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Resources Allocation in Service Planning Using Discrete-Event Simulation
Objective: Calculate the required personnel and resources needed to fulfill the service promise agreed with the customer. Methods and materials: This paper presents a discrete event simulation (DES) model developed to select and implement a Point of Sale (POS) for a company providing financial produ...
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Published in: | Ingeniería y universidad 2021, Vol.25 |
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description | Objective: Calculate the required personnel and resources needed to fulfill the service promise agreed with the customer. Methods and materials: This paper presents a discrete event simulation (DES) model developed to select and implement a Point of Sale (POS) for a company providing financial products. First, the paper shows the characterization of the system components and times per process. Then, hypothesis testing and goodness-of-fit statistics are estimated. Subsequently, the simulation scenarios assess the times between arrivals and the number of commercial advisers. Results and discussion: This model allows us to assess the allocation of resources to fulfill the service promise, which is that 80 % of customers must be served within one hour or less. This paper provided the service isoquants allowing us to observe the behavior of the performance metrics (service promise fulfillment) among different scenarios. Conclusions: The use of DES techniques allows for the evaluation of the assignment of personnel to achieve the fulfillment of the service promise, including facilities, equipment, and the evaluation of related processes. These methods can be extended to the analysis of resource allocation in the development of other processes, observing the relationship between service quality and operating costs. |
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Methods and materials: This paper presents a discrete event simulation (DES) model developed to select and implement a Point of Sale (POS) for a company providing financial products. First, the paper shows the characterization of the system components and times per process. Then, hypothesis testing and goodness-of-fit statistics are estimated. Subsequently, the simulation scenarios assess the times between arrivals and the number of commercial advisers. Results and discussion: This model allows us to assess the allocation of resources to fulfill the service promise, which is that 80 % of customers must be served within one hour or less. This paper provided the service isoquants allowing us to observe the behavior of the performance metrics (service promise fulfillment) among different scenarios. Conclusions: The use of DES techniques allows for the evaluation of the assignment of personnel to achieve the fulfillment of the service promise, including facilities, equipment, and the evaluation of related processes. These methods can be extended to the analysis of resource allocation in the development of other processes, observing the relationship between service quality and operating costs.</description><identifier>ISSN: 0123-2126</identifier><identifier>EISSN: 2011-2769</identifier><identifier>DOI: 10.11144/Javeriana.iued25.rasp</identifier><language>eng</language><publisher>Bogotá: Editorial Pontificia Universidad Javeriana</publisher><subject>Algorithms ; asignación de recursos ; Case studies ; Costs ; Credit cards ; Customer services ; Customers ; Decision making ; Discrete event simulation ; Discrete event systems ; Financial institutions ; Financial services ; Goodness of fit ; isocuantas ; isoquants ; Lean manufacturing ; líneas de espera ; Order quantity ; Patient satisfaction ; Performance measurement ; Personnel ; planeación de servicios ; Resource allocation ; Scheduling ; service planning ; simulación de eventos discretos ; Simulation ; Statistical tests ; waiting lines</subject><ispartof>Ingeniería y universidad, 2021, Vol.25</ispartof><rights>2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>LICENCIA DE USO: Los documentos a texto completo incluidos en Dialnet son de acceso libre y propiedad de sus autores y/o editores. Por tanto, cualquier acto de reproducción, distribución, comunicación pública y/o transformación total o parcial requiere el consentimiento expreso y escrito de aquéllos. Cualquier enlace al texto completo de estos documentos deberá hacerse a través de la URL oficial de éstos en Dialnet. Más información: https://dialnet.unirioja.es/info/derechosOAI | INTELLECTUAL PROPERTY RIGHTS STATEMENT: Full text documents hosted by Dialnet are protected by copyright and/or related rights. This digital object is accessible without charge, but its use is subject to the licensing conditions set by its authors or editors. 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More info: https://dialnet.unirioja.es/info/derechosOAI</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c251t-7eac3e497aeb218eec88a55e1aab8ecbe0c414a3ad2a07362fddc34b74b21b543</citedby><orcidid>0000-0001-9259-6670 ; 0000-0003-1060-2198 ; 0000-0002-0766-8391 ; 0000-0003-4983-7277</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/3039086006/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/3039086006?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,4024,25753,27923,27924,27925,37012,44590,74998</link.rule.ids></links><search><creatorcontrib>Becerra-Fernández, Mauricio</creatorcontrib><creatorcontrib>Herrera, Milton M.</creatorcontrib><creatorcontrib>Trejos, Cristian</creatorcontrib><creatorcontrib>Romero, Olga R.</creatorcontrib><title>Resources Allocation in Service Planning Using Discrete-Event Simulation</title><title>Ingeniería y universidad</title><description>Objective: Calculate the required personnel and resources needed to fulfill the service promise agreed with the customer. 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Conclusions: The use of DES techniques allows for the evaluation of the assignment of personnel to achieve the fulfillment of the service promise, including facilities, equipment, and the evaluation of related processes. 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subjects | Algorithms asignación de recursos Case studies Costs Credit cards Customer services Customers Decision making Discrete event simulation Discrete event systems Financial institutions Financial services Goodness of fit isocuantas isoquants Lean manufacturing líneas de espera Order quantity Patient satisfaction Performance measurement Personnel planeación de servicios Resource allocation Scheduling service planning simulación de eventos discretos Simulation Statistical tests waiting lines |
title | Resources Allocation in Service Planning Using Discrete-Event Simulation |
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