Shibuya Hoppmann

Shibuya Hoppmann: Strengthening Aftermarket Value with CADDi

Case Study

Shibuya Hoppmann: Strengthening Aftermarket Value with CADDi

Shibuya Hoppmann

Established
1955
Number of Employees
Sales
Business Activities
Centrifugal feeding systems, bottle filling and capping systems, and other industrial packaging machines.

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Before

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.

After

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 Hoppman is a leading manufacturer of centrifugal feeding systems, bottle filling and capping systems, and other industrial packaging machines. Their business model required managing hundreds of thousands of design drawings and other data assets, some stretching back to when only paper records existed. They wanted a way to better utilize these assets: reducing costs through standardization and optimization, and reducing unnecessary work to improve lead times.

We’re proud to announce that CADDi was chosen as a powerful platform for achieving these goals. CADDi’s AI-powered search, analysis, and organization functions allow Shibuya Hoppmann’s past data to become a true digital asset.

Optimizing Aftermarket Value by Using CADDi for Design

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.”

Accelerating Quoting with CADDi in Sales

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.

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Shibuya Hoppmann: Strengthening Aftermarket Value with CADDi

Shibuya Hoppmann: Strengthening Aftermarket Value with CADDi

Before

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.

After

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 Hoppman is a leading manufacturer of centrifugal feeding systems, bottle filling and capping systems, and other industrial packaging machines. Their business model required managing hundreds of thousands of design drawings and other data assets, some stretching back to when only paper records existed. They wanted a way to better utilize these assets: reducing costs through standardization and optimization, and reducing unnecessary work to improve lead times.

We’re proud to announce that CADDi was chosen as a powerful platform for achieving these goals. CADDi’s AI-powered search, analysis, and organization functions allow Shibuya Hoppmann’s past data to become a true digital asset.

Optimizing Aftermarket Value by Using CADDi for Design

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.”

Accelerating Quoting with CADDi in Sales

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.

Ready to see CADDi Drawer in action?
Get a personalized demo.

Book a Demo

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Modern stumbling blocks for procurement

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Since its founding in 1954, DCC Automation / Dairy Conveyor Corp. 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.

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