Whole Fleet ManagementThe Barbican, East Street, Farnham, Surrey GU9 7TB01252 738500www.Advantage-Business.co.ukAdvantage Programme Manager: Karen Sparks01252 738576karen.sparks@advantage-business.co.ukCreating the Business CaseDeciding which models/methods to useKaren Sparks MSc

The views expressed in this presentation are those of the authorand not necessarily those of the Whole Fleet Management Integrated Project Team

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Whole Fleet ManagementWFM 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.

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Concept

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Analysis Issues/AgendaThis presentation focuses on OA/OE issues related to the assessment of WFM options:Original modelling intentThe option down-selection processHandling of future uncertaintiesRevised modelling intent

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The Business Case/COEIAObjective is to analyse options and to define the most cost-effective solution:

Level of unit holding.Locations and sizes of pools.

assuming that todays level of training is maintained.

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Cost EffectivenessOEUnit HoldingsPool HoldingsEquipment TransactionsEquipment MovementRepair and maint. changeInfrastructure RequirementsManpower RequirementsTransportation RequirementsCost

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Simulation> 24,000 modelling events

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GoalFor 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).

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GA Two options

323

1503

992

GA for ETF CE

323

1503

992

GA for ETF CE

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Step-wise LP/IP: Step 1Step 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 (Ni)Constraints included:Ni Dij 0 for all i,ji pool locations; j days

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Problem ReductionFor much of the year, the constraint Ni Dij 0 would not affect the solution.

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Step-wise LP/IP: Steps 2 and 3Step 2: Minimise movementKey constraint is that the total number of equipments must not exceed that determined by Step 1.

Step 3: Minimise costKey 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.

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Step-wise LP/IP Results

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

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UncertaintySimulation model is data hungry and specific.Nature of simulation makes uncertain futures difficult:Future Army StructuresGarrison locationsTraining regimesFuture equipment around 2015 +Related business initiatives

Analysis of trends using detailed event logs and influence mapping.Military Judgement Panel.

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Use of Event LogGeographic demand for an option: by unit location; by training locationChange in transportation:equipment, personnelTransactions:between units, unit-pool

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MJPFlexibility 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.

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Down-selection Summary

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Lessons - BalanceSingle 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.

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WFM ToolkitLP/IP optimisationEvent log analysisMJPBenefits mappingInfluence mapping(futures)GA optimisationInformation to select thebest solutionSimulation databaseSIMULATIONHR study

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END of presentation

Questions ?

Cost of procuring and supporting equipment is high.WFM is focused on managing the fleet cost-effectively particularly in peacetime.

Equipment is procured to meet operational need. A process called TFR is used to calculate the requirement taking into account matters such as the repair loop and what is termed irreducable training minimum.

Units would own less equipment sufficient for low-level training.The remaining equipment would be pooled.Modelling assumes training pool(s) and operational pool(s).Pool equipment is held in improved storage conditions improving availability and reducing cost.Training pool equipment is loaned for collective (higher-level) training.Operational pool equipment is ready to deploy.Reality is combined operational/training pools.

One of the early business change programmes for which a formal business case and therefore Combined Operational and Effectiveness Appraisal (COEIA) was required.

WFM is an incremental business change programme enabled by an IS Three decision points (board approval) have been achieved

Many strands to WFM this presentation will concentrate on the OE aspects and process to reduce the number of options taken forward.

This illustrates the key features of the COEIA.High dependency between the OE and cost.

Different management results in changes to availability and the use of spares to maintain and repair equipment.Forming pools creates a requirement for all the necessary infrastructure and manpower. Conducting the loans to and from units requires manpower at the pools and transportation of personnel to and from pools.

Prior to the first COEIA, the IPT were using a simulation to define the start point for trials. So for us, the decision on what model to use had been made.

Model simulates the demand and movement and use of equipment to meet a training plan.Input was a complete directed training plan for the year 2002.Each training activity defined by the location it takes place at, the units involved and the types and numbers of equipments used by each unit.With 163 units, 160 equipment types (and 40,000 equipments in total) that leads to in excess of 24,000 events transactions/movements of groups of equipments.

In concept phase, locations were set by military judgement.Size of pools through iterative runs of the simulation model.The first COEIA examined two unit holding options both defined by military judgement. Found not to be the no brainer assumed before we started the COEIA !For assessment phase: options expanded to 5 as a result of analysing concept phase results and the trials.Need to down-select to a manageable set 3. Rejection of the others required a further COEIA.

For the cost-effectiveness, needed to test the military judgement of pool locations and sizes.IPT planned an all-in-one approach to determining the most cost-effective using GAs.To de-risk this, independent high level modelling using LP/IP was also included in the plan.Originally intended to do the LP/IP work first to inform the detailed simulation/GA but that could not happen and the simulation/GA was carried out first.Study is examining UK and Germany (and to a lesser extent Canada).Example is UK interesting as most geographically dispersed.The first case puts all equipment in a single pool in SW. The second puts a high proportion there but at a different location.

Analysis:Infrastructure costs swamped everything else.Many location and population solutions very close in final cost difference.Taking that analysis into account.Decided to consider a staged approach valid if each stage independent: if there are orders of magnitude between the stages.

Stage 1 minimisation of equipment numbers.

Given the fixed unit holdings of the option, identify a location/population solution that minimises equipment numbers required for training.More obvious objective functions were highly non-linear.Linear one found but with substantially increased number of constraints.

Ni is the number of equipments held in the ith pool.Dij is the demand placed on that pool on day j.

In fact analysis of information shows that many constraints could be removed if required. The absolute minimum allowing no transaction or movement time provides a reference point.

This stage in theory involves an LP run per equipment type and per theatre (326 runs).It allowed us to restrict the analysis if the peak demand with handover and travel time did not exceed the target, then there is no opportunity to optimise numbers.Stage 2 - minimisation of distance travelled. More acceptable to the Army.Given the outcome of Stage 1 as the baseline for this stage of optimisation, this stage minimised the distance each equipment travelled. In effect, this stage identifies different location/population solutions, referred to above, that require the same minimum numbers of equipments but have a better geographical match to requirements.

Stage 3 optimisation of cost.

Given the outcome of Stage 1 as the baseline for this stage of optimisation, this stage minimised pool costs, but with constraints that meant equipment could only be reallocated to pools within 100km of its location resulting from Stage 2, thus maintaining, but relaxing slightly, the priority of low movement. Note due to the method, you cant compare actual numbers. The LP/IP would need to be scaled this was just an investigative approach.

But these are quite different in detail to the sim/GA.Note left in GA a single large pool, dispersed in this method.

Also the two options are more similar as might be expected and suggest a degree of robustness to possible future changes in the option.Just taking the LH option of each again noting you cant compare actual numbers it can be seen that the LP/IP does match the geographic demand better as might be expected.

The work was investigative and supportive and did not try to create costs that could be compared directly.

Important conclusion both methods resulted in the same ranking of the options. It informed the analysis and down-selection not intended to be THE solution.

The other area for down-selection was flexibility and robustness for the future.Detailed event log added to the model based on experience of using the outputs in concept phase, to aid analysis and in preparation for the military judgement panel. MJP given a variety of key information drawn from the event logs. Not provided with the costs.

Provided with a questionnaire focused on soft issues, the future and pool locations.MCDA used to analyse and report on the MJP.

Results of the analysis ranked options according to key criteria not just cost.

The MJP did not particularly discriminate between options or have strong views except for the lowest holding case: it caused the most polarised views but its net score was slightly higher than all the other options.

From the original intent to use a single simulation, the experience gained now suggests a broader toolkit.This provides information which can then be presented for others to make the final decision or for an argument to be constructed that will recommend a particular option.

In conclusion Tenets:Keep an open mind the requirement may well evolveSpend time on analysis - develop an understanding of the key driversFit for purpose models