Exact Measure Of Process Yield Research Articles

Developing a quality-based supplier selection model from the buying company perspective

ABSTRACT In the face of increased competition and globalization, supply chain management has become a crucial aspect of securing competitive advantage. Within this, the quality offered by suppliers is vital. Thus, in this paper, we develop a model to select the best supplier based on process quality from the perspective of the buyer. We first employ the process capability index , which can provide an exact measure of process yield, to evaluate the process quality of suppliers. However, must be estimated from preliminary samples or obtained subsamples. This increases uncertainty and can lead to miscalculation. To ensure the reliability of assessment, we derive the lower confidence limit of to serve as a standard of process quality assessment and construct a process capability analysis chart to select the best supplier. The chart also provides recommendations for process improvement to serve as a reference for suppliers with poor performance. Finally, we present two real-world case studies to demonstrate the practical applicability of the proposed method.

Measuring the Process Yield for Circular Profiles

Two indices Cp(circular) and Cpk(circular) based on the functional method have been proposed to measure the process capability of circular profiles. However, these two indices only provide potential capability and a lower bound on the process yield, respectively. In this paper, we develop a new yield index Spk(circular) for circular profiles. This index provides an exact measure of process yield. The asymptotic normal distribution of the estimated index is derived. The statistical inferences such as hypothesis testing, confidence interval, and lower confidence interval can be easily constructed. A simulation study is conducted to assess the performance of the proposed method. The simulation results confirm that the estimates are close to the true value and the coverage rates of the confidence intervals are greater than the 95% lower limit of the stated nominal in most cases. One real data set is used to illustrate the applicability of the proposed method. Copyright © 2013 John Wiley & Sons, Ltd.

Fuzzy nonlinear programming approach for evaluating and ranking process yields with imprecise data

Process yield, the percentage of processed product units passing inspection, is a standard numerical measure of process performance in the manufacturing industry. On the basis of the process yield expression, an index Spk was developed to provide an exact measure of process yield for normally distributed processes. Most traditional studies measuring process capability are based on crisp estimates in which the output process measurements are precise. However, it is common that the measurements of process quality characteristics are insufficiently precise. Traditional approaches for evaluating process yield become unreliable in such cases. Therefore, this study formulates fuzzy numbers to describe the quality characteristic measurements and applies two methods to construct the fuzzy estimation for Spk. A nonlinear programming approach is provided to solve the α-level sets of the estimator, and a testing procedure is presented for making decisions. Finally, this concept is illustrated with an example and extended to solve the ranking problem of multiple yield indices.

Generalized Inference for Measuring Process Yield With the Contamination of Measurement Errors—Quality Control for Silicon Wafer Manufacturing Processes in the Semiconductor Industry

The yield index Spk provides an exact measure of process yield for normally distributed processes, and it has been popularly accepted by many engineers and shop floor controllers as communication tools for evaluating and improving the manufacturing quality. Most research works related to Spk are carried out under the assumption of no gauge measurement errors. Unfortunately, such an assumption does not accommodate real situations closely even with modern and highly sophisticated measuring instruments. Conclusions drawn from process capability analysis without considering measurement errors are hence unreliable. Recently, Wang studied the impact of the process yield estimation and judgment on the true process yield in the presence of measurement errors and indicated that the presence of measurement errors in the data leads to different behaviors of the estimator according to the entity of the contamination degree. To remedy this, this paper applies the concept of generalized pivotal quantities to construct generalized confidence intervals for Spk with consideration of measurement errors. A series of simulations was undertaken to evaluate the performance of the proposed generalized inference method. The results reveal that the generalized inference method performs very well for measuring process yield in the presence of measurement errors.

Estimating and testing process yield with imprecise data

The index S PK provides an exact measure of process yield for normally distributed processes, and has been widely used in manufacturing industry for measuring process performance. Most studies on estimating and testing process yield are based on crisp estimates involving precise output process measurements. However, it is not uncommon for measurements of product quality to be lack precision. This study designs a realistic approach for assessing process yield that considers a certain degree of imprecision on the sample data. By adopting an extended version of the approach of Buckley, the membership function of fuzzy estimator of S PK index is constructed. With normal approximation to the distribution of the estimated S PK , two useful criteria for fuzzy hypothesis testing, critical value and fuzzy p-value, are developed to assess process yield based on S PK . Finally, an application example is presented to demonstrate the application of the proposed approach.

Applying Capability Index to the Supply Network Analysis

Supply Chain Management (SCM) adopts a systematic and integrative approach to managing the operations and relationships of various parties in a supply chain. The objective of the SCM is to reduce inventory costs and increase customer satisfaction levels. Supply chains span from raw materials to manufacturing, distribution, transportation, warehousing, and retailers for product sales. The ultimate goal of supply chain coordination is to maximize the benefits of the whole supply chain. One way of doing that is to synchronize delivery performance. To evaluate the delivery performance of a supply chain network, the supply chain process can be decomposed into detailed business processes. This paper investigates supply chain performance based on the capability index, Spk, which establishes the relationship between customer specification and actual process performance, providing an exact measure of process yield. A case from the thin-film transistor liquid crystal display (TFT-LCD) industry is used to demonstrate the proposed methodology.