Statistics Are More Than Numbers In Industrial Processes.

Statistics can be extremely helpful for any process in an industry, but statistical quality control can be better for maintaining quality standards and increasing productivity. Statistical quality control is the term used to describe the set of statistical tools used by quality professionals. In fact, many quality processes depend on this kind of statistics. It is important to mention, the use of statistical quality control, its close relation with descriptive statistics like: mean variance and standard deviation and the statistical methods that are implemented in order to improve quality.

On the first place, the use and efficiency of statistical quality control can improve not only processes quality, but also products in an industry. Furthermore, quality plays an important role in an organization overall strategy. The biggest challenge for an International company in gaining global market share is to first understand the global customer. Apart from this, Industries need to fit with Total Quality Managements (TQM), which is as a set of management processes and systems that create delighted customers through empowered employees, leading to higher revenue and lower cost. If an industry can implemented TQM it will be easier to maintain control of production and to satisfied the costumer´s requirement. Be capable of using statistical tools can open many doors to progress and higher profit (Kairong, 2009).

In addition to this, it is necessary to talk about the close relation between Statistical quality control and descriptive statistics. Research has shown that a new methodology based on a multivariate quality control system can increase levels of productivity in industries. This system is basically composed by descriptive statistics and quality tools which can assure a better production. manufacturing environment during later several decades has changed very much, it became modern and competitive for mastering design and manufacturing methods in many industrial fields; In many quality control settings, the product (process) under examination may have two or more correlated quality characteristics (variables); hence, an appropriate approach is needed to monitor all these characteristics simultaneously. The most common variables used are mean, variance and standard deviation, besides, the greatest advantage of this system is that we can focus in a sample of data, since all the variables or the descriptive statistics work only with small samples of the universe; therefore it would be easier for the administration use a sample of data, in order to improve industrial processes and obtain quality facilities (García-Díaz, 2010).

In practice, Statistical methods, involves inspecting a random sample of the output from a process and deciding whether the process is producing products with characteristics that fall within a predetermined range. What is more, the methods of Statistical quality control are

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