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CONTENTS

VIEWS: 8 PAGES: 34

									Optimizing Technical Inventory through
 Advanced Provisioning and Process
             Engineering



                  Presentation for AVIATION WEEK
                             1 Aug 2007




FCE Aeroconsult            1                       01/08/2007
                     Who is FCE Aeroconsult?
 Consultancy - airline logistics (spares planning)

 Partnership with Frankfurt Consulting Engineers

 Experience with heavy plant based industries

 Extensive experience with airlines, aircraft manufacturers,
  vendors

 Committee leadership for AEA, alliance partnerships, vendor
  management tools and various industry working groups




   FCE Aeroconsult           2                         01/08/2007
                   Current Customers

   Audi AG                         Porsche AG
   Aventis Pharma Deutschland
                                    Siemens VDO AG
   BMW of North America
   Continental-Teves
                                    Siemens Power Generation
   Conti TEMIC                     Still GmbH
   Deutsche Bahn AG                Swiss Rail
   EADS                            ZF Friedrichshafen
   Électricité de France
   GEHE Pharmaceutical
   Harman-Becker
   Hella
   Kapferer Pharmaceutical
   Linde
   Megapharm
   Mercedes-Benz USA




FCE Aeroconsult            3                       01/08/2007
                                  Contents
                         Advanced Provisioning
1. Access to spares
          1.1 Effect of stocked spares
          1.2 Efficiency indicators
          1.3 Process or inventory
          1.4 Inventory for dispatch
              reliability
2. Provisioning industry method
          2.1 Limitations
          2.2 TAT is wrong
          2.3 Categories of material
          2.4 Previous approaches to
              improving selections
3. Elements of Advanced provisioning
          3.1 Addresses statistical methods
          3.2 Continuously improves and
              optimises
          3.3 Validates through simulation




    FCE Aeroconsult                 4            01/08/2007
                         Advanced Provisioning
                         1.1 Effect of stocked spares
                                 What exactly do you get from spares ?
100
                                                              OR
 99
                                                              OR+NS
 98                                                           OR+NS+NM
 97
                                                       OR: Technical ground/air
 96                                                    interruptions due to A/C
                                                       basic malfunction
 95
 94                                                    NS: Technical ground/air
                                                       interruptions due to lack
 93                                                    of spares
 92
                                                       NM: Technical ground/air
 91                                                    interruptions due to
 90                                                    maintenance
  Dec'96           Feb     Apr   Jun    Aug     Oct

 - Logbook defect list leads to passenger/crew dissatisfaction
 - Delay in maintenance due to missing parts leads to excess cost

 FCE Aeroconsult                  5                            01/08/2007
                    Advanced Provisioning
 1.2 Efficiency indicators -Popular KPIs for heavy plant


 Inventory value / plant value
 Availability
 Stockout cost
 Percentage of units never used




  FCE Aeroconsult         6                 01/08/2007
                                                                         J:\SM2\AIS-PRES.PPT


               Advanced Provisioning-efficiency
                1.2 - Usage of stocked spares for a known a/c type
% OF INITIAL STOCK

120



100                                                           More used if less
                                                             pressure on efficient
                                                         a procurement/repair cycle
 80                                                 5%
                                                              Less used due to:
                                                20%      b • airline learning
 60                                                        • reliability improvement
                                                             modifications
                                                           • technical obsolescence
 40



 20
                                                                         Used
                                                                         Remaining
  0
           0            1      2       3        4           5     Year



      maximum 60 % usage.

      FCE Aeroconsult              7                             01/08/2007
                            Advanced Provisioning
                          1.3 Process or spares stocking

Service Result
                           Logistics
                           processes




                                                            Inventory


                                                                   Cost
                                                                   Influence
  Improvements in logistics are likely to improve service at lower cost than spares holdings


        FCE Aeroconsult                     8                                    01/08/2007
                                                                     Advanced Provisioning
                   1.4 Stocked spares for dispatch reliability?
                    A319/320/321 SPARES INVESTMENT PER AIRCRAFT
 6                                                                                                                                                                                                          100




                                                                                                            < 5 A/C




                                                                                                                                                                                             10 - 20 A/C
 5                                                                                                                                                                                                          99.5




                                                                                                                                                         10 - 20 A/C
                                                                                                                                                                                                                   Operational
 4                                                                    5 - 10 A/C                                                                                                                            99      Reliability
                                                                                                                                                                                                                       (%)
                                < 5 A/C




                                                                                                                                           10 - 20 A/C
 3                                                                                                                                                                                                          98.5
                                                                                    < 5 A/C




                                                                                                                                                                                  > 20 A/C
      > 10 A/C


                  5 - 10 A/C




 2                                                                                                                                                                                                          98




                                                                                                                                                                       > 20 A/C
                                                       10 - 20 A/C
                                           > 20 A/C




                                                                                                                                                                                                                    Investment

                                                                                                                                < 5 A/C
                                                                                              < 5 A/C




                                                                                                                      < 5 A/C
                                                                                                                                                                                                                        per A/C ($
 1                                                                                                                                                                                                          97.5
                                                                                                                                                                                                                        millions)


 0                                                                                                                                                                                                          97
     A/L1        A/L2          A/L3       A/L4        A/L5           A/L6          A/L7       A/L8      A/L9 A/L10 A/L11                  A/L12 A/L13 A/L14 A/L15 A/L16


More spares does NOT mean higher operational reliability?

            FCE Aeroconsult                                                                             9                                                                                                  01/08/2007
                  Advanced Provisioning
                   3.2 Lean provisioning




FCE Aeroconsult          10            01/08/2007
                         Advanced Provisioning
                      2.1 ATA methodology limitations
1) Poisson distribution is often not applicable to the data
2) Wear out processes not covered for mechanical assemblies
3) Reliability and TAT are often very wrong
4) No cognisance of variance in Reliability or TAT
5) No automatic updates from customized experience
6) No systemized cognisance of costs
7) No cognisance of go if conditionalities
8) No cognisance of commercial no go items
9) Reductions are at best “ad hoc”

The ATA methodology is not good enough


    FCE Aeroconsult              11                    01/08/2007
                        Advanced Provisioning
                          2.3       Categories of material
    High        Standard
                Hardware

                       Consumable
                       Materiel
                             structural Parts,
                             Tools & Mod. kits


                                          Minor Vendors
 Order
                                                 GSE and Tools
Volume
    low                                                    Major Vendors

                                                                   Engines

                 low                  Materiel $ Value                     high


Spares holding cost is with the major vendor parts

   FCE Aeroconsult                         13                                     01/08/2007
                    Advanced Provisioning
                    2.4 Initiatives to reduce IP

0%      8%         10%   11%        18%   30%
                                                    Spares protection/cost
                                                    analysis
                                                    MTBUR review

                                                    Breakdown parts instead of
                                                    complete assemblies
                                                    Expendables for start-up
                                                    only
                                                    Minimum equipment list
                                                    optimization



 Innovative Initial Provisioning

                               Current methods are crude

 FCE Aeroconsult                   14                     01/08/2007
                     Advanced Provisioning
             3 Elements of Advanced Provisioning

 Failure and Repair Time Statistics are rigorously
  checked

 Combinatorial Optimization prioritizes planning
  selections automatically

 Validation by Monte-Carlo Simulation checks
  whether part statistics and distributions are valid
  and corrects them


Advanced provisioning is much more effective

   FCE Aeroconsult           15                01/08/2007
                   Advanced Provisioning
            3.1 Failure and Repair Time Statistics




        Lifetime Estimation




FCE Aeroconsult               17             01/08/2007
                  Advanced Provisioning
            3.1 Failure and Repair Time Statistics
Confidence Interval for MTBUR in case of Normal   Distribution

                                       1 n
Point Estimate for MTBUR
                                    :  Ti
                                   ˆ
                                       n i 1
                                         1 n
                                   :       (Ti   )²
 Point Estimate for
                                   ˆ                ˆ
                                       n  1 i 1
   Standard Deviation



  Confidence Interval for MTBUR                   ˆ                      ˆ
                                  (   u 97, 5
                                    ˆ                  ,   u97 , 5
                                                         ˆ                     )
                                                   n                       n



FCE Aeroconsult             18                               01/08/2007
                  Advanced Provisioning
          3.1 Failure and Repair Time Statistics




        Query to create lifetime sample




FCE Aeroconsult                   19       01/08/2007
                     Advanced Provisioning
                     3.2 Combinatorial optimization

 Rigorous probabilistic model using a stochastic process
  approach
     Lower IP investment at better service level
     Possibility to trade off stockout cost versus inventory cost

 Stockout forecasts

 Detailed cost functions possible for total inventory cost and
  stockout cost for extended ESS codes




Optimization selects the best spares planning choices automatically

   FCE Aeroconsult              20                           01/08/2007
                      Advanced Provisioning
                     3.2 Basic Optimization Problem
                                               In reality, this is
                                               not a 2-dimensional,
                                               but an (n+1) dimensional
                                               situation !




Optimum found for a single partnumber

   FCE Aeroconsult             21                01/08/2007
                  Advanced Provisioning
                3.2 Combinatorial optimization

 Objective
 Function


                     Cost




 Solution
                                                Qty Part 2
                            Qty Part 1




Optimum quantities found for combination of thousands of PNRs

   FCE Aeroconsult                       22                  01/08/2007
                          Advanced Provisioning
        3.2 Combinatorial optimization-Extended ESS
ESS                     Unit Characteristic                        Stockout Penalty

1.0                     Dispatch critical                                                   100,000.00


                        Go If with high probability of causing
2.0                         AOG if missing                                                  100,000.00


                        Go If with low probability of causing
2.1                         AOG if missing                                                    50,000.00


                        Go but makes F or J class seat
3.0                         unsellable of a toilet out                                        60,000.00



                        Go but inables a Y seat or functionality
3.1                         restriction on F or J class seat                                  20,000.00


3.2                     Go and no significant CFD                                              2,000.00

Some parts are more important than others

      FCE Aeroconsult                         23                                      01/08/2007
                       Advanced Provisioning
                      3.2 Results for a 30 B777 fleet

                        PL                        STOCKOUTS                INVESTMENT


 ESS       STANDARD         OPTIMISED       STANDARD     OPTIMISED    STANDARD        OPTIMISED



       1             0,99               1         1362          149         24,3                22,8


       2             0,98         0,99            1379          369         41,6                25,7


       3             0,97         0,98            2966          617          2,7                 2,3


 TOTAL                                                                68,3 M USD      60,8 M USD



8 M USD saved and much less stockouts

   FCE Aeroconsult                           24                                    01/08/2007
                      Advanced Provisioning
         3.3 Validation by Monte-Carlo-Simulation

 Monte-Carlo simulation module allowing for
      Testing validity of model/distribution
      Replacing exponential lifetime distributions by the more
       appropriate Weibull distribution where suitable
      Robustness tests
            How much do IP quantities change, when certain input
             parameters change ?




What if the distribution is wrong or the data too weak or, or,

    FCE Aeroconsult              25                              01/08/2007
                  Advanced Provisioning
3.3 Validation by Monte-Carlo-Simulation




FCE Aeroconsult         26            01/08/2007
    1.3 Process or spares holding

Service Result
                        Logistics
                        processes




                                             Inventory


                                                    Cost
                                                    Influence
   Especially for expendibles, we need to work on the supply chain

      FCE Aeroconsult               27                       01/08/2007
 Categories of material
   High         Standard
                Hardware

                       Consumable
                       Materiel
                             structural Parts,
                             Tools & Mod. kits


                                          Minor Vendors
 Order
                                                 GSE and Tools
Volume
    low                                                    Major Vendors

                                                                   Engines

                 low                  Materiel $ Value                     high


Expendibles consumption is mostly unpredictable

   FCE Aeroconsult                         28                                     01/08/2007
 Hangar Check materiel
       Repetitive Replacement of Airbus proprietary part
                      on eight-year checks
                                  80

                                  70
        % of all required parts




                                  60

                                  50

                                  40

                                  30

                                  20

                                  10

                                  0
                                       1         2           3      4           5
                                           Number of times same part required


Most parts are used on only one check – AEA check data

   FCE Aeroconsult                                      29                          01/08/2007
        Structure parts from the manufacturer



                                     Pre CLT stock

                                CLT stock


Stock
Level

             Reorder point

              Safety

                                         Time
                             Lead Time


   parts with Savings
              no demand characteristic, supply needs to be very quick

         FCE Aeroconsult                    30                01/08/2007
So FCE Aeroconsult, what
do you suggest then?

    -Advanced provisioning
    -Process improvement

FCE Aeroconsult   31         01/08/2007
                    Backup Slides

 Combinatorial Optimization - Flow Diagram
 Query to create total operating hours and total
  number of failures
 TAT distributions




  FCE Aeroconsult      33                     01/08/2007
Query to create total operating hours and
        total number of failures




 FCE Aeroconsult   35            01/08/2007
     Effect of wear out processes on
            initial capital cost




FCE Aeroconsult   36          01/08/2007
     Effect of wear out processes on
            initial capital cost




FCE Aeroconsult   37          01/08/2007
                    TAT distributions

 TAT and MTBUR have the           Can be truncated by contractual
  same effect on the stock          measures
  level. Proof:
    Poisson distribution
     requires Expected demand
     during turn around time
    Expected demand during
     TAT = Fleet Size x QPA x
     (1/MTBUR) x TAT
 TAT distributions
    Must be taken just as
     serious as lifetime
     distributions
    Tend to have a long right
     tail



  FCE Aeroconsult            38                      01/08/2007

								
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