Leading Tire Manufacturer Reduces Equipment Troubleshooting Time with C3 Generative AI

Introduction

With over 125 years of history, a U.S.-based tire manufacturer is a global leader in producing high-performance tires for a wide range of industries, including passenger vehicles, commercial trucks, aviation, military, and heavy off-road machinery. With over $20 billion in annual revenue and a workforce of over 70,000 employees, the company operates over 50 manufacturing facilities across 20+ countries and maintains a vast network of over 1,000 auto and tire service centers. The company’s success relies on the seamless operation of its manufacturing equipment, which plays a critical role in production efficiency, quality control, and overall business continuity.

Downtime in key manufacturing processes can result in production halts lasting over eight hours, causing significant financial and operational impacts. With millions of dollars in revenue at stake, reducing equipment failure and maintenance delays is essential to sustaining output levels, meeting customer demand, and maintaining competitive advantage. The company implemented AI-powered maintenance solutions, integrating over 1,000 documents and live SAP work order and spare parts data to consolidate sources of truth and streamline troubleshooting.

The solutions accelerated speed to insight, resulting in increased technician efficiency. The initiative reduced machine repair times by up to 50%, vastly improving uptime and operational resilience, minimizing costly disruptions, and, most importantly, ensuring continuous production flow.

Challenges

The manufacturer experienced prolonged and costly equipment downtime, often exceeding eight hours, when troubleshooting critical tire manufacturing assets. These assets were mission-critical to production, directly impacting output, quality, and revenue. With millions of dollars at stake, any disruption in these processes led to financial losses, supply chain inefficiencies, and customer delays.

The troubleshooting process was slow and highly manual, relying on technician experience, legacy knowledge, and fragmented documentation. Maintenance planners had to prioritize issues, while technicians spent valuable time parsing through disparate data sources such as work orders, equipment manuals, and technician notes to identify and resolve problems.

Compounding this challenge, the company faced its highest technician attrition rate in over a century, further eroding institutional knowledge and increasing the risk of prolonged downtime. To maintain operational efficiency and ensure consistent, high-quality production, the manufacturer needed to modernize its maintenance and troubleshooting approach, enabling technicians to access critical insights faster, reduce repair times, and minimize revenue losses associated with unplanned downtime.

Solution

To address these challenges, the manufacturer implemented C3 Generative AI for Equipment Troubleshooting, an AI-driven application designed to streamline the review and analysis of troubleshooting documents. By leveraging large language models and enterprise data integration, the solution enabled technicians to quickly diagnose machinery issues, reducing the time spent searching through work orders, equipment manuals, and technician notes.

C3 Generative AI for Equipment Troubleshooting ingested over 1,000 unstructured and structured data sources, including technician notes, equipment manuals, SAP work order data, and spare parts data. With instant access to critical insights through an intuitive search and chat interface, technicians could resolve equipment failures faster and more efficiently.

This solution significantly reduced downtime in key processes, enabling improved production efficiency, enhanced technician productivity, and ensured consistent, high-quality tire manufacturing while minimizing financial losses associated with unplanned downtime.

Results

50% Reduction in Average Time to Repair Machine Downtime
The AI-powered solution streamlined troubleshooting and maintenance workflows, cutting the average repair time in half and minimizing costly production delays.

Improved Technician Efficiency and Knowledge Retention
With instant access to over 1,000 critical maintenance documents, including equipment manuals and technician notes, and connections to SAP work order and spare parts data, teams could resolve issues faster and reduce dependence on legacy knowledge, mitigating the impact of high attrition rates.

About the Company

• Leading U.S.-based tire manufacturer
• ~$20B in annual revenue
• Global customer base, operating in 55 manufacturing facilities in 20+ countries
• 72,000 employees worldwide
• Provides tires for a wide range of vehicles from motorcycles to RVs

Project Highlights

• Achieved 90% answer accuracy, ensuring confidence in AI-generated responses across 1,000+ ingested documents
• Configured a live data ingestion pipeline to SAP work order data, enabling real-time access to facility issues
• Established 20+ structured data archetypes for SAP work order and spare parts data, enhancing search precision
• Conducted weekly engagements with technicians and engineers to continuously refine the application
• Led onsite user acceptance testing (UAT), where 100% of survey respondents found the application easy to use