AI-supported master data maintenance for healthcare facilities
Automated allocation of articles in grocery shopping.
The challenges of master data maintenance
Manual master data maintenance takes a lot of time, ties up valuable capacity and often cannot be completed in full.
Conventional working methods lead to errors such as inaccurate entries or duplicate data records, which significantly impairs data quality.
The commitment of human and time resources results in high costs for recording, maintaining and checking the data.
AI-supported master data maintenance
Our AI-supported solution automates master data maintenance and optimizes the assignment of articles to product groups. First, specialized AI models analyse the article texts to identify relevant characteristics. The product group depth in the article master is then recognized, which enables precise classification into existing structures. Finally, the articles are classified and created to ensure that the data is entered consistently and correctly in the system and stored in the online store. This automation significantly increases master data quality, minimizes errors and prevents duplicate data records.
With a reliable database, food buyers in hospital catering are able to carry out well-founded price comparisons and make strategic, cost-efficient purchasing decisions. In addition, the improved master data quality ensures smooth and more efficient handling of the entire ordering process.
Increase data quality
AI-supported master data maintenance enables a significant improvement in data quality and the correct classification of items into the right product groups.
- Time saving: AI-supported allocation takes 1 second, whereas manual processing takes 30 seconds or more per item.
- Data quality: Increase in data accuracy to almost 100%.
- Cost reduction: Up to 80% lower process costs for data maintenance.
- Scalability: The AI solution can be expanded to several hundred thousand items without additional effort.
- Price comparisons: Fast, precise price comparisons and an improved shopping experience increase the likelihood of repeat purchases in the online store.
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Discover your potential
Discover with us how artificial intelligence can take your performance to a new level. Answer four short questions and we’ll show you customized ways to strengthen your strategy and overcome obstacles.
- How are you currently using artificial intelligence?
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- What are your biggest hurdles on the way to successful AI solutions?
- See our modules: What helps you achieve your goals?
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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.
