Aggregate planning for probabilistic demand with internal and external storage
Autor(a) principal: | |
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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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JOSCM. Journal of Operations and Supply Chain Management |
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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 |
_version_ |
1798943730689900544 |