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 down onboarding and daily operations for the entire team.

Nichirin

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.This shift has significantly improved the onboarding process. New team members can locate technical specifications and past customer relationships without prior context. In the past, new employees frequently relied on managers to identify relevant data—an approach that was time-consuming for both trainers and trainees. Today, many of these tasks are handled independently.

As a result, Nichirin has significantly reduced the time required to search for information, directly improving quoting turnaround and allowing a focus on higher-value tasks. More importantly, the project is fostering a cultural shift: instead of immediately relying on supervisors, employees are encouraged to first search within CADDi, think independently, and take ownership of their work.

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, turning individual expertise into shared organizational strength. Building on this success, Nichirin is now expanding the initiative to Nichirin Flex USA, spanning manufacturing facilities in El Paso and Mexico.Nichirin’s initiative demonstrates how digital transformation begins with people—with the willingness to rethink existing workflows and the determination to turn individual knowledge into shared organizational strength. As the company continues to scale this model across its global operations, it is setting a strong example of how to build a truly connected, data-driven manufacturing organization.

Any team member can now instantly retrieve drawings and related technical or commercial data via keywords or similarity search, regardless of tenure. This has democratized information, enabling independent work and turning individual expertise into a shared organizational asset.

Democratizing knowledge to empower independence with Nichirin Tennessee

60 years worth of data was spread across many different systems, requiring a major time investment to use. Locating prior work, reference designs, or application context often required manual effort, limiting how quickly teams could build on existing expertise.

Sumitomo Drive Technologies

SDT discovered CADDi while searching for ways to reduce this search time. After loading up their historical data into CADDi’s system, our manufacturing-specific AI was able to centralize them into one searchable, organized database.&nbsp;The benefits were dramatic and immediate. What used to be a tedious, many-stage search process across many systems has become a speedy singular search in CADDi. SDT estimates that the new searching process is 90% faster than before.

When new orders come in to SDT, they’re often complex, requiring a lot of time spent to parse requirements and develop a production plan. The design pre-planning can be a significant contributor to lead time. However, if a similar project was produced in the past, many decisions can be carried over, resulting in huge time savings. The problem is that tracking down the similar project often took longer than the time it saved: searching through multiple systems, correlating different pieces of data, and manually checking if the previous designs overlapped enough.With CADDi, things are different. Similarity searching on designs can instantly surface past designs that closely match new orders. This allows for a huge head start in fulfilling the new order, saving weeks of design work, shorting lead times for customers, and increasing the capacity of the shop.When time is saved in these areas, it can be spent on high value problem solving instead. Engineers can work more strategically, innovating and iterating on past lessons, rather than having to constantly reinvent the wheel. SDT has increased productivity on big challenges, such as product innovation, global standardization, and design revision.SDT plans on expanding their usage of CADDi to assist in their design review process, allowing for continuous improvement without sacrificing rigor or efficiency.

As SDT got up to speed with CADDi, they realized that this wasn’t just a tool to add to their process, or even just the basis of a new process. It was an entirely new way to think about their data. Things were possible now that hadn’t been imaginable before.In order to ensure that SMA was able to get the most out of CADDi, our customer success team worked with them to discover opportunities for deeper utilization and change.Want to see how CADDi can unlock valuable time for your organization? Sign up for a free demo today.

With over 50,000 documents and drawings centralized with CADDi, design engineers could find any reference material with ease, opening up time for high-value tasks and process transformation

Transforming “Searching” into “Solving” with Sumitomo Drive Technologies

Shibuya Hoppmann’s aftermarket team fields many requests from customers for parts and enhancements. These requests have commonalities between new requests and previous designs. Finding past designs to reference, though: with hundreds of thousands of past designs, traditional search methods can be time consuming and inconsistent.

Shibuya Hoppmann

Shibuya Hoppmann’s aftermarket team fields many requests from customers for parts and enhancements. These requests are inherently going to have unique aspects, as each customer’s setup and aftermarket needs are different. However, there will also be commonalities between new requests and previous designs. Finding relevant past designs is no easy task, however: with hundreds of thousands of past designs, traditional search methods can be time consuming and inconsistent.CADDi streamlines and accelerates the process of surfacing the most similar past designs with AI-empowered search tools. It can find drawings based on text contained on the drawing, any metadata associated with the drawing, or even the shape of the drawings themselves. Once similar drawings have been found, CADDi automatically links relevant data such as design revisions and notes, production information, and quality reports. This makes it easy to design new aftermarket requests while incorporating all the lessons and best practices learned from previous designs.One aftermarket team member summarizes the revolutionary change of CADDi: “Before CADDi, it could take up to an hour to find a reference drawing from a customer inquiry, whereas with CADDi, it takes only a few seconds... I haven't not been able to find what I need yet.”

Alongside the aftermarket engineering team, the Shibuya Hoppmann sales team sees benefit from using CADDi to accelerate their quoting process. Using CADDi’s robust search options, the sales team can take whatever data is provided with the RFQ and find the most similar previous projects. CADDi links historical sales data for each drawing, instantly providing a starting point for making an accurate estimate quote.The aftermarket and sales team can use CADDi to help collaborate to get to the most accurate quote quickly. By proactively sharing information like customer expectations, previous design revisions, and performance reports, engineering and sales teams can move synchronously to produce an accurate estimate and finalized design quickly and reliably.Shibuya Hoppmann is excited to continue integrating CADDi into their teams and workflows. As more data is processed into the system and more connections are formed, their efficiency will only continue to increase.

CADDi streamlines and accelerates the process of surfacing the most similar past designs with AI-empowered search tools. It can find drawings based on text contained on the drawing, any metadata associated with the drawing, or even the shape of the drawings themselves.

Shibuya Hoppmann: Strengthening Aftermarket Value with CADDi

DCC faced pressure to reduce spend on outsourced and fabricated parts, launching a company-wide initiative. However, data was siloed and not easily accessible, making it difficult to make informed buying decisions.

Dairy Conveyor Corporation

Since its founding in 1954, DCC Automation / Dairy Conveyor Corp.&nbsp;has become a trusted name in hygienic and performance-driven automation. The company designs and manufactures high-quality conveyor systems, robotic palletizers, custom control panels, and end-of-line packaging solutions. DCC’s Evolution Line featuring the Auto-Pack Caser, Round Bottle Caser, and Slant Caser demonstrates its commitment to precision, cleanliness, and flexibility. Each system is engineered to meet the diverse needs of today’s dairy, food, beverage, and household industries. With recent recognition such as the 2024 Rockwell Automation PartnerNetwork™ OEM Innovation Award, DCC Automation continues to redefine performance standards and drive progress across the global manufacturing landscape.Their key projects, including palletizers and casers, often involved up to 800 separate line items, resulting in a lengthy procurement process. External factors further complicated this process, making efficiency a challenge. In the modern era of supply chain disruption and complexity, DCC recognized the need to re-evaluate their procurement costs. Factors such as geopolitical relations, ongoing and upcoming tariffs, and material shortages can make previously viable purchasing strategies less sustainable, prompting a strategic re-evaluation.Unfortunately, making these new procurement strategic decisions requires a lot of experience and expertise. DCC found that the required knowledge was inadequately distributed among different teams, ending up in silos and known only by specific individuals. Existing data management structures, like ERP tools or Solidworks, made the data technically available, but not easily accessible. Different teams working in different systems had a hard time sharing insights and information.On top of this, a specific initiative in one of DCC’s branches was to consolidate suppliers based on expertise. This is a complicated procurement initiative that requires a lot of manual cross-referencing and expertise – knowing where to find categories of component parts that are similar enough, and finding the ideal quality-price tradeoff point for each category. Processes such as these, that require specific experts to track down data, slow the entire company’s progress towards their goals by taking these people away from other valuable work. The most valuable procurement experts were being stretched too thin.

Fortunately, this problem, very typical in modern procurement departments, has a modern solution: CADDi. By implementing CADDi, DCC achieved a reduction in fabricated part spend of 22%, achieving the target of their initiative.Monica Merando, Director of Purchasing at DCC discovered CADDi while researching cost reduction strategies. She searched online for tools that could help consolidate suppliers and find similarity across parts in an automated and consistent way. This searching led her to CADDi’s case studies. On the first introduction call, Monica illustrated that her company relied on vendors to provide non-standard cost guidance for a wide variety of parts. This amplified the cost differential between similar parts with little rationale for the variance, leading to reduced profitability and longer lead time for full custom machines. CADDi solved these discrepancies by linking data and making it searchable.After CADDi was adopted, the process of evaluating suppliers became a far more automatic initiative, even for new employees. Junior buyers at DCC could start making more impactful decisions without relying on the expertise of a few people. Monica Merando, Director of Purchasing, used to personally review each print and recommend a supplier. With CADDi, she can focus on more value-added tasks, spending less time on answering questions and tracking down data.And after a year of working with CADDi, cost optimization has been even more significant. By using CADDi to extract material information from drawings and generate reports, Monica can group parts and make requests from vendors in a way that minimizes the required sheet metal, leading to less scrap and lower costs. Compared to her total spend in 2024, she was able to reduce total spend in 2025 by 49% – an especially impressive accomplishment considering that machine build volume was 65% higher in 2025 compared to 2024. <h5 class="">"By using CADDi Drawer, I have reduced my workflow time by over 80%- what once took an entire week can now be completed in a single afternoon, giving me more time to focus on other projects."</h5><h6 class="">- Monica Merando, Director of Purchasing</h6> This massive unlock in efficiency reduces the time it takes to onboard new people into the procurement department. Katrina, a junior member of the procurement team, has already made over 702 keyword searches with CADDi. Each of these searches would ordinarily require digging through ERP systems manually, with each search taking an order of magnitude longer. A search process that would have taken 40 hours before now takes only 4.After a year of working with CADDi, the procurement team’s search efficiency has only increased. Monica estimates that roughly 12000 searches were required to procure parts for the year’s operations. CADDi has reduced the time of each search by 98%, saving a total of almost 600 working hours for the team. <h5 class="">"It makes my life so much easier, I can sit at my desk and look at CADDi instead of running around with a tape measure."</h5><h6 class="">- Danny Harris, Expediter</h6>

CADDi achieves these goals by connecting data across tools and silos, and surfacing it with more intuitive searches. It functions as a <a href="https://us.caddi.com/resources/insights/manufacturing-data-lake" rel="noopener noreferrer" target="_blank" class="">data lake</a>, taking data from all sources and connecting it. DCC uses this to find past order pricing, quality data, and design data for any given part simultaneously, rather than requiring manual cross-referencing.For example, here’s a common task that was vastly accelerated by CADDi. Monica needs to proactively negotiate with suppliers to ensure optimal profitability. This required surfacing and comparing hundreds of individual orders for each project, an exercise that would typically take five business days. With CADDi, Monica can now just paste a list of IDs into the advanced search function and instantly get a CSV file with all of the old order information. She could skip immediately to the analysis and evaluation stage, reducing this exercise from a whole business week to a few hours. With the time she saved on information gathering, she was able to implement a brand-new inventory barcode system. Automating low-value tedious tasks led to compounding efficiency and accelerating high-value additions.Other high-value tasks that can be difficult to focus on proactively can be unlocked with CADDi. Supplier consolidation becomes far easier. Using cutting-edge machine vision technology, CADDi can automatically group together similar parts using the design of the part itself. You can search with one part drawing to find all the other similar part drawings in your system. <img src="https://cdn.builder.io/api/v1/image/assets%2F209ee507cedf4c298e100edae4bba2df%2Ffa9042ef947b43aaac10e22af9c60921?format=webp" class=""> When DCC launched their initiative to consolidate suppliers for their caser production in Minnesota, they first needed to understand which components were currently coming from each supplier. That is, they needed to know which suppliers were providing wheels, which were providing housings, etc. and what prices each supplier was quoting for each type of part. Before CADDi, this would be an extremely time consuming task, requiring a lot of expert judgment. There would be no way to simply search for “housing” or “wheel”. It would require manual sorting to categorize correctly.DCC implemented CADDi in their initiative for supplier consolidation in caser production. This allowed them to categorize parts into these categories automatically, just by the components of the drawing. CADDi also automatically surfaces all of the relevant information for each part to decide on supplier – previous costs, quality data, design revisions, and more. <h5 class="">"By digitizing and organizing our design data, CADDi enables us to improve team collaboration across sites and streamline the procurement process.</h5><h5 class="">This partnership positions us to more effectively manage costs and elevate our efficiency across the board."</h5><h6 class="">- Monica Merando, Director of Purchasing</h6> ‍Procurement will likely only increase in complexity as tariffs and shortages complicate existing plans. With CADDi, DCC has the fast and consistent access to data required to make the right changes, fast.Monica is excited to continue implementing CADDi across DCC. By integrating it into engineering and aftermarket teams, and adopting the new CADDi Quote, she anticipates even more success in improved efficiency and cost reduction.

CADDi allows knowledge to be shared across all departments, searchable without requiring specific expertise. Data that used to take a whole afternoon to track down can now be found in minutes. Major opportunities for cost savings were discovered through analysis in CADDi.

Driving company-wide cost savings at DCC with CADDi

As the company expanded quickly, the large workload overwhelmed the staff and negatively impacted work quality. The team had minimal dissemination of knowledge, and their manual effort to analyze drawings at scale failed.

Kawasaki Robotic Industries

Ono Takushi

Sakai Koichi

Hayashi Kazuki

‍Kawasaki Robotics, a comprehensive engineering manufacturer, operates in a wide range of businesses, including ships, railway vehicles, motorcycles, industrial plants, precision machinery, and robots. In 1968, Kawasaki Robotics developed and began manufacturing the first domestically produced industrial robot in Japan, establishing itself as a pioneer in Japan's industrial robot sector. The Robot Division of the Precision Machinery &amp; Robot Company, which is the core of the robot business, has been engaged in the development, design, manufacturing, and sales of various industrial robots for industries such as the automotive and semiconductor sectors. In recent years, the company has also ventured into medical robots for surgical assistance. As the robot market has grown, the sales scale of the Robot Division has expanded annually, with a target to quadruple the current sales by 2030 as part of the Group Vision 2030. With the expansion of sales and the organization, the Robot Division faced various challenges. One of the major issues was the need to improve productivity per employee in response to business growth. The allocation of personnel could not keep up with the business expansion, resulting in a continuous increase in the workload for each employee. In such a situation, standardizing knowledge and promoting horizontal dissemination were often delayed, leading to increased dependence on individual employees. The dependence on individuals caused various problems in operations. For instance, in the design department, the lack of utilization of past knowledge led to significant differences in design outcomes among designers. Similarly, in the procurement department, there were disparities in procurement prices and quotations depending on the buyer, resulting in inconsistencies in work quality. To address these issues, the Robot Division began various initiatives, such as trying to standardize operations by creating manuals. In the procurement department, a system was internally developed using Excel to calculate reasonable costs by inputting information from drawings. Takushi Ono"When creating the system, we categorized representative drawings into product groups such as sheet metal parts, machined parts, and purchased parts, and grouped similar items. We then extracted parameters that affect prices, such as shape characteristics, materials, and processing methods, from these drawings. Based on this, we developed a calculation algorithm. The system was created by about seven or eight people. Each person reviewed around 1,000 to 2,000 drawings, so in total, we reviewed approximately 10,000 drawings." The system, developed over about a year, helped reduce price variations and standardize work quality, although the degree of success varied depending on the type of parts. However, as time went on, the system gradually began to encounter problems.‍

The costs of raw materials and processing, which affect the prices of parts, change over time. The system required continuous updates to these price-influencing numbers, but manually updating them became a significant burden. Additionally, the system struggled to handle complex machined parts with many input parameters, leading to a gradual narrowing of the system's usage scope. This necessitated the consideration of a new system that could be used continuously with minimal operational burden. The Robot Division, feeling the limitations of internally developed and manually operated systems, began exploring various external systems. It was during this exploration that they were introduced to CADDi Drawer, a cloud-based drawing data utilization service. CADDi Drawer had already been implemented in the vehicle design department of Kawasaki Railcar Corporation, another group company. Although they were somewhat familiar with the service, the Robot Company had initially hesitated to consider it because they envisioned its use in procurement rather than design, making it difficult to imagine the potential benefits and effects of its use. Koichi Sakai"I was attracted to the idea of 'assetizing drawing data' when I heard the explanation. There are often similar parts, but the drawing methods can differ. CADDi DRAWER's similar item search feature can mechanically detect these differences. You can link drawings to data, allowing you to refer to past unit prices while looking at the drawings. It can also extract data using OCR from drawings. We had struggled significantly with verifying similar drawings and extracting information when we were developing internally. I thought CADDi DRAWER would make such tasks much easier. <h5 class="">"CADDi DRAWER's similar item search feature can mechanically detect differences in drawing methods. You can link drawings to data, allowing you to refer to past unit prices while looking at the drawings.”‍</h5><h6 class="">- Koichi Sakai, General Manager</h6> Robots tend to have many similar products. The ability to handle and view such data with CADDi DRAWER is essential for the future. Also, I felt that with this system, even if the organization expands and new people join, they can naturally carry out their tasks." After receiving the explanation, the Robot Division conducted a trial using actual drawings they typically use and checked the usability with a project team that included both management and field members. As a result, they confirmed that they could find the desired drawings with intuitive keywords, anyone could view past similar drawings and procurement information with one click during quotations, and data collection for the integration and standardization of similar parts was easy. This allowed them to envision the actual use of the system in their operations, leading to the decision to implement it.

The preparation for implementation progressed at an astonishing speed. In the past, it took at least six months to a year to implement a system in the Robot Division, but this time the implementation was achieved in about three months. Kazuki Hayashi"The response speed was incredibly fast, allowing us to communicate without stress. When the response is quick, internal communications also proceed swiftly, creating a synergistic effect for both parties. Furthermore, CADDi's Customer Success team worked alongside us to organize the current challenges in our operations and discuss how to proceed with solving them, enabling us to move from contract to implementation at an unprecedented pace." <h5 class="">"The response speed was incredibly fast, allowing us to communicate without stress. When the response is quick, internal communications also proceed swiftly, creating a synergistic effect for both parties.”</h5><h6 class="">- Kazuki Hayashi, Procurement Section 1</h6>‍Not only during implementation, but CADDi also provides extensive support after implementation. Takushi Ono"The Customer Success support structure was very attractive. Simply introducing the tool does not lead to its adoption. It requires a lot of effort to expand it with only internal members. Many cases have ended without fully utilizing the tool. CADDi, however, does not just sell the tool; they provide solid support and help integrate it into our actual operations. The consulting-like cooperation after implementation was a significant advantage. We hope to continue working together towards achieving the business goals set out in our Group Vision 2030." <h5 class="">"CADDi provides solid support and helps integrate the tool into operations… This consulting-like cooperation was a significant advantage for achieving our Group Vision 2030."</h5><h6 class="">- Takushi Ono, Senior Staff, Procurement Department</h6>‍ Currently, the Robot Division mainly uses CADDi DRAWER in the procurement department, but they are considering expanding its use in the future. The plan is to extend its application to the design department after mastering its use in the production division responsible for procurement. Ultimately, they aim to expand its use throughout the entire company to create synergies. By leveraging CADDi DRAWER, they plan to improve production efficiency, establish a solid foundation for business operations, and achieve further business expansion.

With CADDi DRAWER, it became possible to quickly search for similar past projects and purchase records. CADDi allowed the team to establish a structure capable of handling the impending business expansion.

Operational Speed Increased, and a System for Delivering Consistent Results Was Established with CADDi

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