Browsing by Subject "Manufactures -- Costs"
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Item A destructive sampling method designed for high quality production processes (DSM-HQ)(Texas Tech University, 2004-12) Delgadillo, FranciscoIn manufacturing and assembly, the sampling of units produced is important since in many situations not all of the units can be tested. Destructive sampling, which commonly occurs in the assembly and manufacturing industry, is a form of sampling where all units produced cannot be tested since the parts are destructively tested. In this situation, sampling techniques are used to determine if an entire lot should be accepted or rejected based on the sampling results. The traditional sampling techniques include single or classical sampling, double sampling, multiple sampling, skip-lot sampling, chain sampling and MIL-STD-105E. However, in the modem era of high quality production, traditional sampling techniques require a high number of units tested in order to guarantee a high level of quality resulting in very high sampling costs. Therefore, to keep costs down, manufacturers and assemblers have used these techniques with lower sampling numbers in order to monitor quality. A goal of this research is to develop a sophisticated technique that monitors quality and outperforms the existing techniques in situations where quality is high and tests are destructive. The proposed technique. Destructive Sampling Method for High Quality production processes (DSM-HQ), is based on a cost function, which balances the costs of sampling versus the costs of finding a defect on the field. DSM-HQ assumes to have a Poisson process defect pattern and uses an Empirical Bayesian analysis to allow the researcher to include prior knowledge. The research simulation and results are separated in two stages. Stage 1 fine tunes DSM-HQ and examines its properties, while Stage 2 compares DSM-HQ to the traditional methods. The simulation results from Stage 2 show that DSM-HQ is superior to the traditional methods in most cases at the 5-sigma level. As the quality increases to 6-sigma, DSM-HQ proves to be significantly superior to all traditional methods in every cost case considered and in both random events combined with out-of-control events case and the random-event-only case. Although DSM-HQ sampling method has some limitations, which will be explored in future research, and the case examined here is limited in scope, which will be expanded in future research, the results and comparisons to traditional methods are very encouraging.Item The integrated economic production quantity model for inventory and quality(Texas Tech University, 2004-12) Ittharat, TharatDetermining the optimal production lot sizing has been widely used by the classical economic production quantity (EPQ) model. However, the analysis for finding an EPQ has several weaknesses which lead many researchers to make extensions in several aspects on the original EPQ model. The cost of quality is one of good aspects to be added to the EPQ model since there are a lot of costs incurred such as prevention, appraisal, failure, warranty (products returned from customer), inspection, and rework costs. The integration of cost of quality and EPQ should be able to link and classify each cost of quality in practical way of inventory management. This paper deals with the finite production inventory model integrated with quality costs for a single product imperfect manufacturing system. This problem assumes that the product quality is not always perfect unlike the traditional EPQ model. The defect rate is considered as a proportion of the production rate, and defective items are reworked at some cost either before, or after sales (products returned by the customer). The prevention, appraisal, and inspection costs have somewhat inverse relationships to the defective rate. The replacement rate from products returned by the customer is also considered to be another random variable with known failure rate in the field. The purpose of this research is to investigate the quality cost factors in the economic production quantity inventory model in order to find the optimal lot size. The objective is to develop mathematical models in order to minimize the annual total cost of inventory and quality.