TY - JOUR
T1 - Flexible production data generator for manufacturing companies
AU - Fernandes, Ederson Carvalhar
AU - Dos Santos, Lucas Iuri
AU - Camatti, Juliane Andressa
AU - Brown, Liam
AU - Borsato, Milton
N1 - Publisher Copyright:
© 2020 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the FAIM 2021.
PY - 2020
Y1 - 2020
N2 - Advances in technology over last decades have been followed by many challenges of industrial competitiveness at different levels. Production planning and control generally demand too much time to data capture, and it makes it difficult to understand the analysis of the process flow and to obtain satisfactory results. These results can help operations managers make the best decisions and implement them. Companies that do not have tracking systems to automatically capture data from their machines, spend a lot of time trying to achieve a visual correlation which is consistent with the reality of their processes and goals. Companies without this technology can use data generator systems to simulate data variables in different sizes and previously identify suitable graphical correlations. Current's data generators work with several variables for a lot of situations. On the other hand, it can become confusing and error-prone due to excess of information. However, there is no automated data generator focused on process-flow data which has usability enough to meet the manufacturing company's needs. This study proposes a flexible production-oriented data generator for different physical arrangements in manufacturing companies, along with graphical correlations of production parameters to aid planning and decision making.
AB - Advances in technology over last decades have been followed by many challenges of industrial competitiveness at different levels. Production planning and control generally demand too much time to data capture, and it makes it difficult to understand the analysis of the process flow and to obtain satisfactory results. These results can help operations managers make the best decisions and implement them. Companies that do not have tracking systems to automatically capture data from their machines, spend a lot of time trying to achieve a visual correlation which is consistent with the reality of their processes and goals. Companies without this technology can use data generator systems to simulate data variables in different sizes and previously identify suitable graphical correlations. Current's data generators work with several variables for a lot of situations. On the other hand, it can become confusing and error-prone due to excess of information. However, there is no automated data generator focused on process-flow data which has usability enough to meet the manufacturing company's needs. This study proposes a flexible production-oriented data generator for different physical arrangements in manufacturing companies, along with graphical correlations of production parameters to aid planning and decision making.
KW - Data Generator
KW - Data Visualization
KW - Flexibility
KW - Manufacturing
UR - http://www.scopus.com/inward/record.url?scp=85099837878&partnerID=8YFLogxK
U2 - 10.1016/j.promfg.2020.10.205
DO - 10.1016/j.promfg.2020.10.205
M3 - Conference article
AN - SCOPUS:85099837878
SN - 2351-9789
VL - 51
SP - 1478
EP - 1484
JO - Procedia Manufacturing
JF - Procedia Manufacturing
T2 - 30th International Conference on Flexible Automation and Intelligent Manufacturing, FAIM 2021
Y2 - 15 June 2021 through 18 June 2021
ER -