1. Whole Fleet Management The Barbican, East Street, Farnham, Surrey GU9 7TB 01252 738500 www.Advantage-Business.co.uk Advantage Programme Manager: Karen Sparks 01252 738576 karen.sparks@advantage-business.co.uk Creating the Business Case Deciding which models/methods to use Karen Sparks MSc The views expressed in this presentation are those of the author and not necessarily those of the Whole Fleet Management Integrated Project Team

2. ISMOR 2005 2 Whole Fleet Management  WFM is: The process of managing a fleet of equipment through global visibility in the most supportable, effective and economic way in order to meet the stated operational, training and support requirements.  Whole Fleet Management (WFM) is essential because equipment is now procured in accordance with Total Fleet Requirement (TFR). Fleets in the future will be smaller.

3. ISMOR 2005 3 Concept Reduced Unit Holding WFM Pooled Equipment U TPs OPs Training fleets/pools Operational fleets/pools U U U U U U U Unit holdings

4. ISMOR 2005 4 Analysis Issues/Agenda  This presentation focuses on OA/OE issues related to the assessment of WFM options:  Original modelling intent  The option down-selection process  Handling of future uncertainties  Revised modelling intent

5. ISMOR 2005 5 The Business Case/COEIA  Objective is to analyse options and to define the most cost-effective solution:  Level of unit holding.  Locations and sizes of pools.  …… assuming that today’s level of training is maintained.

6. ISMOR 2005 6 Cost Effectiveness OE Unit Holdings Pool Holdings Equipment Transactions Equipment Movement Repair and maint. change Infrastructure Requirements Manpower Requirements Transportation Requirements Cost

7. ISMOR 2005 7 Simulation n1 Equipment 1 n2 Equipment 2 n3 Equipment 3 160 equipment types ~40,000 equipments > 24,000 modelling events Directed Training Plan 2002 Location Unit 1 Unit 2 163 units Training Activity

8. ISMOR 2005 8  For each defined unit holding option:  What is the best set of pool locations and pool populations ?  Optimisation:  Genetic algorithms (GA) with the simulation.  Independent Linear Programming (LP/IP). Goal

9. ISMOR 2005 9 GA – Two options

10. ISMOR 2005 10 Step-wise LP/IP: Step 1  Step 1 – Minimise total number of equipments required for training.  Unit holdings are fixed. So this is achieved by minimising the number of equipments in TPs for each equipment type: MIN Σ(N i )  Constraints included: N i – D ij ≥ 0 for all i,j i pool locations; j days

11. ISMOR 2005 11 Problem Reduction  For much of the year, the constraint N i – D ij ≥ 0 would not affect the solution.

12. ISMOR 2005 12 Step-wise LP/IP: Steps 2 and 3  Step 2: Minimise movement  Key constraint is that the total number of equipments must not exceed that determined by Step 1.  Step 3: Minimise cost  Key constraints are:  The total number of equipments must not exceed that determined by Step 1.  Equipments cannot be located more that 100 km away from their location determined in Step 2.

13. ISMOR 2005 13 Step-wise LP/IP Results

14. ISMOR 2005 14 Geographic Demand NB. Not valid to compare the actual numbers between the GA runs and LP/IP runs

15. ISMOR 2005 15 Uncertainty  Simulation model is data hungry and specific.  Nature of simulation makes uncertain futures difficult:  Future Army Structures  Garrison locations  Training regimes  Future equipment around 2015 +  Related business initiatives  Analysis of trends using detailed event logs and influence mapping.  Military Judgement Panel.

16. ISMOR 2005 16 Use of Event Log Geographic demand for an option: by unit location; by training location Change in transportation: equipment, personnel Transactions: between units, unit-pool

17. ISMOR 2005 17 MJP  Flexibility for uncertain futures. and …  The simulation takes account of the equipment – but not the impact on command and control and human factors.  The optimisation focused the information on possible sites – but did not take account of wider issues.  Military judgement panel captured preferences in these areas.

18. ISMOR 2005 18 Down-selection Summary

19. ISMOR 2005 19 Lessons - Balance  Single ‘all-in-one’ approach may hide business drivers.  Adding complexity (greater ‘realism’) may add little value.  Analysis of underlying trends leads to a better understanding than taking final outputs from a large number of simulation runs.  Optimisation needs to be used with care.

20. ISMOR 2005 20 WFM Toolkit LP/IP optimisation Event log analysis MJP Benefits mapping Influence mapping (futures) GA optimisation Information to select the best solution Simulation database SIMULATION HR study

21. ISMOR 2005 21 END of presentation Questions ?