Joint Replenishment Problem in Drug Inventory Management of Pharmacies under Stochastic Demand
Autor(a) principal: | |
---|---|
Data de Publicação: | 2018 |
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/446 |
Resumo: | In today’s fast-paced and competitive market place, organizations need every edge available to them to ensure success in planning and managing inventory of items with demand uncertainty. In such an effort, cost effective methods in determining optimal replenishment policies are paramount. In this paper, a mathematical model is proposed that optimize inventory replenishment policies of a periodic review inventory system under stochastic demand; with particular focus on malaria drugs in Ugandan pharmacies. Adopting a Markov decision process approach, the states of a Markov chain represent possible states of demand for drugs that treat malaria. Using weekly equal intervals, the decisions of whether or not to replenish additional units of drugs were made using discrete time Markov chains and dynamic programming over a finite period planning horizon. Empirical data was collected from two pharmacies in Uganda. The customer transactions of drugs were taken on a weekly basis; where data collected was analyzed and tested to establish the optimal replenishment policy and inventory costs of drugs. Results from the study indicated the existence of an optimal state-dependent replenishment policy and inventory costs of drugs at the respective pharmacies. |
id |
ABEPRO_fe385ec976be499ebcb640cb160e002b |
---|---|
oai_identifier_str |
oai:ojs.bjopm.org.br:article/446 |
network_acronym_str |
ABEPRO |
network_name_str |
Brazilian Journal of Operations & Production Management (Online) |
repository_id_str |
|
spelling |
Joint Replenishment Problem in Drug Inventory Management of Pharmacies under Stochastic Demanddrug inventoryjoint replenishmentpharmaciiesstochastic demandIn today’s fast-paced and competitive market place, organizations need every edge available to them to ensure success in planning and managing inventory of items with demand uncertainty. In such an effort, cost effective methods in determining optimal replenishment policies are paramount. In this paper, a mathematical model is proposed that optimize inventory replenishment policies of a periodic review inventory system under stochastic demand; with particular focus on malaria drugs in Ugandan pharmacies. Adopting a Markov decision process approach, the states of a Markov chain represent possible states of demand for drugs that treat malaria. Using weekly equal intervals, the decisions of whether or not to replenish additional units of drugs were made using discrete time Markov chains and dynamic programming over a finite period planning horizon. Empirical data was collected from two pharmacies in Uganda. The customer transactions of drugs were taken on a weekly basis; where data collected was analyzed and tested to establish the optimal replenishment policy and inventory costs of drugs. Results from the study indicated the existence of an optimal state-dependent replenishment policy and inventory costs of drugs at the respective pharmacies.Brazilian Association for Industrial Engineering and Operations Management (ABEPRO)2018-06-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionPeer-reviewed Articlemathematical modelingtext/htmlapplication/pdfhttps://bjopm.org.br/bjopm/article/view/44610.14488/BJOPM.2018.v15.n2.a12Brazilian Journal of Operations & Production Management; Vol. 15 No. 2 (2018): June, 2018; 302-3102237-8960reponame:Brazilian Journal of Operations & Production Management (Online)instname:Associação Brasileira de Engenharia de Produção (ABEPRO)instacron:ABEPROenghttps://bjopm.org.br/bjopm/article/view/446/615https://bjopm.org.br/bjopm/article/view/446/583Copyright (c) 2018 Brazilian Journal of Operations & Production Managementinfo:eu-repo/semantics/openAccessMubiru, Kizito Paul2021-07-13T14:14:34Zoai:ojs.bjopm.org.br:article/446Revistahttps://bjopm.org.br/bjopmONGhttps://bjopm.org.br/bjopm/oaibjopm.journal@gmail.com2237-89601679-8171opendoar:2023-03-13T09:45:17.671291Brazilian Journal of Operations & Production Management (Online) - Associação Brasileira de Engenharia de Produção (ABEPRO)false |
dc.title.none.fl_str_mv |
Joint Replenishment Problem in Drug Inventory Management of Pharmacies under Stochastic Demand |
title |
Joint Replenishment Problem in Drug Inventory Management of Pharmacies under Stochastic Demand |
spellingShingle |
Joint Replenishment Problem in Drug Inventory Management of Pharmacies under Stochastic Demand Mubiru, Kizito Paul drug inventory joint replenishment pharmaciies stochastic demand |
title_short |
Joint Replenishment Problem in Drug Inventory Management of Pharmacies under Stochastic Demand |
title_full |
Joint Replenishment Problem in Drug Inventory Management of Pharmacies under Stochastic Demand |
title_fullStr |
Joint Replenishment Problem in Drug Inventory Management of Pharmacies under Stochastic Demand |
title_full_unstemmed |
Joint Replenishment Problem in Drug Inventory Management of Pharmacies under Stochastic Demand |
title_sort |
Joint Replenishment Problem in Drug Inventory Management of Pharmacies under Stochastic Demand |
author |
Mubiru, Kizito Paul |
author_facet |
Mubiru, Kizito Paul |
author_role |
author |
dc.contributor.author.fl_str_mv |
Mubiru, Kizito Paul |
dc.subject.por.fl_str_mv |
drug inventory joint replenishment pharmaciies stochastic demand |
topic |
drug inventory joint replenishment pharmaciies stochastic demand |
description |
In today’s fast-paced and competitive market place, organizations need every edge available to them to ensure success in planning and managing inventory of items with demand uncertainty. In such an effort, cost effective methods in determining optimal replenishment policies are paramount. In this paper, a mathematical model is proposed that optimize inventory replenishment policies of a periodic review inventory system under stochastic demand; with particular focus on malaria drugs in Ugandan pharmacies. Adopting a Markov decision process approach, the states of a Markov chain represent possible states of demand for drugs that treat malaria. Using weekly equal intervals, the decisions of whether or not to replenish additional units of drugs were made using discrete time Markov chains and dynamic programming over a finite period planning horizon. Empirical data was collected from two pharmacies in Uganda. The customer transactions of drugs were taken on a weekly basis; where data collected was analyzed and tested to establish the optimal replenishment policy and inventory costs of drugs. Results from the study indicated the existence of an optimal state-dependent replenishment policy and inventory costs of drugs at the respective pharmacies. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-06-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article mathematical modeling |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://bjopm.org.br/bjopm/article/view/446 10.14488/BJOPM.2018.v15.n2.a12 |
url |
https://bjopm.org.br/bjopm/article/view/446 |
identifier_str_mv |
10.14488/BJOPM.2018.v15.n2.a12 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://bjopm.org.br/bjopm/article/view/446/615 https://bjopm.org.br/bjopm/article/view/446/583 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2018 Brazilian Journal of Operations & Production Management info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2018 Brazilian Journal of Operations & Production Management |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html 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. 15 No. 2 (2018): June, 2018; 302-310 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 |
_version_ |
1797051460926570496 |