Extended Warranty Management in the Department of Defense

Naval Postgraduate School Acquisition Research Progra

Complexity and Self-Sustainment in Disaster Response Supply Chains

Governmental organizations play a major role in disaster relief operations. Supply chains set up to respond to disasters differ dramatically in many dimensions that affect the cost of relief efforts. One factor that has been described recently is self-sustainment, which occurs when supplies consumed by intermediate stages of a supply chain must be provided via the chain itself because they are not locally available. This paper applies the concept of self-sustainment to response supply chains. A mathematical model of a self-sustaining response supply chain is developed. Analysis of this model yields insights about the relationships and interactions among self-sustainment, speed of disaster onset, dispersion of impact, and the cost of the relief efforts

Warranty inventory management and supplier decision models

In warranty inventory management, customers return allegedly malfunctioning units to a company for replacement or credit. Useful units may be recovered through testing and/or remanufacturing processes; the company can use these recovered units to fulfill future warranty requests. The company also has the option of purchasing new units from a production line. In high-volume situations, warranty inventory management involves many complexities such as stochastic demand rates, probabilistic requests for credit instead of replacement, probabilistic repairs, multiple sources of supply originating from both the stochastic reverse channel and the company's purchasing decisions, and, in some cases, batching of remanufacturing. First, we formulate several related warranty inventory system models that aim to minimize inventory levels while meeting service level constraints. These models study periodic, single-location, inventory systems that are dedicated to warranty returns and include the following complexities: random warranty claims, random requests for replacement or credit, three sources of supply (testing, remanufacturing, and new product), random flows of returned products into testing and remanufacturing, random yields from testing and remanufacturing, different lead times for each resupply process, remanufacturing lead time variability, and random batching of remanufacturing. Using well-developed heuristics, these models produce results that provide near-optimal inventory-control policies in this complex environment and demonstrate the payoffs that result from reducing production lead times and batching in remanufacturing. Next, we consider several related models that incorporate the intricate cost structures that are often present in warranty inventory systems. We formulate two-stage models in which the company first makes a strategic decision on testing and remanufacturing capacities; then, in the second stage, the company aims to minimize inventory costs when faced with various levels of visibility into pipeline inventory in the reverse channel. "The Curse of Dimensionality" prohibits us from solving for optimal policies in most practical cases. Thus, we develop heuristic dynamic programs that allow for tractable models while incorporating information gained from the reverse channel visibility; we call this latter concept Advance Supply Information. Supplier decision models. Learning curves are a well-studied phenomenon in both the theoretical and empirical disciplines. As it gains experience in manufacturing a product, a factory/supplier is able to reduce that product's production costs. Buyers can choose their factory locations based upon the rate of learning that occurs at the factory. Many times, the lowest cost factory today becomes the high cost factory tomorrow due to slow learning. In order to explore these relationships, we have created several related models that solve for the optimal purchasing decisions under various supply chain configurations. We also evaluate the cost of ignoring the learning curve. First, we explore a model with deterministic parameters and then expand the model to include a stochastic learning portion

Fully-Burdened Cost of Supply in Self-Sustaining Logistics Networks

Disclaimer: The views represented in this report are those of the authors and do not reflect the official policy position of the Navy, the Department of Defense, or the federal government.Excerpt from the Proceedings of the Tenth Annual Acquisition Research Symposium Logistics ManagementThe research presented in this report was supported by the Acquisition Research Program of the Graduate School of Business & Public Policy at the Naval Postgraduate School. To request defense acquisition research, to become a research sponsor, or to print additional copies of reports, please contact any of the staff listed on the Acquisition Research Program website (www.acquisitionresearch.net).Prepared for the Naval Postgraduate School, Monterey, CA 93943.Approved for public release; distribution is unlimited

The Logistics Burden in Fully Burdened Cost Estimates

Naval Postgraduate School Acquisition Research Progra

Issues and Challenges in Self-Sustaining Response Supply Chains

Acquisitions Research Program Sponsored Report SeriesThe most basic representation of a supply chain has three elements: supply demand, and the flow between the two. A humanitarian response supply chain (RSC) tends to have unknown demand and at best uncertain supply with disrupted flow. A self-sustaining supply chain requires that the supply chain itself provide all resources consumed while transporting supplies, thus complicating the operations with numerous challenges and unfamiliar issues. If an RSC is self-sustaining, it will reduce some of the uncertainties in supply. However, self-sustaining response supply chains (SSRSC) generate significant additional cost. We explore the issues and challenges of SSRSC that arise in logistics networks in order to understand the costs associated with SSRSC observed in special operations and humanitarian assistance and disaster relief.The research presented in this report was supported by the Acquisition Research Program of the Graduate School of Business & Public Policy at the Naval Postgraduate School.Approved for public release; distribution is unlimited

Buyer and Nonprofit Levers to Improve Supplier Environmental Performance

Material IQ (MiQ) is a new decision tool designed by GreenBlue to help suppliers safely share sensitive chemical-toxicity data with their customers. As GreenBlue takes MiQ to market, it must determine under what market conditions to promote the use of MiQ and when to recommend that a buyer uses its implementation as an opportunity to work with an existing supplier. We study GreenBlue's problem in two parts. First, we investigate when a buyer can use a wholesale price premium and/or buyer-supplier cost sharing to improve a supplier's environmental performance. Based on our findings, we then develop insights into GreenBlue's strategy. We model both a single-supplier and a supplier-competition setting. We find that in the single-supplier setting, if the buyer's optimal strategy is to offer the supplier a premium, then he also fully subsidizes her investment cost to build quality. By developing the supplier's capabilities, the buyer can increase the impact of the premium he offers. In the supplier-competition setting, although cost sharing is less effective as a lever, cases can occur in which the buyer chooses to share costs and prevent the incumbent supplier from having to compete. From GreenBlue's perspective, promoting the use of MiQ and cost sharing are often viable strategies when there exists a one-to-one relationship between a buyer and a supplier. However, GreenBlue's strategy becomes more restricted when competition exists between suppliers. Only when the relative market awareness of quality is high and there is a dominant party in the supply chain should GreenBlue recommend the use of MiQ

Model-Based Optimization for Pilot Training

Presented at Western Decision Sciences Institute (WDSI 2012) conference April 3 - 6, 2012Pilot training in the Air Force is modeled as a supply chain where each step in the process is seen as the “supplier” of the next step. We use a model-based approach for optimizing the cost of pilot training while improving the war-fighting capability of the Air Force. A linear programming model that synchronizes and balances the flow of pilots through the various stages of the supply chain is developed. The model includes constraints such as capacity and manpower flows reflecting hiring and training of pilots