Appendix A
DSES-6620 SIMULATION MODELING AND ANALYSIS
Spring 2002
Project Proposal (revised) – R. Sewersky
Summary: Using a previously developed Supply Chain Simulation (developed in TaylorED)
complete an analysis to verify and validate it. The model focuses on predicting fill rate
performance for overhauled parts given a particular inventory position.
Background: Industry research has recently developed as series of TaylorED simulation
modules to simulate the US Navy helicopter components supply chain. The chain includes:
 demand for components due to wear/fatigue and maintenance given certain levels of
operational flight activities
 repair or overhaul of these components in the Navy and contractor shops
 replacement of certain parts by vendors or contractor manufacturing
The scope of the entire system is too large to consider complete validation as a class project so
only one component will be used for this verification and validation (V&V) exercise. This
module was an early prototype that considered cumulated demand levels, processing at
component turnaround time in the shop and component inventory levels (for which some V&V
was already done). The specific V&V tasks to be performed will also be downselected based on
data availability and previous V&V that was done.
Verification Tasks: The following are a list of the Verification tasks to be completed for the
project.
 Check literature regarding standard commercial or military practices that define
simulation qualification or validation. Report on those found relevant and consider
their guidance.
 Develop logic diagrams for the previously developed module.
 Analyze sensitivity to input variable excursions and assess whether the simulated
response seems correct. Provide supporting graphs and descriptions.
 Review animation output for reasonableness.
 Trace execution at key steps using debugging tools.
Validation Tasks: The following are a list of the Validation tasks to be completed for the
project.
 Compare simulation outputs with any available analytical tools that can provide
comparable outputs. In this domain, a commercial tool called OPUS is available to
model supply chain performance statically given average demand levels and average
processing times and inventory levels. Other OR tools such as Queuing or Inventory
theory will also be considered.
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OPUS assumes demands are Poisson distributed. Characterize actual demand data for
suitability of this assumption or propose a better distribution match using StatFit.
Compare model output variability with variability of available real performance data.
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