Sumitomo Rubber: Optimizing Manufacturing With Automation & AI

Sumitomo Rubber Industries, Ltd., a prominent player in the global tire and rubber manufacturing sector, is renowned for its commitment to innovation and quality of high-performance tires and rubber products. As a leader in the space, the Japanese company serves diverse markets ranging from automotive to industrial applications. 

As part of its ongoing pursuit of operational excellence, the company has increasingly embraced data-driven solutions to enhance its manufacturing processes and improve product quality. 

This commitment to innovation is exemplified in their recent initiative to optimize the vulcanization process using AI-driven solutions, specifically Dataiku. 

Process Hurdles: Inefficiencies & Quality Control Challenges

The tire manufacturing process faced several key issues prior to this transformation, starting with manual inefficiencies. Notably, the previous system did not support real-time data processing.

Operators were required to manually input production data and determine the optimal manufacturing phase by analyzing reports, which was both time-consuming and reduced operational efficiency.
This manual approach also increased the risk of human error, as determining the correct vulcanization phase required comparing multiple reports. This heightened the likelihood of misreading data or making calculation errors, which in turn made it difficult to maintain consistent product quality and created a significant challenge to maintain high manufacturing standards across the board.

From Manual to Automated: Enhancing Manufacturing With Dataiku

Discovering the benefits of Dataiku, Sumitomo devised a solution. This project aimed to enhance efficiency and accuracy of the manufacturing process by automating several manual steps, including key calculations, using a system built around Dataiku. 

Key improvements from the AI system included the automatic removal of anomalous data and the use of wavelet averaging methods to calculate the best manufacturing parameter. 

The impact of this use case was significant. It streamlined the entire production process by reducing manual data input and report generation, resulting in notable improvements in product uniformity. A comparison between traditional methods and the AI-optimized vulcanization process revealed that 80% of product sizes tested showed enhanced consistency!

The system also seamlessly integrated with another tool utilized by Sumitomo — the Thingworx platform — to provide real-time feedback to ensure that production adjustments were immediately applied based on the calculated optimal parameters.

The Lasting Impact From the Project

Leveraging Dataiku’s capabilities, the company not only improved its core manufacturing process but also demonstrated how AI drives significant enterprise-wide operational improvements within demanding industrial settings.

Dataiku allowed the organization to democratize access to data, empowering non-technical team members to contribute to analytics projects, fostering a more collaborative and data-driven culture across departments. Additionally, the system reduced the cognitive load on operators, allowing them to shift their focus to more strategic tasks.

One of the key changes is the speed at which we can derive insights from our data. Dataiku’s ability to streamline workflows, integrate various data sources, and leverage machine learning models means that insights are generated faster and with greater accuracy. This has improved our decision-making process across departments, allowing for more informed and timely business actions. Shuichi Kaneko Data Product Manager, Sumitomo Rubber

Dataiku has significantly boosted the organization’s efficiency and improved decision-making, resulting in an estimated business value increase of $75,000 to $100,000. It has also fostered a more inclusive approach to data, empowering teams and paving the way for future AI-driven innovations at the leading global manufacturer for years to come.