Ted Klastorin

Coordinating orders in supply chains through price discounts

In this paper, we examine the issue of order coordination between a supplier and multiple retailers in a decentralized multi-echelon inventory/distribution system where the supplier provides a product to multiple retailers who experience static demand and standard inventory costs. Specifically, we propose and analyze a new policy where a manufacturer, who outsources production to an Original Equipment Manufacturer, offers a price discount to retailers when they coordinate the timing of their orders with the manufacturers order cycle. We show that our proposed policy can lead to more efficient supply chains under certain conditions, and present a straightforward method for finding the manufacturers optimal price discount in this decentralized supply chain. A numerical experiment illustrates the managerial implications of our model as well as conditions when a manufacturer should consider adopting such a policy.

Impact of material flow policies and goals on job outcomes

A manual production line was examined for effects of different material flow policies and 3 different goal-setting policies. The line used a push system, where workers work at their own pace (assuming available work ) and pass work to the next station as soon as the work is completed, and a pull system, where workers pass work only when the next worker needs it. Three different goal-setting policies involved no specified goals, individual goals, or group goals confounded with monetary incentives and feedback. Measurements were taken from unobtrusive videotaping and worker questionnaires. Analyses indicated productivity increased approximately 25% when group goals were matched to a pull policy (compared to a push policy with no specified goals). Other results relating to productivity and job satisfaction are discussed.

An effective subgradient algorithm for the generalized assignment problem

Abstract A modified subgradient algorithm is presented for the generalized assignment problem, which, like the classical assignment problem, is concerned with the minimum cost assignment of agents to jobs. The generalized assignment problem, however, permits differences in job performance efficiencies among agents and thereby allows the possibility that each agent may be assigned more than a single job, as long as each job is ultimately assigned and the total resources available to every agent are not exceeded. A two stage heuristic algorithm using a modified subgradient approach and branch and bound is developed for solving the problem. By computing step sizes precisely and using the dual as a bound, the algorithm is shown to be particularly effective and easy to program and implement. A numerical example is presented to illustrate the model and method, and computational experience is cited for problems containing up to 12,000 0–1 variables.

Production classification system: concepts, models and strategies

Production processes are most frequently classified in the literature into one of three categories: projects, intermittent processes (i.e., job or flow shops), or continuous processes (i.e., assembly lines or flow processes). A major shortcoming of this classification system is that it often fails to capture important aspects of real world production processes, thus providing manufacturing managers with little conceptual work to guide their decision-making. In this paper, a more comprehensive framework (which we denote as the production classification system, or PCS) is developed which defines and relates many types of production processes, In addition, a direct linkage between this framework and corporate competitive strategy is described.

An effective methodology for the stochastic project compression problem

In this paper, we consider the problem of planning a complex project when task durations are random. Specifically, we consider the problem of deciding how much to compress tasks in order to minimize the expected total cost that is defined by the sum of direct, indirect, and incentive costs. We initially consider this problem under the assumption that task durations can be modeled by a negative exponential distribution although we later relax this assumption and show that our methodology can be applied to any general distribution. To solve this problem, we develop an effective heuristic algorithm that we call the Stochastic COmpression Project (SCOP) algorithm; the SCOP algorithm is straightforward to implement and our numerical tests indicate that the algorithm performs significantly better than previously reported heuristics. In addition, we compare our approach to solutions found using expected values embedded in a deterministic approach (an approach that is frequently used to solve this problem in practice). Using our results, we show that the deterministic approximation approach, such as the classic PERT model, provides biased results and should be avoided.

On the Measurement of Hospital Case Mix

This article discusses a number of issues related to the measurement of hospital diagnostic case mix. We initially examine a number of previous attempts to measure case mix based on surrogate measures (e.g., facilities and services) and information from predetermined discharge-classification systems. Since a number of researchers have attempted to reduce diagnostic classification data into a single-valued (i.e., scalar) case mix index, we then discuss a number of concepts and assumptions implicit in the construction of such indices. Among these assumptions is the property of functional homogeneity; this property and a methodology based on Q-type factor analysis for testing for the presence of this property are defined. In order to illustrate the use of the methodology, it is applied to data from 153 hospitals in downstate New York.

A current reappraisal of berry’s hospital typology

In an earlier article, Berry1 published the results of a study that examined possible relationships among hospital facilities and services. In general, Berry found that hospitals behaved in a reasonably consistent fashion; that is, hospitals added facilities and services in a well-defined order, and these facilities and services were added in distinct groups. Berry defined five such groups and hypothesized that each group represents an increasing level of case-mix complexity. Given the current interest in using Berrys results to measure hospital case mix, this study attempted to replicate Berrys results using recent data from the American Hospital Association. Using a number of statistical methodologies (including cluster analysis and Guttman scale analysis), we found that hospitals continue to add facilities and services in a well-defined order (although this order contains some notable differences from the order found by Berry). However, our results indicate that hospitals no longer add facilities and services in well-defined groups but in a more continuous fashion. Thus, it would appear that hospitals have become more differentiated.

Production Lot-Sizing Under Learning Effects: An Efficient Solution Technique

Abstract In this paper, we consider a production-inventory model which assumes that learning occurs as a function of the number of units produced. We analyze two cases: the first case allows for no forgetting between production runs and the second case (a generalization of the first case) allows for some given degree of forgetting between production runs. In the first case, we show that learning only has an impact on initial lot-sizes for large order quantities and that steady state lot-sizes will approach the traditional EOQ amount. In addition, we show that succeeding lot-sizes are always nonincreasing. Applying these results to the second case when forgetting occurs, we develop efficient heuristic algorithms with complexity O(N logN) to determine order quantities. Results from our algorithms are compared to optimal solutions; these comparisons indicate that our algorithms usually provide solutions within one percent of the optimal cost.

The impact of small lot ordering on traffic congestion in a physical distribution system

In recent years, some managers and researchers have advocated reducing lot sizes by decreasing setup costs, arguing that smaller lot sizes improve quality while reducing inventory levels and associated holding costs. However, smaller lot sizes result in an increased number of shipments which, in turn, exacerbates traffic congestion. This results in longer delivery times and, thereby, higher inventory levels. In this paper we study the relation between lot sizes and traffic congestion by constructing a model with numerous retailers who share a common congested delivery road. Using a numerical example, we illustrate the models managerial implications with respect to several factors, including lot sizes, traffic congestion, and inventory levels. Our findings suggest that in a physical distribution system, if there are a relatively large number of retailers, no single retailer has an incentive to increase batch sizes because one retailers effect on reducing traffic congestion will be negligible. If all retailers increase their lotsizes, however, traffic congestion will be reduced and all retailers will experience lower costs.

Incentive Contracts in Serial Stochastic Projects

In this paper we propose an incentive payment contract for stochastic projects defined by a series of stages or tasks that are outsourced to independent subcontractors. Projects defined by sequentially completed independent stages are common in new product development and other high-risk projects. Our goal is to maximize the clients expected discounted profit. Our proposed contract reflects the convex time-cost trade-off that is well known in the project scheduling literature. We show that this type of contract dominates a fixed price contract with respect to expected clients profit and schedule performance, regardless of payment timing considerations. Using a piecewise linear approximation, we show that our contract is a generalization of an incentive/disincentive contract that is frequently used in practice. We show how our contract can be used to find the optimal due date and penalties/bonuses in an incentive/disincentive contract. We compare this contract with several variations and discuss implications for both the client and subcontractors.