering Management Software. To learn more, visit <a href="https://us.caddi.com/" rel="noopener noreferrer" target="_blank">us.caddi.com</a>.Media ContactMilbien MedinaSenior Manager, Brand MarketingCADDi USA<a href="mailto:milbien_medina@caddi.com" rel="noopener noreferrer">milbien_medina@caddi.com</a>

nVenia Adopts the CADDi AI Data Platform to Assetize 750,000 Drawings and Strengthen Aftermarket Value Across Multiple Brands

What's One Underrated Leadership Skill in Logistics and Supply Chain Management?

CADDi has launched an ITAR compliant version of its CADDi Drawer platform, enabling U.S. aerospace and defense manufacturers to apply AI to sensitive design and supply chain data while maintaining strict data residency and U.S. person access requirements. The company has also registered with the U.S. Department of State’s Directorate of Defense Trade Controls, positioning the platform for broader use across the defense industrial base.<h2>Addressing Data Challenges in Aerospace Engineering Workflows</h2>Aerospace and defense organizations accumulate decades of design files, legacy drawings and institutional knowledge, much of which is unstructured or undocumented. As senior engineers retire and programs evolve, teams often recreate work, re source parts already in use and spend weeks generating quotes or design comparisons. These inefficiencies are compounded by export control requirements that limit the use of many commercial AI tools.CADDi Drawer is designed to centralize and analyze this fragmented data, making historical designs and engineering decisions more accessible. By offering a U.S. only tier with controlled access, CADDi can now support manufacturers whose data cannot leave an ITAR compliant perimeter.<h2>Multi Layer Controls for Sensitive Data</h2>The ITAR tier builds on CADDi’s existing SOC 2 Type II and ISO 27001 certifications. The company has implemented additional controls to ensure all data storage remains within the United States and that only U.S. persons can access or manage the environment. These measures allow aerospace and defense manufacturers to apply AI driven search, classification and analysis to their design archives without violating export control regulations.CADDi leadership emphasized that this release is an early step in expanding support for the defense industrial base and enabling AI transformation in sectors where data sensitivity has historically limited adoption.<h2>Platform Availability and Use Cases</h2>The ITAR compliant tier of CADDi Drawer is now available to qualifying U.S. manufacturers. The platform is intended to help engineering and supply chain teams reduce redundant design work, accelerate quoting, improve sourcing decisions and preserve institutional knowledge across long running aerospace programs.<h2>Company Background</h2>Founded in 2017, CADDi develops AI powered tools that make design and supply chain data more accessible for manufacturing organizations. Its CADDi Drawer platform analyzes unstructured engineering and production data to improve efficiency and support innovation across industries including aerospace, automotive and heavy manufacturing.<h5>Originally posted on <a href="https://www.aerospace-trends.com/caddi-introduces-itar-compliant-tier-of-its-ai-data-platform-for-u-s-aerospace-and-defense-manufacturers/" rel="noopener noreferrer" target="_blank">Aerospace Trends</a></h5>

CADDi Introduces ITAR Compliant Tier of Its AI Data Platform for U.S. Aerospace and Defense Manufacturers

Nichirin Group has manufactured automotive components for more than 100 years. In factories, drones equipped with sensors handle inventory checks that previously took employees days to complete walking the floor. North America is leading much of this work, with successful approaches earmarked for rollout to facilities in Europe, Asia, India and Japan.The company holds a clear view: It cannot remain stable without continuing to innovate and adapt. That stability depends on its people, and as the company adds new locations and functions across the U.S., it wants employees at every level to take ownership.<h2>An Archive That Required an Expert to Navigate</h2>In its Tennessee division, the company is investing in AI data tools to make 24 years of engineering history accessible to every employee on day one. The <a href="https://us.caddi.com/" rel="noopener noreferrer" target="_blank">CADDi</a> AI data platform deployment is one part of that larger investment, and the results are already shaping how Nichirin builds elsewhere.Before CADDi, accessing drawings, BOMs and historical data required exact part numbers and a heavy reliance on the "tribal knowledge" of senior staff. This created significant information silos that slowed onboarding and daily operations for the entire team.<h2>Preserving and Activating Global Veteran Knowledge</h2>Although Nichirin has operated globally for more than a century, leadership recognized that sustaining growth required making local employees more self-sufficient by creating a knowledge-sharing infrastructure that could help drive innovation and long-term expansion.Japanese expatriate and veteran staff embodied years of experience and contextual knowledge. Other employees routinely routed questions through them, and newer hires spent close to a year building enough familiarity with drawing structures and part-number logic before they could handle related tasks independently.The most important documents, including engineering drawings and technical reports, were controlled by only a few people, with local staff often having to ask where specific files were located.When <a href="https://us.caddi.com/product-overview" rel="noopener noreferrer" target="_blank">CADDi Drawer</a> was first proposed, there were concerns about the scale of effort required: Uploading over two decades of drawings, documenting historical conventions, linking engineering records to commercial data across systems that had never been connected. Instead, the value showed up before the work was even finished.<h2>Building an Intelligence Hub for Company-wide Access and Enablement</h2>Nichirin's engineering and commercial records were saved in separate systems with no shared structure connecting them. Drawing formats and part-numbering conventions had shifted multiple times, and sales teams searching for historical pricing had to move across spreadsheets, PDFs and archived folders, which meant producing estimates rather than verified figures if the right record couldn't be located in time.The CADDi team began by interviewing Nichirin staff to map how part numbers had been structured across different periods of the company's history. CADDi Drawer extends standard optical character recognition with configurable logic that identifies which numbering rule applies to a given drawing based on its date, then extracts the correct part number from there.The system was configured to apply the right numbering rules based on a drawing's date, extract the correct part number and link it to related BOM and commercial records through that shared key. A sales employee could then enter a customer name, material type or component description and retrieve drawings alongside verified pricing history in a single workflow.After implementing CADDi, the team reduced reliance on individual knowledge to improve quoting speed and cross-functional decision making. This cultural shift resulted in notable behavior changes:&nbsp;<ol><li>Employees stopped routing basic location questions through higher-level employees, freeing them to focus on work that actually required their experience and judgment.</li><li>Employees began going directly into CADDi Drawer instead of asking repeated questions.</li><li>The quality of questions improved because employees were doing their own research first, saving time for veteran staff and building confidence for staff in training.</li></ol><h2>Bridging Technology Adoption Hurdles with Visible Impact</h2>Experienced employees are often hesitant about digitization efforts because they can feel disruptive to long-established workflows. What gets them on board is positioning the transition as a way to preserve operational knowledge for the next generation of employees, especially as manufacturers face growing workforce and succession challenges.Previously, drawing-related tasks required close to a year of accumulated context before a new hire could handle them independently. With CADDi Drawer in place, new employees accessed engineering and commercial records from the start and moved into substantive work months earlier than before.For younger sales employees, access to historical project data changed what they could do independently. Before, estimating pricing or quantities meant either tracking down someone with historical context or working from guesswork. Now they search for past projects directly and build estimates from verified precedents, improving both the speed and accuracy of customer responses.With aggressive team expansion, investment in AI and data tools signals what kind of environment new hires can expect to work in, which is increasingly important for younger workers evaluating long-term career growth in U.S. manufacturing.<h2>AI-Driven and Built to Scale Workforce Empowerment</h2>With the success of the Tennessee technology deployment, Nichirin is applying the strategy to expand the intelligence into more sites, expanding to Nichirin Flex USA, with facilities in El Paso, Texas, and Juarez, Mexico, so new sites start with a searchable, connected archive rather than waiting years for institutional context to develop.Nichirin Tennessee is also transitioning responsibilities currently held by senior staff to local management. North America is serving as early proving ground, with the possibility of similar approaches being explored across its global group companies down the road.Across U.S. manufacturers facing a tighter labor market, the same dynamic appears: Critical operational knowledge concentrated in a small number of long-tenured employees. The companies that succeed at localization are the ones that treat that knowledge as something to engineer into the operation, not something to recruit around.<h5>Originally posted on <a href="https://www.manufacturing.net/automotive/article/22968781/how-automotive-component-maker-democratized-24-years-of-engineering-knowledge" rel="noopener noreferrer" target="_blank">manufacturing.net</a></h5>

How Automotive Component Maker Democratized 24 Years of Engineering Knowledge

CADDi leverages AI to digitize drawings and procurement data, making manufacturing insights searchable by shape, text, dimensions and part names.

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