Naval Supply Systems Command
Manuel D. Rossetti, Vikram L. Desai, Ashish Achlerkar, Mohammad H. Al-Rifai
University of Arkansas
The objective of this project is to develop a Multi-Echelon data generation mechanism to generate a data set that represents a generic multi-echelon inventory systems, develop a backorder Multi-Echelon, Multi-Indenture optimization model, develop a Multi-echelon, Multi-Indenture inventory segmentation methodology that reduces computational time, inventory investment and increases management convenience, implement the inventory segmentation methodology and use the backorder model to set inventory control policy parameters. . provide general recommendations for generic inventory segmentation and control strategies, and provide general guidance for extending the Multi-Echelon, Multi-Indenture segmentation methodology to:
A repairable spare parts case
Non identical-retailer case
The purpose of the 2004-2005 CELDi Research Project is to investigate the methodologies for segmenting the inventory in a multi echelon system so as to achieve a balance between managerial convenience and Navy’s supply chain goals at the lowest possible cost. The project will develop a methodology to generate dataset representative of the generic inventory system (Reparable, Consumable). The research aims at developing a methodology for segmenting the inventory based on attributes which are essential to meet the supply chain goals. A backorder multi-echelon, multi-product optimization model that reduces the total inventory cost of the system will be developed to test the benefits of segmenting the inventory.
The research is intended to find a rationale alternative to traditional ABC analysis for grouping and setting up inventory policies so as to attain the overall supply chain goals and provide differentiated customer service.
Large multi-echelons, multi-indenture inventory systems usually consists of hundreds of thousands of items
Calculating inventory policy for each item is a computational burden that necessitates the need for:
More efficient policy setting techniques that reduce computational time,
Improves the ability of item managers to more effectively manage the supply chain
Inventory segmentation is a prominent way to overcome these problems
Future research area would be to optimize and cluster simultaneously.