Production & Maintenance

Maintenance as a Productivity Bottleneck

‍

As part of its broader digital strategy, an automotive manufacturer set out to reduce machine downtime, make maintenance processes more efficient, and unlock hidden potential in its maintenance operations through the targeted use of AI.
However, the status quo was defined by a multitude of scattered data sources, hard-to-access knowledge buried in manuals and SAP logs, and manual search processes during technical failures.

As a result, maintenance times were unnecessarily prolonged, error analysis depended heavily on individual expertise, and new employees required lengthy onboarding periods.
The vision was clear: a digital maintenance plan that accelerates, structures, and standardizes maintenance processes by intelligently linking existing information—accessible to all employees regardless of experience level or location.

‍

AI-Powered Maintenance Assistant to Organize Knowledge and Processes

‍

Through a series of ideation sprints in the ada Future Lab (LINK), the following solution was developed: an AI-powered maintenance assistant.
This “Maintenance Co-Pilot” supports technicians by providing fast, context-specific access to information. It can analyze and interpret both structured and unstructured data from various sources and deliver precise answers in natural language.

The digital maintenance plan draws on technical documentation, manuals, historical maintenance records, and SAP logs.
The system identifies error codes, links them to past incidents, and generates maintenance recommendations—including prioritization and step-by-step instructions—based on proven measures.
This reduces dependence on individual expertise while standardizing and scaling maintenance quality across the organization.

‍

Technology and System Integration: From Data Access to SAP Connectivity

‍

Technologically, the Co-Pilot is built on Azure OpenAI (GPT-4) for natural language processing.
Azure Blob Storage and custom Python scripts were used to capture and organize data, while deep integration with the SAP system ensures automated access to operational maintenance data—eliminating the need for manual interfaces.

Because the solution is connected directly to existing IT infrastructures, the digital maintenance plan can be seamlessly embedded in the day-to-day operations of maintenance teams.
Users don’t need to learn a new interface, as access is provided through the familiar systems—minimizing training requirements and significantly increasing user adoption.

‍

Stakeholder Benefits: Value for Maintenance, IT, and Management

‍

The Co-Pilot delivers measurable benefits on multiple levels:

• Maintenance teams receive instant, accurate information without the need for time-consuming research or colleague consultations. Fault analysis and repair recommendations become faster, more structured, and more reliable.
• IT teams gain a scalable framework for knowledge distribution that leverages existing data and can be flexibly expanded with new content.
• Management benefits from shorter reaction times, reduced machine downtime, and an improved data foundation that supports strategic decision-making, investment planning, and KPI-based monitoring.

The Co-Pilot also safeguards organizational knowledge by systematically documenting and reusing practical experience—independent of any single employee’s expertise.

‍

More Efficient Processes, Less Downtime

‍

The introduction of the digital maintenance plan quickly delivered measurable results:

• Machine downtime was significantly reduced because potential faults were identified and resolved more quickly.
• The average processing time per maintenance task dropped noticeably, while execution quality increased thanks to standardized instructions.
• New employees could be onboarded much faster, as they had access to the same structured knowledge base as experienced colleagues.

Overall, the digital maintenance plan improved the productivity of the entire maintenance organization while reducing the workload for skilled technicians.

‍

Scaling the Digital Maintenance System

‍

Following the successful MVP launch, the company now plans to scale the solution by:

• Integrating additional data sources, such as IoT sensor data for early detection of technical anomalies.
• Expanding functionality to include predictive maintenance and automated maintenance scheduling.
• Rolling out the system to additional sites and production lines.
• Using it as a central knowledge platform for training and technical qualification.

The goal is clear: to establish the digital maintenance plan as a standard tool for modern maintenance—intelligent, scalable, user-centric, and a direct driver of reduced machine downtime.