On returns and network configuration in supply chain dynamics

This research focuses on how two common modeling assumptions in the Bullwhip Effect (BWE) literature (i.e., assuming the return of the excess of goods and assuming a serial network) may distort the results obtained. We perform a robust design of experiments where the return condition (return vs. no return) and the configuration of the Supply Chain Network (SCN) (serial vs. divergent) are systematically analyzed. We find an important interaction between these assumptions: the impact of returns on the BWE strongly depends on the SCN configuration. This study highlights the importance of accurately modeling SCNs to properly assess SCNs managers.Junta de Andalucía P08-TEP-0363

Mean-Variance Analysis of Supply Chain Contracts

Institute of Textiles and Clothin

Smart-Contract Enabled Blockchains for the Control of Supply Chain Order Variance

Conflicts between supply chain members emerge because individually strategic actions may not be jointly optimal. Efforts to forecast consumer demand represent a source of conflict. Coordination of forecasts require a powerful incentive alignment approach. This work proposes a smart-contract equipped consortium blockchain system that creates an incentive structure which makes coordination with respect to forecasts economically appealing to a retailer and supplier. Distortions of demand due to uncoordinated forecasting is captured by a bullwhip measure which factors both forecast error and variance. Cooperation under the system is shown to help minimize this bullwhip measure. New outcomes to supply chain participants that allow for a higher reward is provided by the system. Under a fixed payout structure, the system achieves credibility of continued cooperation thus promoting an optimal coordinated equilibrium between retailer and supplier. Blockchain technology represents a consensus formation mechanism which can intermediate the behavior of supply chain network

Smart-Contract Enabled Blockchains for the Control of Supply Chain Order Variance

Conflicts between supply chain members emerge because individually strategic actions may not be jointly optimal. Efforts to forecast consumer demand represent a source of conflict. Coordination of forecasts require a powerful incentive alignment approach. This work proposes a smart-contract equipped consortium blockchain system that creates an incentive structure which makes coordination with respect to forecasts economically appealing to a retailer and supplier. Distortions of demand due to uncoordinated forecasting is captured by a bullwhip measure which factors both forecast error and variance. Cooperation under the system is shown to help minimize this bullwhip measure. New outcomes to supply chain participants that allow for a higher reward is provided by the system. Under a fixed payout structure, the system achieves credibility of continued cooperation thus promoting an optimal coordinated equilibrium between retailer and supplier. Blockchain technology represents a consensus formation mechanism which can intermediate the behavior of supply chain network

Inventory Policies and Information Sharing: An Efficient Frontier Approach

We consider a two-tier inventory management system with one retailer and one supplier. The retailer serves a demand driven by a stationary moving average process (of possibly infinite order) and places periodic inventory replenishment orders to the supplier. In this setting, we study the value of information sharing and its impact on the retailer’s optimal ordering strategy. We argue that information sharing affects performance through two key cost drivers: (i) on-hand inventory variability and (ii) replenishment order variability. We characterize a “Pareto frontier” between these two sources of variability by identifying optimal inventory replenishment strategies that trade-off one type of variability for the other in a cost efficient way. For the case in which the retailer is able to share her complete demand history, we provide a full characterization of the efficient frontier, as well as of an optimal replenishment policy. On the other hand, when the retailer is not able (or willing) to share any demand information we provide a partial characterization of an optimal solution and show that information sharing does not always add value. We also show that the question of identifying conditions under which information sharing does offer value reduces to a delicate analysis of the invertibility (in a time series sense) of a specific stationary process.Operations Management Working Papers Serie

Vertical integration and firm boundaries : the evidence

Since Ronald H. Coase's (1937) seminal paper, a rich set of theories has been developed that deal with firm boundaries in vertical or input–output structures. In the last twenty-five years, empirical evidence that can shed light on those theories also has been accumulating. We review the findings of empirical studies that have addressed two main interrelated questions: First, what types of transactions are best brought within the firm and, second, what are the consequences of vertical integration decisions for economic outcomes such as prices, quantities, investment, and profits. Throughout, we highlight areas of potential cross-fertilization and promising areas for future work

Essays in inventory decisions under uncertainty

Uncertainty is a norm in business decisions. In this research, we focus on the inventory decisions for companies with uncertain customer demands. We first investigate forward buying strategies for single stage inventory decisions. The situation is common in commodity industry where prices often fluctuate significantly from one purchasing opportunity to the next and demands are random. We propose a combined heuristic to determine the optimal number of future periods a firm should purchase at each ordering opportunity in order to maximize total expected profit when there is uncertainty in future demand and future buying price. Second, we study the complexities of bundling of products in an Assemble-To-Order (ATO) environment. We outline a salvage manipulator mechanism that coordinates the decentralized supply chain. Third, we extend our salvage manipulator mechanism to a two stage supply chain with a long cumulative lead time. With significant lead times, the assumption that the suppliers all see the same demand distribution as the retailer cannot be used.Ph.D.Committee Chair: Yih-Long Chang; Committee Member: Paul Griffin; Committee Member: Ravi Subramanian; Committee Member: Soumen Ghosh; Committee Member: Srinagesh Gavirnen

Inventory management of remanufacturable products

Includes bibliographical references (p. 24-25).L. Beril Toktay, Lawrence M. Wein, Stefanos A. Zenios

Approximate Order-up-to Policies for Inventory Systems with Binomial Yield

This paper studies an inventory policy for a retailer who orders his products from a supplier whose deliveries only partially satisfy the quality require- ments. We model this situation by an infinite-horizon periodic-review model with binomial random yield and positive lead time. We propose an order- up-to policy based on approximating the inventory model with unreliable supplier by a model with a reliable supplier and suitably modified demand distribution. The performance of the order-up-to policy is verified by com- paring it with both the optimal policy and the safety stock policy proposed in Inderfurth & Vogelgesang (2013). Further, we extend our approximation to a dual-sourcing model with two suppliers: the first slow and unreliable, and the other fast and fully reliable. Compared to the dual-index order- up-to policy for the model with full information on the yield, the proposed approximation gives promising results