This paper develops a new model for studying multiechelon inventory systems with stochastic demand. For the model we assume that each site in the system orders at preset times according to an order-up-to policy, that delivery times are deterministic, and that the demand processes are stochastic with independent increments. We introduce a new scheme for allocating stock in short supply, which we call virtual allocation and which permits significant tractability. We exercise the model on a set of test problems for two-echelon systems to get insight into the structure of good policies. The primary findings are that both the central warehouse (upper echelon) and the retail sites (lower echelon) should hold safety stock, but that most of the safety stock should be at the retail sites. Consequently, the central warehouse will stock out with high probability. Furthermore, we show that the virtual allocation rule is near optimal for the set of test problems.

This study compared the relationships between organizational context and the designs of environmental scanning systems for organizational with effective and ineffective scanning systems. The study analyzed data from over 100 North American business organizations. Results indicate that organizations with effective scanning systems tend to align their scanning designs with the requirements of their context. On the other hand, the results show that organizations with ineffective scanning systems typically fail to exhibit the requisite level of alignment between contexts and scanning design.

We develop an algorithm that solves the constant capacities economic lot-sizing problem with concave production costs and linear holding in O(T 3 ) time. The algorithm is based on the standard dynamic programming approach which requires the computation of the minimal costs for all possible subplans of the production plan. Instead of computing these costs in a straightforward manner, we use structural properties of optimal subplans to arrive at a more efficient implementation. Our algorithm improves upon the O(T 4 ) running time of an earlier algorithm.

Date Envelopment Analysis (DEA) employs mathematical programming to measure the relative efficiency of Decision Making Units (DMUs). This paper is concerned with development of indicators to determine whether or not the specification of the input and output space is supported by data in the sense that the variation in data is sufficient for estimation of a frontier of the same dimension as the input output space. Insufficient variation in data implies that some inputs/outputs can be substituted along the efficient frontier but only in fixed proportions. Data thus locally supports variation in a subspace of a lower dimension rather than in the input output space of full dimension. Each segment of the efficient frontier is in this sense subject to local collinearity. Insufficient variation in data provides a bound on admissible disaggregations in cases where substitution in fixed proportions is incompatible with a priori information concerning the production process. A data set incapable of estimating a frontier of full dimension will in this case be denoted ill-conditioned. It is shown that the existence of well-defined marginal rates of substitution along the estimated strongly efficient frontier segments requires the existence of Full Dimensional Efficient Facets (FDEFs). A test for the existence of FDEFs is developed, and an operational two-stage procedure for efficiency evaluation relative to an over-all non-fixed technology is developed; the two-stage procedure provides a lower and an upper bound on the efficiency index for each DMU.

The United Network for Organ Sharing system of allocating cadaveric kidneys for transplantation permits only minimal involvement of patients in the selection process. The system ignores potential variations in the importance that patients may attach to outcomes associated with the transplant decision. For instance, some patients may prefer only kidneys that will give them a very favorable chance to achieve a successful transplant. We designed a decision model in which patients are surveyed about their preferences for health states that in turn affect decisions about the type of donor kidneys that would be acceptable for transplantation. Our analyses show that patients with favorable transplant characteristics (e.g., young age, good health, good immunologic match between the donor kidney and the recipient) or who expect a minimal improvement in quality of life after successful transplantation can afford to be selective when considering which kidneys to accept for transplantation. Involving patients in selecting the optimal donor-kidney should improve patients' quality and duration of life with end-stage renal disease, and thereby improve the overall efficiency of the U.S. kidney transplantation program.

Four antecedents, it is argued, are necessary precursors for a firm to capture rents from innovation. The antecedents are causal understanding; innovation team proficiency; emergence and mobilization of new competences; and creation of competitive advantages, each of which are conceptually distinct and precisely defined in the paper. These constructs are linked together in a stage model and subsequently operationalized and tested using LISREL. Substantial support is found for the central thesis, that achieving each of the four antecedent processes increases the predicted rents from an innovation project.