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Whole Fleet Management

Whole Fleet Management. 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.

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Whole Fleet Management

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  1. Whole Fleet Management 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 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

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

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

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

  8. Goal • 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).

  9. GA – Two options

  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(Ni) • Constraints included: Ni – Dij≥ 0 for all i,j i pool locations; j days

  11. Problem Reduction • For much of the year, the constraint Ni – Dij≥ 0 would not affect the solution.

  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. Step-wise LP/IP Results

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

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

  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. Down-selection Summary

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

  21. END of presentation Questions ?

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