What would be the optimum pre-positioned stock?

This pillar focuses on defining the optimum pre-positioned stock in each location, based on a combination of historical disaster response data, a set of humanitarian logistics information (price per ton per kilometre), and other variables which are relevant to the humanitarian context (i.e. absorption capacity, replenishment times, etc).

Stock data are analysed through mathematical models elaborated in partnership with Penn State University and other academic institutions. The models highlight gaps and overlays, allowing in-country partners to coordinate better and supporting the definition of effective pre-positioning strategies at regional and country level. The ultimate goal of these strategies is helping humanitarian actors to optimise average response times and costs.

The Data Analysis work:

  • Is carried out by academic researchers in close collaboration with humanitarian practitioners
  • Is based on historical data about disasters size, impact and locations as well as on key logistical variables such as time to contract transportation, distances, price per ton per kilometre, absorption capacity, etc.
  • Covers core relief items to store, quantities and locations
  • Allows users to make informed decisions towards the definition of a coordinated pre-positioning strategy