Abstract
the article considers the algorithmic and mathematical justification for the development of a new software module for product quality control and statistical data processing at the Auditor enterprises. The software is designed for automated calculation of product quality control and evaluation indicators. The program has a wide range of functions for statistical data processing and product quality control tools at the enterprise. The functional characteristics of the software include: comparing risks for a given set of factors for two cases, building a histogram of the distribution of cases over a given numerical interval, comparing objects for a given set of values using a generalized estimate, calculating global priorities for a given set of factors for several cases, calculating the I-th predicted value for a linear regression model, calculating the i-th predicted value for an exponential regression model, calculating the i-th predicted value for a parabolic regression model, calculating the consistency of experts for a given set of factors, calculating the n+1 value of a time series, and evaluating the level of employee satisfaction.
Highlights
In the current global environment, there is an urgent need to improve the quality and automation of production
To improve and ensure the quality of products, as well as the convenience of statistical processing of data obtained in production, a software module was developed as part of the information system being developed
Calculation algorithm : 1) Building a histogram of the output distribution, the intervals are plotted on the abscissa axis, and the number of observations is plotted on the ordinate axis
Summary
In the current global environment, there is an urgent need to improve the quality and automation of production. There are information systems that can be used to manage enterprises for the production of any product These systems are far from perfect and require constant improvement. To improve and ensure the quality of products, as well as the convenience of statistical processing of data obtained in production, a software module was developed as part of the information system being developed. We will determine the weighted average value of the analyzed parameter, which is a causal risk factor:. Using the value of the found variance, we determine the standard deviation of the expected value of the analyzed parameter from its average value:. To compare different solutions with different expected results and different risks, you can use the coefficient of variation:. The option that has the lowest value should be chosen as the least risky (with a lower relative risk )
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