The Buy-Online-Pick-Up-in-Store Retailing Model: Optimization Strategies for In-Store Picking and Packing
Online shopping is growing fast due to the increasingly widespread use of digital services. During the COVID-19 pandemic, the desire for contactless shopping has further changed consumer behavior and accelerated the acceptance of online grocery purchases. Consequently, traditional brick-and-mortar r...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/14/12/350 |
| _version_ | 1797507035239022592 |
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| author | Nicola Ognibene Pietri Xiaochen Chou Dominic Loske Matthias Klumpp Roberto Montemanni |
| author_facet | Nicola Ognibene Pietri Xiaochen Chou Dominic Loske Matthias Klumpp Roberto Montemanni |
| author_sort | Nicola Ognibene Pietri |
| collection | DOAJ |
| description | Online shopping is growing fast due to the increasingly widespread use of digital services. During the COVID-19 pandemic, the desire for contactless shopping has further changed consumer behavior and accelerated the acceptance of online grocery purchases. Consequently, traditional brick-and-mortar retailers are developing omnichannel solutions such as click-and-collect services to fulfill the increasing demand. In this work, we consider the Buy-Online-Pick-up-in-Store concept, in which online orders are collected by employees of the conventional stores. As labor is a major cost driver, we apply and discuss different optimizing strategies in the picking and packing process based on real-world data from a German retailer. With comparison of different methods, we estimate the improvements in efficiency in terms of time spent during the picking process. Additionally, the time spent on the packing process can be further decreased by applying a mathematical model that guides the employees on how to organize the articles in different shopping bags during the picking process. In general, we put forward effective strategies for the Buy-Online-Pick-up-in-Store paradigm that can be easily implemented by stores with different topologies. |
| first_indexed | 2024-03-10T04:40:57Z |
| format | Article |
| id | doaj.art-d2d42dda8c7643ada5bc953651cb7064 |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-03-10T04:40:57Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-d2d42dda8c7643ada5bc953651cb70642023-11-23T03:24:49ZengMDPI AGAlgorithms1999-48932021-11-01141235010.3390/a14120350The Buy-Online-Pick-Up-in-Store Retailing Model: Optimization Strategies for In-Store Picking and PackingNicola Ognibene Pietri0Xiaochen Chou1Dominic Loske2Matthias Klumpp3Roberto Montemanni4Department of Sciences and Methods for Engineering, University of Modena and Reggio Emilia, 42121 Reggio Emilia, ItalyDepartment of Sciences and Methods for Engineering, University of Modena and Reggio Emilia, 42121 Reggio Emilia, ItalyDepartment of Business Administration, Georg-August-University of Göttingen, 37077 Göttingen, GermanyDepartment of Business Administration, Georg-August-University of Göttingen, 37077 Göttingen, GermanyDepartment of Sciences and Methods for Engineering, University of Modena and Reggio Emilia, 42121 Reggio Emilia, ItalyOnline shopping is growing fast due to the increasingly widespread use of digital services. During the COVID-19 pandemic, the desire for contactless shopping has further changed consumer behavior and accelerated the acceptance of online grocery purchases. Consequently, traditional brick-and-mortar retailers are developing omnichannel solutions such as click-and-collect services to fulfill the increasing demand. In this work, we consider the Buy-Online-Pick-up-in-Store concept, in which online orders are collected by employees of the conventional stores. As labor is a major cost driver, we apply and discuss different optimizing strategies in the picking and packing process based on real-world data from a German retailer. With comparison of different methods, we estimate the improvements in efficiency in terms of time spent during the picking process. Additionally, the time spent on the packing process can be further decreased by applying a mathematical model that guides the employees on how to organize the articles in different shopping bags during the picking process. In general, we put forward effective strategies for the Buy-Online-Pick-up-in-Store paradigm that can be easily implemented by stores with different topologies.https://www.mdpi.com/1999-4893/14/12/350buy-online-pick-up-in-storein-store order picking and packingomnichannel grocery retailingoptimizationroutingmathematical programming |
| spellingShingle | Nicola Ognibene Pietri Xiaochen Chou Dominic Loske Matthias Klumpp Roberto Montemanni The Buy-Online-Pick-Up-in-Store Retailing Model: Optimization Strategies for In-Store Picking and Packing Algorithms buy-online-pick-up-in-store in-store order picking and packing omnichannel grocery retailing optimization routing mathematical programming |
| title | The Buy-Online-Pick-Up-in-Store Retailing Model: Optimization Strategies for In-Store Picking and Packing |
| title_full | The Buy-Online-Pick-Up-in-Store Retailing Model: Optimization Strategies for In-Store Picking and Packing |
| title_fullStr | The Buy-Online-Pick-Up-in-Store Retailing Model: Optimization Strategies for In-Store Picking and Packing |
| title_full_unstemmed | The Buy-Online-Pick-Up-in-Store Retailing Model: Optimization Strategies for In-Store Picking and Packing |
| title_short | The Buy-Online-Pick-Up-in-Store Retailing Model: Optimization Strategies for In-Store Picking and Packing |
| title_sort | buy online pick up in store retailing model optimization strategies for in store picking and packing |
| topic | buy-online-pick-up-in-store in-store order picking and packing omnichannel grocery retailing optimization routing mathematical programming |
| url | https://www.mdpi.com/1999-4893/14/12/350 |
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