Some Issue in Statistical Process Control

The object of this paper is to discuss some of the issues in statistical process control (SPC), along with some methods to deal with them, so that the SPC users would get the most benefit out of their quality control efforts. An alternative approach to process control, i.e., process adjustment, which might be preferred over process control in some situations, will also be briefly discussed

An Alternative Method to Test the Residuals in a Regression Model

In this paper we propose an alternative method to the Durbin-Watson(DW) test for the fitness of a regression model (see, for example, Makridakis [5, pp. 267-258; 303-304; 630-631] and Wilson & Keating [8, pp. 182-184; 234-236] for DW statistic). The proposed method tests whether the residual terms (i.e., actual – model) display any sign of non-randomness by comparing two variance estimators of the residuals. This is a transformation of the DW statistic into a standardized normal statistic, N(0,1) which is readily interpreted; and unlike the DW test does not require a special table. A numerical example is provided

Data precision - Do you have enough?

The control chart on page 4, gives a clear signal that the data has a problem with precision. There are only four or five plateaus in the data. This would occur for example, if you were measuring thickness with a ruler calibrated in units of 1/16ths and there was little chance for variation in the data

Modeling in statistical process control

Statistical Process Control (SPC), which is based on statistical theory, helps to monitor the performance of a process. SPC techniques were first introduced by Shewhart [1] in the 1930\u27s. They are used to identify, control and eliminate variation in the process. To control and reduce variation, one should understand it\u27s sources

Estimating Potential Capability of an Unstable Process

In this paper we describe several methods for providing benchmark estimate of the potential width capability of a process when the process becomes stable in the statistical process control (SPC) sense of the word. We define continually meet customer requirements which are expressed in terms of product specifications. Product specifications are generally concerned with both the center and the width of the various characteristics of the process output. The term capability is appropriate for process sense. Should the process not be in control, the term process capability (Cp) should be replaced by process performance (Pp); and the ratio calculations should be based on the capability standard deviation and the performance standard deviation respectively. This point is describes several methods for evaluating the capability standard deviation which do not require the use of control charts. A numerical example is given

Process Performance Measures When Process Distribution is Non-Normal

This paper addresses the problems associated with process performance measures, such as Cp. Cpk, when the frequency distribution of the variable being evaluation is not Normal. These measures, also known as capability indices, are commonly used in industry, yet they may not reflect the true process performance if the process distribution is not Normal. Gunter (1), in his four-part series articles, emphasized this point and other problems associated with measures like Cpk. In this paper, we discuss various scenarios with respect to process stability and frequency distribution, and provide an example using non-Normal process curve

Acceptance Charts for Low Data Environments

The purpose of acceptance charts is to evaluate a process to determine whether or not it can be expected to satisfy product specifications. Conventional application of acceptance charts involves use of subgroup averages (X\u27s), which requires frequent and large amounts of data. This paper, instead, proposes the use of moving averages in the acceptance chart. This way the acceptance chart approach can be used in low data environment. An example is included to illustrate the proposed concept

Building an Acceptance Chart Around the Process Mean

In this paper we will propose a new way of building an acceptance chart which could be utilized in Six Sigma environment. The proposed method is to build the acceptance chart around the desired value for the process mean rather than around the specification limits. An example is included to illustrate the proposed method

A Tool to Monitor Processes

The purpose of this paper is to present a method which will enable the manager to make comparisons of the quality performance of a set of product lines so that the management\u27s attention is concentrated where it is most needed. The comparison is usually difficult because the different lines may produce different specifications. The method proposed in this paper uses three Z values, which are described below

Integrating Several Variance Estimators

It is crucial to understand the proper variance estimator in statistical process control (SPC) when you are trying to answer the questions on the process behavior both in the short term and the long term. In this paper we will discuss various variance estimators including their calculation using range and/or range squares, their potential use in SPC, their similarities and differences