Efficient processing of fault messages in facility management with AI
Automation of tens of thousands of processes every year.
The challenges of manual processing
Manually processing fault reports takes a lot of time and ties up valuable human resources. Converting each ticket into a maintenance order costs many working hours that are not spent elsewhere.
The manual recording and conversion of tickets often leads to errors, such as incomplete or incorrectly transmitted information. These errors lead to delays and increased effort in subsequent processes.
Any contractual specifics of the respective operating site must be taken into account in order to ensure forwarding to the correct employee group. This necessary detailed knowledge increases complexity and requires constant employee training.
Automated processing with AI - fast, precise and effective
Fault reports in facility management with AI
The AI solution for processing fault reports in facility management was developed to sustainably optimize processes through the targeted use of artificial intelligence. By automating up to 70,000 processes per year, sources of error are effectively reduced, while time and human resources are saved. Thanks to the combination of in-depth industry expertise and state-of-the-art AI technology, precise, error-free and efficient processing is guaranteed.
Our AI solution automates the processing of fault messages and converts them directly into maintenance orders. This saves you time, minimizes administrative effort and ensures error-free implementation. The specific requirements of each operating site are automatically taken into account so that the responsible technician is notified quickly and accurately – for smooth and efficient processing.
Maximize efficiency and reduce costs
Our AI solution reduces manual effort, minimizes errors and significantly increases efficiency. As a result, you benefit from higher service quality and improved operational reliability.
- Cost savings: Automation significantly reduces manual effort and lowers operating costs.
- Efficient use of resources: Up to 85% less effort means more time for strategically important activities.
- Error reduction: On average 50% fewer errors than with manual ticket assignment.
- Faster response times: Minimization of downtimes during operation.
- Scalability: The solution can be easily applied to additional objects.
- Greater satisfaction: Improved service quality for technicians and users.
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FAQ
Frequently asked questions
Here you will find an overview of frequently asked questions:
How does the collaboration between Open Logic Systems (Open LS) and FACT work?
FACT and Open LS combine their expertise to offer you customized AI solutions. FACT has in-depth knowledge of facility management, while Open LS has extensive experience in the development of innovative AI technologies. Together, we offer a practical and seamless integration of solutions.
How does the collaboration between Open Logic Systems (Open LS) and FACT work?
FACT and Open LS combine their expertise to offer you customized AI solutions. FACT has in-depth knowledge of facility management, while Open LS has extensive experience in the development of innovative AI technologies. Together, we offer a practical and seamless integration of solutions.
How quickly can AI solutions be implemented?
The speed of implementation varies depending on the use case. As a rule, a proof of concept can often be implemented in 1-2 weeks, with full implementation taking place in the following months. The availability of data and the complexity of the requirements are decisive for the time frame.
How does a workshop work?
Our workshops are led by 2-3 facilitators, including data scientists and AI specialists. We use the design thinking method to develop innovative ideas, which are then prioritized and concretized in an AI canvas, resulting in 2-3 concrete project outlines.
How do you proceed in a proof of concept?
In a proof of concept, we use the CRISP-DM approach (Cross Industry Standard Process for Data Mining), which offers a structured procedure for validating AI use cases. This proven approach is also used in the complete implementation support to ensure successful and sustainable integration of the solution into your business processes.
What are the typical phases and timeframe for implementation?
We start with a one-day workshop to identify possible areas of application. This is followed by a proof of concept, which can often be implemented in 1-2 weeks to check feasibility. This is followed by the actual project phase, which can be completed in a further 3-6 months.
