Knowledge that Transforms

A model is developed from which welfare‐optimal prices, capacities, and reliabilities for a service provider are simultaneously determined. Solutions are determined under conditions of stochastic demand subject to a reliability constraint on service quality. Both quality of service provided, as well as price, impact on demand for services rendered. Results indicate that (i) optimal prices are equated to the reliability‐constrained marginal costs, (ii) optimal reliabilities require that the marginal benefits of increasing reliability are equated to the marginal costs of doing so, and (iii) optimal capacity allocation involves minimizing the system's expected costs subject to meeting the prespecified reliability constraint for service quality. The model is applied to postal delivery services in light of the growing competition that has emerged in this industry.

We consider the problem of determining the allocation of demand from different customer orders to production batches and the schedule of resulting batches to minimize the total weighted earliness and tardiness penalties in context of batch chemical processing. The problem is formulated as a mixed‐integer nonlinear programming model. An iterative heuristic procedure that makes use of the network nature of the problem formulation is presented to approximate an optimal solution. An algorithm polynomial in the number of batches to produce is also presented that optimally solves the problem under special cost structures.

Managing throughput time and its relation to work‐in‐process (wip) inventory and customer service is the focus of this paper. This research combines theory, simulation results, and the analysis of corporate data in an effort to address the issues associated with how one company (Eli Lilly) managed a reduction in their throughput times and an improvement in their delivery reliability. The results for this company suggest that production control decisions—expediting and de‐expediting—can lead to a vicious circle of decisions, which in turn can lead to increased levels of WIP inventory and higher and more unpredictable throughput times.

This paper proposes a realistic queueing model of automated guided vehicle (agv) systems in just‐in‐time production systems. The model takes into consideration return paths, Erlang distributed service times, and pull‐type dispatching rule, assuming finite buffer capacities. Since it has no product‐form solution and natural decomposability due to complex nontree fork‐cum‐join architecture and dynamic dispatching rules, we propose a machine‐based decomposition algorithm for the performance evaluation of the model. Each decomposed module consists of the processing machine and its dispatching station. Three flow probabilities, derived from flow conservation analysis, relate the modules, which are updated iteratively until the parameters converge. The numerical results from a real‐life Agv system application show that the algorithm is reasonably accurate.

We examine how process choice links with design decisions about capital and human resources. Our analysis confirms empirically most expected differences between process‐ and product‐focused plants for these decisions. What is unexpected is how top‐performing plants resemble other plants within the same process choice category in most respects, while distinguishing themselves on a few selected process attributes. For example, better performing process‐focused plants not only achieve higher machine flexibility levels and lower overhead costs as expected, but also have more intensive preventive maintenance programs. Similarly, product‐focused plants that achieve certain hallmarks of process‐focused plants also enjoy superior performance.

In this paper we review the use of tradeoff curves in the design of manufacturing systems that can be modeled as open queueing networks. We focus particularly on the tradeoff between expected work‐in‐process (or product leadtime) and capacity investment in job shops. We review the algorithms in the literature to derive tradeoff curves and illustrate their application in evaluating the efficiency of the system, in deciding how much capacity to have, how to allocate resources between the reduction of uncertainty and the introduction of new technologies, and how to assess the impact of changes in products throughput and product mix. The methodology is illustrated with an example derived from an actual application in the semiconductor industry.

This paper explores the effect of expectations and information on customer dissatisfaction in unfavorable, nonroutine service encounters. In complex services (e.g., health care) with multiple encounters and wide range of services, customers use some of the services rarely or only once. In such encounters, customers may not have clear expectations regarding the process and/or outcome of the impending service delivery. This may increase the likelihood of the customer to perceive poor service or be dissatisfied. Hypotheses regarding the nature of expectations—levels, uncertainty, consistency—and its affects on customer dissatisfaction are tested using a dynamic process model of customer dissatisfaction.

This paper describes a periodic review, fixed lead time, single‐product, single‐facility model with random demand, lost sales and service constraints that was developed for potential application at a Western Canadian retailer. The objective of this study was to determine optimal ( s, S) policies for a large number of products and locations. To this end, we evaluate the long run average cost and service level for a fixed ( s, S) policy and then used a search procedure to locate an optimal policy. The search procedure is based on an efficient updating scheme for the transition probability matrix of the underlying Markov chain, bounds on S and monotonicity assumptions on the cost and service level functions. A distinguishing feature of this model is that lead times are shorter than review periods so that the stationary analysis underlying computation of costs and service levels requires subtle analyses. We compared the computed policies to those currently in use on a test bed of 420 products and found that stores currently hold inventories that are 40% to 50% higher than those recommended by our model and estimate that implementing the proposed policies for the entire system would result in significant cost savings.

Service quality improvement has become an imperative in today's service firms. In this paper, we present a modeling framework that combines marketing and operations viewpoints for resource allocation. The framework can be used to allocate resources to the different stages of a multistage service system, where the manager's goal is to improve customers' perceptions of service quality, given some budget. Optimal allocation guidelines are provided, and the interplay of three factors on the resulting allocation scheme is captured. These factors are the current level of customers' perceptions of service quality at each stage, the cost of implementing a service quality improvement at each stage, and the importance placed by customers at each stage. Sensitivity analysis to provide additional managerial insights is also performed. We demonstrate the applicability of the modeling framework, using data from a real life health care environment. Model limitations and future research are also discussed.

We consider a single product, single level, stochastic master production scheduling (Mps) model where decisions are made under rolling planning horizons. Outcomes of interest are cost, service level, and schedule stability. The subject of this research is the Mps control system: the method used in determining the amount of stock planned for production in each time period. Typically, Mps control systems utilize a single buffer stock. Here, two Mps dual‐buffer stock systems are developed and tested by simulation. We extend the data envelopment analysis (dea) methodology to aid in the evaluation of the simulation results, where Dea serves to increase the scope of the experimental design. Results indicate that the dual‐buffer control systems outperform existing policies.