Open AccessParametric identification of the organizational maturity management system
Author(s) -
Mikhail Dorrer
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/537/4/042054
Subject(s) - maturity (psychological) , service integration maturity model , capability maturity model , identification (biology) , business process , process (computing) , industrial engineering , parametric statistics , computer science , control (management) , process management , parametric programming , operations research , engineering , work in process , operations management , mathematics , artificial intelligence , statistics , psychology , developmental psychology , botany , operating system , software , biology , programming language
The article describes the solution of the problem of constructing a mathematical model that describes the behavior of indicators of the level of maturity of a company’s business processes as a dynamic management system. This work was carried out in the course of solving the problem of developing a system for managing the level of maturity of the enterprise’s business processes. The set of maturity indicators for a company’s business processes is described as a dynamic model in discrete time of a control system in a deterministic formulation. The identification of the parameters of the dynamic model is made on the basis of the data on the maturity of the company’s business processes collected at the operating machine-building enterprise. It is shown that a dynamic model of a control system in discrete time in a deterministic formulation adequately describes the behavior of a system of indicators of the maturity of a company’s business processes. The resulting model reproduces the available experimental data on changes in the levels of organizational maturity in the company. It shows plausible behavior when predicting a process based on various input data in a real-world expected range of input parameters.