Simulating an optimal continuous review inventory policy online retail
In the online channel of grocery retailers, customers typically define a specific date and time at which they want to receive their orders. This customer order time window provides additional flexibility, which can be used to improve the policy for optimizing inventory. This improvement allows to be...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2017 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/110776 |
| Acceso en línea: | https://hdl.handle.net/2117/110776 |
| Access Level: | acceso abierto |
| Palabra clave: | Business management BM Empreses -- Direcció i administració Àrees temàtiques de la UPC::Economia i organització d'empreses |
| Sumario: | In the online channel of grocery retailers, customers typically define a specific date and time at which they want to receive their orders. This customer order time window provides additional flexibility, which can be used to improve the policy for optimizing inventory. This improvement allows to better adjust the ordering point and the ordering quantity, leading to a decrease in the total cost, which comprises the ordering, holding and stockout costs. This study is based on a previous thesis, where the (s, Q) inventory policy explicitly accounts for the ordering window. The policy considers that the customer demand as well as the customer ordering window are stochastic. In this thesis, we extend and refine the policy by exploring the multiple on order setting, which introduces more complex and extreme scenarios to the inventory system with ordering windows. The validations were performed using simulation for a variety of parameter configurations. The simulator was implemented in Excel, using VBA, and the policy was optimized via numerical optimization using MatLab. The revised policy proved to be appropriate both for single and multiple on order scenarios. There is an avenue of future research to be explored in this online retail setting, particularly in the exploration of inventory policies that account for the ordering window. |
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