| Issue |
MATEC Web Conf.
Volume 196, 2018
XXVII R-S-P Seminar, Theoretical Foundation of Civil Engineering (27RSP) (TFoCE 2018)
|
|
|---|---|---|
| Article Number | 04023 | |
| Number of page(s) | 8 | |
| Section | Building Materials, Technologies, Organization and Management in Construction | |
| DOI | https://doi.org/10.1051/matecconf/201819604023 | |
| Published online | 03 September 2018 | |
Methods of estimating reserves of productivity growth
Moscow State University of Civil Engineering,
Yaroslavskoe shosse, 26,
Moscow,
129337,
Russia
*
Corresponding author: KuzminaTK@mgsu.ru
Two levels of the estimation of labor productivity reserves are considered: for lower-level (secondary) subdivisions and a construction enterprise. For secondary subdivisions it is recommended to implement the full-scale approach through interconnected single-factor, multifactor and predictable methods. The ground of these methods is based on corre-lation-regressive models of changes in natural output by types of construc-tion and installation works. For the construction enterprise, the authors have allocated groups of factors that determine the average annual output per worker-production of contract works, their structure, production assets and circulating assets, labor, prime cost and operational management. The foregoing provisions are illustrated by examples based on a large body of statistical information from construction practice.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.