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You are here: Home » Case Studies » Minimising whole-life costs of vehicle fleets

Minimising whole-life costs of vehicle fleets


  • A client (QinetiQ) is responsible for helping the MOD decide on alternative designs for new vehicles that have to meet a particular operational requirement
  • The choice is between adopting a particular vehicle with or without a new class of technology
  • The decision on which alternative to adopt must take account of the whole-life fleet cost. Thus, for example, adopting the new fleet with the new technology may involve much higher up-front development and unit costs, but over its life-time greater reliability and maintainability may require fewer vehicles to meet the operational requirement. Hence the whole life-costs could be lower
  • There was previously no method for a) dealing with the mixture of objective and subjective data required to solve this problem and b) quantifying the inevitable uncertainty


  • A model was built in AgenaRisk that incorporated all the factors impacting on repair efficiency and reliability as well as the whole-life cost factors
  • To run the model users simply input the operational requirements (the number of vehicles that have to be available at the end of a combat operation) together with the features that characterize the different alternative designs. AgenaRisk then calculates (for each alternative) the number of vehicles that need to be bought and the total whole-life costs
  • The results for the two alternative are plotted against each other
  • The unique simulation capability in AgenaRisk was used to full effect: numeric variables were created without any need to create artificial discretisations, and this also ensured highly accurate results
  • The model incorporates periods of peacetime and wartime missions


  • AgenaRisk provides a rigorous, auditable prediction of the relative whole life costs and hence provides a clear basis for making the critical decision
  • AgenaRisk enables extensive "what-if?" and other types of sensitivity analysis to enable users to identify the impact of changes to different factors and assumptions
  • AgenaRisk identifies the causes of increased cost and poorer quality