Empirical and Hierarchical Bayesian Estimation in Finite Population Sampling under Structural Measurement Error Models

Abstract.  This paper considers simultaneous estimation of means from several strata. A model-based approach is taken, where the covariates in the superpopulation model are subject to measurement errors. Empirical Bayes (EB) and Hierarchical Bayes estimators of the strata means are developed and asymptotic optimality of EB estimators is proved. Their performances are examined and compared with that of the sample mean in a simulation study as well as in data analysis.     

A multicriteria model for supporting setup reduction investment decisions

One critical manufacturing challenge of the 1990s is for firms to effectively apply new operations management techniques while embracing wider philosophies such as total quality management (TQM) and computer integrated manufacturing (CIM), etc. Setup cost and/or time reduction is one such technique capable of producing many benefits for manufacturing firms, including reduced inventory, better equipment utilization, and improved quality. It is thereby viewed as an important component of just-in-time (JIT) manufacturing practice. Existing problems with the setup reduction decision include the many factors that must be considered, as well of an absence of validated and usable models for estimating potential benefits from setup reduction investment made in different contexts. This paper discusses the attainment of gains from setup reduction mainly by improving existing equipment and work practices rather than purchasing new equipment or technology. The model proposed in this paper is based on the application of the analytic hierarchical process (AHP) on seven weighted factors to obtain a preliminary indication as to whether investment in setup reduction is desirable in a given manufacturing context, and the expected benefits of such investment. A flexible scaling system, thus obtained, allows the model to handle a wide range of managerial predispositions to setup reduction.     

Optimal investment in setup reduction for finite horizon periodic review inventory system under deterministic time varying demand

This paper examines the impact of setup reduction on a finite horizon, periodic review inventory system, under deterministic time varying demand. A total relevant cost function is developed for such systems. Using this, the impact of setup reduction is examined under various forms of setup reduction cost functions that have been suggested in the literature. The operating characteristics and optimization of the various scenarios are discussed. Our analysis shows that the effects of setup reduction in a periodic review system are similar to those in a reorder point system. Our results are likely to help practitioners who use similar periodic review systems towards decreasing total inventory related costs by investing in setup reduction.     

MAIL SURVEY RESPONSE RATE: A META-ANALYSIS OF SELECTED TECHNIQUES FOR INDUCING RESPONSE

This article reports the results of a meta-analysis of experimentalstudies which have examined ten different factors felt to influenceresponse rates to mail surveys. The form of meta-analysis usedclearly defines the individual impact of each of the factorsexamined. Results indicate that prenotification and follow-upsincrease the response rate, as does the type of outgoing postageused. Furthermore, studies sponsored by a university receivegreater returns. Increases in the size of the monetary incentiveused appear to have decreasing marginal gains. Finally, someevidence exists to suggest that the color of the questionnaireinfluences response rate.     

EVALUATING ORDER RELEASE MECHANISMS IN A JOB SHOP WITH SEQUENCE-DEPENDENT SETUP TIMES

Job-release mechanisms in job-shop control are receiving increased recognition in recent research. However, there is considerable disagreement over the benefits of sophisticated job-release mechanisms, and they remain to be tested in various operating environments. In our study, we extend previous job-releasing research by examining several job-release mechanisms in a different operating environment-a sequence-dependent setup job shop. We tested these release mechanisms, originally developed for a machine-constrained job shop, in conjunction with two groups of sequencing rules-ordinary and setup-oriented sequencing rules. We measured shop performance in terms of total cost, the sum of inventory-holding and late-penalty costs. We developed a simulation model of a nine-machine job shop for our experiment.     

SETUP REDUCTION AND MACHINE AVAILABILITY

This paper is an extension of Billington, who used the framework of the economic production quantity (EPQ) to model setup cost reduction. In the present paper, we use the EPQ model as a starting point to investigate the nature of setup costs and the effect of setup time reduction on the increase in available capacity. Reducing setup is vital to a company's success because a lengthy changeover of machinery is expensive: it demands long production runs to justify its cost, and these, in turn, lead to excessive inventory and to a slow response to customer needs. As in Billington, setup reduction is modeled as a function of an annual amortized investment. The paper examines the behavior of the setup time, the inventory cost, the lot size, and the freeing up of machine time in the face of a capacity constraint. A solution algorithm is provided to find setup times that minimize the sum of setup and holding cost, subject to a constraint on machine availability. The analysis sheds light on the true nature of setup cost and on the opportunity cost of not reducing setups. In the constrained optimization, the Lagrangian multiplier gives an estimate of the marginal value of adding one time unit of machine capacity, or, alternatively, of reducing one unit of setup time.