Issue |
MATEC Web Conf.
Volume 137, 2017
Modern Technologies in Manufacturing (MTeM 2017 - AMaTUC)
|
|
---|---|---|
Article Number | 01010 | |
Number of page(s) | 6 | |
Section | Manufacturing Engineering | |
DOI | https://doi.org/10.1051/matecconf/201713701010 | |
Published online | 22 November 2017 |
Forecasting of steel consumption with use of nearest neighbors method
1 Poznan University of Technology, Chair of Management and Production, Piotrowo 3 Street, Poland
2 Poznan University of Technology, Institute of Material Technology, Piotrowo 3 Street, Poland
3 BiB Systems LLC, Lutycka 11, Poland
* Corresponding author: michal.rogalewicz@put.poznan.pl
In the process of building a steel construction, its design is usually commissioned to the design office. Then a quotation is made and the finished offer is delivered to the customer. Its final shape is influenced by steel consumption to a great extent. Correct determination of the potential consumption of this material most often determines the profitability of the project. Because of a long waiting time for a final project from the design office, it is worthwhile to pre-analyze the project’s profitability and feasibility using historical data on already realized orders. The paper presents an innovative approach to decision-making support in one of the Polish construction companies. The authors have defined and prioritized the most important factors that differentiate the executed orders and have the greatest impact on steel consumption. These are, among others: height and width of steel structure, number of aisles, type of roof, etc. Then they applied and adapted the method of k-nearest neighbors to the specificity of the discussed problem. The goal was to search a set of historical orders and find the most similar to the analyzed one. On this basis, consumption of steel can be estimated. The method was programmed within the EXPLOR application.
© The authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (http://creativecommons.org/licenses/by/4.0/).
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.