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Integrated modeling of “soft” and “hard” variables in manufacturing

This paper presents a novel holistic modeling approach for investigating and analyzing the relationship of qualitative variables such as training and absenteeism with quantifiable shopfloor key performance indicators such as quality, inventory, and production rate. Soft variables, supervisor support...

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Autores principales: Afy-Shararah, Mohamed, Salonitis, Konstantinos
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer London 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9363146/
https://www.ncbi.nlm.nih.gov/pubmed/35968034
http://dx.doi.org/10.1007/s00170-022-09872-z
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author Afy-Shararah, Mohamed
Salonitis, Konstantinos
author_facet Afy-Shararah, Mohamed
Salonitis, Konstantinos
author_sort Afy-Shararah, Mohamed
collection PubMed
description This paper presents a novel holistic modeling approach for investigating and analyzing the relationship of qualitative variables such as training and absenteeism with quantifiable shopfloor key performance indicators such as quality, inventory, and production rate. Soft variables, supervisor support and work environment, and their relationships with the hard variables, facility layout, and production strategies were investigated in this research. It was found in the literature that increasing absenteeism reduces the rate of production and causes a decrease in motivation, while training can increase the level of motivation if effective. A causal loop diagram was developed based on the evidence in the literature, and a system dynamics simulation model was created to depict these relations. It was confirmed that absenteeism affected the cycle time and motivation inversely, but it was not possible to always maintain a desired level of motivation. A discrete event simulation model was also built for the current and the future state maps of the production system. The model used output from the system dynamics model as its input to investigate the effects of the qualitative variables on the production system performance. This paper discusses in detail the stages of building the simulation models and the results recorded.
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spelling pubmed-93631462022-08-10 Integrated modeling of “soft” and “hard” variables in manufacturing Afy-Shararah, Mohamed Salonitis, Konstantinos Int J Adv Manuf Technol Original Article This paper presents a novel holistic modeling approach for investigating and analyzing the relationship of qualitative variables such as training and absenteeism with quantifiable shopfloor key performance indicators such as quality, inventory, and production rate. Soft variables, supervisor support and work environment, and their relationships with the hard variables, facility layout, and production strategies were investigated in this research. It was found in the literature that increasing absenteeism reduces the rate of production and causes a decrease in motivation, while training can increase the level of motivation if effective. A causal loop diagram was developed based on the evidence in the literature, and a system dynamics simulation model was created to depict these relations. It was confirmed that absenteeism affected the cycle time and motivation inversely, but it was not possible to always maintain a desired level of motivation. A discrete event simulation model was also built for the current and the future state maps of the production system. The model used output from the system dynamics model as its input to investigate the effects of the qualitative variables on the production system performance. This paper discusses in detail the stages of building the simulation models and the results recorded. Springer London 2022-08-09 2022 /pmc/articles/PMC9363146/ /pubmed/35968034 http://dx.doi.org/10.1007/s00170-022-09872-z Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/ Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Original Article
Afy-Shararah, Mohamed
Salonitis, Konstantinos
Integrated modeling of “soft” and “hard” variables in manufacturing
title Integrated modeling of “soft” and “hard” variables in manufacturing
title_full Integrated modeling of “soft” and “hard” variables in manufacturing
title_fullStr Integrated modeling of “soft” and “hard” variables in manufacturing
title_full_unstemmed Integrated modeling of “soft” and “hard” variables in manufacturing
title_short Integrated modeling of “soft” and “hard” variables in manufacturing
title_sort integrated modeling of “soft” and “hard” variables in manufacturing
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9363146/
https://www.ncbi.nlm.nih.gov/pubmed/35968034
http://dx.doi.org/10.1007/s00170-022-09872-z
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