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<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">Read the article in Assembly Magazine</a>New research from CADDi, a company that builds a manufacturing AI data platform, signals three important takeaways for manufacturers as they head into 2026.Manufacturers report that the skilled labor shortage is no longer a looming threat; it is the defining constraint on manufacturing operations heading into 2026. Nearly 80% of respondents identified labor availability as their biggest external challenge, and 71% said it is already directly impacting their ability to meet production goals. What's changed is the scope of the problem. This is no longer confined to shop-floor operators. Shortages are spreading into maintenance, operations and even design and engineering. Retirements are accelerating faster than training pipelines can respond.As a result, manufacturers are being forced to rethink how work gets done. Sixty-two percent are prioritizing recruitment, training and retention, but the study suggests workforce strategy alone may not be enough.One of the clearest shifts in the study is how manufacturers are using data to blunt the impact of labor shortages. Access to accurate, real-time parts and production data is emerging as one of the most effective ways to preserve productivity with smaller teams.Manufacturers report that employees spend roughly an hour a day searching for parts data, an inefficiency that compounds across departments. More than half of respondents cited poor cross-department collaboration around data, while nearly 40% said fragmented or outdated systems are actively slowing decision-making.In response, companies are investing in centralized data platforms and data lakes that connect parts, supplier and production information. Predictive maintenance, automated inventory tracking and AI-driven procurement tools are allowing teams to shift from reactive troubleshooting to planned execution.The takeaway is that data infrastructure is no longer about visibility or reporting. It's about labor efficiency. Manufacturers are using data to make fewer people more effective.After years of volatility, manufacturers are entering 2026 with a markedly different capital mindset. Growth is no longer defined by adding capacity or new systems, but by extracting more value from what already exists. The study shows a clear pivot toward physical assets that deliver immediate, measurable returns on the shop floor. Sixty-nine percent of respondents plan to invest in robots and equipment in 2026, up from 60% the prior year. Planned investment in broad operational software systems like ERP and MES fell to 33%, from 60%.The findings are based on a survey conducted by CADDi in partnership with SME and Mercury Research LLC in fall 2025. The independent study collected responses from 195 manufacturing professionals holding management-level positions or higher.

Skilled Labor Gap Reshapes How Manufacturers Invest for 2026 According to New Study

<a href="https://us.caddi.com/" rel="noopener noreferrer" target="_blank">CADDi</a> has released a new customer case study detailing how Mitsubishi Materials USA is using CADDi Drawing Intelligence to improve access to engineering data and streamline custom engineering and quoting processes. The case study highlights a broader shift within manufacturing organizations toward shared access to historical design and production data, reducing reliance on individual expertise as teams expand and experienced staff retire.Manufacturers supporting high-mix and custom production often maintain extensive archives of engineering drawings, pricing records, and quality documentation. However, these materials are frequently spread across multiple systems or managed by individuals, making them difficult to locate and reuse efficiently. This fragmentation can slow response times and lead to repeated work.Mitsubishi Materials USA, part of the Mitsubishi Materials Group headquartered in Japan, supplies cutting tools and engineered carbide products to manufacturers across North America. Founded in 1984, the company supports applications including turning, milling, drilling, and wear-resistant tooling.By using the CADDi platform, including the CADDi Drawer application, Mitsubishi Materials USA enabled its sales and engineering teams to search for information using customer names, tool numbers, or quote identifiers. This allowed teams to quickly retrieve drawings, inspection reports, and prior decisions without relying on manual coordination between departments.Improved access to historical data helped reduce duplicate engineering work and shortened response times for custom requests. Sales teams gained the ability to estimate and respond to customer needs independently, while engineering teams were able to reuse existing designs and reference prior outcomes more effectively.The case study also notes that centralized access to engineering records improved consistency across quoting and design workflows. By making past decisions and quality results visible across teams, the company reduced dependency on individual knowledge holders and supported continuity as projects scaled and personnel changed. According to a regional sales manager at Mitsubishi Materials USA, the ability to quickly search and retrieve information has significantly changed daily workflows and reduced time spent tracking down records.<a href="https://themachinemaker.com/news/dee-development-engineers-limited-expands-capacity-at-anjar-facility/" rel="noopener noreferrer" target="_blank">CADDi</a> is an AI-based data platform designed to make design and supply chain information more accessible to manufacturing teams. Founded in 2017 and headquartered in Tokyo and Chicago, the company develops tools that help manufacturers organize and analyze unstructured engineering and production data to improve efficiency and decision-making.<a href="https://themachinemaker.com/news/mitsubishi-materials-usa-improves-engineering-and-quoting-processes-using-caddi-drawing-intelligence/" rel="noopener noreferrer" target="_blank">Read the article in MachineMaker</a>

Mitsubishi Materials USA Improves Engineering and Quoting Processes Using CADDi Drawing Intelligence

<a href="https://www.designworldonline.com/voices/voices-yongli-deng-head-of-customer-success-caddi/" rel="noopener noreferrer" target="_blank" style="font-size: 16px;">Read the article in Design World</a><img src="https://cdn.builder.io/api/v1/image/assets%2F209ee507cedf4c298e100edae4bba2df%2Ff2d98c09a5724d718d45d25037542f24?format=webp" alt="Design World Voices interview banner featuring a portrait of Yongli Deng, Head of Customer Success at CADDi">By Sana Kazilbash | May 11, 2026This article is sponsored by <a href="https://us.caddi.com/company?ref=US-PR-2026-Q3-WTWH-DesignWorld-Voices3-SC" rel="noopener noreferrer" target="_blank">CADDi</a>. In this Voices interview, Design World spoke with Yongli Deng, Head of Customer Success at CADDi, who shared a look at adoption of data intelligence tools, and the challenges and rewards of close partnerships and collaboration to drive a successful implementation.Design World: Tell us about yourself and your role at CADDi.Yongli Deng: I lead Customer Success for CADDi in the U.S. Since we’re a fast-growing startup, the work moves quickly and no two weeks look the same, which has been a fun challenge. I joined about eight months ago after spending eight years at Boston Consulting Group, and I made the move because I wanted to shift from advising companies to building alongside customers and driving real outcomes with them.Looking back at the early adopters of data intelligence technology, we often see a disconnect between the department head’s grand AI vision and the daily reality of the engineers expected to use the technology. As an adviser who steps in post-sale to manage expectations, what is the most critical step accountable leaders must take to bridge this gap and ensure the platform gets adopted?I think there are three main points.The most important element for success is to work together as one team in a partnership with our customer, rather than just the implementation of a software or a tool. CADDi brings our product, our expertise, our change management experience, our technical support. Our customers bring their data access, time, champions and leadership reinforcement to help us really drive success. It’s important, too, for the customer to name one single accountable project owner for the success of the CADDi implementation.Second, there’s often a gap between how users perceive and expect to use the technology, and the company’s vision. Executives talk about outcomes; what they care about is revenue or cost, and bottom-line improvement. But engineers are users that live in steps. The CADDi team helps translate that vision into day-to-day workflows the engineering team can understand and benefit from.&nbsp;We work together with the customer to define success by addressing both the management agenda and the ultimate benefit to the users — faster decision making, different use cases, standardized parts and reduced costs, for example.And lastly, it’s important for CADDi to be honest and transparent with our customers about what we can and what we cannot do. Adoption gets killed when the expectations are vague or magical, or because users get frustrated early on, and it’s very common because AI is a buzzword, people get excited and then they expect AI to do everything to solve their problem. So, during our onboarding process we educate our customers about what AI can do and cannot do.Some manufacturers’ teams initially adopt an AI platform simply to save on administrative and search time. However, high software login usage doesn’t automatically mean the business is seeing true value. When advising first-movers, how do you guide them past that ‘digital filing cabinet’ mindset to track specific, measurable outcomes — like increased decision velocity or procurement cost reductions?This is critical, specifically for our business. We do not want customers to just see us as a search platform. But search time is tangible, and users can feel the difference immediately when they start using CADDi Drawer. If we don’t intentionally educate our customers, they tend to think that search time is where CADDi’s value comes from. But our value is in how many problems we can help our customers to solve.For us to educate our customers about CADDi’s value, we spend time understanding their business, their team, and their processes. Then the CADDi team helps them come up with ideas and share inspiration, presenting real examples where CADDi can create value for them — not just reducing search time, but also contributing to their financial improvement or breaking silos across functions.Ultimately, all this education is necessary because CADDi Drawer and our platform is first to market. There wasn’t a comparable product existing in this market before, so customers may not know what to expect. It’s easy for them to think, CADDi is just a search platform. In reality, we’re not just about search. We’re here to be a partner who can create value.The manufacturing industry is currently facing a massive skilled labor shortage and the loss of ‘tribal knowledge’ as veteran engineers retire. You have mentioned that a key metric for CADDi’s success is ‘better onboarding and employee support.’ How do you help companies utilize their historical data to actually accelerate new-hire training and capture that expertise before it leaves the building?When we onboard our customers, it’s common for the new hires to become the power users the fastest because they don’t have 10 years of muscle memory to unlearn. But they also don’t have access to all that expertise because they are so new, and without CADDi, they would have to ask a more experienced member for help, either through training, hand-holding, or learning by doing. Our platform helps reduce the barrier of “I don’t know who to ask.”One way we help keep knowledge within the organization is by structuring historical data into onboarding workflows. CADDi is a platform that consolidates drawings and drawing-related documents, information and data into one place so it’s easy for people to gain access to all the information that they need. During the onboarding process, we help new members see us as the first stop when they need a reference, or have a question.At one recent manufacturer we worked with, the VP of Engineering was hiring multiple engineers, and shared that he used their CADDi partnership as a talking point to attract young engineers to his organization. Not all manufacturers are adopting AI as CADDi does, so he used this to signal his company is modern, serious about engineering excellence, and at the forefront of adopting AI in the engineering design world.When deploying complex intelligence platforms, a buyer’s initial expectations rarely match the immediate reality of the rollout. Looking back at the implementations your team has successfully turned around, why is a deeply consultative, “co-building” partnership more critical for ensuring long-term ROI than just relying on standard IT tech support?In this era of AI, everyone is excited about it, using it and building pilots. The barrier to build a tool yourself in-house is a lot lower. So increasingly often, the customer will not just want to pay for a tool, they want to pay for impact. For us to overcome the natural inclination to build in-house, we need to provide impact that transcends what their IT department would be able to implement.For us to deliver impact requires, first, that CADDi’s implementation be closely tied to customers’ strategic agenda. Otherwise, it won’t be prioritized, and the impact will be non-existent or minimal to their business.Second, the foundation of creating value is adoption, and driving that adoption requires collaboration between CADDi and our customers. A lot of manufacturers have used the same tools or same processes for years. It’s understandable, because there’s a very low tolerance for making mistakes in the manufacturing process. Imagine you send the wrong version of your drawing to your supplier or your customer. The cost of that mistake can be massive.To build the necessary momentum for change, our customers typically put a mandate in place to get through the harder part, which is going from zero to adoption. After that, our customers continue encouraging their users for adoption, while CADDi is here to help drive that change, to do training, share best practices, and come up with creative ideas like sending newsletters, creating champions, doing on-site training or office hours.Third, delivering value beyond adoption requires shaping our implementation to make sure it is aligned with our customers’ strategic goals, and ensuring we have aligned and defined the success criteria at the beginning of the project. It’s very much a partnership.For some real-life examples, walk us through how your team acted as a strategic adviser during a successful vs. challenging implementation, and explain why unifying multiple teams onto a CADDi-supported workflow drove tangible ROI from the POV of different key stakeholders.We recently published a <a href="https://us.caddi.com/case-studies/amerequip?ref=US-PR-2026-Q3-WTWH-DesignWorld-Voices3-SC" rel="noopener noreferrer" target="_blank">case study</a> on a successful integration with Amerequip. We are in the second quarter of their implementation, which is still early, but we have already provided strong value to the customer. They were skeptical of CADDi’s value proposition at first, and whether they could justify the ROI. I led this project myself with the CS team, and once we started the partnership with them we dedicated one resource to sit down with them to understand their needs, their pain points and their data, to co-design the environment and workflow with them, to educate them about our platform.During onboarding, we are not just implementing a tool, we are also helping our customers to prioritize. Customers will have lots of ideas. My role and my team’s role in this process is not just taking customers’ requests and implementing them. Instead, we actively help our customers to prioritize what’s most important and what’s less important, to build a roadmap for our implementation.Lastly, we’re building credibility through little actions, like recurring weekly check-ins and making sure every meeting has a clear agenda, clear outcome, clear action items. Even with something we needed to deprioritize, a few weeks later our team is proactively coming back to the customer saying, this was something we discussed a few weeks ago, now it’s time to bring that back to our agenda. We’re not letting anything fall through.We have been very transparent with Amerequip in terms of what we can and cannot do. Customers really appreciate that honesty and transparency. It helps them understand our capabilities, and also builds trust and credibility with them. Now Amerequip is connecting us to other companies in the area, and attending our workshops to help our other customers.A challenging implementation was with an American manufacturer who initially expected CADDi to deliver a function we’re not capable of. There was some miscommunication, and the customer expected us to do something different, which made the initial implementation very challenging because the customer was frustrated we couldn’t deliver what they initially asked for.But our CS team proactively helped guide the customer by sharing other relevant use cases with them, and redefining workflows that we later realized are even more connected to their goal of improving their quotation win rates and quotation speed. A year later, this customer became another champion of CADDi. They came to our customer event and shared their experience of this journey with other customers. It’s very inspiring to see that, when this customer was originally in a very challenging situation.This is the value of CADDi’s strategic partnership and the critical role our Customer Success team plays. The partnership starts from the first touchpoint and deepens as customers implement new technologies. Driven by our unique customer focus and the vision of our <a href="https://www.designworldonline.com/voices/voices-yushiro-kato-co-founder-and-ceo-caddi/" rel="noopener noreferrer" target="_blank">CEO Yushiro Kato</a>, CADDi operates more like a top-tier management consulting firm than a traditional SaaS development vendor.Through a high-intensity, white-glove partnership model and manufacturing-exclusive AI data platform technology, CADDi helps manufacturers strengthen operational performance, reduce interoperability friction and build the unified data intelligence foundation needed for sustainable growth in American manufacturing.To learn more about how CADDi AI platform first-movers are improving productivity by unlocking faster, smarter decisions, explore our collection of featured customer case studies at <a href="http://us.caddi.com/case-studies?ref=US-PR-2026-Q3-WTWH-DesignWorld-Voices3-SC" rel="noopener noreferrer" target="_blank">us.caddi.com/case-studies</a>. Or <a href="https://us.caddi.com/resources/whitepaper/decision-velocity-unlocking-faster-smarter-decisions-in-manufacturing?ref=US-PR-2026-Q3-WTWH-DesignWorld-Voices3-SC" rel="noopener noreferrer" target="_blank">read CADDi’s new white paper</a> which explains how improving decision velocity can drive significant productivity gains without large capital investments, while also improving the quality of decisions.

Manufacturing Voices: Yongli Deng, Head of Customer Success, CADDi

<a href="https://www.manufacturingdive.com/spons/data-intelligence-drives-total-cost-of-ownership-in-industry-40/818640/" rel="noopener noreferrer" target="_blank">Read the article in Manufacturing Dive</a>By Philip Carpenito, Industry Advisor &amp; Former Chief Procurement Officer at L3Harris Commercial Aviation<img src="https://cdn.builder.io/api/v1/image/assets%2F209ee507cedf4c298e100edae4bba2df%2F3df874fa846d465dbd28fc02f46c0d21?format=webp" alt="Person working on a laptop showing a CADDi Quote quotation project with a bearing drawing, in front of windows overlooking an automated factory floor with robotic arms.">We are in an era of extended supply chain uncertainty. Is the Strait of Hormuz open as you read this? Will it be open tomorrow?&nbsp;What will the next geopolitical issue bring?Companies need to get comfortable with uncertainty and implement real-time software to help manage it. Preparing for a new technological era under these global constraints means extending digital transformation beyond the production environment to become more knowledgeable, agile and flexible in procurement, engineering and overall supply chain. This enables companies to truly understand the Total Cost of Ownership (TCO).Industry 4.0 is often defined by automation and connectivity on the manufacturing floor. In reality, it depends on how decisions are made across procurement, engineering, and supply chain. Bringing the level of data visibility required for Industry 4.0 procurement decisions and processes is even more critical than modernizing manufacturing operations. It provides the connected data foundation that AI needs to generate intelligence for faster, better sourcing decisions. From a procurement standpoint, the challenge is the inability to see supplier capability, cost history, and part requirements in one place when decisions have to be made. This data is also necessary when designing new products to ensure the best cost at launch and a robust long-term supply chain.Organizations that empower more functions with component data intelligence move away from reactive crisis management and into sustained operational efficiencies backed by data-driven decisions. That said, improving upstream tools and systems is only part of the answer. The leaders running procurement have to evolve, too.<h4>Why Procurement Can’t Afford to Be Left Behind</h4>Sixty percent of procurement professionals report sourcing parts at higher costs than necessary due to insufficient component and supplier data for effective negotiations. In practice, procurement teams operate under time pressure, often without a complete view of supplier performance, cost history, or prior part sourcing decisions.Similar to other upstream functions, procurement teams remain burdened by siloed systems, outdated tools, static reports and spreadsheets that don’t match future-forward Industry 4.0 changes in decision-making. Most procurement professionals often must search across multiple ERPs, PLMs and shared drives to answer basic questions about suppliers, pricing, and parts history. This is sometimes referred to as “search tax”; that time loss translates into slower quote cycles, delayed product launches and higher costs when people can’t see what’s been purchased, from whom, and for what.Bringing procurement into the digital transformation is beyond critical for two key reasons:<ul><li>Decision blind spots. What slows procurement down is the time it takes to find, validate, and trust the information needed to make a decision. For many manufacturers, decades’ worth of information about which suppliers can handle specific parts, where they are located, why quality issues tend to surface, and what trade-offs have been made often live only in the memory of key decision-makers within the organization. Without capturing this context, organizations will repeat past mistakes. Increase cost, lead times, and supplier qualifications</li><li>Knowledge loss. When seasoned employees retire, those insights leave with them unless they’re captured, digitized, and democratized. Digitizing data is no longer a nice-to-have but an absolute necessity, and must be accomplished before it is no longer available. Digitizing procurement intelligence preserves institutional knowledge and reduces the need to reinvent the wheel.</li></ul>Just like operations teams need real-time visibility into production, procurement teams need real-time visibility into factors that drive sourcing decisions. Visibility is now a foundational requirement for running a global manufacturing business. You have to know what’s going on with suppliers, costs, and risks if you want to run your organization. In the end, it comes down to communication. Procurement decisions require a clear view of engineering requirements, supplier capability, and historical outcomes. This is what Industry 4.0 looks like in procurement.Implementing AI for procurement enables it to operate strategically. Organizations that unify procurement intelligence can reclaim this lost time and redirect it toward supplier collaboration, cost optimization, quicker time to market for new products and overall innovation.<h4>A More Complex Role Demands Better Data</h4>In Industry 4.0, procurement is no longer a downstream function. It is part of how cost, risk, and supplier decisions are managed in real time.<ul><li>The chief procurement officer or CPO’s mandate has shifted drastically from placing POs and chasing cost reductions to managing a lack of manpower, tariffs, supply chain disruptions, supplier concentration, global trade shifts, logistics and risk exposure across regions. Every sourcing decision is a trade-off between cost, supply, lead time, and risk. The challenge is making that trade-off without full visibility into what has already been done before.</li><li>Modern procurement teams function as intelligence hubs informing executives on decision-making.</li></ul>This all plays out in a world where data can change every hour on the hour; where supplier conditions, costs, and availability shift constantly — often faster than teams can validate the data behind their decisions.&nbsp;It has never been more important for the CPO to understand the financial health of their suppliers and the immense logistics challenges that now define global sourcing. Without better tools and connectivity, these procurement leaders are being asked to manage modern risks with yesterday’s infrastructure.With this level of responsibility and exposure. The new CPO has become a strategic advisor to the executive team and is responsible for designing resilient global supply chains and networks rather than simply managing purchasing transactions. This is exactly why the technology and data environment must catch up and support these new responsibilities<h4>Closing the Digital Disconnect in Total Cost of Ownership</h4>If fragmented systems and workarounds prevent procurement from using AI effectively, the first step is to consolidate data into a single, unified view. For many businesses, that means building a data warehouse that connects the core systems procurement depends on, so teams can access part numbers, part specifications, pricing, supplier, and cost context in one place.&nbsp;This includes enterprise resource and demand planning software, quality systems, product lifecycle management tools, bills of materials, and engineering drawings.Cleaning up and consolidating data does more than simply break down silos; it also creates a foundation for AI-powered tools to turn that unified data into actionable data intelligence. Once data is connected, AI can be deployed to support faster, smarter decisions and total cost. Teams describe what they need, and the system surfaces the information in minutes. Industry 4.0 data is consolidated and built to support decisions at the speed required by supply chain operations.Connected data helps procurement, engineering, quality, and manufacturing to tap into real-time supplier intelligence instead of working from snapshots in time. For instance, when a labor strike hits one region or a natural disaster threatens production in another, you can quickly see which suppliers and parts are exposed, along with possible alternatives. The question is not just who can supply the part. It is who has supplied it successfully before, what location they are in, under what conditions, and at what cost. It also supports predictive cost and supply risk insights to provide a forward-looking view of where material prices or lead times will be heading in the future.The result of these insights is better communication and faster sourcing decisions. AI can connect historical sourcing data — such as pricing, supplier performance, delivery, and defect history — to drive data-driven outcomes. Teams know who their best or approved suppliers are, where costs are headed, and how to stay within budget before decisions become too expensive to reverse. This reduces the need to re-source known parts, revisit prior decisions, or absorb unnecessary cost from incomplete information. You have a system to share with your supplier; your engineer can share design tools and capabilities. This collaboration helps optimize sourcing and supplier collaboration.<h4>Crucial Steps to Effectively Embrace the Fourth Industrial Revolution</h4>Adopting a system of insight can accelerate sourcing decision velocity, improve supplier selection, collaboration, and enable early cost optimization. AI software partner selection is critical to ultimately ensure the technology adoption within procurement and the full supply chain workflow. You’re looking for a technology adviser who can provide not only implementation but also training and ongoing support. This will help manufacturers reduce sourcing cycles, achieve cost savings and improve quality.Verify that their platform will integrate with the systems you run without a disruptive overhaul. And finally, prioritize ease of use — a system that intimidates users won’t be adopted, and unused systems produce no value. The data you need to work smarter already exists within your organization. You just need to put it to work. The lack of integration makes it difficult for teams to get a unified view of operations and can delay decision-making processes. Yet, redefining operational strategies can drive substantial cost savings.To see what this might look like, <a href="https://us.caddi.com/resources/insights/the-procurement-playbook" rel="noopener noreferrer" target="_blank">CADDi offers a free procurement playbook</a> that defines 11 actionable strategies and a data-driven approach to start driving Total Cost of Ownership. Investing in a data intelligence platform isn’t like cutting a PO, putting it in a drawer, and calling it a day. These systems directly affect how data is stored, and how procurement decisions are made and a new system will be as effective as your strategic approach to gain buy-in at all levels of the organization.Finally, Industry 4.0 in procurement is not defined by systems. It is defined by how decisions are made. Cost control depends on how quickly and confidently teams can act with complete, connected information.What is it costing you to operate without the full picture?Author BioPhilip Carpenito is a Strategic Advisor and Consultant at CADDi and former Chief Procurement Officer at L3Harris Technologies Commercial Aviation. He brings more than three decades of global supply chain and procurement leadership across complex manufacturing environments and industries. His experience spans procurement, operations, NPI, supply chain, subcontracts, and program management, where he has led large-scale initiatives focused on cost control, supplier strategy, supplier relationship and operational performance. He currently advises organizations on how to strengthen decision-making across engineering, procurement, and supply chain functions. He has also held leadership roles at Nature’s Bounty, Motorola, Raytheon and Bose.About CADDiCADDi 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="http://us.caddi.com/company?ref=US-PR-2026-Q3-TechTarget-ManufacturingDive-Interview2-SC" rel="noopener noreferrer" target="_blank">us.caddi.com/company</a>

Data intelligence drives total cost of ownership in Industry 4.0

<a href="https://themachinemaker.com/news/nichirin-tennessee-and-caddi-inc-transform-24-years-of-engineering-data-into-actionable-manufacturing-intelligence/" rel="noopener noreferrer" target="_blank" style="font-size: 16px;">Read the article in MachineMaker</a><img src="https://cdn.builder.io/api/v1/image/assets%2F209ee507cedf4c298e100edae4bba2df%2F2f4300796a5349a88770d8a5e7d0bdde" alt="Long hose production line inside a factory, with rows of blue tubing on a conveyor, pressure gauges and a control panel in the foreground, and a stack light beside it">Image Courtesy: NICHIRIN Group<a href="https://www.nichirin.co.jp/" rel="noopener noreferrer" target="_blank">﻿Nichirin Tennessee Inc</a>, a US-based manufacturer of brake and power steering hoses for passenger vehicles and motorcycles, has successfully modernized its operations through the implementation of CADDi Drawer, an AI-driven data platform developed by <a href="https://us.caddi.com/company" rel="noopener noreferrer" target="_blank">CADDi Inc</a>. The initiative marks a significant step toward reducing reliance on individual expertise and creating a more transparent, efficient, and self-reliant organization.Founded in 2002 and operating as a subsidiary of Nichirin Co, Ltd, a global leader in automotive hose manufacturing, Nichirin Tennessee has long depended on accumulated experience and institutional knowledge to manage engineering data. Accessing critical information such as drawings, bills of materials (BOMs), and historical records traditionally required precise part numbers and guidance from experienced personnel. This approach created operational bottlenecks, slowed onboarding, and limited responsiveness to customer needs.To address these challenges, Nichirin Tennessee deployed CADDi Drawer as an integration layer across its systems. The platform centralizes engineering and commercial data, linking drawings, BOMs, quotations, and control documents into a unified, searchable environment. As a result, employees across departments now have immediate access to critical information without relying on specialized knowledge or manual retrieval processes.The transformation has significantly improved operational efficiency. Team members can now locate relevant data using keyword or similarity searches, eliminating the need for exact identifiers. This shift has reduced time spent on information retrieval, accelerated quoting processes, and enabled employees to focus on higher-value tasks.In addition to operational gains, the initiative has fostered a cultural shift within the organization. Employees are encouraged to independently access and analyze data, promoting accountability and cross-functional collaboration. New hires, in particular, benefit from faster onboarding, as the platform allows them to navigate complex datasets with minimal supervision.Company representatives emphasized that the success of the implementation extends beyond technology. By making knowledge widely accessible, Nichirin Tennessee has strengthened its ability to operate as a unified team while leveraging decades of accumulated expertise as a shared organizational asset.<a href="https://themachinemaker.com/technology/euroblech-trumpfs-machine-diagnostics-minimizes-production-downtime/" rel="noopener noreferrer" target="_blank">Building on</a> these results, the company plans to expand the use of CADDi Drawer to additional facilities, including operations in Texas and Mexico, further enhancing efficiency and knowledge sharing across its manufacturing network. CADDi Inc, founded in 2017 and headquartered in Tokyo and Chicago, continues to develop AI-powered solutions designed to unlock the value of design and supply chain data for manufacturers worldwide.

Nichirin Tennessee and CADDi Inc. Transform 24 Years of Engineering Data into Actionable Manufacturing Intelligence

<a href="https://www.thefabricator.com/thefabricator/news/cadcamsoftware/caddi-delivers-ai-platform-to-nichirin-tennessee" rel="noopener noreferrer" target="_blank">Read the article in The Fabricator</a><a href="https://us.caddi.com/" rel="noopener noreferrer" target="_blank">CADDi Inc.</a> has announced that Nichirin Tennessee Inc. has implemented CADDi Drawer, a platform that uses AI to centralize and analyze unstructured design and production data, in its operations in Lewisburg, Tenn. The company manufactures brake and power steering hoses for use in passenger cars and motorcycles.By linking drawings, BOM data, quotations, and control cards, Nichirin is helping to ensure that every department can use the platform as a single source of truth across disconnected systems. In addition, the platform has turned individual expertise into scalable knowledge reuse shared across the organization.“Our goal wasn’t just to implement new software—it was to eliminate barriers and operate as one unified team,” said Takehiro Ishimoto, sales manager.

CADDi delivers AI platform to Nichirin Tennessee

<a href="https://www.manufacturingtomorrow.com/news/2026/04/21/nichirin-tennessee-and-caddi-inc-turn-24-years-of-engineering-data-into-manufacturing-intelligence-reducing-reliance-on-tribal-knowledge/27441/" rel="noopener noreferrer" target="_blank">Read the article in Manufacturing Tomorrow</a>New case study reveals how long-time manufacturer Nichirin Tennessee reduced reliance on individual knowledge to improve quoting speed and cross-functional decision makingCADDi Inc., the global developer of a manufacturing-exclusive AI data platform, announced today that Nichirin Tennessee Inc. has implemented CADDi Drawer to modernize its operations and reduce reliance on individual expertise. Established in April 2002, Nichirin Tennessee is the U.S.-based manufacturer of brake and power steering hoses for use in passenger cars and motorcycles and one of the U.S. subsidiaries of Nichirin Co., Ltd., a century-long leader in automotive hoses for global OEMs. Nichirin Tennessee strategically deployed the platform to build a more transparent, accessible, and self-reliant organization under real operating conditions.Manufacturers historically rely on individual experience to access drawings, BOMs, and historical data. This approach slows onboarding, delays decision-making, and limits how quickly teams can respond to customer demand. The new case study delves into how Nichirin Tennessee Inc. addressed this constraint by centralizing access to engineering and commercial data with CADDi, enabling faster execution across teams.Reducing dependency on individual knowledge and manual data retrievalHistorically, accessing drawings, BOMs, and historical data required exact part numbers and a heavy reliance on the "tribal knowledge" of senior Japanese expatriate staff. This concentration of expertise created significant information silos that slowed down onboarding and daily operations for the entire team. In the past, new employees frequently relied on managers to identify relevant data, an approach that was time-consuming for both trainers and trainees.Centralizing access to engineering and commercial data across systemsBy adopting CADDi as an integration layer, Nichirin has successfully democratized its organizational knowledge. By linking drawings, BOM data, quotations, and control cards, the company is ensuring that every department can use CADDi as their single source of truth across disconnected systems."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," said Takehiro Ishimoto, Sales Manager at Nichirin Tennessee.Today, any team member can instantly retrieve drawings, BOM data, and necessary historical information using keywords or similarity search, without relying on drawing numbers or individual experience. Information that was once concentrated among a few veteran engineers is now broadly accessible across departments, supporting cross-functional execution across engineering, procurement, sales, and operations.Improving quoting speed and enabling independent execution across teamsThis structured engineering knowledge has significantly reduced the time required to search for information, directly improving quoting turnaround and allowing a focus on higher-value tasks. Furthermore, the project is fostering a cultural shift toward empowerment where employees are encouraged to first search within CADDi, think independently, and take ownership of their work before relying on supervisors.Nathan Yifrach, a member of the sales staff, experienced this firsthand: "I was able to start working smoothly from day one. Even without prior knowledge or detailed instructions, the system was intuitive enough to search and navigate on my own". By turning individual expertise into scalable knowledge reuse and a shared organizational strength, Nichirin is ensuring that every department can leverage expert insight.The case study, "Democratizing knowledge to empower independence with Nichirin Tennessee" explores how CADDi Inc. and Nichirin Tennessee democratized decades of information, enabling independent work and turning individual expertise into a shared organizational asset. Building on this success, Nichirin is now expanding the initiative to Nichirin Flex USA, spanning manufacturing facilities in El Paso, Texas, as well as Juarez, Chihuahua in Mexico.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 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 us.caddi.com.

Nichirin Tennessee and CADDi Inc. Turn 24 Years of Engineering Data into Manufacturing Intelligence, Reducing Reliance on Tribal Knowledge

<a href="https://www.techtarget.com/searchenterpriseai/feature/GenAI-in-product-manufacturing-cuts-costs-but-adds-risks" rel="noopener noreferrer" target="_blank">Read the article in Industry Today / TechTarget</a><h5>In product design and manufacturing, GenAI systems are consolidating supplier data, improving processes and cutting significant software costs -- yet they raise unique concerns.</h5>The third-party manufacturing worksheet scheduler didn't work to his satisfaction, so Moulydharan Vallal, manufacturing design and implementation manager at battery material maker Sila Nanotechnologies, built his own in a few months <a href="https://www.techtarget.com/searchenterpriseai/feature/Battle-of-the-bots-Best-GenAI-chatbots-for-business" rel="noopener noreferrer" target="_blank">using ChatGPT</a>.The <a href="https://www.techtarget.com/searchenterpriseai/definition/generative-AI" rel="noopener noreferrer" target="_blank">generative AI</a>-built scheduler replaced a spreadsheet and checks equipment availability, upcoming maintenance, material levels and storage capacity. The Sila team has been building other systems, including a scenario-planning model that tracks the cost of goods sold. Over the past two years, build-your-own systems has saved the manufacturer "hundreds of thousands of dollars" in software costs, according to Vallal, who credits GenAI for the savings. The cost of building systems, he said, "is almost collapsing to zero."GenAI-developed systems have the potential to replace third-party software applications in several industries, but <a href="https://www.techtarget.com/searcherp/tip/Use-cases-for-generative-AI-in-manufacturing" rel="noopener noreferrer" target="_blank">GenAI's use in manufacturing</a> poses different kinds of concerns. When integrating AI-related capabilities into manufacturing systems, any system failure can shut down production.AI explainability is critical, said Austin Locke, principal and global lead for data science and AI at Rockwell Automation. "If the AI system is designed as a black box, there's definitely resistance to that in manufacturing," Locke said. Still, manufacturers aren't resistant to advanced technology.Manufacturers have long used high-performance computing (HPC) systems, including leasing the U.S. government's most powerful supercomputers, to test and design products in virtual environments, such as the wings of airplanes. Before the advent of HPC, manufacturers had to build physical prototypes and test each one.HPC runs its <a href="https://www.techtarget.com/searchenterpriseai/news/366640672/How-simulations-and-digital-twins-are-advancing-robotics" rel="noopener noreferrer" target="_blank">computationally intensive simulations</a> based on physics to produce an output "closer to nature" and more realistic, said Steve Conway, independent consultant for AI and HPC. AI is "just one more step removed from nature -- from realism," he explained. It examines historical data to identify the best design options. As a result, HPC makers are integrating AI into their systems, Conway added.<h2>GenAI in product design: Who's the inventor?</h2>While GenAI can help design products, select materials and produce innovative components, it can also create legal risk because U.S. copyright law doesn't recognize machines as inventors. "To be able to get a patent, the inventor has to be a human being. And the question becomes, Who is the inventor?" conjectured Shabbi Khan, a partner at consultancy Foley &amp; Lardner and co‑chair of AI, automation and robotics group.Until recently, large language models (<a href="https://www.techtarget.com/whatis/definition/large-language-model-LLM" rel="noopener noreferrer" target="_blank">LLMs</a>) were viewed as <a href="https://www.techtarget.com/whatis/feature/12-of-the-best-large-language-models" rel="noopener noreferrer" target="_blank">tools for generating ideas and producing content</a>. But "we're pretty much at the point now where LLMs can give you an answer -- and that's the invention," Khan explained. "You have an obligation to the patent office. You have the duty of good faith and candor, which includes declaring that you are the true inventor of this technology."A business can claim that a human, not AI, produced the invention, but there could be <a href="https://www.techtarget.com/searchcontentmanagement/answer/Is-AI-generated-content-copyrighted" rel="noopener noreferrer" target="_blank">significant legal risk in doing so</a>. "You may get away with it, and nobody will notice until something gets litigated," Khan noted. The real inventor can surface in discovery, when a party is required to "provide all documentation relating to the conception of the invention," he added. "And if somebody comes across your LLM history, it could really jeopardize certain things."A practical guardrail is to treat AI's involvement as a starting point. "Don't use the output as is," Khan advised. "Think about tweaks, go experiment with it, modify it."<h2>GenAI in autonomous processes</h2>Geminus AI makes an intelligence layer that sits on top of existing industrial control systems, such as those from Schneider Electric and Rockwell, said Greg Fallon, CEO of Geminus.AI. The platform uses GenAI to ingest the operations data and return specific control settings to improve process performance. In manufacturing plants that rely on HPC and physics‑based simulations, Geminus AI's platform augments those systems by using what it calls "physics‑informed" AI models to drive continuous, real‑time control decisions.While the <a href="https://www.techtarget.com/searchenterpriseai/feature/GenAI-is-ready-for-more-autonomy-but-supply-chains-are-not" rel="noopener noreferrer" target="_blank">process can run autonomously</a>, Fallon noted, the decision depends on the customers. "We have some customers who prefer to have a human in the loop because they don't have the autonomous backups in place," he said. "We have other customers that are fully comfortable running autonomously."There are also <a href="https://www.techtarget.com/searchenterpriseai/tip/Top-generative-AI-tool-categories" rel="noopener noreferrer" target="_blank">GenAI-infused tools</a> that can improve manufacturing speed and efficiency. "Engineers spend more time searching for information than using it," said Chris Cope, vice president of engineering at CADDi, whose tools are designed to centralize data, such as drawings, spreadsheets and data, stored in manufacturing supplier systems.In addition, "generative AI is really good at extracting knowledge and intent from engineers," said Nathan Evans, co-founder and head of climate and AI at digital manufacturing platform provider Fictiv. That kind of context from engineers is vital. GenAI can build at the "speed of thought," Sila's Vallal said, but​ "without context [from people], it will break."Patrick Thibodeau has worked for several decades as an enterprise reporter, focusing on IT and workforce management, ERP, high-skills immigration, tech policy, and high-performance computing.

GenAI in product manufacturing cuts costs but adds risks

<a href="https://www.designworldonline.com/voices/voices-yushiro-kato-co-founder-and-ceo-caddi/" rel="noopener noreferrer" target="_blank" style="font-size: 16px;">Read the article in Design World</a><img src="https://cdn.builder.io/api/v1/image/assets%2F209ee507cedf4c298e100edae4bba2df%2F8fa76e901d7648f089ceae494c52d559?format=webp" alt="Design World Voices banner featuring a headshot of Yushiro Kato, Co-Founder and CEO, CADDi">By Sana Kazilbash | April 2, 2026This article is sponsored by <a href="https://us.caddi.com/company?ref=US-PR-2026-Q3-WTWH-DesignWorld-Voices2-SC" rel="noopener noreferrer" target="_blank">CADDi</a>. In this Voices interview, Design World spoke with Yushiro Kato, co-founder and CEO at CADDi, to discuss his founder journey and why unlocking historical engineering knowledge is the key to driving more effective Value Analysis and Value Engineering (VA/VE) initiatives in American manufacturing.Design World: Tell us about yourself and your role as co-founder and CEO of CADDi.Yushiro Kato: I founded CADDi at the end of 2017 with my co-founder, Aki Kobashi, who’s the CTO at CADDi. I used to work for McKinsey, a management consulting firm, for three and a half years as an engagement manager, co-leading procurement for the manufacturing sector and spending plenty of time on the shop floor helping expand the firm’s IoT and AI practice. I moved to Chicago almost three years ago to lead the U.S. market expansion of CADDi, and I’m currently the global CEO as well. I call it “Chief Everything Officer”.You worked with American manufacturers at McKinsey and launched CADDi first in Japan. How have those experiences shaped CADDi’s platform and advisory approach for the U.S. market today?I had my own business when I was at university before McKinsey. Then I joined McKinsey to learn more about the issues in traditional industries like manufacturing. I didn’t specifically target manufacturing at the time, but I wanted to do something bigger.I did a lot of projects at McKinsey, including manufacturing, construction, and investing firms, among others. Manufacturing was the most fascinating industry for me because I had three criteria at the time for the industries that I wanted to target. One was the size of the industry, as large as possible, and second the depth of pain points. I didn’t want to do something that I could solve within two or three years with simple technology and action. And then three, how common the industry was globally. Those are the three criteria I had, and manufacturing was the best match.I spent about two years at McKinsey focusing on manufacturing, especially supply chain transformation. This included procurement cost reduction, new supplier development and design improvement for manufacturability, and cost reduction — which is called value analysis and value engineering (VA/VE). I worked with some U.S. manufacturers, large enterprise companies in Milwaukee, Nebraska, Kansas City, and New Jersey. Honestly, I saw a lot of commonalities across Japan and the U.S. in terms of the pain points they have and the solutions that can impact those pain points.We actually started with a business called CADDi Manufacturing, which is a procurement platform for custom-made parts. We were not selling software. We were selling physical parts like Amazon. Behind it, we had implemented a lot of software to streamline our own operations because we had 5,000 customers on the manufacturing platform and 600 suppliers. Every company has its own format and its own strengths and weaknesses. We couldn’t handle it manually. Naturally, we implemented software, and now we are selling the software we used internally.I learned about a lot of pain points, especially in the supply chain area at McKinsey, and I leveraged that foundation to establish CADDi.Manufacturers generate decades of engineering knowledge through drawings, supplier decisions and product revisions. Why do you think so much of that knowledge remains underutilized inside organizations?We did CADDi Manufacturing for seven years from the foundation and became one of the largest on-demand manufacturing platforms in the world. What we learned at the time was that we owned more than ten physical inspection centers to assure the quality of the parts, and we delivered the parts to customers. Like Amazon, we had a lot of warehouses and inspection centers.One purpose was to track quality issues, but the second purpose was to gain data to identify what causes quality defects. We identified that more than 50% of quality defects are caused by the lack of quality in drawings and designs. That’s why we developed CADDi Drawer and launched it four years ago to change that.Engineers have their own systems, like product lifecycle management (PLM) systems and drawing management systems. They design based on past designs and their tribal knowledge. But to prevent defects, you need to understand what kind of defects can happen if you change this design to another design, or if you tighten a tolerance. The systems are siloed. Procurement people use their own supply chain management systems. Quality people use their own quality management systems, QMS or Excel files, and track past quality reports in PDFs. It’s all separated and disconnected.To change quality defects, you need to change designs, but you don’t have that data or visibility. That’s one of the biggest reasons why transformation is hard to achieve.What we did was connect all the data from siloed systems. We also developed our own proprietary shape analysis AI model to understand the similarity across parts, even from 2D drawings. A human brain can understand that these parts are similar to another part, so we can leverage the learnings from that similar part in the past for the part I’m working on. But AI or software couldn’t do that. So we developed it to connect different systems through part number or part shape.That’s our innovation. That’s the storyline of why it was not easy to solve and now why we can solve it.At what point did you realize that unlocking historical engineering knowledge could influence not only engineering productivity but also sourcing, supply chain decisions and overall competitiveness?I realized the power of engineering because we were a supplier, a platform for engineers and procurement buyers. More than 50% of the quality defects happened because of the lack of quality of engineering and drawings.Customers always asked us to reduce costs when we were doing CADDi Manufacturing. Oftentimes, if you want to reduce costs, there are some commercial levers to negotiate with suppliers, but you can realize more impact through changing designs. If you change materials, it’s the easiest way to reduce costs, for example. Or if you change the tolerance, if you can loosen one tolerance, you can gain cost reduction impact.We learned that through millions of parts transactions at CADDi Manufacturing. After we launched CADDi Drawer, all the data was connected and a lot of engineers used it to learn past quality defects and cost reduction histories of similar parts using the similarity search that we have. A lot of customers, like Subaru, an automotive company, realized the cost reduction impact very quickly.The CADDi Manufacturing experience helped us realize that engineering is super-important and it has a lot of impact on the downstream funnel in terms of cost reduction, lead time and more, through inspections. Then through the experience of CADDi Drawer and software implementation, we learned that this impact can be realized in real life with specific customers.Many engineering teams struggle to execute cost-down initiatives like Value Analysis and Value Engineering (VA/VE) because they lack access to historical design data. How do you expect manufacturers that successfully unlock this knowledge to operate differently when it comes to VA/VE and continuous innovation?If you are a senior engineer, you have a lot of experience. If you change this material from A material to B material, you will realize this cost reduction. Or if you change this feature to that feature, you can realize this. This is what VA/VE is, changing designs to reduce cost and reduce quality defects.But for most younger engineers, they don’t have that tribal knowledge. They don’t have that much experience. So it’s hard to come up with VA/VE ideas.What we are doing is creating visibility across similar parts from the past, regardless of the designers. Even if you haven’t designed similar parts before, we can identify similar previous parts and what kind of quality defects happened, or what kind of design reviews and design changes happened to those drawings. Then they can learn from past experiences rather than recreating similar parts without understanding the historical context.That’s what we are doing. Basically, AI identifies similar parts and similar VA/VE cost reduction history and quality defects to give insights to younger engineers for VA/VE initiatives.Looking ahead, how do you see AI changing the way manufacturers use historical engineering knowledge to make faster and more informed decisions?The concept of our product is to prevent reinvention of the wheel, which is happening everywhere in manufacturing. It is said 99% of the activities manufacturing workers are doing are just reinventing the wheel to some extent. Someone in your company has likely done similar things in the past. Oftentimes a lot of knowledge is only in human brains. It’s not tacit knowledge, it’s just tribal knowledge. If people leave the company, that knowledge will also walk out.What we are doing is to assetize, to capitalize on people’s knowledge, past data and experiences as a company-wide asset like a knowledge base, so that you don’t have to reinvent the wheel. Instead, you can spend your time creating new things, innovative stuff that no one else in the company has ever worked on. That’s what we want to achieve, and AI is the best tool to do that.We do not just leverage large language models (LLMs) that are available in the market. The biggest advantage we have is the shape analysis model that we created based on the proprietary data we accumulated through CADDi Manufacturing and real manufacturing experiences. This is the biggest difference between CADDi and normal system vendors, because we were a manufacturer. I think the most important asset is the human experiences, historical data, and failures in the company—and AI can unlock the potential of all that historical data and leverage it as an asset.<a href="https://us.caddi.com/company?ref=US-PR-2026-Q3-WTWH-DesignWorld-Voices2-SC" rel="noopener noreferrer" target="_blank">CADDi</a> is a global technology company that develops a manufacturing-exclusive data intelligence platform. Headquartered in Tokyo and Chicago, CADDi brings proven expertise in helping American Manufacturers preserve decades of engineering knowledge. Its flagship product, CADDi Drawer, uses advanced AI to structure fragmented engineering data into searchable intelligence. 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.Learn more in CADDi’s whitepaper, <a href="https://us.caddi.com/resources/whitepaper/value-analysis-value-engineering-for-manufacturing?ref=US-PR-2026-Q3-WTWH-DesignWorld-Voices2-SC" rel="noopener noreferrer" target="_blank">Value Analysis &amp; Value Engineering for Manufacturing: The Challenges and Solutions of Implementation</a>.

Manufacturing Voices: Yushiro Kato, Co-Founder and CEO, CADDi

<a href="https://digitaleditions.walsworth.com/publication/?i=861841&amp;article_id=5122529&amp;view=articleBrowser" rel="noopener noreferrer" target="_blank">Read the article in SME / Manufacturing Engineering &amp; Technology Magazine</a>Written by: Chris Cope, CADDi VP of Engineering&nbsp;AI in manufacturing is shifting from early adoption to operational use at large manufacturers across automotive, heavy machinery and aerospace. New AI applications are purposefully built to support maintenance planning, scheduling, and quoting across the supply chain.&nbsp;According to new research by MIT Sloan, manufacturing AI adoption shows mixed or early-stage results, emphasizing that AI introduction frequently leads to long-term growth output, increased revenue, and higher employment. For most decision-makers, AI investment is now prioritized by how it improves cross-functional engineering and sourcing workflows and supports more informed decisions, especially when teams are lean, timelines are compressed, and critical decisions require quick data-driven judgment. These transformations show real P&amp;L impact at global manufacturers.Data from CADDi’s 2026 Manufacturing Outlook Study reflects this shift. Nearly 70 percent of 200 participating business leaders plan to invest in physical assets such as robotics and equipment. Nearly 80 percent continue to identify skilled labor shortages as their most significant external limitation. Respondents also confirmed that while most manufacturing organizations operate PLM, PDM, ERP, or engineering document management systems, most professionals have struggled to find and useutilize information during fast-paced project design, sourcing, and production decisions.<h3>Operational realities of infrastructure gaps</h3>In discussions with department leads, data fragmentation is consistently mentioned as a core barrier to streamlining processes. Locating and synthesizing this information has historically taken days, sometimes weeks. In most cases, a part number file does not include why a design changed, which supplier met tolerance requirements, or how cost and lead time were affected in prior programs. In some cases, engineers have reported spending more than one hour per day searching for drawings, supplier information, or quality records.A unified AI platform becomes an operational advantage when it strengthens decision infrastructure. For enterprise, that means inviting in a system and enforcing new technology adoption to preserve traceability, keeping project leads in control while applying insights into design, sourcing, and execution workflows. Manufacturing organizations maximize AI value when historical engineering and sourcing decisions become reusable across teams and time.&nbsp;<h3>Planning ahead for a multi-generational workforce</h3>A significant number of the manufacturing workforce is expected to retire by 2030, and these skilled professionals shaped multiple product generations, teams and production environments. When experienced engineers retire, access to valuable context risks disappearance unless companies planned a structured exit strategy and hand-off.Unless a system of insights is in place for a manufacturing enterprise, tribal knowledge and historical learnings exist primarily in individual documentation or memory. By shifting from “tribal knowledge in people’s heads” to “institutional knowledge, accessible to all”, an AI data platform like CADDi makes critical information visible to anyone, including new employees.<h3>Bridging the generic AI gap</h3>Even when PLM or ERP systems are in place, engineering and sourcing knowledge remains fractured across teams and tools. CADDi’s recent survey found manufacturing business leaders identified inefficient cross-department collaboration, poor search functionality, and lack of centralized access as primary contributors to lost productivity.While AI technology has evolved significantly, these systems still depend on human judgment and interpretation. Drawings vary by naming convention, revision practice, and format. Supplier performance history is distributed across purchasing systems, quality records, and email threads. Quality issues appear in inspection reports, annotations, and meeting notes.Applying generic AI to fragmented manufacturing data increases inconsistency in engineering and sourcing decisions. Without domain-specific models, pattern recognition cannot reliably interpret geometry, tolerance trade-offs, or sourcing decisions without shared definitions. Scalable implementation requires vertically trained AI, particularly when meaning varies by team, plant, or region, because models amplify the structure they are given.Most importantly, domain-specific AI surfaces evidence and patterns while leaving engineering judgment, accountability, and final decisions with people at every level of expertise.&nbsp;<h3>Making a case for data interoperability</h3>AI is most effective when prior decisions and historical data are visible to users and cross-functional teams. Procurement teams reference supplier performance tied to specific design outcomes. Quality teams link defects to historical decisions. Manufacturing teams remain responsible for decisions, with full traceability back to source documentation. Manufacturing AI only becomes operational when historical decisions can be compared and reused across teams, programs, and across shifts in business transformations.Advanced AI models extract text, numeric values, and geometry, then apply consistent interpretation across time and programs. Having custom-trained AI models for particular tasks provides that structure by defining how parts, designs, suppliers, and engineering outcomes relate across programs and teams so information is interpreted consistently. Historical drawings, handwritten notes, inspection records, and quotations can be ingested in their existing form.&nbsp;For organizations operating under constraint, converting history into an operational asset is a prerequisite for consistent execution.<h3>Conclusion</h3>While other industries are being transformed by AI, manufacturers still see their departments siloed and dependent on disappearing tribal knowledge. ERP and PLM systems were built to solve department-level problems, and they do that well. What they were not built to do is connect engineering knowledge across departments. That gap is where institutional knowledge gets lost. The high-context engineering designs, which are the heart of all operations, are incomprehensible to general-purpose AI.&nbsp;Today’s manufacturing leaders are not deciding whether to transform their companies, but how. They are looking for effective systems to preserve skills and knowledge as business conditions and workforce dynamics change. Leaders need engineering and sourcing decisions that remain accessible, explainable, and reusable as teams become leaner and product complexity increases.&nbsp;For manufacturing organizations facing skilled labor constraints, cost volatility, and compressed timelines, knowledge systems now determine execution speed and decision quality. Businesses that continue to rely on fragmented data and tribal knowledge place growth, consistency, and resilience at risk as digital transformation moves into daily operations.About CADDiCADDi is a global technology company that develops manufacturing-exclusive data intelligence platforms. Its flagship product, CADDi Drawer, uses advanced AI to structure fragmented engineering data into searchable intelligence, enabling teams to locate historical designs, understand part evolution, and apply proven solutions across engineering, procurement, quality, and operations. To learn more, visit <a href="http://us.caddi.com/company?ref=CompanyDescription2026" rel="noopener noreferrer" target="_blank">us.caddi.com/company</a>

Turn AI Into a Manufacturing Advantage

<a href="https://briefglance.com/articles/us-factories-face-triple-threat-labor-tariffs-instability" rel="noopener noreferrer" target="_blank">Read the article in Brief Glance</a>Experts agree that the U.S. manufacturing sector is facing a critical inflection point, requiring radical improvements in efficiency and data-driven strategies to navigate labor shortages, economic pressures, and geopolitical instability.CHICAGO, IL – January 20, 2026 – The American manufacturing sector is facing a severe and multifaceted crisis, with a staggering 79% of industry executives identifying the skilled labor shortage as their most significant challenge, according to a new study. This critical workforce gap is being dangerously compounded by rising costs, tariff uncertainties, and geopolitical instability, creating a perfect storm of challenges for manufacturers heading into 2026.The findings, detailed in the "2026 Manufacturing Outlook Study" released by AI data intelligence firm CADDi in partnership with the Society of Manufacturing Engineers (SME), paint a stark picture of an industry under immense strain. The research, based on a survey of over 200 U.S. manufacturing professionals, reveals that the talent crisis has intensified, with the current shortage representing a 7% increase over the 72% reported in 2025.The skilled labor deficit is not a new problem for manufacturers, but its intensity has reached a critical peak. The CADDi study reveals that 90% of leaders say their core manufacturing departments are the most impacted by the shortage, a figure that underscores the strain on the shop floor itself. Operations teams are also heavily affected (48%), as are design and engineering departments (40%).In response, 62% of companies are doubling down on efforts to improve employee recruitment, enablement, and retention. However, with the pool of available talent shrinking, many are realizing that simply hiring their way out of the problem is no longer a feasible strategy.While the labor crisis erodes capacity from within, a barrage of external forces is squeezing manufacturers from all sides. According to the study, 61% of executives cite rising costs and persistent inflation as a major roadblock, complicating pricing strategies and eroding profit margins. Adding to the complexity, nearly half of manufacturers (47%) report that tariffs and unclear trade policies are making planning harder. Furthermore, 38% of leaders are actively bracing for supply chain disruptions tied to ongoing geopolitical instability.Faced with this triple threat, manufacturers are making a significant pivot in their investment strategies. A remarkable 69% of companies plan to invest in physical assets such as robots and equipment. This surge in automation investment comes at the direct expense of other technology spending. Investment in operational systems like Enterprise Resource Planning (ERP) and Manufacturing Execution Systems (MES) has fallen sharply to 33%, down from 60% in the previous year."Manufacturing teams are facing shrinking headcounts despite rising volatility and pressure," said Yushiro Kato, CEO and co-founder of CADDi. He noted that companies need faster answers and stronger cost visibility to navigate the current climate."Our research shows manufacturing growth in 2026 will not come from broad expansion. Instead, it will come from the ability to extract more value from the assets companies already own," Kato stated. "Smarter use of parts data can help leaders automate inventory tracking, streamline procurement, and free up engineers to focus on innovation rather than administration."

US Factories Face Triple Threat: Labor, Tariffs & Instability

New case study reveals how long-time manufacturer Nichirin Tennessee reduced reliance on individual knowledge to improve quoting speed and cross-functional decision makingCHICAGO, Ill.--<a href="https://us.caddi.com/company?ref=US-PR-2026-Q3-Nichirin-BW" rel="noopener noreferrer" target="_blank">CADDi Inc.</a>, the global developer of a manufacturing-exclusive AI data platform, announced today that Nichirin Tennessee Inc. has implemented CADDi Drawer to modernize its operations and reduce reliance on individual expertise. Established in April 2002, Nichirin Tennessee is the U.S.-based manufacturer of brake and power steering hoses for use in passenger cars and motorcycles and one of the U.S. subsidiaries of <a href="https://www-nichirin-co-jp.translate.goog/corporate?_x_tr_sl=ja&amp;_x_tr_tl=en&amp;_x_tr_hl=en-US" rel="noopener noreferrer" target="_blank">Nichirin Co., Ltd.</a>, a century-long leader in automotive hoses for global OEMs. Nichirin Tennessee strategically deployed the platform to build a more transparent, accessible, and self-reliant organization under real operating conditions.&nbsp;Manufacturers historically rely on individual experience to access drawings, BOMs, and historical data. This approach slows onboarding, delays decision-making, and limits how quickly teams can respond to customer demand. The new case study delves into how Nichirin Tennessee Inc. addressed this constraint by centralizing access to engineering and commercial data with CADDi, enabling faster execution across teams.Reducing dependency on individual knowledge and manual data retrievalHistorically, accessing drawings, BOMs, and historical data required exact part numbers and a heavy reliance on the "tribal knowledge" of senior Japanese expatriate staff. This concentration of expertise created significant information silos that slowed down onboarding and daily operations for the entire team. In the past, new employees frequently relied on managers to identify relevant data, an approach that was time-consuming for both trainers and trainees.Centralizing access to engineering and commercial data across systemsBy adopting CADDi as an integration layer, Nichirin has successfully democratized its organizational knowledge. By linking drawings, BOM data, quotations, and control cards, the company is ensuring that every department can use CADDi as their single source of truth across disconnected systems.&nbsp;“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,” said Takehiro Ishimoto, Sales Manager at Nichirin Tennessee.&nbsp;Today, any team member can instantly retrieve drawings, BOM data, and necessary historical information using keywords or similarity search, without relying on drawing numbers or individual experience. Information that was once concentrated among a few veteran engineers is now broadly accessible across departments, supporting cross-functional execution across engineering, procurement, sales, and operations.Improving quoting speed and enabling independent execution across teamsThis structured engineering knowledge has significantly reduced the time required to search for information, directly improving quoting turnaround and allowing a focus on higher-value tasks. Furthermore, the project is fostering a cultural shift toward empowerment where employees are encouraged to first search within CADDi, think independently, and take ownership of their work before relying on supervisors.Nathan Yifrach, a member of the sales staff, experienced this firsthand: “I was able to start working smoothly from day one. Even without prior knowledge or detailed instructions, the system was intuitive enough to search and navigate on my own”. By turning individual expertise into scalable knowledge reuse and a shared organizational strength, Nichirin is ensuring that every department can leverage expert insight.&nbsp;The case study, “<a href="https://us.caddi.com/case-studies/nichirin?ref=US-PR-2026-Q3-Nichirin-BW" rel="noopener noreferrer" target="_blank">Democratizing knowledge to empower independence with Nichirin Tennessee</a>” explores how CADDi Inc. and Nichirin Tennessee democratized decades of information, enabling independent work and turning individual expertise into a shared organizational asset.&nbsp;Building on this success, Nichirin is now expanding the initiative to Nichirin Flex USA, spanning manufacturing facilities in El Paso, Texas, as well as Juarez, Chihuahua in Mexico.&nbsp;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 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/company?ref=US-PR-2026-Q3-Nichirin-BW" rel="noopener noreferrer" target="_blank">us.caddi.com</a>.&nbsp;Media ContactMilbien MedinaSenior Manager, Brand Marketing at CADDi USA<a href="mailto:milbien_medina@caddi.com" rel="noopener noreferrer">milbien_medina@caddi.com</a>&nbsp;

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

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