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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

<img src="https://cdn.builder.io/api/v1/image/assets%2F209ee507cedf4c298e100edae4bba2df%2F5b7606bc963644f4b4cfb4f1330bb925?format=webp">Getty ImagesAn engineering team quotes a new part, projecting 100 parts per hour based on process planning and machine capability. But once the part launches, it becomes clear that the line can only achieve 50 per hour; tight specifications reduced machine output. What engineering learned from a similar part two years ago didn’t reach the quoting team because systems weren’t connected. Critical details were buried in historical production data. The result: a massive cost overrun caused by a quote that wasn’t built on historical learnings.Scenarios like this play out more often than most manufacturing leaders realize. While Industry 4.0 investments have transformed plant floors around the world, engineering — where manufacturing costs are determined — still operates on legacy tools and siloed data. <a href="https://caddi.com/en-us/resources/white-paper/supercharge-engineering-efficiency/" rel="noopener noreferrer" target="_blank">A Rolls-Royce analysis</a> found that 80 percent of cost reduction opportunities required changes to part designs. The stakes are especially high in industries like automotive, where products have 15-year lifespans and can take five to seven years to develop. There’s not much opportunity to get it wrong when timelines are this long.As retirements accelerate the loss of institutional knowledge, tariff volatility complicates supply chains, and supplier consolidation reduces sourcing options, manufacturers have a decision to make: connect engineering to the rest of the business and use AI to gain insight, or fall further behind global competitors that have already figured out how to streamline the process with more mature data intelligence.<h4>Where Design and Engineering Break Down</h4>This quoting problem — just like most engineering failures — is a symptom of how engineering works. It’s inherently iterative: test, break, redesign, test again. Engineering processes are also complex and disconnected by nature; they span years, involve multiple systems, and require collaboration across functions.Each manufacturing team has traditionally operated in a legacy system that wasn’t designed to share information, leaving them completely unaware of what useful data was available in other siloed environments, such as similar parts produced last month, last year, or at a different facility. As such, part consolidation opportunities are also often missed.When this data isn’t accessible across functions, the overall process breaks down:<ul><li>Procurement can’t see engineering specifications from past similar parts when sourcing new suppliers, so they negotiate without knowing what drove cost on similar components.</li><li>Quality can’t access and consider design rationale if problems emerge on the floor, which means troubleshooting starts from zero instead of incorporating original intent.</li><li>Quoting teams can’t draw on historical production data, so they keep repeating estimating errors.</li><li>Sales can’t respond quickly to RFQs because they’re waiting on engineering, which means opportunities close before quotes arrive.</li></ul>The same disconnect exists across multiple sites. A five-plant operation spanning one or more continents may have three stamping plants that share little data on a regular basis. The knowledge at each location stays there, and the company expends resources solving the same problems three times. Making that knowledge accessible across plants supports knowledge retention, training and faster problem-solving, ultimately mitigating the chances of the same mistake twice.Making the right engineering and design data accessible to non-engineering functions enables teams to move faster and make smarter decisions along the way, including eliminating waste and proliferation.<h4>Extracting Value from Current Platform Investments</h4>Legacy systems themselves aren’t to blame for this problem. The significant investments made in product lifecycle management (PLM), enterprise resource planning (ERP), and other enterprise platforms are necessary and valuable; they’re just often underutilized. In fact, very few companies, if any, achieve maximum effectiveness from these platforms. Expert users are often the only ones who know how to extract value from them at all. What if there was a tool that could look through all these systems to connect data and surface insights from joined-up thinking?This challenge intensifies in multinational companies: Each site runs different systems with little unification across locations. Fragmentation slows collaboration and prevents knowledge transfer across borders. Every team dedicates time and resources to solving the same problems independently.Here’s the good news: Existing investments don’t have to be abandoned. Instead, they can be connected by a data intelligence platform that catalyzes the extraction of value from all of them. In other words, by connecting information across disparate systems and applying an AI data platform, people from all over the organization can access the necessary data to surface insights and support ideas and decisions that would otherwise remain buried or require a huge expenditure of resources, and the associated delays, to find.There is a financial-impact dimension to this as well. A data intelligence platform that draws from PLM, ERP and other enterprise systems can reduce the number of specialist licenses an organization needs for those systems, since non-expert users can access the data they need through the connecting platform instead.How does this work? Consider a manufacturer making similar parts for different customers across different plants. Without the ability to connect systems, no one ever knows about opportunities to consolidate costs. To spot them, teams would have to connect the dots manually amid other operational demands. A data intelligence platform connects these dots. It surfaces similar parts and cost differences and presents supplier comparisons so leaders can identify consolidation opportunities.Giving easy and rapid access to such information enables engineers to find new solutions, avoid duplication and won’t make the same mistakes twice because they can quickly learn from what’s happened in the past. New employees get up to speed faster, bringing fresh curiosity that surfaces new insights, and the organization builds upon digitalized knowledge from veteran employees.<h4>Competing on Quality, Speed and Connection</h4>Connecting engineering data across the organization is especially urgent for manufacturers seeking to grow. Response speed on RFQs translates directly to win rate. The faster a quote can be built on real production data, the more accurately it can be priced and the more new business it can capture. Global manufacturers have to move faster while becoming more cost-effective in engineering and design. Chinese manufacturers have already developed vehicles at sub-$10,000 price points by accelerating development cycles and standardizing parts that don’t differentiate the product for the consumer. Competing with that in North America or Europe means getting faster and more cost-effective in development, but organizations operating with fragmented internal operations start every race behind. Accelerating momentum with less resource consumption can boost business and financial performance.With real-time access to historical data made possible by a connected platform, manufacturers can quote faster and launch more right designs the first time. The right platform also supports organizational change by making data accessible and useful without requiring every plant to use the same systems. This flexibility matters when companies are being added to or restructured constantly.<h4>A Measured Path to ROI</h4>Knowing where to start fixing this issue is often the hardest part, but Subaru has a practical model for manufacturers to follow. Before working with CADDi, formal inquiries to the company’s design department were required to access drawings; responses took a week or longer. To address this, Subaru implemented CADDi in one department first. After proving ROI, they scaled the platform to 50 departments. Now, team members across the organization save thousands of hours by accessing critical engineering information directly and without distracting engineers from their primary work. By digitizing legacy data, Subaru reported nearly 2,400 hours per month in drawing-search time savings.Whether the goal is to reduce engineering hours, compress development cycles, achieve first-time-right designs that eliminate costly churn or consolidate parts across sites, the approach is the same: Begin where pain is greatest to prove ROI, and then expand from there.For manufacturers ready to capture the value of a data intelligence platform, the Supercharge Engineering: Efficiency Using Strategic Parts Data whitepaper outlines the implementation path with pragmatic steps to turn engineering data into competitive advantage.<a href="https://caddi.com/en-us/resources/white-paper/bridging-the-digital-divide-enhancing-manufacturing-efficiency-with-caddi-drawer/" rel="noopener noreferrer" target="_blank">View the Whitepaper</a>

AI-driven engineering and design insights: Manufacturing’s next competitive edge

<img src="https://cdn.builder.io/api/v1/image/assets%2F209ee507cedf4c298e100edae4bba2df%2F2ecd4e9d029a4bf1bc4c57f8373b0ae0" class=""><a href="https://www.nvenia.com/services/" rel="noopener noreferrer" target="_blank" class="">nVenia</a> builds highly engineered-to-order equipment across five brands, where closely similar parts had proliferated across internal brands faster than manual searches could reconcile them. Ben Snyder, Vice President of Lifecycle Services, evaluated engineering AI early, paused the investment when the technology was not yet ready, and re-engaged CADDi once it had matured. In this VOICES article, Design World spoke with Snyder about controlling SKU proliferation, reducing inventory risk, and proving out engineering AI in a production environment. Design World: Tell us a bit about yourself and your role at nVenia.Ben Snyder: I am Vice President of Lifecycle Services, which is our aftermarket department. I’m relatively new in that role, having started in February of this year. Prior to that, I was in the operations role at nVenia for two and a half years. Thanks to my time with operations, I already had a good understanding of the execution and operational excellence side of the business, and now I also understand this from the commercial side dealing with questions like how we’re using new tools, such as CADDi, to really drive revenue increases at the same time as we streamline efficiencies inside our factory.nVenia builds highly engineered-to-order equipment across five distinct brands. How does the engineering team fulfill the core business mission for your customers when managing complex product variations across internal brands?We build a combination of what we call engineered-to-order (ETO) style machines, as well as configured-to-order machines.Configured-to-order is easier to describe. It’s like ordering a car. You have a set number of options, you select the combination you want, and then we will go and build your unit. The difference is that we have 30,000 to 45,000 different options, so there is a lot to choose from.On the ETO side, these are unique opportunities that come in. For example, a ramen company or a bottling company. We have machines that have a common core, but every product is slightly different in size. This requires significant customization, for how that specific product comes to the machine, how it gets bundled and packed, how we wrap the product and place it onto a pallet with a robotic palletizer, how that gets sent to a semi-truck or storage area.All of these unique customer opportunities require special components. Prior to CADDi, the idea was that we must build a new part, make a new SKU, release it to the factory and go from there.What we learned doing manual searches is that we have many components that are very similar. A part can have the same shape but the hole is in a different location, or we might find two parts with an identical shape but one is not used because someone in the engineering department designed it 15 years ago and newer engineers don’t know about it. We use CADDi to help us locate those similar parts so that we don’t have SKU proliferation.Engineers spend roughly an hour a day searching for fragmented parts data. What specific operational bottleneck made eliminating SKU proliferation the primary catalyst for evaluating a new software solution?That goes back to what I said earlier, and the idea that every time you produce a new part, there’s a lot of work that goes into that. It’s not just creating a drawing. It’s needing stock and a stocking strategy. You have to set it up in your enterprise resource planning (ERP) system. There are quality inspection steps. If we’re buying this from outside, I need a procurement plan. I need a ton of different things that come into play just to create a nut, for example.We wanted to stop that confusion. We have hundreds of thousands of drawings inside of our facility. How do we optimize using them over anything else before we create something new?nVenia currently holds sizable inventory to allow for decreased lead time with the many combinations of product. How will centralized part data help reduce excess working capital while generating new project revenue through component association?The first thing we wanted to do is stop the bleeding. In order to do that, don’t make new parts, to avoid SKU proliferation. We’re using CADDi on the design side, and we’re only producing new parts when we absolutely must.The second thing is dealing with what we call excess and obsolete (E&amp;O) inventory. What we want to do is be able to utilize those parts in a new design. When we get these ETO machines coming down the pipe, we want to design in those existing parts. Instead of using a 1.2 horsepower motor, we can use 1.3 horsepower because that’s in inventory, so we can save some money there.The last thing is what happens when a customer calls. We have this inventory because it’s been used somewhere; we’re not just buying parts that never get used. Typically, a customer has a custom machine and the part might only break every five or 10 or 15 years. So it becomes about us reminding the customer. What CADDi helps us do now is call customers to say, this is an at-risk inventory part. We can give you a discount if you buy two of these, or 10 of these. It flags us as a customer service representative, to make sure we’re letting customers know that we don’t plan to stock this and there’s a great opportunity to put some safety stock of this part on their shelf.So we’re stopping the bleeding, and not producing more SKUs. We’re using CADDi internally, so I have a list of parts that engineers can search where they can say, I need something that looks like this or I need a motor that does this. CADDi will suggest ways to use up existing internal inventory.Finally, we want to sell it. When a customer calls in for a spare part order, we’re able to identify the part quickly and say, ‘We have that and we have more in stock. Would you like some?’You previously paused engineering AI software investment because the early technology lacked a direct link to your engineering vault. What specific real-time synchronization capabilities convinced your team to reconsider the CADDi platform as a mature system ready for nVenia’s needs today?We engaged in CADDi about two years ago, and did a year-long trial. During that trial, we figured out that due to the engineered-to-order nature of our business, we were syncing about once every week, maybe once every two weeks, and that was too slow for us. We did this as a manual sync. Data would come in, but in real time when we’re designing something I need to find something new now, not two weeks from now.The game changer came when CADDi developed an API that allowed us to transfer this data almost in real time. That also allowed us to use another tool called CADDi Quote. Before, if drawings would come on Monday at noon, and I needed to quote them by three or four that afternoon, I couldn’t. Now we can.The core of CADDi is that I put in all the drawings and then I can look at those drawings through the AI filter. I can ask, ‘find me a pipe with this diameter’ and it’ll go search, or shape match, and find me something that looks the same. On top of that is the CADDi Quote add-on module. When we look at going out to our vendors and saying, ‘I need to manufacture this outside of our four walls,’ it’s to ensure we’re getting good pricing. We wanted to use this tool to help our procurement team get the best possible cost.These days, we get those new parts in real time. The procurement team gets a notification to go bid these out. We bid them out, usually to three manufacturers, and see which ones get the best price. We can also pull in quality data from that manufacturing group. We can say, ‘I got a good price from a good quality manufacturer—let’s move forward, let’s issue a PO to them.’ CADDi Quote really helped us on that front more than anything.Evaluating a returning software vendor requires clear evidence that their system can handle high-volume production data. What specific results from uploading 3,000 parts at once confirmed the software had evolved enough to process your real drawings accurately?They have approximately 750,000 drawings of ours right now. 3,000 were from doing the initial demo, which was really interesting.We grabbed 3,000 random parts, gave them the files, and they were able to find some neat information about commonalities. The biggest one was a right-angle bracket that we use to hold up sensors. Randomly in those 3,000 parts, we had three or four matches. The only difference was that the holes were in different locations.So the idea became, can we put all four holes in this at the same time so I only have one part? Or is this a modification where I can take the three parts down to two, or can I slot it instead? It raises a lot of questions about why we have so many similar parts. Just in that quick demo of 3,000 drawings, we found amazing opportunities.<a href="https://us.caddi.com/company?ref=US-PR-2026-Q3-WTWH-DesignWorld-Voices5-SC" rel="noopener noreferrer" target="_blank" class="">CADDi</a> is a global technology company that develops a vertical data intelligence platform for engineering, design and manufacturing. CADDi brings AI technology expertise to help manufacturers preserve decades of engineering knowledge. Rather than replacing PLM or ERP systems, CADDi operates as a shared intelligence layer across manufacturing teams.CADDi’s <a href="https://us.caddi.com/resources/whitepaper/decision-velocity-unlocking-faster-smarter-decisions-in-manufacturing?ref=US-PR-2026-Q3-WTWH-DesignWorld-Voices5-SC" rel="noopener noreferrer" target="_blank" class="">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, reduce friction in engineering and sourcing processes, and build the intelligence foundation needed for long-term competitiveness. More case studies and insights are available at <a href="http://us.caddi.com/case-studies?ref=US-PR-2026-Q3-WTWH-DesignWorld-Voices5-SC" rel="noopener noreferrer" target="_blank" class="">us.caddi.com/case-studies</a>.

Manufacturing Voices: Ben Snyder, Vice President, Lifecycle Services, nVenia

As retirement rises, manufacturers risk losing more than workers. Institutional knowledge is disappearing – technology can help preserve it.By Aaron Lober, VP of Marketing and Manufacturing Intelligence Lead at <a href="https://us.caddi.com/" rel="noopener noreferrer" target="_blank">CADDi</a>Hiring issues take the heat for many of manufacturing’s greatest problems. <a href="https://us.caddi.com/resources/whitepaper/2026-manufacturing-outlook-study" rel="noopener noreferrer" target="_blank">Eighty percent of manufacturing executives</a> say the skilled labor shortage is their biggest challenge today. But once leaders take a closer look under the hood, a more critical challenge than low headcounts begins to emerge. Institutional knowledge is disappearing right before our eyes.Seasoned manufacturing professionals are retiring. The practical experience that keeps production running is becoming less and less available as shrinking teams need it most. Leaders can try to fill the roughly <a href="https://www.bls.gov/news.release/jolts.t01.htm" rel="noopener noreferrer" target="_blank">433,000 open manufacturing jobs</a> recorded at the end of 2025, but increasing headcount alone won’t fill this growing gap. We need transferable know-how – and we need it fast.To overcome the knowledge crisis, businesses must go beyond hiring. It’s time to digitize the storage and transfer of tribal information for better resilience.<h2>Manufacturing’s Complex Labor Landscape</h2>Manufacturing today is under rising pressure. Tariffs are unpredictable, cost volatility remains high, and many teams are running lean as production expectations increase.In January, the <a href="https://economics.td.com/us-ism-manufacturing-index" rel="noopener noreferrer" target="_blank">ISM Manufacturing Index</a> returned to expansion at <a href="https://economics.td.com/us-ism-manufacturing-index" rel="noopener noreferrer" target="_blank">52.6</a>, signaling that the sector had moved back into growth after more than a year of contraction. However, the employment component remained in contraction at <a href="https://economics.td.com/us-ism-manufacturing-index" rel="noopener noreferrer" target="_blank">48.1</a>, suggesting manufacturers are still cautious about rebuilding their workforce. On the other hand, those that’ve decided to grow their teams are struggling to do so with <a href="https://nam.org/mfgdata/facts-about-manufacturing-expanded/" rel="noopener noreferrer" target="_blank">1.9 million manufacturing jobs expected to go unfilled</a> in the next few years. As a result, organizations are operating with smaller headcounts and asking existing employees, who are often less experienced than those who left, to carry more responsibility.Another complexity is that the nature of the work itself is changing. Manufacturers now need workers with both traditional trade knowledge and digital fluency with new hires expected to navigate automation systems, production data, and increasingly complex workflows from day one. Yet most organizations haven’t built the training infrastructure to match that reality. Only <a href="https://us.caddi.com/resources/whitepaper/manufacturing-outlook-study-2025" rel="noopener noreferrer" target="_blank">20% of manufacturers</a> report making significant investments in upskilling. Just <a href="https://us.caddi.com/resources/whitepaper/manufacturing-outlook-study-2025" rel="noopener noreferrer" target="_blank">8% of employees</a> say their company actively supports the reskilling needed to keep up with new technologies.To add fuel to the fire, the industry is losing many of its veterans with no way to store what they’ve learned over the years. <a href="https://us.caddi.com/resources/whitepaper/manufacturing-outlook-study-2025" rel="noopener noreferrer" target="_blank">Seventy-three percent of senior manufacturing leaders expect to retire within the next decade</a>, taking with them the practical knowledge that was never fully documented to outlive them.Without their institutional knowledge answers become inaccessible. Every urgent decision gets harder, slower, and more expensive. As a result, people start to default to “good enough” assumptions and the impacts are felt immediately.<img src="https://cdn.builder.io/api/v1/image/assets%2F209ee507cedf4c298e100edae4bba2df%2F60fae2a064314c99bff2f524f5287a30" alt="Control console screen showing production-line dashboards with charts and robot icons, with orange industrial robot arms blurred in the factory background.">Connected manufacturing data gives teams faster access to drawings, decisions, and production insights.<h2>What the Knowledge Crisis Looks Like on the Floor</h2>For many manufacturers, the knowledge crisis creates wasted time on administrative execution. Teams spend <a href="https://www.assemblymag.com/articles/99806-skilled-labor-gap-reshapes-how-manufacturers-invest-for-2026-according-to-new-study" rel="noopener noreferrer" target="_blank">an hour a day</a> searching for drawings, part histories, supplier records, and past decisions that should be easy to find. The information exists, but it is scattered across disconnected systems, spreadsheets, file folders, and the memories of a few experienced employees.The impact builds quickly. Work bottlenecks around a handful of “go-to” people. New hires take longer to ramp. Teams repeat work that has already been done because finding the answer takes longer than starting over.In practice, that can be as simple as not knowing which revision is current, why a design change was made, or how a similar issue was handled last time. On the floor, it means defects take longer to troubleshoot, rework builds up faster, and small problems turn into bigger delays because the context behind past decisions is hard to access.Most manufacturers try to manage this with the tools they already have: shared drives, PLM folders, ERP notes, internal wikis, and stricter naming conventions. The intent is understandable. But those systems still depend on people knowing what to search for, where to look, and how the company has documented information over time.This is where the real gap exists. Manufacturers need a way to connect drawings, revisions, and downstream outcomes into a usable record of what happened, what changed, and why. That starts with making past work easier to find and use in the moment it matters.<img src="https://cdn.builder.io/api/v1/image/assets%2F209ee507cedf4c298e100edae4bba2df%2F67f2787976d74ec082267299feb8d648" alt="Laser cutting head cutting a shape out of a steel sheet on a CNC machine bed, throwing a spray of sparks">Manufacturers are using AI and automation to reduce knowledge gaps and improve operational efficiency.<h2>﻿Overcoming the Knowledge Crisis with AI</h2>For most organizations, the answers are already there in drawings, revisions, quotes, supplier records, inspection results, and scattered notes. The problem is that this knowledge is fragmented across systems and formats, which forces teams to rely on memory, workarounds, and a small number of tenured folks to fill in the gaps.Modern manufacturing platforms are helping close that gap by unifying these sources and making them easier to search in plain language with Artificial Intelligence (AI). Instead of digging through ERP fields or scanned PDFs, AI enables teams to search across drawings and related records in one place, even when they do not know the part number or where the file should live. In practice, that gives newer employees faster access to the context behind prior decisions and makes it easier for experienced teams to build on work that has already been done.That matters because in a complex labor market, manufacturers can no longer depend on processes that only work when the most senior person is nearby. They need systems that make design intent, sourcing history, and past learnings easier to carry into the next decision. When engineers can quickly find similar designs instead of recreating them, and when teams can trace what changed and why, productivity stops depending so heavily on individual memory.<h2>The Next Generation of Manufacturing</h2>The labor market will stay volatile, but manufacturers can remain resilient. The teams that implement AI-powered systems that democratize knowledge will move faster with smaller, less experienced teams. The next generation of leaders will be the ones to digitize their career’s most important learnings and end the knowledge crisis.About the Author<a href="https://www.linkedin.com/in/aaron-lober-67349287/" rel="noopener noreferrer" target="_blank">Aaron Lober</a> is Vice President of Marketing at CADDi, where he leads U.S. marketing strategy focused on accelerating digital transformation in manufacturing. He brings 15+ years of experience launching and scaling products across multiple industries, including building and managing portfolios exceeding $100 million. Before joining CADDi, Lober served as Head of Marketing at Blameless. Prior to that, he spent several years at Procore, where he helped guide the company through its path to IPO and led major product launches, including Procore’s first data and analytics offering, Procore Analytics, and Procore Estimating.<a href="https://industrytoday.com/manufacturings-growing-knowledge-crisis/" rel="noopener noreferrer" target="_blank">Read the article on IndustryToday.com</a>

Manufacturing's Growing Knowledge Crisis

CADDi Inc. announced that Amerequip has implemented CADDi Drawer to centralize engineering data and change how teams search and reuse historical design information.Centralizing a century of engineering knowledgeSince 1920, Amerequip has designed and manufactured custom equipment and solutions for companies such as John Deere, Caterpillar and Oshkosh Corporation. The company supports OEM customers with equipment manufacturing, hydraulic systems, product design and component production. Over time, it has accumulated a large library of design data. However, the size and complexity of this library made it difficult for engineers to locate and use relevant information.<img src="https://cdn.builder.io/api/v1/image/assets%2F209ee507cedf4c298e100edae4bba2df%2F26ca61c7e4bd40d794a3593d262d6e9a" alt="Four people standing in front of a large black eagle wall emblem at Amerequip: from left, CADDi sales director Marcin Zmarzly, Amerequip head of quality Bryon Nolan, mechanical designer Stella Rezabek, and CADDi VP customer success Yongli Deng">Shown standing (from left) are CADDi sales director Marcin Zmarzly, Amerequip’s head of quality Bryon Nolan and mechanical designer Stella Rezabek, and CADDi VP customer success Yongli Deng. Taken during a recent site visit at Amerequip. (CADDi photo)One issue involved mismatched part numbers between customers and Amerequip’s internal system. When a customer referenced a different part number, the process required searching the ERP system for a cross-reference, accessing a drawing server, opening multiple files and manually verifying matches. This workflow relied on familiarity with internal systems and took multiple steps.With CADDi Drawer, Amerequip moved to a single searchable environment. Engineers can search drawings, part numbers and keywords together, with results showing related drawings and cross-referenced internal numbers. The system also allows teams to identify similar components listed under different internal descriptions, which can reduce duplication and additional orders.Access to information across engineering and quality teamsThe system also provides visibility into how components are used in assemblies. Instead of generating reports in the ERP system and reviewing drawings separately, teams can search a part number and view related assemblies and documents in one place.This approach reduces the need to switch between systems and allows teams to review design relationships during quality checks, change evaluations and customer requests.Additional details are available in the case study titled “<a href="https://us.caddi.com/case-studies/amerequip" rel="noopener noreferrer" target="_blank">Amerequip: From Hunting for Drawings to a True “One-Search” Engineering and Quality Workflow</a>” on CADDi’s website.CADDi<a href="https://us.caddi.com/" rel="noopener noreferrer" target="_blank">us.caddi.com</a><a href="https://www.designworldonline.com/amerequip-adopts-caddi-drawer-to-centralize-design-data/" rel="noopener noreferrer" target="_blank">Read the article in Design World</a>

Amerequip Adopts CADDi Drawer to Centralize Design Data

Single-search workflow unifies Amerequip engineering and quality teams with CADDi Drawer – connecting drawings, assemblies, specifications, and cross-referenced part numbers into one accessible systemCHICAGO, Ill.--CADDi Inc., a global technology company developing an AI data platform for manufacturers, today announced that Amerequip has implemented CADDi Drawer to centralize engineering data and improve how teams search and reuse historical design information.Centralizing a century of engineering knowledgeSince 1920, Amerequip has designed and manufactured custom equipment and solutions for brands such as John Deere, Caterpillar and Oshkosh Corporation. The company supports OEM customers with complex equipment manufacturing, hydraulics systems, new product design and component production. Through over a century of innovation and design, Amerequip has built up a substantial library of past designs. This library should be a useful reference point to improve new work, but its size and complexity created issues when engineers tried to search through it.One recurring issue involved mismatched part numbers between customers and Amerequip's internal system. When a customer referenced a part number that differed from Amerequip's internal numbering convention, the traditional workflow required searching the ERP for a cross-reference, navigating to a drawing server, opening multiple prints, and confirming the match manually. It was a multi-step process that depended heavily on experience and familiarity with the systems.By implementing the CADDi Drawer platform, Amerequip replaced a multi-system workflow with a single searchable environment. Engineers can now search drawings, part numbers and keywords simultaneously, surfacing relevant drawings alongside cross-referenced internal numbers embedded within the documents. The system also enables teams to identify existing components that appear under different internal descriptions, helping avoid duplicate parts and unnecessary purchase orders.According to Tim Dorn, Vice President of Engineering at Amerequip, "The breadth of our operations drives a high volume of data across the organization. Ultimately, our effectiveness depends on how quickly we turn data into decisions, accelerating speed to market in support of our partner customers."Faster answers across engineering and quality teamsCADDi Drawer also improved visibility into how components are used across assemblies. Instead of running "where-used" reports in ERP and manually reviewing drawings, engineers and quality teams can search a part number and immediately see related assemblies and documentation in a single view.This consolidated workflow reduces system switching and helps teams confirm design relationships faster during quality reviews, change assessments and customer inquiries. Amerequip Head of Quality Bryon Nolan shared, "Many times, we're working on a part and we want to understand what it goes into. With CADDi, we type in the part number we are reviewing, and in addition to seeing that part, we are also seeing drawings for assemblies that use it. One search. All the results we want."To learn more about how CADDi is helping manufacturers unlock the value of historical engineering data, read the full case study, "Amerequip: From Hunting for Drawings to a True "One-Search" Engineering and Quality Workflow" on CADDi's website.About CADDiCADDi 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, Apple, and Lockheed Martin. 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 us.caddi.com.<a href="https://www.manufacturingtomorrow.com/news/2026/03/20/amerequip-transforms-100-years-of-engineering-data-into-actionable-intelligence-with-caddi-ai-data-platform-/27234/" rel="noopener noreferrer" target="_blank">Read the article in Manufacturing Tomorrow</a>

Amerequip Transforms 100 Years of Engineering Data into Actionable Intelligence with CADDi AI Data Platform

The investment thesis here is not about a single product, but about positioning at the inflection point of an exponential shift. The global AI in manufacturing market is projected to grow from <a href="https://www.marketsandmarkets.com/Market-Reports/artificial-intelligence-manufacturing-market-72679105.html" rel="noopener noreferrer" target="_blank">$34.18 billion in 2025 to $155.04 billion by 2030</a>, a compound annual growth rate of 35.3%. This isn't linear expansion; it's the steepening of an S-curve where adoption is accelerating from niche experimentation toward strategic integration. The catalysts are powerful and structural.Two fundamental problems are forcing this acceleration. First, there's a severe <a href="https://us.caddi.com/" rel="noopener noreferrer" target="_blank">engineering resource gap</a> as senior talent retires, taking critical knowledge out of the system. Second, manufacturing data is fragmented across PLM, ERP, spreadsheets, and tribal knowledge, creating a costly bottleneck for innovation. These are not minor inefficiencies; they are the friction points that make AI adoption a necessity, not a luxury. The market is moving from pilots to production because the cost of not acting is rising faster than the cost of implementation.This shift is now a top-down priority. According to recent analysis, <a href="https://iot-analytics.com/industrial-ai-market-insights-how-ai-is-transforming-manufacturing/" rel="noopener noreferrer" target="_blank">most large manufacturers now have a formalized, CEO-driven AI strategy</a>. The focus areas emerging from these strategies are telling: industrial data management and architectures are paramount. This signals a move beyond point solutions to building the foundational infrastructure that can handle the data deluge required for true AI value. The paradigm is clear: the next wave of industrial productivity will be powered by AI, but only if the data plumbing is in place.Amerequip's CADDi implementation is a direct bet on this infrastructure layer. It's an attempt to solve the data fragmentation problem at the source-engineering design and manufacturing planning-thereby capturing value as the entire industrial AI S-curve steepens. The company is positioning itself not just as a software vendor, but as a builder of the rails for this new paradigm.<h4>CADDi as Foundational Infrastructure: Solving the Data Bottleneck</h4>The core problem Amerequip is solving with CADDi is a classic friction point in high-mix manufacturing: data is everywhere, but not where you need it. This fragmentation is a major source of rework and inefficiency. As one engineer put it, the traditional workflow for a simple part number match was a <a href="https://www.businesswire.com/news/home/20260319179061/en/Amerequip-Transforms-100-Years-of-Engineering-Data-into-Actionable-Intelligence-with-CADDi-AI-Data-Platform" rel="noopener noreferrer" target="_blank">multi-step process that depended heavily on experience</a>, requiring engineers to jump between ERP systems, drawing servers, and spreadsheets. This isn't just a minor annoyance; it's a fundamental bottleneck that slows down the entire product development cycle.CADDi Drawer is built as a first-principles solution to this. It creates a single searchable environment that unifies engineering and quality teams by connecting drawings, assemblies, specifications, and cross-referenced part numbers into one accessible system. The functionality is straightforward but powerful. Instead of hunting through multiple silos, a user can search a part number and instantly see the part itself, its related assemblies, and the cross-references embedded in the documents. As the Head of Quality noted, this replaces a fragmented hunt with one search. All the results we want.This centralization is strategic infrastructure. Amerequip has built up a substantial library of past designs over its 100-year history, a potential goldmine for reuse. Yet that library was a liability because of its complexity. CADDi turns that century of engineering knowledge into an active asset. By surfacing existing components that appear under different internal descriptions, the platform helps avoid duplicate parts and unnecessary purchases. More importantly, it accelerates the speed-to-market for OEM customers like John Deere and Caterpillar. The company's own statement is telling: "Ultimately, our effectiveness depends on how quickly we turn data into decisions, accelerating speed to market."This is the essence of building foundational rails. CADDi isn't just a tool for internal efficiency; it's a platform that solves the data bottleneck at the source-engineering design. By making historical knowledge instantly accessible and actionable, it removes a key constraint on innovation. For a company facing an <a href="https://www.amerequip.com/blog/our-complete-oem-engineering-services-from-cad-to-prototyping-to-production/" rel="noopener noreferrer" target="_blank">engineering resource gap</a>, this ability to exponentially reuse existing work is a critical lever. It positions Amerequip not just as a manufacturer, but as a partner that can rapidly scale its design capacity, directly supporting the industrial AI paradigm where data velocity is the new competitive moat.<h4>Financial Impact and Scalability: From Tactical Tool to Network Effect</h4>The financial payoff from CADDi hinges on converting data centralization into tangible operational leverage. Success is measured in reduced design cycle times and less engineering rework. Every hour saved hunting for a part number or verifying a cross-reference is an hour that can be spent on innovation or production planning. For a company operating in high-mix manufacturing, where <a href="https://us.caddi.com/" rel="noopener noreferrer" target="_blank">more SKUs, more complexity, fewer skilled workers</a> are killing efficiency, this directly impacts project margins and throughput. The earlier evidence noted that the traditional workflow was a <a href="https://www.businesswire.com/news/home/20260319179061/en/Amerequip-Transforms-100-Years-of-Engineering-Data-Into-Actionable-Intelligence-with-CADDi-AI-Data-Platform" rel="noopener noreferrer" target="_blank">multi-step process that depended heavily on experience</a>. By replacing that with a single search, CADDi attacks a known source of costly rework. The financial impact is a function of how much of that wasted effort is eliminated and how quickly new projects can be initiated.The path to exponential scaling, however, lies beyond internal efficiency. The real catalyst is expansion. If CADDi remains a tool for Amerequip's own engineers, its value is linear. But if it becomes the shared data layer for its OEM partners like John Deere and Caterpillar, a network effect kicks in. Imagine integrating CADDi with a partner's PLM or ERP system. This would create a unified data architecture where design knowledge flows seamlessly across the supply chain. The moat here isn't just in the software, but in the data itself-the historical library of past designs that becomes more valuable as more partners contribute and consume from it. This integration depth is what transforms a tactical tool into a defensible infrastructure layer.From a strategic investment perspective, this is a bet on data as the new competitive moat. The ROI, therefore, depends entirely on two factors: the depth of integration with key partners and the rate of user adoption across the entire organization. The company's own <a href="https://www.amerequip.com/blog/our-complete-oem-engineering-services-from-cad-to-prototyping-to-production/" rel="noopener noreferrer" target="_blank">engineering resource gap</a> makes internal adoption critical for immediate relief. But the exponential growth story requires that same platform to be adopted by external partners. The financial model shifts from cost savings to revenue acceleration and margin expansion as the platform enables faster, more innovative product development for the entire ecosystem. The risk is that integration proves complex or adoption stalls, leaving the platform as a valuable but contained internal asset. The opportunity is that it becomes the foundational data layer for a network of industrial AI, capturing value as the entire S-curve steepens.<h4>Catalysts and Risks: Navigating the Exponential Growth Path</h4>The path from a tactical data tool to an exponential growth engine is paved with specific catalysts and guarded by a key risk. The forward view hinges on measurable outcomes and the quality of the foundation being built.The primary catalyst is public validation. For investors, the critical watchpoint is Amerequip's own disclosure of measurable key performance indicators post-implementation. Success will be demonstrated not by internal satisfaction, but by concrete metrics like a <a href="https://www.businesswire.com/news/home/20260319179061/en/Amerequip-Transforms-100-Years-of-Engineering-Data-Into-Actionable-Intelligence-with-CADDi-AI-Data-Platform" rel="noopener noreferrer" target="_blank">reduction in design time</a> or a quantified drop in rework costs. Without these numbers, the platform risks being seen as a costly internal project rather than a scalable solution. The company's own statement that its effectiveness depends on how quickly we turn data into decisions sets the benchmark. If Amerequip can show that CADDi accelerates that cycle, it provides the proof-of-concept needed to push the platform beyond its own walls.The most significant risk is also foundational: the platform's value is only as good as the data it ingests. If the historical design library is poorly labeled, inconsistent, or contains errors, the system will amplify those issues. This is the classic "garbage in, garbage out" problem, but in a high-mix manufacturing context, it can lead to costly mistakes in procurement or production. The risk is that the very data centralization CADDi provides becomes a single point of failure if the underlying hygiene is poor. The solution requires disciplined data governance, but the burden falls on Amerequip to ensure the quality of its own century-old knowledge base before expecting partners to trust it.Yet the macro tailwind is powerful and persistent. The skilled labor shortage is not a temporary headwind but a structural, multi-year challenge. According to recent research, <a href="https://us.caddi.com/resources/news/79-of-manufacturing-executives-say-skilled-labor-shortage-is-greatest-challenge-according-to-new-caddi-research" rel="noopener noreferrer" target="_blank">79% of manufacturing leaders cite the skilled labor shortage as their greatest challenge</a>. This creates a relentless demand driver for any technology that can extract more value from existing assets and reduce reliance on scarce expertise. The platform's value proposition-accelerating speed to market by reusing historical design knowledge-directly addresses this pain point. In a market where <a href="https://us.caddi.com/" rel="noopener noreferrer" target="_blank">more SKUs, more complexity, fewer skilled workers</a> are killing efficiency, a tool that makes engineering knowledge instantly accessible is not a luxury; it's a necessity for survival.The bottom line is that Amerequip is navigating a classic S-curve inflection. The catalysts are clear: prove the internal ROI, then expand the platform. The risk is data quality. But the tailwind is structural and immense. The company's bet is on becoming the essential data layer for industrial AI, and its success will be measured by how well it converts its own century of engineering knowledge into the fuel for that exponential growth.<a href="https://www.ainvest.com/news/amerequip-caddi-seizes-industrial-ai-data-bottleneck-curve-steepens-2603/" rel="noopener noreferrer" target="_blank">Read the article in AI Invest</a>

Amerequip's CADDi Seizes Industrial AI's Data Bottleneck as S-Curve Steepens

Manufacturing skills and digital shift‍A survey by ECI Solutions (in Dutch) shows that Dutch manufacturing firms expect continued pressure in 2026 from labour shortages, cost volatility and supply chain disruptions. The report describes increased investment in ERP systems, automation and data integration to improve planning and productivity. You can download the report (in Dutch) after filling in your details.Research by CADDi finds that 79% of executives see the shortage of skilled labour as their main challenge. Companies are expanding training and redesigning workflows to compensate. You can download the report after filling in your details.AI moves to accountability‍A report (8.1 MB) by Deloitte indicates that AI adoption in enterprises is moving from pilot projects to scaled deployment. The report highlights stronger focus on governance, data quality and measurable business value. A Lewis Silkin report points to rising legal and ethical risks linked to AI use, including transparency and employee monitoring. You can download the report after filling in your details. Meanwhile, a report (6.43 MB) by Deloitte Digital describes how customer service is shifting to AI-first models, with automation handling routine queries and employees managing complex cases.Human skills in demand‍A study by Deloitte shows that team performance increasingly depends on collaboration, communication and adaptability alongside technical skills. The survey describes how human capabilities support productivity in technology-rich environments.Data in the Udemy Global Learning &amp; Skills Trends Report show rising demand for AI and data skills as well as leadership and problem-solving training. Organisations are updating learning strategies to combine technical and behavioural development. You can download the report after filling in your details. The conclusion is that continuous learning and balanced skillsets are essential for workforce effectiveness.Labour market rotation‍The LinkedIn labour market report (4.12 MB) suggests that the global labour market is rotating rather than shrinking. The report describes growing mobility between sectors and rising demand for digital and analytical skills. Employers are recruiting for transferable skills and reskilling existing staff, while workers are shifting towards technology-related roles. The impact is an increased need for ongoing training and flexible career paths.Hybrid work and safety‍A scenario study (in Dutch, 1.98 MB) by TNO (in Dutch) outlines four possible futures for healthy and safe work towards 2040, shaped by technology adoption, demographics and organisational choices. The report describes outcomes ranging from highly automated workplaces to more human-centred models.Research (in Dutch, 2.24 MB) from Instituut Gak (in Dutch) highlights legal and organisational challenges linked to hybrid work, including employer responsibility, working conditions and equal treatment. Organisations are updating policies and safety frameworks. The conclusion is that sustainable hybrid work requires clear governance, prevention measures and attention to employee wellbeing.<a href="https://www.en.nvc.nl/news/item/hrm-en-competentieontwikkeling---februari-2026/" rel="noopener noreferrer" target="_blank">Read the article in NVC Packaging Centre</a>

HRM and skills development, February 2026

<a href="https://lnkd.in/gvskyqHH" rel="noopener noreferrer" target="_blank">Read the article in Fastener Technology International</a>Fastener Technology International featured coverage of CADDi's AI data platform and its applications for the fastener and precision manufacturing industries. The article highlighted how CADDi helps manufacturers centralize engineering drawings and historical production data, enabling faster quoting and reduced reliance on tribal knowledge.

R&D teams reducing engineering and search time

CADDi could replace the manual, non-AI workflows that engineers use to search, compare, and reuse historical engineering data.<h4>How specifically will this AI tool help engineers?</h4><ul><li>Converts legacy drawings into usable engineering data. CADDi applies advanced OCR and AI models to extract text, dimensions, GD&amp;T symbols, annotations, and handwritten notes from 2D drawings and scanned documents. Information that was previously locked in PDFs, images, or paper archives becomes searchable and analyzable.</li><li>Enables geometry-based similarity search. Beyond keyword search, CADDi extracts shape and geometric features from drawings and builds an internal geometric representation. Engineers can search for parts based on visual and geometric similarity, even when part names, file structures, or naming conventions differ across systems or acquisitions. This allows teams to identify duplicate parts, create part families, and uncover prior designs that can be reused or adapted rather than recreated.</li><li>Automates drawing comparison and revision analysis. The platform highlights differences between revisions or similar drawings, allowing engineers to quickly see what changed and assess implications for manufacturability, cost, or quality without manual side-by-side review.</li><li>Preserves design intent and institutional knowledge. CADDi links drawings with design review notes, quality inspections, NCRs, and historical decisions. Engineers gain visibility into why a design was changed, what problems were encountered, and how those issues were resolved, even when the original engineers are no longer available. This prevents the loss of tribal knowledge as experienced engineers retire.</li><li>Connects engineering decisions to manufacturing outcomes. By linking engineering data with procurement history, supplier performance, lead times, margins, and quality outcomes, engineers can evaluate tradeoffs earlier in the design process using real operational evidence rather than assumptions.</li><li>Supports assembly and BOM-level navigation. For assemblies and subassemblies, CADDi automatically extracts BOMs and links components across documents. Engineers can navigate from a decades-old assembly drawing to individual components, identify alternatives, and evaluate reuse or replacement options for maintenance, repair, or aftermarket needs.</li><li>Enables AI-assisted exploration with human oversight. Engineers can use conversational AI to query historical documentation, such as identifying recurring quality issues or summarizing past defects. Every AI-generated response is linked back to source documents, keeping engineers in control of decisions and maintaining traceability.</li><li>Reduces non-design work. By eliminating time spent searching shared drives, PLM exports, PDFs, and paper archives, engineers spend less time hunting for information and more time designing, reviewing, and improving products.</li></ul><h4>Will this AI tool replace a non-AI tool or process?</h4>Yes. CADDi replaces the manual, non-AI workflows that engineers use to search, compare, and reuse historical engineering data.It eliminates activities such as:<ul><li>Navigating shared drives and legacy folder structures</li><li>Manually searching PDFs and scanned drawings</li><li>Relying on individual memory to locate past designs or suppliers</li><li>Hand-driven drawing comparisons</li><li>Recreating designs that already exist elsewhere in the organization</li></ul>CADDi does not replace CAD, PLM, or ERP systems. It replaces the fragmented processes engineers use to extract insight from those systems.The result is faster access to prior work, reduced duplicate design effort, improved consistency, and engineering decisions grounded in accumulated knowledge rather than individual recollection.<a href="https://www.designnews.com/artificial-intelligence/ai-tools-for-engineers-caddi-drawer-caddi-quote" rel="noopener noreferrer" target="_blank">Read the article in Design News</a>

AI Tools for Engineers: CADDi Drawer & CADDi Quote

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

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