Objective. To develop insights on the impact of size, average length of stay, variability, and organization of clinical services on the relationship between occupancy rates and delays for beds. Data Sources. The primary data source was Beth Israel Deaconess Medical Center in Boston. Secondary data were obtained from the United Hospital Fund of New York reflecting data from about 150 hospitals.
The subject of this paper is autoregressive (AR) modeling of a stationary, Gaussian discrete time process, based on a finite sequence of observations. The process is assumed to admit an AR(∞) representation with exponentially decaying coefficients. We adopt the nonparametric minimax framework and study how well the process can be approximated by a finite-order AR model. A lower bound on the accuracy of AR approximations is derived, and a nonasymptotic upper bound on the accuracy of the regularized least squares estimator is established.
We address multi-item inventory systems with random and seasonally fluctuating, and possibly correlated, demands. The items are produced in two stages, each with its own lead-time; in the first stage a common intermediate product is manufactured. The production volumes in the first stage are bounded by given capacity liits. We develop an accurate lower bound and close-to-optimal heuristic strategies of simple structure. The gap between them, evaluated in an extensive numerical study, is on average only 0.45%.
The authors investigate two competing hypotheses about how chronic vividness of imagery interacts with the vividness and salience of information in decision making. Results from four studies, covering a variety of decision domains, indicate that chronic imagery vividness rarely amplifies the effects of vivid and salient information. Imagery vividness may, in fact, attenuate the effects of vivid and salient information. This is because, relative to nonvivid imagers, vivid imagers rely less on information that appears obvious and rely more on information that seems less obvious.
It has been recently suggested that sponsor identification may be biased in favor of prominent brands. All things equal, consumers are more likely to attribute sponsorship to brands that they perceive to be more prominent in the marketplace, such as large-share brands. This article offers additional empirical evidence for this phenomenon and examines the underlying processes. The results of a controlled laboratory experiment replicate the phenomenon and show that this bias arises only when consumers are unable to retrieve the name of the sponsor directly from memory.
A monopolist sells a single product to a market where the customers may be enticed to accept a delay as to when their orders are shipped. The enticement is a discounted price for the product. The market consists of several segments with different degrees of aversion to delays. The firm offers a price schedule under which the customers each self-select the price they pay and when their orders are to be shipped. When a customer agrees to wait, the firm gains advanced demand information that can be used to reduce its supply chain costs.
We analyze a model of inventory competition among n firms that provide competing, substitutable goods. Each firm chooses initial inventory levels for their good in a single period (newsboy-like) inventory model. Customers choose dynamically based on current availability, so the inventory levels at one firm affect the demand of all competing firms. This creates a strategic interaction among the firms' inventory decisions. Our work extends earlier work on variations of this problem by Karjalainen (1992), Lippman and McCardle (1997) and Parlar (1988).
We analyze a single-period, stochastic inventory model (newsboy-like model) in which a sequence of heterogeneous customers dynamically substitute among product variants within a retail assortment when inventory is depleted. The customer choice decisions are based on a natural and classical utility maximization criterion. Faced with such substitution behavior, the retailer must choose initial inventory levels for the assortment to maximize expected profits.
Recent papers have developed analytical models to explain and quantify the benefits of delayed differentiation and quick response programs. These models assume that while demands in each period are random, they are independent across time and their distribution is perfectly known, i.e., sales forecasts do not need to be updated as time progresses. In this paper, we characterize these benefits in more general settings, where parameters of the demand distributions fail to be known with accuracy or where consecutive demands are correlated.