Aggregate planning for probabilistic demand with internal and external storage

Detalhes bibliográficos
Autor(a) principal: Biazzi, Jorge Luiz
Data de Publicação: 2018
Tipo de documento: Artigo
Idioma: eng
Título da fonte: JOSCM. Journal of Operations and Supply Chain Management
Texto Completo: https://periodicos.fgv.br/joscm/article/view/69583
Resumo: This paper presents three approaches to support decision-making for production planning, sales and inventory problems. They work in a situation with: non-stationary probabilistic demand; production capacity in regular hours and overtime; shortage leads to lost sales; limited internal storage space; and ordering costs resulting from machine preparation are negligible. In the first approach, we consider the problem as linear and deterministic. In the second, safety inventories are used to fill a probabilistic demand, but the possibility of stockout is not considered. The third approach estimates shortage resulting from demand uncertainty. The last two approaches use iterative processes to re-estimate unit holding cost, which is the basis to calculate safety inventories in each period of the horizon. Using Microsoft Excel Solver, with linear programming and nonlinear search functions, a hypothetical example (but strongly based on real-life companies) and some scenarios permit concluding that developing more realistic and complex models may not provide significant benefits.
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spelling Aggregate planning for probabilistic demand with internal and external storageAggregate planning for probabilistic demand with internal and external storageInventorynon-stationary probabilistic demandaggregate production planningsales and operations mathematical modelsno ordering costsInventorynon-stationary probabilistic demandaggregate production planningThis paper presents three approaches to support decision-making for production planning, sales and inventory problems. They work in a situation with: non-stationary probabilistic demand; production capacity in regular hours and overtime; shortage leads to lost sales; limited internal storage space; and ordering costs resulting from machine preparation are negligible. In the first approach, we consider the problem as linear and deterministic. In the second, safety inventories are used to fill a probabilistic demand, but the possibility of stockout is not considered. The third approach estimates shortage resulting from demand uncertainty. The last two approaches use iterative processes to re-estimate unit holding cost, which is the basis to calculate safety inventories in each period of the horizon. Using Microsoft Excel Solver, with linear programming and nonlinear search functions, a hypothetical example (but strongly based on real-life companies) and some scenarios permit concluding that developing more realistic and complex models may not provide significant benefits.This paper presents three approaches to support decision-making for production planning, sales and inventory problems. They work in a situation with: non-stationary probabilistic demand; production capacity in regular hours and overtime; shortage leads to lost sales; limited internal storage space; and ordering costs resulting from machine preparation are negligible. In the first approach, we consider the problem as linear and deterministic. In the second, safety inventories are used to fill a probabilistic demand, but the possibility of stockout is not considered. The third approach estimates shortage resulting from demand uncertainty. The last two approaches use iterative processes to re-estimate unit holding cost, which is the basis to calculate safety inventories in each period of the horizon. Using Microsoft Excel Solver, with linear programming and nonlinear search functions, a hypothetical example (but strongly based on real-life companies) permits concluding that developing more realistic and complex models may not provide significant benefits.FGV EAESP2018-06-15info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://periodicos.fgv.br/joscm/article/view/6958310.12660/joscmv11n1p37-52Journal of Operations and Supply Chain Management; Vol. 11 No. 1 (2018): January - June; 37-52Journal of Operations and Supply Chain Management; v. 11 n. 1 (2018): January - June; 37-521984-3046reponame:JOSCM. Journal of Operations and Supply Chain Managementinstname:Fundação Getulio Vargas (FGV)instacron:FGVenghttps://periodicos.fgv.br/joscm/article/view/69583/pdf_50Copyright (c) 2018 Journal of Operations and Supply Chain Managementinfo:eu-repo/semantics/openAccessBiazzi, Jorge Luiz2018-06-15T14:05:34Zoai:ojs.periodicos.fgv.br:article/69583Revistahttp://bibliotecadigital.fgv.br/ojs/index.php/joscmPRIhttp://bibliotecadigital.fgv.br/ojs/index.php/joscm/oai||joscm@fgv.br1984-30461984-3046opendoar:2018-06-15T14:05:34JOSCM. Journal of Operations and Supply Chain Management - Fundação Getulio Vargas (FGV)false
dc.title.none.fl_str_mv Aggregate planning for probabilistic demand with internal and external storage
Aggregate planning for probabilistic demand with internal and external storage
title Aggregate planning for probabilistic demand with internal and external storage
spellingShingle Aggregate planning for probabilistic demand with internal and external storage
Biazzi, Jorge Luiz
Inventory
non-stationary probabilistic demand
aggregate production planning
sales and operations mathematical models
no ordering costs
Inventory
non-stationary probabilistic demand
aggregate production planning
title_short Aggregate planning for probabilistic demand with internal and external storage
title_full Aggregate planning for probabilistic demand with internal and external storage
title_fullStr Aggregate planning for probabilistic demand with internal and external storage
title_full_unstemmed Aggregate planning for probabilistic demand with internal and external storage
title_sort Aggregate planning for probabilistic demand with internal and external storage
author Biazzi, Jorge Luiz
author_facet Biazzi, Jorge Luiz
author_role author
dc.contributor.author.fl_str_mv Biazzi, Jorge Luiz
dc.subject.por.fl_str_mv Inventory
non-stationary probabilistic demand
aggregate production planning
sales and operations mathematical models
no ordering costs
Inventory
non-stationary probabilistic demand
aggregate production planning
topic Inventory
non-stationary probabilistic demand
aggregate production planning
sales and operations mathematical models
no ordering costs
Inventory
non-stationary probabilistic demand
aggregate production planning
description This paper presents three approaches to support decision-making for production planning, sales and inventory problems. They work in a situation with: non-stationary probabilistic demand; production capacity in regular hours and overtime; shortage leads to lost sales; limited internal storage space; and ordering costs resulting from machine preparation are negligible. In the first approach, we consider the problem as linear and deterministic. In the second, safety inventories are used to fill a probabilistic demand, but the possibility of stockout is not considered. The third approach estimates shortage resulting from demand uncertainty. The last two approaches use iterative processes to re-estimate unit holding cost, which is the basis to calculate safety inventories in each period of the horizon. Using Microsoft Excel Solver, with linear programming and nonlinear search functions, a hypothetical example (but strongly based on real-life companies) and some scenarios permit concluding that developing more realistic and complex models may not provide significant benefits.
publishDate 2018
dc.date.none.fl_str_mv 2018-06-15
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://periodicos.fgv.br/joscm/article/view/69583
10.12660/joscmv11n1p37-52
url https://periodicos.fgv.br/joscm/article/view/69583
identifier_str_mv 10.12660/joscmv11n1p37-52
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://periodicos.fgv.br/joscm/article/view/69583/pdf_50
dc.rights.driver.fl_str_mv Copyright (c) 2018 Journal of Operations and Supply Chain Management
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2018 Journal of Operations and Supply Chain Management
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv FGV EAESP
publisher.none.fl_str_mv FGV EAESP
dc.source.none.fl_str_mv Journal of Operations and Supply Chain Management; Vol. 11 No. 1 (2018): January - June; 37-52
Journal of Operations and Supply Chain Management; v. 11 n. 1 (2018): January - June; 37-52
1984-3046
reponame:JOSCM. Journal of Operations and Supply Chain Management
instname:Fundação Getulio Vargas (FGV)
instacron:FGV
instname_str Fundação Getulio Vargas (FGV)
instacron_str FGV
institution FGV
reponame_str JOSCM. Journal of Operations and Supply Chain Management
collection JOSCM. Journal of Operations and Supply Chain Management
repository.name.fl_str_mv JOSCM. Journal of Operations and Supply Chain Management - Fundação Getulio Vargas (FGV)
repository.mail.fl_str_mv ||joscm@fgv.br
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