1. Evaluation of Segmentation Techniques for Spare Parts Inventory Management: Phase 1
Researcher: Manuel D. Rossetti, Ph. D., P.E.
Graduate Assistant: Ashish V. Achlerkar
Sponsor: Naval Systems Supply Command
Large number of spare parts to manage
High computational time and cost for policy calculations
Conventional grouping techniques do not consider operationally relevant part attributes
Need to provide differentiated service to spare parts depending upon their importance for the overall supply chain goals
Need to revise the inventory segmentation policy suggested in the NISS report in lieu of the “Readiness Based Customer-Wait Time Sparing Model
Supply
Stock
ACWT
RTAT/EXREP
AIMD
4 - 7 days
Repair
Retrograde (21 days)
AWM
Depot
(Attrition)
RTAT (45 days)
Wholesale
Pack & Receipt
(4 days) $
Attrition
Replenishment
(12-24 Months)
BCM Rate 41%
(19/15 days)
BCM
Resupply
Admin Lead Time
Complex structure of multi-echelon inventory systems. Example of the US Navy. Interactions amongst Inventory Holding Points
Reference: Cdr.Ackart presentation slides
Representative example of a Multi-echelon inventory system

2. Research Contributions
Developed two new inventory segmentation techniques to provide an alternative to conventional inventory segmentation techniques
Developed and automated a data-generation mechanism to simulate datasets for different types of inventory systems
Evaluated different inventory segmentation techniques using experimental design
Provided general recommendations for inventory segmentation in multi-indentured, multi-echelon inventory systems
Goal
Best
Second- Best
Cost most important
GMIIP
MIGP
Fill rate most important
Customer Wait Time (CWT) most important
Execution time most important
ABC
Cost, Fill rate, CWT and Execution time all equally important
Center for Engineering Logistics & Distribution
CELDi

3. Expand results of phase 1 to multi-echelon setting
Phase 2: Multi-Echelon, Multi-Indentured Spare Part Segmentation Methodologies
Expand results of phase 1 to multi-echelon setting
Investigate clustering and segmentation policies
Develop new data generation mechanisms
Develop easy to implement multi-echelon models for testing
Expand results to multi-indentured systems
Investigate mathematical formulations of clustering approaches at the same time as policy optimization
Center for Engineering Logistics & Distribution
CELDi