Optimization Model for Cross-Functional Decision Making: A Computational Business Intelligence Approach

Detalhes bibliográficos
Autor(a) principal: Roy, Supryio
Data de Publicação: 2010
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Brazilian Journal of Operations & Production Management (Online)
Texto Completo: https://bjopm.org.br/bjopm/article/view/BJV6N1_2009_2
Resumo: Latest manufacturing technologies enhance cross-functional interaction between manufacturing and marketing. In spite of increasingly emphasizing on the aspect of end user’s demand, many production decision-making processes do not take into account only the dynamic nature of the marketer. Here, anattempt has been made to bridge the gap between marketing and partially integrated production problem, with the objective of developing mathematical model that can act as an optimizer in an add-on advanced planning system within an enterprise. Basic idea of this research is the integration of work on determining production and raw material batch sizes under different ordering and delivery assumptions for heuristically evaluating the two-stage batch production problem. Production rate is considered to be a decision variable. Integrated unit production cost function is formulated by considering the various pertinent factors. Proposed model is developed simultaneouslyby formulating constrained maximization problem for marketing division and minimization problem for production division. Considering the complexities for highly non-linear optimization problem, a Computational Intelligence approach is successfully developed and implemented. The model is practical in natureand may be used as an add-on optimizer that co-ordinates distinct function with an aim of maximizing the profit function in any firm.
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spelling Optimization Model for Cross-Functional Decision Making: A Computational Business Intelligence ApproachLatest manufacturing technologies enhance cross-functional interaction between manufacturing and marketing. In spite of increasingly emphasizing on the aspect of end user’s demand, many production decision-making processes do not take into account only the dynamic nature of the marketer. Here, anattempt has been made to bridge the gap between marketing and partially integrated production problem, with the objective of developing mathematical model that can act as an optimizer in an add-on advanced planning system within an enterprise. Basic idea of this research is the integration of work on determining production and raw material batch sizes under different ordering and delivery assumptions for heuristically evaluating the two-stage batch production problem. Production rate is considered to be a decision variable. Integrated unit production cost function is formulated by considering the various pertinent factors. Proposed model is developed simultaneouslyby formulating constrained maximization problem for marketing division and minimization problem for production division. Considering the complexities for highly non-linear optimization problem, a Computational Intelligence approach is successfully developed and implemented. The model is practical in natureand may be used as an add-on optimizer that co-ordinates distinct function with an aim of maximizing the profit function in any firm.Brazilian Association for Industrial Engineering and Operations Management (ABEPRO)2010-02-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionPeer-reviewed Articleapplication/pdfhttps://bjopm.org.br/bjopm/article/view/BJV6N1_2009_2Brazilian Journal of Operations & Production Management; Vol. 6 No. 1 (2009): July, 2009; 37-622237-8960reponame:Brazilian Journal of Operations & Production Management (Online)instname:Associação Brasileira de Engenharia de Produção (ABEPRO)instacron:ABEPROenghttps://bjopm.org.br/bjopm/article/view/BJV6N1_2009_2/pdf_7Roy, Supryioinfo:eu-repo/semantics/openAccess2019-04-04T07:29:26Zoai:ojs.bjopm.org.br:article/46Revistahttps://bjopm.org.br/bjopmONGhttps://bjopm.org.br/bjopm/oaibjopm.journal@gmail.com2237-89601679-8171opendoar:2023-03-13T09:45:01.735648Brazilian Journal of Operations & Production Management (Online) - Associação Brasileira de Engenharia de Produção (ABEPRO)false
dc.title.none.fl_str_mv Optimization Model for Cross-Functional Decision Making: A Computational Business Intelligence Approach
title Optimization Model for Cross-Functional Decision Making: A Computational Business Intelligence Approach
spellingShingle Optimization Model for Cross-Functional Decision Making: A Computational Business Intelligence Approach
Roy, Supryio
title_short Optimization Model for Cross-Functional Decision Making: A Computational Business Intelligence Approach
title_full Optimization Model for Cross-Functional Decision Making: A Computational Business Intelligence Approach
title_fullStr Optimization Model for Cross-Functional Decision Making: A Computational Business Intelligence Approach
title_full_unstemmed Optimization Model for Cross-Functional Decision Making: A Computational Business Intelligence Approach
title_sort Optimization Model for Cross-Functional Decision Making: A Computational Business Intelligence Approach
author Roy, Supryio
author_facet Roy, Supryio
author_role author
dc.contributor.author.fl_str_mv Roy, Supryio
description Latest manufacturing technologies enhance cross-functional interaction between manufacturing and marketing. In spite of increasingly emphasizing on the aspect of end user’s demand, many production decision-making processes do not take into account only the dynamic nature of the marketer. Here, anattempt has been made to bridge the gap between marketing and partially integrated production problem, with the objective of developing mathematical model that can act as an optimizer in an add-on advanced planning system within an enterprise. Basic idea of this research is the integration of work on determining production and raw material batch sizes under different ordering and delivery assumptions for heuristically evaluating the two-stage batch production problem. Production rate is considered to be a decision variable. Integrated unit production cost function is formulated by considering the various pertinent factors. Proposed model is developed simultaneouslyby formulating constrained maximization problem for marketing division and minimization problem for production division. Considering the complexities for highly non-linear optimization problem, a Computational Intelligence approach is successfully developed and implemented. The model is practical in natureand may be used as an add-on optimizer that co-ordinates distinct function with an aim of maximizing the profit function in any firm.
publishDate 2010
dc.date.none.fl_str_mv 2010-02-07
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://bjopm.org.br/bjopm/article/view/BJV6N1_2009_2
url https://bjopm.org.br/bjopm/article/view/BJV6N1_2009_2
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://bjopm.org.br/bjopm/article/view/BJV6N1_2009_2/pdf_7
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Brazilian Association for Industrial Engineering and Operations Management (ABEPRO)
publisher.none.fl_str_mv Brazilian Association for Industrial Engineering and Operations Management (ABEPRO)
dc.source.none.fl_str_mv Brazilian Journal of Operations & Production Management; Vol. 6 No. 1 (2009): July, 2009; 37-62
2237-8960
reponame:Brazilian Journal of Operations & Production Management (Online)
instname:Associação Brasileira de Engenharia de Produção (ABEPRO)
instacron:ABEPRO
instname_str Associação Brasileira de Engenharia de Produção (ABEPRO)
instacron_str ABEPRO
institution ABEPRO
reponame_str Brazilian Journal of Operations & Production Management (Online)
collection Brazilian Journal of Operations & Production Management (Online)
repository.name.fl_str_mv Brazilian Journal of Operations & Production Management (Online) - Associação Brasileira de Engenharia de Produção (ABEPRO)
repository.mail.fl_str_mv bjopm.journal@gmail.com
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