In many industries, firms consider the option of outsourcing an important service process associated with the goods or services they bring to the market. Often, competing firms outsource this service process to one or more common service suppliers. When they outsource to a common service provider, this gives rise to a service supply chain.
We describe research on a supply chain contracting problem that was sponsored by a major semi-conductor manufacturer. The manufacturer sells products (semi-conductor parts) with varying quality levels through a network of distributors to end consumers (independent computer shops, system configurators, hobbyists, etc.) who have heterogeneous valuations for quality. Since production costs for semi-conductors are essentially independent of quality levels (within a part family), the manufacturer earns much more selling higher quality parts.
This paper considers a profit-maximizing make-to-order manufacturer that offers multiple products to a market of price and delay sensitive users, using a model that captures three aspects of particular interest: first, the joint use of dynamic pricing and lead-time quotation controls to manage demand; second, the presence of a dual sourcing mode that can expedite orders at a cost; and third, the interaction of the aforementioned demand controls with the operational decisions of sequencing and expediting that the firm must employ to optimize revenues and satisfy the quoted lead times.
Dynamic pricing offers the potential to increase revenues. At the same time, it creates an incentive for customers to strategize over the timing of their purchases. A firm should ideally account for this behavior when making its pricing and stocking decisions. In particular, we investigate whether it is optimal for a firm to create rationing risk by deliberately understocking products. Then, the resulting threat of shortages creates an incentive for customers to purchase early at higher prices. But when does such a strategy make sense?
We focus on the problem of adaptive estimation of signal singularities from indirect and noisy observations. A typical example of such a singularity is a discontinuity (change-point) of the signal or of its derivative. We develop a change-point estimator which adapts to the unknown smoothness of a nuisance deterministic component and to an unknown jump amplitude. We show that the proposed estimator attains optimal adaptive rates of convergence. A simulation study demonstrates reasonable practical behavior of the proposed adaptive estimates.
We analyze a planning model for a firm or public organization that needs to cover uncertain demand for a given item by procuring supplies from multiple sources. Each source faces a random yield factor with a general probability distribution. The model considers a single demand season. All supplies need to be ordered before the start of the season. The planning problem amounts to selecting which of the given set of suppliers to retain, and how much to order from each, so as to minimize total procurement costs while ensuring that the uncertain demand is met with a given probability.
We consider the problem of nonparametric estimation of signal singularities from indirect and noisy observations. Here by singularity, we mean a discontinuity (change-point) of the signal or of its derivative. The model of indirect observations we consider is that of a linear transform of the signal, observed in white noise. The estimation problem is analyzed in a minimax framework. We provide lower bounds for minimax risks and propose rate-optimal estimation procedures.
We consider the risk of a portfolio comprising loans, bonds, and financial instruments that are subject to possible default. In particular, we are interested in performance measures such as the probability that the portfolio incurs large losses over a fixed time horizon, and the expected excess loss given that large losses are incurred during this horizon.
This paper develops a simple and systematic approach for obtaining bounds on stationary expectations of Markov processes. Given a function f which one is interested in evaluating, the main idea is to find a function g that satisfies a certain "mean drift" inequality with respect to f, which in turn leads to bounds on the stationary expectation of the latter.