ESUPS second question is: “What should the optimum prepositioned stock be in this location”. In this part of the ESUPS project, logistical data are analysed through in-depth mathematical models defined in partnership with the Penn State University (PSU) and other academics. Hereby, gaps and overlays are highlighted, and recommendations can be made towards the definition of a pre-positioning strategy. Ultimately, this helps optimize the average response time and costs.
The driving principles of the data analysis are:
- Led by Academics researchers (Penn State University) in cooperation with humanitarian practitioners.
- Based on decades of historical data about disasters sizes, impacts and locations as well as on logistical data such as time to contract a transport, distances, price per ton per kilometer, etc…
- Includes the absorption capacity variable.
- The created algorithm provides what could an “optimum stock” be, looking both at quantities and locations.
- Those are displayed on the Data Collection platform allowing the users to collectively see gaps and/or overlays and opening the door for informed discussions towards a coordinated prepositioning strategy