"The production process is hidden in the data." "He who cannot measure has no data." "He who does not have data does not know the process." - František Vaňkát
The use of tools and statistical methods can help clarify process variability, helping organizations solve problems, reduce costs, and improve efficiency. These methods also facilitate better use of available decision support data.
Variability can be observed over the course of many activities and their outputs, even in a state of apparent stability. Such variability can be observed in the measurable features of products and processes, and its existence can be seen at various stages of the product life cycle from market research to customer service and final disposal.
The basic requirement then is to identify the causes of variability and to eliminate them, if possible and appropriate. The aim is to achieve an acceptable stable level of process variability and its gradual reduction.
"The key to quality is in understanding process variability." W. E. Deming
Statistical methods can help measure, describe, analyze, interpret and model such variability, even with a relatively limited amount of data. Statistical analysis of this data can help to better understand the nature, extent and causes of variability, thus helping to solve problems and even prevent such problems that may arise from such variability, and may also lead to continuous improvement.
For larger data sets and multidimensional data, the tools and methods alone are no longer sufficient. In such cases, we must already use appropriate statistical methods, using software statistical applications or sophisticated statistical software in the field of quality improvement.
"QUALITY = Variability converted into money." - František VAŇKÁT
We have many years of practical experience in the field of statistical analysis and professional experience in the engineering, metallurgical and automotive industries.
If we can help you solve / reduce your production / non-production costs, we will be happy if you contact us.
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