Navy Supply Chain Model

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Navy Supply Chain Model
DSES-6620 Simulation Modeling and Analysis

• Verification and Validation
• Rich Sewersky
• May 2, 2002

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Background

• New business for Aerospace Provide spare parts
  at fixed cost per flight hour called Performance
  Based Logistics (PBL)
• Key measure and risk is Fill Rate on time
  shipments/total requisitions
• Goal of model is to predict fill rate and other
  measures such as inventory to be able to complete
  business tradeoffs/contingency planning
• Model prepared in TaylorED by researcher,
  Sewersky was Subject Matter Expert
• 14 repairable components (Main Gearbox Module
  chosen for project)
• One year run period with specified starting
  position (contract terms)
• Project goal was to verify and validate model so
  it can be used for business
• Key steps
• Research commercial/military VV guidelines
• Review model logic and code and research report
• Explore model sensitivity to key inputs and
  output variability
• Calibrate output to known commercial simulation
  package
• Terms
• Ready for Issue (RFI) - Components in working
  order
• Carcasses - Broken components

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TaylorED Modeling Paradigm (connect the dots)
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(No Transcript)
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Results - Verification

• Research found DOD has Defense Modeling and
  Simulation Office
• has web site with guidance and educational
  materials, bibliography
• www.dmso.mil/public/transition/vva/
• several working groups including formal VV and
  Accreditation
• key issue is model interoperability and
  contractor sell-off
• many of these models support war games
• Developed logic diagram based on model structure
  and code flow
• (found one code error and corrected it - minor
  performance effect)
• Checked effect of key variables on output
  measures
• Variation turned off
• Demand interarrival rates
• Transport times
• Processing times
• Distribution fitting
• Model reactions made sense

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Results - Validation

• Researcher had checked model outputs against OPUS
  spares modeling tool - OPUS 91 Fill vs. Model
  96
• Sewersky baseline runs averaged 93 with fairly
  high variation (stdev 9.2). (note that the run
  with all variation turned off was 90.6)
• Input data is also highly variable
• Navy demand histories
• Navy transportation times (returned carcasses)
• Shop flow times (part shortage driven)

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Results Summary
Most orders remained unprocessed so
these statistics are misleading (only for those
actually processed)