For attribute data with (very) low rates of defectives, attractive control charts can be based on the maximum of subsequent groups of r failure times, for some suitable r ≥ 1, like r = 5. Such charts combine good performance with often highly needed robustness, as they allow a nonparametric adaptation already for Phase I samples of ordinary size. In the present paper we address the problem of e...

The purpose of this article is to evaluate the application of forecasting models along with the use of residual control charts to assess production processes whose samples have autocorrelation characteristics. The main objective is to determine the efficiency of control charts for individual observations (CCIO) and exponentially weighted moving average (EWMA) charts when they are applied to res...

This paper studies an attribute control chart for monitoring the number of nonconforming items using a triple sampling (TS-np) which has not yet been applied to charts. The design and procedure decision about state process are given. Mathematical expressions average run length (ARL) in-control out-of-control processes sample (ASN) A bi-objective genetic algorithm that seeks minimize ASN probabi...

Monitoring multi-attribute processes is an important issue in many quality control environments. Almost all the priory proposed control charts utilize equal weights for each Attribute Quality Characteristics (AQCs). In such condition, there is no priority among AQCs. But in real-world, compensatory may exist. Hence due to some applied reasons such as function or efficiency, unequal weights for ...

In some multivariate statistical control applications, the data of the process cannot be precise and defined linguistically in practice. Using multivariate control charts in such situations with non-precise data leads to misleading results. In this paper, a new neural network-based monitoring scheme is presented by considering fuzzy multivariate multinomial data. The proposed approach is also a...

1. Foundations for problem solving and improvement 2. Framework for capability: The broader quality system 3. 1.33 as an objective: Factors and relationships to variation, charts 4. Using the charts to solve problems, reduce variation, make improvements 5. Fliers, outliers, trends, and other out of control conditions 6. Systematic use of data and charts: Revisiting basic SPC conditions 7. Quali...