Unlocking the Potential of Hidden Drawings and Purchasing Data: Achieving Unexpected Cost Reductions

Cost Reduction Team Solving Issues in Design, Procurement, Manufacturing, and Cost Planning

Ever since it began manufacturing in 1906, Kawasaki Railcar Manufacturing Co., Ltd. has been at the forefront of technology, playing a significant role in the evolution of railway mobility. With exceptional technical prowess, high-quality production, the company has contributed to the advancement and modernization of railway vehicles, producing several iconic railcars. Their railcars play a vital role in urban transit systems, serving cities such as New York and Washington.

 

As a renowned railcar manufacturer, the company adopts a custom-production approach, designing railcars according to individual railway companies’ needs. While this offers flexibility, it also means a proliferation of parts, making efficiency harder to achieve. Consequently, optimizing procurement, standardizing designs, and reducing costs became critical management issues. To address these, the Technical Control Department DTC Promotion Division was established.

 

Established in 2020, while the company was a division of Kawasaki Heavy Industries, Ltd., DTC, an acronym for “Design to Cost,” was introduced. As the name suggests, its mission revolves around cost reduction from the design stage, acting as a task force to resolve issues from a VE/VA perspective, as explained by Mr. Shiraishi of the division. The division attracts experts from a broad spectrum, including design, cost planning, material procurement, and manufacturing, focusing on price strategy in cost planning and management.

 

Mr. Ashida, in charge of optimizing parts procurement, emphasized that project teams are assembled for each customer project. These teams create designs based on project specifications, including assembly and parts drawings. Although designers sometimes consult past similar drawings, the current management system lacks flexibility in retrieving historical drawing and pricing data, resulting in less than optimal design choices and increased costs.

Bottleneck in Cost Reduction: "Unable to Find the Necessary Drawings"

At this juncture, they received a proposal for the CADDi Drawer, a cloud solution for leveraging drawing data, from their long-time business partner, CADDi. The platform can automatically interpret and analyze characters and shapes from drawing data like PDFs and TIFFs. Once the company’s drawing data is uploaded to the cloud, based on this information, processing methods, materials, and other user-friendly keywords can be freely searched. Furthermore, it’s possible to instantly refer to past drawings with similar shapes. If order history data is also uploaded, it can be linked with the drawings.

 

The system was adopted in August 2022, shortly after its release. According to Mr. Shiraishi, the pivotal factor behind this decision was to address the specific challenge of not being able to locate relevant past drawings when required for various vehicle models.

 

The company had previously introduced a system to manage drawing data. However, this system didn’t allow text-based search on drawings. The keyword for searching was limited to part numbers because data filenames were managed by part numbers. Furthermore, for overseas projects, part numbers changed with every modification. This made decisions on repeats difficult, and it was hard to trace back to the original drawing.

Optimized Procurement Operations Through Utilization of Drawings & Order Data

The biggest challenge arising from the inability to refer to past drawings and order history data was the ballooning costs in both design and procurement. Designers, when trying to refer to reused drawings, would often rely on their memory. If they couldn’t find the needed drawing quickly, some would decide it’s faster to create a new one, leading to an increase in new drawings.

 

In procurement, previously, even if another colleague in the company had ordered a similar part in the past, the current person responsible for the order might find it challenging to access that information. Thus, order operations often depended on the individual’s experience and insights, determining the supplier and order price. Mr. Ashida recalls, “There were instances where orders for even simple parts were placed with suppliers specializing in high-precision processing, leading to increased prices. Especially in overseas factories, frequent changes in order personnel made such occurrences more common.”

 

However, with the introduction of CADDi Drawer, signs of resolution are emerging. One example is the optimization of procurement operations. In the CADDi Drawer, order information (like supplier names and prices) can also be automatically linked to each drawing. When making ordering decisions for new drawings, it’s possible to quickly find similar drawings from accumulated data, referring to relevant order history data, such as the price.

 

With this system, anyone can instantly access past similar drawing orders, and there’s no longer a need for the individual to rely on their personal judgment or memory. Mr. Shiraishi states, “The number of cases where we end up ordering from a high-priced supplier has dropped considerably. Even if there’s a personnel change, we can always choose the most appropriate supplier.”

 

The next goal is to connect CADDi Drawer with the company’s ERP system, which will enable a more seamless and efficient process. They’re also considering introducing it to overseas bases to standardize procurement operations globally.

 

In summary, Kawasaki Railcar Manufacturing Co., Ltd. is taking progressive steps to harness the power of its data, unlocking cost reductions that were once unrealized. The journey has begun, and the road ahead looks promising.