DUNLOP

This solution targets all manufacturing companies and organizations (factories and shop floors) that manage large volumes of data.

Even if there are no major quality issues today, are you facing challenges like these?

◆ Productivity & Efficiency
・Further improve productivity (output, workforce optimization, line efficiency)
・Reduce time spent on reporting and analysis tasks
・Overcome manual processes and reliance on individual expertise

◆ Data Integration & Utilization
・Fragmented data across departments limits effective use
・Time-consuming data search and visualization delays decisions
・Growing data volumes exceed current system capabilities

◆ Data Quality & Standardization
・Inconsistent data (missing values, formats, granularity)
・Lack of standardized tools and formats across teams
Note: This solution enables effective use of diverse and inconsistent data without requiring standardization.

◆ Knowledge Dependency & Adoption
・Reliance on individuals makes standardization difficult
・Challenges in passing on skills and expertise
・Low adoption of AI/tools at the operational level

etc...

This is an integrated analytics platform centered on a data foundation that consolidates operational data across departments and systems.

Data stored in the platform can be searched and analyzed across domains without being constrained by its original source, department, or format, enabling advanced analysis by combining data from quality, production, and equipment.

In addition, generative AI analyzes actual data on the platform, improving the efficiency of analytical tasks such as root cause analysis and report generation.

The platform also offers flexible data integration capabilities, allowing data to be aggregated without changing existing formats or structures, thereby expanding data utilization without disrupting on-site operations.

This solution targets manufacturers of passenger vehicles, commercial vehicles, specialized vehicles, motorcycles, construction and agricultural machinery, as well as other transportation equipment, along with all companies and organizations that utilize vehicle, sensor, and warranty claim data.

Are you facing these quality-related challenges?

◆ Failure Analysis & Predictive Maintenance
・Unable to fully leverage vehicle/equipment data for claims analysis and root cause identification
・Delays in identifying root causes, slowing preventive actions
・Struggling to reduce warranty costs due to limited root cause analysis and early detection
・Need to automate early anomaly detection and failure risk visualization
・Increasing quality and safety requirements demand urgent improvements

◆ Data Integration & Infrastructure
・Fragmented data across systems leads to inefficient analysis
・Lack of data integration limits early detection of quality issues
・No unified global data and quality management
・Data volume growth exceeds conventional processing capabilities

◆ Data Preparation & AI Implementation
・Challenges in building end-to-end capabilities from data prep to AI operations
・Disconnect between domain expertise and data analysis limits practical models
・AI tools are difficult to use and not adopted on-site

◆ Management & Investment Decisions
・Recognize the need for AI and data utilization, but unclear ROI hinders investment decisions

etc...

This solution builds a dedicated AI model to accurately detect early signs of specific failure modes in particular product models and assess the risk of failure occurrence.

Based on our proprietary patented technology, the model analyzes not only individual sensor values but also correlations and behavioral patterns across multiple sensors to calculate failure risk.

This algorithm enables the detection of failure indicators that are difficult to identify using single metrics, resulting in a highly optimized predictive model tailored to each target product.