ABSTRACT
PurposeThis study examines the different effects of service recovery strategies on customers' future intentions when online shoppers were experiencing delivery failures. Two types of problem severity are evaluated: wrong-product delivery (issues with the product quality or quantity) and late delivery. This study also investigates the impact of service criticality on the relationship between service recovery strategies and customers' future intentions.Design/methodology/approachThis study employs experimental research with 123 online shoppers as participants. Following the results, a subsequent test is conducted to examine the effect of participants' demographics on future intentions. Finally, the current study elaborates the findings using qualitative research, interviewing both sides impacted by the service failures: online shoppers and e-retail managers.FindingsThe findings show that complementing product replacement with monetary compensation is the most effective strategy to improve repurchase intention after a dissatisfaction moment. This effect is indifferent to service criticality and severity. Age influences the participants' repurchase intentions, in which younger people are less tolerant of service failures. In contrast, gender and education level do not provide any differences. To prevent delivery failures, managers participating in this study suggest several best practices regarding systems and infrastructure, people and coordination and collaboration with logistics partners.Research limitations/implicationsThe study mainly examines a limited type of service and service failures. Further studies are encouraged to expand the variables and scenarios, as well as to employ more distinctive methods, to enrich the findings related to recovery strategy in the e-commerce industry.Practical implicationsGiven proper compensation, service failure could create momentum for online retailers to boost customer loyalty. This study suggests that managers design the most effective service recovery to win customers back to the business.Originality/valueThis paper enriches the literature related to a service recovery strategy, particularly within the online shopping context.
ABSTRACT
With the rapid increase of digitization and desire for contactless shopping during the COVID-19 pandemic, online grocery sales keep growing fast. Correspondingly, optimized policies for order picking are nowadays central in omnichannel supply chains, not only within dedicated warehouses but also in grocery stores while processing online orders. In this work, we apply the Buy-Online-Pick-up-in-Store concept and optimize the in-store picking and packing procedure. The approach we propose, which is based on two mathematical programming models, guides pickers on how to organize articles into bags while collecting items. In this way bags are filled up evenly and they are ready to be handled to the customers at the end of each picking task, with no further rearrangement needed.
ABSTRACT
Community retail is an important research issue in the field of fresh agriproduct e-commerce. This paper focuses on the problem of last-mile multi-temperature joint distribution (MTJD), which combines time coupling, order allocation, and vehicle scheduling. Firstly, according to the temperature of a refrigerated truck in multi-temperature zones, a split-order packing decision is proposed to integrate the different types of fresh agriproduct. Then, the order allocation strategy is incorporated into a comprehensive picking and distribution schedule, while taking into account the time-coupling of picking, distribution, and delivery time limit. To improve consumer satisfaction and reduce order fulfillment costs, an optimization model combining multi-item order allocation and vehicle scheduling is established, to determine the optimal order allocation scheme and distribution route. Finally, taking fresh agriproduct community retail in the Gulou District of Nanjing as an example, the effectiveness and feasibility of the model are illustrated. The numerical results of medium- to large-scale examples show that, compared with the variable neighborhood search algorithm (VNS) and genetic algorithm (GA), the mixed genetic algorithm (MGA) can save 29% of CPU time and 65% of iterations. This study considers the integrated optimization of multiple links, to provide scientific decision support for fresh agriproduct e-commerce enterprises.
ABSTRACT
The COVID-19 pandemic has caused critical challenges for e-commerce warehouses that strive to fulfill surging customer demand while facing a high virus infection risk. Current literature on picking optimization overlooks warehouse safety under pandemic conditions. Meanwhile, scattered storage and zone-wave-batch picking have been used in parallel by many large e-commerce warehouses, these two operational policies have not been considered together in picking optimization studies. This paper fills these gaps by solving an order batching problem considering scattered storage, zone-wave-batch picking, and pickers' proximity simultaneously. We formulate and solve the mathematical model of the discussed problem and propose the Aisle-Based Constructive Batching Algorithm (ABCBA) to help warehouses pick more efficiently and safely. Experiments with extensive datasets from a major third-party logistics (3PL) company show that, compared to the current picking strategy, ABCBA can reduce the total picking time and the virus infection risk due to pickers' proximity by 46% and 72%, respectively. Compared to other heuristics like tabu + nLSA3 (Yang, Zhao, and Guo 2020), ABCBA gets better results using less computation time.
ABSTRACT
Purpose>This paper identifies, configures and analyses a solution aimed at increasing the efficiency of in-store picking for e-grocers and combining the traditional store-based option with a warehouse-based logic (creating a back area dedicated to the most required online items).Design/methodology/approach>The adopted methodology is a multi-method approach combining analytical modelling and interviews with practitioners. Interviews were performed with managers, whose collaboration allowed the development and application of an empirically-grounded model, aimed to estimate the performances of the proposed picking solution in its different configurations. Various scenarios are modelled and different policies are evaluated.Findings>The proposed solution entails time benefits compared to traditional store-based picking for three main reasons: lower travel time (due to the absence of offline customers), lower retrieval time (tied to the more efficient product allocation in the back) and lower time to manage stock-outs (since there are no missing items in the back). Considering the batching policies, order picking is always outperformed by batch and zone picking, as they allow for the reduction of the average travelled distance per order. Conversely, zone picking is more efficient than batch picking when demand volumes are high.Originality/value>From an academic perspective, this work proposes a picking solution that combines the store-based and warehouse-based logics (traditionally seen as opposite/alternative choices). From a managerial perspective, it may support the definition of the picking process for traditional grocers that are offering – or aim to offer – e-commerce services to their customers.
ABSTRACT
Picking operations is the most time-consuming and laborious warehousing activity. Managers have been seeking smart manufacturing methods to increase picking efficiency. Because storage location planning profoundly affects the efficiency of picking operations, this study uses clustering methods to propose an optimal storage location planning-based consolidated picking methodology for driving the smart manufacturing of wireless modules. Firstly, based on the requirements of components derived by the customer orders, this research analyzes the storage space demands for these components. Next, this research uses the data of the received dates and the pick-up dates for these components to calculate the average duration of stay (DoS) values. Using the DoS values and the storage space demands, this paper executes the analysis of optimal storage location planning to decide the optimal storage location of each component. In accordance with the optimal storage location, this research can evaluate the similarity among the picking lists and then separately applies hierarchical clustering and K-means clustering to formulate the optimal consolidated picking strategy. Finally, the proposed method was verified by using the real case of company H. The result shows that the travel time and the distance for the picking operation can be diminished drastically.
ABSTRACT
In the aftermath of the COVID-19 pandemic, supply chains experienced an unprecedented challenge to fulfill consumers' demand. As a vital operational component, manual order picking operations are highly prone to infection spread among the workers, and thus, susceptible to interruption. This study revisits the well-known order batching problem by considering a new overlap objective that measures the time pickers work in close vicinity of each other and acts as a proxy of infection spread risk. For this purpose, a multi-objective optimization model and three multi-objective metaheuristics with an effective seeding procedure are proposed and are tested on the data obtained from a major US-based logistics company. Through extensive numerical experiments and comparison with the company's current practices, the results are discussed, and some managerial insights are offered. It is found that the picking capacity can have a determining impact on reducing the risk of infection spread through minimizing the picking overlap.