title: ÐÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð¸Ð½ÑÑÑÑÐ¼ÐµÐ½ÑÐ¾Ð² Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð´Ð»Ñ Ð¿Ð¾Ð²ÑÑÐµÐ½Ð¸Ñ Ð¿Ð¾ÐºÐ°Ð·Ð°ÑÐµÐ»ÐµÐ¹ ÐºÐ°ÑÐµÑÑÐ²Ð° Ð¿ÑÐ¾ÑÐµÑÑÐ¾Ð²
title: Using machine learning tools to improve process quality indicators
Creators: name: ÐÐ¸ÑÐ¾Ð²Ð¸Ðº, Ð Ð¾Ð¼Ð°Ð½
name: ÐÑÐ»ÑÑÐ¸Ð½, ÐÐ¸ÐºÐ¸ÑÐ°
name: "Ð£Ð¿ÑÐ°Ð²Ð»ÐµÐ½Ð¸Ðµ Ð¸Ð½Ð½Ð¾Ð²Ð°ÑÐ¸ÑÐ¼Ð¸ Ð² ÑÑÐ»Ð¾Ð²Ð¸ÑÑ  ÑÐ¸ÑÑÐ¾Ð²Ð¾Ð¹ ÑÑÐ°Ð½ÑÑÐ¾ÑÐ¼Ð°ÑÐ¸Ð¸", Ð²ÑÐµÑÐ¾ÑÑÐ¸Ð¹ÑÐºÐ°Ñ ÑÑÑÐ´ÐµÐ½ÑÐµÑÐºÐ°Ñ ÑÑÐµÐ±Ð½Ð¾-Ð½Ð°ÑÑÐ½Ð°Ñ ÐºÐ¾Ð½ÑÐµÑÐµÐ½ÑÐ¸Ñ (2023; Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³)
Publisher: name: ÐÐÐÐÐ¢ÐÐ¥-ÐÐ ÐÐ¡Ð¡
Year: 2023
Subjects: ÐÐ°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð±Ð¸Ð·Ð½ÐµÑ-Ð¿ÑÐ¾ÑÐµÑÑÑ
Ð¸Ð½ÑÐµÐ³ÑÐ°Ð»ÑÐ½ÑÐ¹ Ð¿Ð¾ÐºÐ°Ð·Ð°ÑÐµÐ»Ñ ÐºÐ°ÑÐµÑÑÐ²Ð°
ÐºÐ°ÑÐµÑÑÐ²Ð¾
Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ Ð¸Ð½ÑÐµÐ½ÑÐ¸Ð²Ð½Ð¾ÑÑÐ¸ Ð¾ÑÐºÐ°Ð·Ð¾Ð²
business processes
integral quality indicator
quality
failure rate prediction

description: Ð Ð°ÑÑÐ¼Ð¾ÑÑÐµÐ½Ð° Ð²Ð¾Ð·Ð¼Ð¾Ð¶Ð½Ð¾ÑÑÑ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ñ ÑÐµÑÐ½Ð¾Ð»Ð¾Ð³Ð¸Ð¸ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð´Ð»Ñ Ð¿Ð¾Ð²ÑÑÐµÐ½Ð¸Ñ Ð¿Ð¾ÐºÐ°Ð·Ð°ÑÐµÐ»ÐµÐ¹ ÐºÐ°ÑÐµÑÑÐ²Ð° Ð¿ÑÐ¾ÑÐµÑÑÐ¾Ð². ÐÐ±Ð¾ÑÐ½Ð¾Ð²Ð°Ð½Ð° Ð°ÐºÑÑÐ°Ð»ÑÐ½Ð¾ÑÑÑ Ð¿ÑÐ¾Ð±Ð»ÐµÐ¼Ñ. Ð¦ÐµÐ»ÑÑ ÑÐ°Ð±Ð¾ÑÑ ÑÐ²Ð»ÑÐµÑÑÑ Ð¾Ð±Ð·Ð¾Ñ ÑÐµÑÐ½Ð¾Ð»Ð¾Ð³Ð¸Ð¸ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ, ÑÐ¿Ð¾ÑÐ¾Ð±Ð¾Ð² Ð¿Ð¾Ð²ÑÑÐµÐ½Ð¸Ñ ÐºÐ°ÑÐµÑÑÐ²Ð° Ð¿ÑÐ¾ÑÐµÑÑÐ¾Ð² Ð¿ÑÐ¸ Ð¿Ð¾Ð¼Ð¾ÑÐ¸ Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ½Ð¾Ð»Ð¾Ð³Ð¸Ð¸. Ð Ð°ÑÑÐ¼Ð¾ÑÑÐµÐ½Ñ Ð¾ÑÐ½Ð¾Ð²Ð½ÑÐµ Ð²Ð¸Ð´Ñ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ (ÐÐ). Ð ÐºÐ°ÑÐµÑÑÐ²Ðµ Ð¾Ð±Ð»Ð°ÑÑÐµÐ¹ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ñ ÑÐµÑÐ½Ð¾Ð»Ð¾Ð³Ð¸Ð¸ Ð² ÑÐ¿ÑÐ°Ð²Ð»ÐµÐ½Ð¸Ð¸ ÐºÐ°ÑÐµÑÑÐ²Ð¾Ð¼ Ð±Ð¸Ð·Ð½ÐµÑ-Ð¿ÑÐ¾ÑÐµÑÑÐ¾Ð², Ð¸Ð·ÑÑÐµÐ½Ñ Ð°Ð²ÑÐ¾Ð¼Ð¾Ð±Ð¸Ð»ÑÐ½Ð°Ñ Ð¿ÑÐ¾Ð¼ÑÑÐ»ÐµÐ½Ð½Ð¾ÑÑÑ Ð¸ Ð¾ÑÐµÐ½ÐºÐ° Ð¿Ð¾ÑÑÐ°Ð²ÑÐ¸ÐºÐ¾Ð². Ð£ÑÑÐ°Ð½Ð¾Ð²Ð»ÐµÐ½Ð¾, ÑÑÐ¾ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ Ð² Ð°Ð²ÑÐ¾Ð¼Ð¾Ð±Ð¸Ð»ÐµÑÑÑÐ¾ÐµÐ½Ð¸Ð¸ Ð¿Ð¾Ð·Ð²Ð¾Ð»ÑÐµÑ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°ÑÑ Ð½ÐµÐ¸ÑÐ¿ÑÐ°Ð²Ð½Ð¾ÑÑÐ¸, ÑÐ½Ð¸Ð¶Ð°ÐµÑ Ð²ÑÐµÐ¼Ñ Ð²ÑÐ¿Ð¾Ð»Ð½ÐµÐ½Ð¸Ñ Ð¿ÑÐ¾ÑÐµÑÑÐ°. ÐÐ¾Ð¼Ð¸Ð¼Ð¾ ÑÑÐ¾Ð³Ð¾, Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼ Ð´ÐµÑÐµÐ²Ð° ÑÐµÑÐµÐ½Ð¸Ð¹ Ð¿Ð¾Ð·Ð²Ð¾Ð»ÑÐµÑ Ð¿ÑÐ¾Ð²Ð¾Ð´Ð¸ÑÑ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ Ð¿Ð¾ÑÑÐ°Ð²ÑÐ¸ÐºÐ¾Ð², ÑÑÐ¾ ÑÐ²Ð»ÑÐµÑÑÑ Ð¾Ð´Ð½Ð¸Ð¼ Ð¸Ð· ÐºÑÐ¸ÑÐµÑÐ¸ÐµÐ² Ð²Ð½ÐµÐ´ÑÐµÐ½Ð¸Ñ Ð¡ÐÐ. ÐÐ¾ÐºÐ°Ð·Ð°Ð½Ð¾, ÑÑÐ¾ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ Ð¾Ð±Ð»Ð°Ð´Ð°ÐµÑ Ð±Ð¾Ð»ÑÑÐ¸Ð¼ Ð¿Ð¾ÑÐµÐ½ÑÐ¸Ð°Ð»Ð¾Ð¼ Ð² ÑÐµÑÐµÐ½Ð¸Ð¸ ÑÐ°Ð·Ð»Ð¸ÑÐ½ÑÑ Ð·Ð°Ð´Ð°Ñ.
description: The possibility of applying machine learning technology to improve process quality indicators is considered. The relevance of the problem is justified. The purpose of this paper is to review machine learning technology, ways to improve the quality of processes using this technology. The main types of machine learning (ML) are considered. The automotive industry and supplier evaluation are studied as areas of technology use in business process quality management. It is found that machine learning in automotive industry can predict faults, reduce process execution time. In addition, the decision tree algorithm allows the classification of suppliers, which is one of the criteria for QMS implementation. It is shown that machine learning has great potential in solving various problems.