Proceedings of the 2018 Winter Simulation Conference M. Rabe, A.A. Juan, N. Mustafee, A. Skoogh, S. Jain, and B. Johansson, eds. LIFE-CYCLE ENGINE FLEET SIMULATION FOR SPARE PART INVENTORY MANAGEMENT WITH ADVANCED CONDITION INFORMATION Ana Muriel Michael Prokle Robert Tomastik Dept. of Mechanical & Industrial Engineering Global Materials & Logistics University of Massachusetts Amherst Pratt & Whitney 160 Governor’s Drive 400 Main Street Amherst, MA 01003, USA East Hartford, CT 06118, USA ABSTRACT The cost efficient management of spare parts for low-volume high-tech equipment is inherently difficult. In this on-going study, we seek to improve the OEM’s spare parts inventory management by incorporating the condition information from a large number of distributed working units in the field. For that purpose, the condition information relayed by sensors is put in context with usage parameters, preventive replacement policies, customer plans, and current economic indicators to create an aggregate forecast and inventory ordering policy. This requires a synthesis of the state of the art knowledge from multiple research streams. In this paper, we outline a simulation environment of the maintenance management of a jet engine program over its life cycle, and provide preliminary results highlighting several modules for future research to improve the performance of spare part inventory policies and assess the value of health monitoring. 1 INTRODUCTION Stochastic part deterioration makes the prediction of equipment maintenance and associated spare part demand very difficult. Yet, accurate forecasting and spare part inventory management is crucial to keeping expensive equipment running and maintenance costs manageable. Condition monitoring involves collecting real-time sensor information from a functioning device to make predictions regarding the health condition and lifetime of each unit, and is thus posed to improve maintenance decisions. By aggregating over the condition of an entire fleet, this information not only promises improved maintenance scheduling but also better management of the resources needed - in particular spare parts. Our work is part of a multipronged and interdisciplinary study that develops the methodologies necessary to utilize sensor readings from a large number of distributed working units in the forecasting and inventory control of the spare parts necessary for maintaining those units. The research consists of four key milestones (as outlined in the NSF Abstract #1301188): 1. “Advancing sensing methods and the interpretation of signals to diagnose equipment condition". 2. “Developing procedures for transforming these data into predictions of time-to-overhaul and resource-requirements". 3. “Building part forecasting methods and inventory policies that aggregate this information across equipment, under consideration of field usage and economic conditions". 4. “Creating a simulation tool for the monitoring and maintenance of a large fleet to validate the methodology". This paper focuses on the last milestone, building a simulation environment to validate, compare, and further optimize the study's proposed methodologies and inventory policies. The ultimate objective is to highlight the economic value of advanced sensing techniques. We focus on a particularly complex and 2309 978-1-5386-6572-5/18/$31.00 ©2018 IEEE