Note: This command consists of the following components:
1. Study of the decisive parameters
2. Drawing up an analytical predictive model
3. total integration into a high-performance software platform
The assignment is divided into 2 parts:
1. Delivering the software platform, including study + creation predictive model
2. Providing a non-exclusive right of use (including maintenance and support) in the form of a license to use the software platform
Context:
To serve its members, the Solidaris health insurance fund uses an office network of more than 200 locations in Flanders as well as a number of online platforms.
In the context of changing circumstances (extensive digitization, financial accountability, ...) Solidaris wants to optimize its physical network.
It is important that the ratio between the number of offices and the accessibility to the members is optimal and that it is also checked which services are best offered.
It will therefore be necessary not only to understand the needs of the current members but also to assess the needs of the future members.
Solidaris has a lot of geographical data on member behaviour. Through thorough analysis of this historical data, the right future actions can be taken in order to draw up a future-proof location strategy.
Solidaris is looking for a partner who can design the network of the future with the help of A.I. The partner will have to analyze the data, draw up a predictive model based on this and make this model accessible to Solidaris via a user interface.
The assignment can be extended 3 times.
Note: This command consists of the following components:
1. Study of the decisive parameters
2. Drawing up an analytical predictive model
3. total integration into a high-performance software platform
The assignment is divided into 2 parts:
1. Delivering the software platform, including study + creation predictive model
2. Providing a non-exclusive right of use (including maintenance and support) in the form of a license to use the software platform
Context:
To serve its members, the Solidaris health insurance fund uses an office network of more than 200 locations in Flanders as well as a number of online platforms.
In the context of changing circumstances (extensive digitization, financial accountability, ...) Solidaris wants to optimize its physical network.
It is important that the ratio between the number of offices and the accessibility to the members is optimal and that it is also checked which services are best offered.
It will therefore be necessary not only to understand the needs of the current members but also to assess the needs of the future members.
Solidaris has a lot of geographical data on member behaviour. Through thorough analysis of this historical data, the right future actions can be taken in order to draw up a future-proof location strategy.
Solidaris is looking for a partner who can design the network of the future with the help of A.I. The partner will have to analyze the data, draw up a predictive model based on this and make this model accessible to Solidaris via a user interface.
The assignment can be extended 3 times.