In some statistical process control applications in the real world, quality of a process or a product is characterized by a relationship between two or more variables, which is referred to as profile. Understanding and checking the stability of process over time can be facilitated by monitoring the quality profile in phase I. Sometimes the quality characteristics may be linguistic, imprecise, v...

Agriculture in Russia has some specifics such as large distribution, inclement climate and big market competition. Appearing of resources-economy, precision and management technologies demanded from agriculture companies new planning and control methods. The paper is dedicated to assembly charts planning in agriculture with multi-agent approach. For resources distribution we used method of virt...

statistical process control (spc) charts play a major role in quality control systems, and their correct interpretation leads to discovering probable irregularities and errors of the production system. in this regard, various artificial neural networks have been developed to identify mainly singular patterns of spc charts, while having drawbacks in handling multiple concurrent patterns. in this...

Standard control charts are based on the assumption that the observations are normally distributed. In practice, normality often fails and consequently the false alarm rate is seriously in error. Application of a nonparametric approach is only possible with many Phase I observations. Since nowadays such very large sample sizes are usually not available, there is need for an intermediate approac...

Industrial production requires multivariate control charts to enable monitoring of several components. Recently there has been an increased interest also in other areas such as detection of bioterrorism, spatial surveillance and transaction strategies in finance. In the literature, several types of multivariate counterparts to the univariate Shewhart, EWMA and CUSUM methods have been proposed. ...

Control charts such as X and R charts are widely used in industry to monitor quality. These charts are effective in detecting large departures from the in-control condition. Other process monitoring methods, such as Cumulative Sum (CUSUM) charts are sequential in nature as they accumulate information from previous observations. CUSUM charts are good at detecting more moderate presistent process...

Quality control charts are limited to controlling one characteristic of a production process, and it needs large amount data determine limits the process. Another limitation traditional chart is that doesn’t deal with vague environment. The fuzzy work uncertainty exists in data. Also, investigate random variations found between samples. In modern industries, productivity often different designs...