Open AccessEconomic aspects of measuring technological processes
Author(s) -
Elena Vorozhbit,
Anna Bakhireva,
A. A. Vyskrebentseva,
K. I. Gulkin,
M. V. Laskina
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1515/3/032007
Subject(s) - computer science , process (computing) , control (management) , technological change , acceleration , industrial engineering , identification (biology) , trajectory , risk analysis (engineering) , operations research , engineering , artificial intelligence , medicine , physics , botany , classical mechanics , astronomy , biology , operating system
The theory and practice of efficient organization the technological processes has significant developments. However, the issues of measuring technological processes and monitoring their execution remain poorly studied. On the one hand, there should be a lot of control points to ensure a timely assessment of the current state of the system, to form an effective management impact to adjust its trajectory. On the other hand, continuous monitoring is too costly, burdens the information system with redundant data. This article describes the theoretical aspects of measuring technological processes and monitoring their performance. The proposed approach to determining the frequency and scale of control procedures is based on the critical principles of rationality and efficiency. It allows you to determine the necessary and sufficient number of control points. Another problem is the presence of a large number of benchmarks, the number of which increases with the development of management theory. This problem is solved by applying universal metrics that characterize any technological process of the company: “work”, “speed”, “time”, “acceleration”. This approach unifies the economic space of measurement, makes it visible and accessible to any level of management, allows for accurate identification of process parameters at any time at minimal cost.