AI-supported demand forecast for medical goods in hospitals
Improving hospital logistics through data-based demand forecasting.
The challenges of demand planning
The needs assessment is carried out by nursing staff or care assistants by visual inspection on site.
Decisions about the required order quantities are often made on the basis of subjective assessments.
Inaccurate demand forecasts lead to incorrect inventories and supply uncertainties.
AI-supported demand forecasting - precise, resource-saving and efficient
Our AI solution for forecasting the demand for medical supplies helps nursing staff and care assistants to precisely determine the actual demand for medical consumables. To do this, the AI analyzes past consumption data and relevant information from the wards and functional areas to create needs-based order proposals. These suggestions can be reviewed and adjusted by the orderers before they are approved, significantly optimizing the ordering process. Once the orders have been approved, the AI uses the information obtained to further improve route planning for deliveries and increase delivery reliability.
Maximize efficiency and reduce costs
Automated demand forecasting reduces manual effort, optimizes stock levels and ensures a reliable supply chain.
- Flexibilization: Nursing staff and care assistants are relieved and can invest their time more efficiently in patient care.
- Savings potential: Reduction of process and material costs in procurement logistics.
- Reduction of stock levels: Precise demand forecasts avoid overstocking, thereby reducing warehousing costs and the space required on the wards.
- Secure and efficient supply: Reliable forecasts ensure a continuous supply, thus avoiding bottlenecks.
- Sustainable logistics: The integration of order and delivery rhythms avoids unnecessary orders and improves logistics processes.
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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 an AI-supported demand forecast 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.
