Principal Investigator: Manuel D. Rossetti, Ph.D., P.E. Research Assistants: Ashish Achlerkar, Mohammad H. Al-Rif

1. CELDI Project Evaluation of Segmentation Techniques for Spare Parts Inventory Management Principal Investigator: Manuel D. Rossetti, Ph.D., P.E. Research Assistants: Ashish Achlerkar, Mohammad H. Al-Rifai, Vikram L. Desai Department of Industrial Engineering University of Arkansas, Fayetteville

2. Overview • Introduction and Research Motivations • Research Objectives • Project Phase I • Model Description, • Results and Conclusions • Project Phase II • Research Contributions • Research Tools & Techniques • Research Methodology • Conclusions & Directions for Future Research

3. WORKSHOP ECHELON-5 ECHELON-4 C-DEPOT ECHELON-3 BRIG - A BRIG - B ECHELON-2 BATA– A1 BATA-A2 BATA-B1 ECHELON-1 COMP-A11 COMP-A12 COMP-A21 COMP-B11 COMP-B12 Introduction & Research Motivations • Large complex Multi-Indentured, Multi-Echelon inventory systems • Large number and variety of spare parts • Stochastic nature of demand, lead-time • Availability of data that represent generic inventory systems A typical five-echelon inventory system structure

4. ACWT Admin Lead Time (4 days) Supply Stock AIMD 4 - 7 days RTAT/EXREP Resupply Repair BCM (19/15 days) BCM Rate 41% Retrograde (21 days) Wholesale Stock $ Attrition Replenishment (12-24 Months) AWM Depot RTAT (45 days) Pack & Receipt (4 days) (Attrition) Introduction & Motivations • Computational time, system costs, and management convenience • Selecting inventory control policy • Conventional grouping techniques & Inventory systems • Need to provide differentiated service to spare parts depending upon their importance for the overall supply chain goals Reference: Cdr.Ackart presentation slides Representative example of a Multi-echelon inventory system

5. Research Objectives • 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 • Provide general guidance for extending the Multi-Echelon, Multi-Indenture segmentation methodology to: • A repairable spare parts case • Non identical-retailer case

6. Phase IOverview • In this phase, a single echelon, single indenture inventory segmentation methodologies are developed to provide an alternative to conventional inventory segmentation techniques, e.g. ABC: • Multi-Item Group Policies (MIGP) • In this policy a large number of items are reduced into small number of groups. • A generic group statistics for each variable are calculated • The backorder model is used to set up generic group inventory policy parameters, while trading off between groups • Grouped Multi-Item Group Policies (GMIIP) • In this policy a large number of items are also reduced into small number of groups. • The backorder model is used to set up inventory policy parameters for each item inside the groups without trading off between groups

7. Phase IContributions • The items were clustered using the Unweighted Pair Method Arithmetic Averages UPGMA method • Multi-product backorder inventory model (Hopp and Spearman, 2001) was used to calculate inventory control policies for grouped and ungrouped datasets • 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

8. Phase IResults

9. Phase II:Research Contributions • Develop a backorder Multi-Echelon, Multi-Indenture optimization model, that sets inventory control policy parameters • We developed the following backorder Multi-Echelon, Multi-Item optimization model that uses (R, Q) inventory policy at all echelons: • Our objective function is to minimize total inventory investment at both echelons subject to the following constraints: • We represent the above model mathematically as follows:

10. Phase II:Research Contributions • In order to solve the above model we decomposed it into two levels, the warehouse and the retailer levels: • The warehouse level: • The retailer level:

11. Phase II:Research Contributions • Develop data generation mechanism that generates data sets for generic inventory systems. • Investigate the applicability of available clustering techniques on inventory systems • Develop multi-echelon, multi-indenture inventory segmentation methodologies • Provide general recommendations for generic inventory segmentation and control strategies

12. Phase II:Research Tools & Techniques • Clustering techniques: • Unweighted Pair Method Using Arithmetic Averages (UPGMA) • Nearest Neighbor • Inventory control model: • (R, Q) policy setting policy • Analytical models for calculating performance metrics • Heuristics • We are planning on using JAVA language to implement: • backorder Multi-Echelon, Multi-Indenture optimization model • data generation mechanism • SAS & MINITAB statistical analysis software's: • Implementing the clustering technique using SAS • Analyze the results using both SAS & MINITAB • Microsoft tools • Microsoft ACCESS to store and call data sets • Microsoft EXCEL to implement

13. Phase II:Research Methodology • Build a data generation mechanism & generate data set that represent generic Multi-Echelon, Multi-Indenture inventory systems • Implement a clustering algorithm and use it to reduce large number of items into groups of items that characterized by: • A maximum between groups variance for each variable, • Minimum within groups variance for each variable, and • Limited number of groups • Build a backorder multi-echelon, multi-indenture optimization model • Develop a simple heuristics to approximate exact model • Implement the model in JAVA • Use the model to set up items and groups inventory policies • Experimental design and analysis • Investigates techniques to extend the segmentation methodologies to: • A repairable spare parts case • Non identical-retailer case

14. Phase II:Conclusions & Directions for Future Research • 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 • Direction for future research to extend inventory segmentation methodology to optimize and cluster at the same time.