The automotive industry is experiencing rapid growth, and with it comes increasing pressure to maintain equipment reliability, reduce maintenance costs and ensure consistent production quality. For large automotive enterprises, traditional approaches to equipment management and maintenance are no longer sufficient. The key challenges include:
- Technical challenges in maintenance: As technology advances at a fast pace, maintenance workers are required to constantly update their skills and invest significant time in accessing and interpreting equipment data. These requirements lead to inefficiencies, lower job satisfaction and decreased employee retention.
- High maintenance costs: Automotive equipment is complex, and diagnosing faults requires specialized knowledge. These issues increase the demand for skilled personnel, raise labor costs and limit the flexibility in workforce allocation.
- Inconsistent maintenance quality: The reliance on personal experience for maintenance tasks results in varying outcomes, making it difficult to diagnose issues accurately and maintain consistent equipment performance. This inconsistency negatively impacts production quality and reduces equipment lifecycle stability.
To address these challenges, a major joint venture automotive enterprise in China sought an integrated solution that would automate, digitize and standardize their maintenance processes. The goal was to enhance efficiency, reduce costs and improve user satisfaction while establishing a robust framework for enterprise asset management.
AI-powered tools drive efficiency and productivity
Leveraging the AI-powered capabilities of the IBM® Maximo® Application Suite, Shuto Technology, in collaboration with the IBM Client Success and Lab teams in China, developed a comprehensive solution to enhance maintenance operations. This integrated solution combines intelligent knowledge mapping, AI-driven maintenance diagnostics and augmented reality (AR)-powered remote assistance to provide an AI-enhanced enterprise asset management (EAM) assistant for the automotive enterprise.
Key features of the solution include:
- Intelligent knowledge graph: Using AI, the Maximo solution creates a dynamic knowledge base that gives maintenance teams a quick way to locate critical equipment data, drastically reducing time spent on manual searches.
- AI-driven maintenance diagnosis: Advanced AI models perform fault analysis, offer fault probability assessments and continually improve through machine learning (ML), helping ensure faster and more accurate diagnostics.
- AR remote maintenance assistance: Augmented reality tools provide real-time, interactive expert guidance, enhancing troubleshooting accuracy and facilitating seamless remote collaboration.
This combination of AI and AR empowered maintenance teams with a comprehensive suite of tools, reducing mean time to repair (MTTR) by 25% and increasing overall efficiency. By seamlessly integrating with existing systems, the solution builds a unified, scalable ecosystem for managing and optimizing maintenance processes across the enterprise.
Improved efficiency, empowered teams and sustainable growth
The deployment of IBM Maximo Application Suite with its intelligent maintenance assistant, IBM Maximo Assistant, has delivered substantial benefits, optimizing maintenance operations and driving efficiency:
- Enhanced information acquisition efficiency:
IBM Maximo Application Suite has streamlined access to maintenance data, replacing manual searches with intelligent data positioning, improving transparency, standardizing workflows and strengthening corporate knowledge management.
- Reduced repair time:
AI-driven fault diagnosis models accurately identify root causes and offer actionable solutions, cutting MTTR. IBM Maximo Assistant also provides relevant repair documentation and best practice recommendations, improving troubleshooting efficiency.
- Enhanced integration with existing IT systems:
IBM Maximo Application Suite integrates seamlessly with the client’s existing systems, generating dynamic maintenance knowledge graphs and helping ensure timely, accurate data reflection within the asset management platform, fostering greater system connectivity and data sharing.
- Creation of the “four-pool” knowledge system:
The “four-pool” knowledge system—equipment, course, talent, expert pools—supports ongoing training and skill development, improving employee competence and providing performance-based incentives for continuous improvement.
Looking ahead: The solution has already reduced average repair time from 13 minutes to 9 minutes at the automotive enterprise. Moving forward, Shuto Technology and IBM will expand the solution’s capabilities by integrating with additional systems, such as production and Internet of Things (IoT) platforms, to further optimize data sharing and improve cross-regional maintenance.
Beijing Shuto Technology Co., Ltd. (link resides outside of ibm.com) is a leading provider of EAM solutions, based in Beijing, China. The company specializes in delivering advanced technological solutions to industries requiring efficient and intelligent maintenance systems, including automotive, manufacturing and industrial sectors. Founded in 2005, Shuto Technology has become a prominent player in the field, with a strong focus on integrating AI, IoT and AR technologies into maintenance workflows. The company serves a diverse range of clients, enhancing operational efficiency and helping organizations optimize asset management. With a team of over 1,000 employees, Shuto Technology continues to drive innovation and transform traditional maintenance practices for enterprises globally.
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Examples presented as illustrative only. Actual results will vary based on client configurations and conditions and, therefore, generally expected results cannot be provided.