An important observation in supply chain management, known as the bullwhip effect, suggests that demand variability increases as one moves up a supply chain. In this paper we quantify this effect for simple, two-stage supply chains consisting of a single retailer and a single manufacturer. Our model includes two of the factors commonly assumed to cause the bullwhip effect: demand forecasting and order lead times. We extend these results to multiple-stage supply chains with and without centralized customer demand information and demonstrate that the bullwhip effect can be reduced, but not completely eliminated, by centralizing demand information.bullwhip effect, forecasting, information, inventory, lead time, supply chain, variability

Real-time condition monitoring is becoming an important tool in maintenance decision-making. Condition monitoring is the process of collecting real-time sensor information from a functioning device in order to reason about the health of the device. To make effective use of condition information, it is useful to characterize a device degradation signal, a quantity computed from condition information that captures the current state of the device and provides information on how that condition is likely to evolve in the future. If properly modeled, the degradation signal can be used to compute a residual-life distribution for the device being monitored, which can then be used in decision models. In this work, we develop Bayesian updating methods that use real-time condition monitoring information to update the stochastic parameters of exponential degradation models. We use these degradation models to develop a closed-form residual-life distribution for the monitored device. Finally, we apply these degradation and residual-life models to degradation signals obtained through the accelerated testing of bearings.

An important phenomenon often observed in supply chain management, known as the bullwhip effect, implies that demand variability increases as one moves up the supply chain, i.e., as one moves away from customer demand. In this paper we quantify this effect for simple, two-stage, supply chains consisting of a single retailer and a single manufacturer. We demonstrate that the use of an exponential smoothing forecast by the retailer can cause the bullwhip effect and contrast these results with the increase in variability due to the use of a moving average forecast. We consider two types of demand processes, a correlated demand process and a demand process with a linear trend. We then discuss several important managerial insights that can be drawn from this research.

O nline marketplaces, such as those operated by Amazon, have seen rapid growth in recent years. These marketplaces serve as an intermediary, matching buyers with sellers, whereas control of the good is left to the seller. In some cases, e.g., the Amazon marketplace system, the firm that owns and manages the marketplace system will also sell competing products through the marketplace system. This creates a new form of channel conflict, which is a focus of this article. We consider a setting in which a marketplace firm operates an online marketplace through which retailers can sell their products directly to consumers. We consider a single retailer, who currently sells its product only through its own website, but who may choose to contract with Amazon to sell its product through the marketplace system. Selling the product through the marketplace expands the available market for the retailer, but comes at some expense, e.g., a fixed participation fee or a revenue sharing requirement. Thus, a key question for the retailer is whether she should choose to sell through the marketplace system, and if so, at what price. We analyze the optimal decisions for both the retailer and the marketplace firm and characterize the system equilibrium.

An important phenomenon often observed in supply chain management, known as the bullwhip effect, implies that demand variability increases as one moves up the supply chain, i.e., as one moves away from customer demand. In this paper we quantify this effect for simple, two-stage, supply chains consisting of a single retailer and a single manufacturer. We demonstrate that the use of an exponential smoothing forecast by the retailer can cause the bullwhip effect and contrast these results with the increase in variability due to the use of a moving average forecast. We consider two types of demand processes, a correlated demand process and a demand process with a linear trend. We then discuss several important managerial insights that can be drawn from this research.

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In this paper we study a class of one-sided post-processing techniques to enhance the accuracy of the discontinuous Galerkin methods. The applications considered in this paper are linear hyperbolic equations, however the technique can be used for the solution to a discontinuous Galerkin method solving other types of partial differential equations, or more general approximations, as long as there is a higher order negative norm error estimate for the numerical solution. The advantage of the one-sided post-processing is that it uses information only from one side, hence it can be applied up to domain boundaries, a discontinuity in the solution, or an interface of different mesh sizes. This technique allows us to obtain an improvement in the order of accuracy from k+1 of the discontinuous Galerkin method to 2k+1 of the post-processed solution, using piecewise polynomials of degree k, throughout the entire domain and not just away from the boundaries, discontinuities, or interfaces of different mesh sizes.

Given an infeasible system of linear inequalities, we show that the problem of identifying all minimally infeasible subsystems can be reduced to the problem of finding all vertices of a related polyhedron. This results in a shorter enumeration than that performed by previous method to solve this problem. INFORMS Journal on Computing, ISSN 1091-9856, was published as ORSA Journal on Computing from 1989 to 1995 under ISSN 0899-1499.