Impact of dampening demand variability in a production/inventory system with multiple retailers
We study a supply chain consisting of a single manufacturer and two retailers. The manufacturer produces goods on a maketoorder basis, while both retailers maintain an inventory and use a periodic replenishment rule. As opposed to the traditional (r, S) policy, where a retailer at the end of each...
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Formato:  Capítulo de libro (Book Chapter) 
Lenguaje:  Inglés (English) 
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Springer Science
2013

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Acceso en línea:  https://repository.urosario.edu.co/handle/10336/28527 https://doi.org/10.1007/9781461449096_11 
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ir103362852720200828T15:50:27Z Impact of dampening demand variability in a production/inventory system with multiple retailers Impacto de atenuar la variabilidad de la demanda en un sistema de producción / inventario con múltiples minoristas Van Houdt B. Pérez J.F. Structured markov chains Supply chain Inventory MSC: primary 60J22 Secondary 90B30 90B05 We study a supply chain consisting of a single manufacturer and two retailers. The manufacturer produces goods on a maketoorder basis, while both retailers maintain an inventory and use a periodic replenishment rule. As opposed to the traditional (r, S) policy, where a retailer at the end of each period orders the demand seen during the previous period, we assume that the retailers dampen their demand variability by smoothing the order size. More specifically, the order placed at the end of a period is equal to ? times the demand seen during the last period plus (1 ? ?) times the previous order size, with ? ? (0, 1] the smoothing parameter. We develop a GI/M/1type Markov chain with only two nonzero blocks A 0 and A d to analyze this supply chain. The dimension of these blocks prohibits us from computing its rate matrix R in order to obtain the steady state probabilities. Instead we rely on fast numerical methods that exploit the structure of the matrices A 0 and A d , i.e., the power method, the Gauss–Seidel iteration, and GMRES, to approximate the steady state probabilities. Finally, we provide various numerical examples that indicate that the smoothing parameters can be set in such a manner that all the involved parties benefit from smoothing. We consider both homogeneous and heterogeneous settings for the smoothing parameters. 2013 20200828T15:49:16Z info:eurepo/semantics/bookPart info:eurepo/semantics/publishedVersion ISBN: 9781461449089 EISBN: 9781461449096 https://repository.urosario.edu.co/handle/10336/28527 https://doi.org/10.1007/9781461449096_11 eng info:eurepo/semantics/restrictedAccess application/pdf Springer Science Business Media MatrixAnalytic Methods in Stochastic Models 
institution 
EdocUR  Universidad del Rosario 
collection 
DSpace 
language 
Inglés (English) 
topic 
Structured markov chains Supply chain Inventory MSC: primary 60J22 Secondary 90B30 90B05 
spellingShingle 
Structured markov chains Supply chain Inventory MSC: primary 60J22 Secondary 90B30 90B05 Van Houdt B. Pérez J.F. Impact of dampening demand variability in a production/inventory system with multiple retailers 
description 
We study a supply chain consisting of a single manufacturer and two retailers. The manufacturer produces goods on a maketoorder basis, while both retailers maintain an inventory and use a periodic replenishment rule. As opposed to the traditional (r, S) policy, where a retailer at the end of each period orders the demand seen during the previous period, we assume that the retailers dampen their demand variability by smoothing the order size. More specifically, the order placed at the end of a period is equal to ? times the demand seen during the last period plus (1 ? ?) times the previous order size, with ? ? (0, 1] the smoothing parameter. We develop a GI/M/1type Markov chain with only two nonzero blocks A 0 and A d to analyze this supply chain. The dimension of these blocks prohibits us from computing its rate matrix R in order to obtain the steady state probabilities. Instead we rely on fast numerical methods that exploit the structure of the matrices A 0 and A d , i.e., the power method, the Gauss–Seidel iteration, and GMRES, to approximate the steady state probabilities. Finally, we provide various numerical examples that indicate that the smoothing parameters can be set in such a manner that all the involved parties benefit from smoothing. We consider both homogeneous and heterogeneous settings for the smoothing parameters. 
format 
Capítulo de libro (Book Chapter) 
author 
Van Houdt B. Pérez J.F. 
author_facet 
Van Houdt B. Pérez J.F. 
author_sort 
Van Houdt B. 
title 
Impact of dampening demand variability in a production/inventory system with multiple retailers 
title_short 
Impact of dampening demand variability in a production/inventory system with multiple retailers 
title_full 
Impact of dampening demand variability in a production/inventory system with multiple retailers 
title_fullStr 
Impact of dampening demand variability in a production/inventory system with multiple retailers 
title_full_unstemmed 
Impact of dampening demand variability in a production/inventory system with multiple retailers 
title_sort 
impact of dampening demand variability in a production/inventory system with multiple retailers 
publisher 
Springer Science 
publishDate 
2013 
url 
https://repository.urosario.edu.co/handle/10336/28527 https://doi.org/10.1007/9781461449096_11 
_version_ 
1676708397079068672 
score 
11,383098 