ON THE USE OF BUFFER INVENTORIES TO MINIMIZE COSTS WITH AN UNRELIABLE MACHINE

In this paper, we discuss the problem of quality control with an unreliable machine which produces defects at a rate of Λ₀, per unit when in-control and a rate of Lambda; ₁, when out-of-control (where Λ₁ Λ ₀). Every h time periods, we sample n units, count the number of defects, and (using a process based on a Shewart c-chart) test the hypothesis that the machine is in control by comparing the total number of defects to an upper control limit (UCL). More important, we introduce the concept that a buffer inventory which immediately follows the unreliable machine may reduce expected total costs. This buffer serves to delay the movement of items from the unreliable machine to the next stage of the production process. In this way, we can isolate and repair most defective items before they are embedded in a product downstream or sold to customers where repair is more costly. To search for the optimal control policy, we find bounds for n, h, and UCL; given values for these variables, we show how the optimal buffer size can be determined directly. Numerical results illustrate the magnitude of potential savings.     

Impact of material flow policies and goals on job outcomes.

A manual production line was examined for effects of 2 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. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

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

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.     

IIE Transactions Special Issue on Planning and Coordination of Supply Chains with Outsourcing

Introduction

IIE Transactions Special Issue on Planning and Coordination of Supply Chains with Outsourcing     

Project performance and the liability of group harmony

There have been numerous debates about the proper makeup and behavior of effective project teams, and the literature is contradictory with regard to this issue. In the present study, 44 members of 14 project teams who worked on the same complex computer-simulated project were observed. A survey administered four times during the life of the project revealed that initial ratings of group attributes were good predictors of later success. Teams which ultimately performed well began the project with lower opinions of their respective groups than did teams which ultimately did not perform well. Peer ratings of individual members' contributions were generally lowest in the high-performing teams. Additionally, technical expertise appears to have been valued more highly in the high-performing groups than in low-performing groups. These results suggest that project teams which begin with harmonious interpersonal relations may not perform as well as those which experience early disharmonics. The managerial implications of these results are discussed in relation to the literature on groups, with special attention to `groupthink' theories     

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 manufacturer's 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 manufacturer's 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.     

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.     

An effective subgradient algorithm for the generalized assignment problem

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.