An Application of Variable Sampling Interval Control Charts

Adaptive control charts allow the components of the quality-monitoring scheme to vary in order to obtain improved performance over non-adaptive control charts. Research has centered on components such as the sample size, time between samples, warning limits, and control limits and has recommended a variety of schemes, many of which are optimal in some sense. In practice, there are many other adaptive schemes that are near optimal, which will still yield considerable improvement over non-adaptive control charts. In addition, the impact of parameter estimation on adaptive control chart performance must be taken into consideration. Based on the simulation results shown here, adaptive control charts should only be used for mature processes, where a sufficient amount of Phase I data have been obtained to ensure that the estimated control limits are accurate. When evaluating control chart performance, we consider initial state performance measures for simplicity and note that the conclusions obtained here apply to steady-state performance measures. The evaluation of performance measures is easily handled by the Markov chain approach detailed in the Appendix.

Section: Relationship Between Settings and Performance Measuresmentioning

confidence: 99%

Design issues for adaptive control charts

Jensen

Bryce

Reynolds

2008

“…Despite these advantages, practitioners are still reluctant to exploit this group of charts, mostly due to perceived implementation difficulties. A notable and unique exception, at least to our knowledge, was presented by Baxley 8 and it concerns the application of a variable sampling interval, exponentially weighted moving average (EWMA) control chart at a nylon fiber plant. In that application the chart parameter values were chosen on the basis of statistical considerations, without explicit consideration of all the relevant quality costs.…”

Section: Introductionmentioning

confidence: 98%

Using economically designed Shewhart and adaptive ―X charts for monitoring the quality of tiles

Nikolaidis

Rigas

Tagaras

2006

This paper presents the economic design ofX control charts for monitoring a critical stage of the main production process at a tile manufacturer in Greece. Two types ofX charts were developed: a Shewhart-type chart with fixed parameters and adaptive charts with variable sampling intervals and/or sample size. Our prime motivation was to improve the statistical control scheme employed for monitoring an important quality characteristic of the process with the objective of minimizing the relevant costs. At the same time we tested and confirmed the applicability of the theoretical models supporting the economic design of control charts with fixed and variable parameters in a practical situation. We also evaluated the economic benefits of moving from the broadly used static charts to the application of the more flexible and effective adaptive control charts. The main result of our study is that, by redesigning the currently employed Shewhart chart using economic criteria, the quality-related cost is expected to decrease by approximately 50% without increasing the implementation complexity. Monitoring the process by means of an adaptiveX chart with variable sampling intervals will increase the expected cost savings by about 10% compared with the economically designed Shewhart chart at the expense of some implementation difficulty.

“…Reynolds et al 2 ), even though the logistical problem associated with the lack of fixed sampling points has made the use of variable sampling intervals awkward (e.g. Baxley 3 ).…”

Section: Introductionmentioning

confidence: 98%

Joint ―X and R Charts with Two‐stage Samplings

Costa

Rahim

2004

When jointX and R charts are in use, samples of fixed size are regularly taken from the process, and their means and ranges are plotted on theX and R charts, respectively. In this article, jointX and R charts have been used for monitoring continuous production processes. The sampling is performed, in two stages. During the first stage, one item of the sample is inspected and, depending on the result, the sampling is interrupted if the process is found to be in control; otherwise, it goes on to the second stage, where the remaining sample items are inspected. The two-stage sampling procedure speeds up the detection of process disturbances. The proposed jointX and R charts are easier to administer and are more efficient than the jointX and R charts with variable sample size where the quality characteristic of interest can be evaluated either by attribute or variable.