This proposal is aimed developing models and providing analysis to aid Ricoh in the development of spare parts supply chain networks with additive manufacturing (AM) capability. The goal is to design and exercise a decision-support model that helps Ricoh evaluate the answers to the following questions: When should I consider adding AM capability into my spare parts logistics network? If I should use AM, where do I locate AM capability in the network? Do the statistical characteristics of my spare parts demand impact the design of my supply network? How can I quantitatively measure the efficiency, resilience and security of the network? After establishing it, how do I best resource, manage, and employ the AM capability? Due to the fast-changing environment and capabilities of AM equipment, the model must be flexible enough to adjust to new evolutions of AM technology as new materials, processes, and capabilities continue to emerge over time. The model will provide optimization of supply chain configurations given the production capabilities, both traditional and AM, available to the supply chain under forecast demand scenarios.

The purpose of this effort is to design, initiate, and manage a serious of workforce development, training, and AM skills workshops to respond to the growing demand facing both industrial, government, and military organizations seeking to build organic additive manufacturing capability.

This STIR grant will advance the current understanding of supply chain performance prediction, capacity planning, and resiliency analysis. It will provide military logistics planners with capabilities that are currently lacking in prevalent logistics planning tools. Research products will be designed to leverage data from the Army's new Enterprise Resource Planning (ERP) system to pursue end-to-end analysis and optimization of military supply chains, incorporating network modeling, queuing theory, and simulation to enable planners to evaluate logistics plans in near-real time. The analysis will focus on expeditionary operations as part of contingency scenarios. The project will construct a network-based model that captures routing alternatives and characterize the solutions to conduct capacity planning and resiliency analysis in near-real time.