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.

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 article 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 is a challenge that many companies must confront. Customers return allegedly defective units to a company for replacement or credit. The company can then economically recover the unit through either a testing or remanufacturing process; it can use recovered units to fulfill future warranty requests. The company also has the option of purchasing a new product from the 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, and tight customer-service constraints. Companies may also have to consider the complexities that a batch remanufacturing process causes. In this paper, we formulate several related models of such warranty inventory systems. In these models, we study a periodic, single-location, inventory system that is dedicated to warranty returns. We find near-optimal policies for each system using well-developed heuristics. The models 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. The results of the models 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. Hitachi GST has gained a great deal from this modeling process. In addition to the direct benefit from the model's calculations, additional sensitivity analyses have shed light on the quantitative importance of various factors, including demand volatility, the percentage of credit requests, the percentage of units successfully remanufactured, and batching effects in remanufacturing.

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