Introduction
In an era where technology is rapidly advancing, automating traditional processes is essential for efficiency and competitiveness. A leading tyre manufacturing company is pioneering this transformation, focusing on improving the warranty claims process for bus and truck tyres.
Objective
The project’s primary goal is to automate the classification of tyre defects. This advancement aims to accelerate and assist the warranty claims process, which is currently burdened by manual methodologies. Integrating an AI-based engine into the existing portal is envisioned to streamline claim processing, leading to quicker and more accurate resolutions.
Problem
The existing warranty claims process for truck and bus radial tyres involves customers taking damaged tyres to dealerships for manual inspection. Quality Engineers manually review photographs to classify tyre defects or reject claims. This procedure is not only time-consuming but also prone to human error and potential manipulation, hindering efficiency and reliability.
Approach/Solution
To tackle these challenges, the company plans to develop a computer vision-based Deep Learning solution, focusing on two key areas:
- Extraction of the Tyre DOT Code: The system will analyze uploaded images to identify the DOT (Department of Transportation) code, validate it with the user, conduct database lookups for matching records, and allow for manual entry in case of discrepancies.
- Classification of Tyre Defects: Utilizing AI, the system will determine the defect category from the tyre images, advising service engineers or supervisors on whether to approve or reject warranty claims.
Feedback from users on the system’s accuracy will be used for continuous improvement and refinement.
Impact
The implementation of this AI-based system is poised to significantly enhance the accuracy and efficiency of the tyre warranty claims process. Automating defect classification and DOT code extraction will reduce the time required to process claims, benefiting both the company and its customers through improved operational efficiency and faster claim resolution. The learning capability of the system also ensures ongoing improvement in accuracy.
Conclusion
This automation project by a leading tyre manufacturing company is a significant step forward in applying AI and machine learning in the automotive sector. It exemplifies the company’s dedication to innovation and superior customer service. As the system evolves, it has the potential to set new industry standards in warranty claim processing, inspiring similar technological advancements across the sector.
At CoReCo Technologies, our focus lies in utilizing technology to solve real-world issues and add value to end-users. Throughout the solutioning phase, our primary focus remains on problem-solving rather than the technology itself. For us, technology is a means to an end, not the final goal. Additionally, we go the extra mile to find optimal solutions within the given constraints such as cost and time.
As of January 2024, we have served 60+ global customers with 100+ digital transformation projects successfully executed. For more details, please visit us at www.corecotechnologies.com or write to us at [email protected].
Umaima Surti
Solution Architect
CoReCo Technologies Private Limited
