Search results

This research investigates how to optimize storage location assignment to decrease the order picking time and the waiting time of orders in the staging area of low-temperature logistics centers, with the goal of reducing food loss caused by temperature abuse.

The authors applied ABC clustering to the products in a simulated database of historical orders modeled after the actual order pattern of a large cold logistics company; then, the authors mined the association rules and calculated the sales volume correlation indices of the ordered products. Finally, the authors generated three different simulated order databases to compare order picking time and waiting time of orders in the staging area under eight different storage location assignment strategies.

All the eight proposed storage location assignment strategies significantly improve the order picking time (by up to 8%) and the waiting time of orders in the staging area (by up to 22%) compared with random placement.

The results of this research are based on a case study and simulated data, which implies that, if the best performing strategies are applied to different environments, the extent of the improvements may vary. Additionally, the authors only considered specific settings in terms of order picker routing, zoning and batching: other settings may lead to different results.

A storage location assignment strategy that adopts dispersion and takes into consideration ABC clustering and shipping frequency provides the best performance in minimizing order picker's travel distance, order picking time, and waiting time of orders in the staging area. Other strategies may be a better fit if the company's objectives differ.

Previous research on optimal storage location assignment rarely considered item association rules based on sales volume correlation. This study combines such rules with several storage planning strategies, ABC clustering, and two warehouse layouts; then, it evaluates their performance compared to the random placement, to find which one minimizes the order picking time and the order waiting time in the staging area, with a 30-min time limit to preserve the integrity of the cold chain. Order picking under these conditions was rarely studied before, because they may be irrelevant when dealing with temperature-insensitive items but become critical in cold warehouses to prevent temperature abuse.

Prior literature has widely established that the design of storage locations impacts order picking task performance. The purpose of this study is to investigate the performance impact of unit loads, e.g. pallets or rolling cages, utilized by pickers to pack products after picking them from storage locations.

An empirical analysis of archival data on a manual order picking system for deep-freeze products was performed in cooperation with a German brick-and-mortar retailer. The dataset comprises N = 343,259 storage location visits from 17 order pickers. The analysis was also supported by the development and the results of a batch assignment model that takes unit load selection into account.

The analysis reveals that unit load selection affects order picking task performance. Standardized rolling cages can decrease processing time by up to 8.42% compared to standardized isolated rolling boxes used in cold retail supply chains. Potential cost savings originating from optimal batch assignment range from 1.03% to 39.29%, depending on batch characteristics.

This study contributes to the literature on factors impacting order picking task performance, considering the characteristics of unit loads where products are packed on after they have been picked from the storage locations. In addition, it provides potential task performance improvements in cold retail supply chains.

Increasing personnel costs and labour shortages have pushed retailers to give increasing attention to their intralogistics operations. We study hybrid order picking systems, in which humans and robots share work time, workspace and objectives and are in permanent contact. This necessitates a collaboration of humans and their mechanical coworkers (cobots).

Through a longitudinal case study on individual-level technology adaption, we accompanied a pilot testing of an industrial truck that automatically follows order pickers in their travel direction. Grounded on empirical field research and a unique large-scale data set comprising N = 2,086,260 storage location visits, where N = 57,239 storage location visits were performed in a hybrid setting and N = 2,029,021 in a manual setting, we applied a multilevel model to estimate the impact of this cobot settings on task performance.

We show that cobot settings can reduce the time required for picking tasks by as much as 33.57%. Furthermore, practical factors such as product weight, pick density and travel distance mitigate this effect, suggesting that cobots are especially beneficial for short-distance orders.

Given that the literature on hybrid order picking systems has primarily applied simulation approaches, the study is among the first to provide empirical evidence from a real-world setting. The results are discussed from the perspective of Industry 5.0 and can prevent managers from making investment decisions into ineffective robotic technology.

Building on Stimulus-Organism-Response theory, the current study examines the influence of pickup service quality in buy-online pickup in-store service (BOPIS) on users' perceived relationship investment with the mediating role of users' perceived experience quality and relationship proneness. This research also demonstrates the subsequent impact of BOPIS users' perceived relationship investment on their relationship performance indicators, like their cross-buying behaviors (breadth), frequency of their purchase (depth) and longevity of their relationship (length) with the store. The moderating role of BOPIS users' service experience consciousness in a few proposed relationships was tested.

The research is descriptive, quantitative and cross-sectional investigation. The study employed a purposive sampling technique. It was conducted using data collected using a validated self-administered questionnaire from 786 Indian omnichannel shoppers who have used BOPIS services in the past. The proposed conceptual model was tested using Partial Least Squares-Structural Equation Modeling.

The results indicate that BOPIS users' perceived experience quality and relationship proneness positively mediate pickup service quality and perceived relationship investment. The users' perceived relationship investment subsequently significantly positively impacts different dimensions of their relationship performance with the store (breadth, depth and length). Additionally, BOPIS users' service experience consciousness has a significant negative moderating effect on the direct relationship between pickup service quality and different dimensions of relationship performance.

The study is conducted in the Indian population, where omnichannel retailing is still nascent.

This study addresses the need to investigate the relationship performance indicators of BOPIS users, like their cross-buying behaviors(breadth), frequency of their purchase(depth) and longevity of their relationship(length) with the store. This study is the first to show that pickup service quality might explain the relationship performance of BOPIS users through their perceived experience quality, relationship proneness and relationship investments. The moderating role of BOPIS users' service experience consciousness in a few proposed relationships was also tested for the first time.

Despite its potential, warehouse managers still struggle to successfully assimilate autonomous mobile robots (AMRs) in their operations. This paper means to identify the moderating factors of AMR assimilation for production warehouses that influence the digital transformation of their intralogistics via AMRs.

Drawing on innovation of assimilation theory (IAT), this study followed an explorative approach using the principles of the case study method in business research. The cases comprised of four AMR end users and six AMR service providers. Data were collected through semi-structured interviews.

Four clusters of moderators that affect each stage of AMR assimilation were identified. These clusters include organizational attributes of end users (i.e. production warehouses), service attributes of service providers, technology attributes of AMRs and relational attributes between the AMR service providers and the AMR end users.

The authors extend the IAT framework by identifying various moderating factors between different stages of the AMR assimilation process. To the authors' knowledge, this is the first study to introduce the perspective of AMR end users in conjunction with AMR service providers to the “Industry 4.0” technology assimilation literature. The study propositions regarding these factors guide future intralogistics and AMR research.

This paper aims to examine why retail firms seldom achieve full integration of online and offline channels as prescribed in omni-channel literature. It examines the intermediate process of channel integration from an internal, operational perspective.

This study is composed of two parts. In the first part, the authors interviewed informants from nine firms that were engaged in channel integration. In the second part, the authors conducted case studies with three firms from the cosmetics and skincare industry against the backdrop of the COVID-19 pandemic to find evidence to support or negate the propositions made in the first part.

The first part identified six operational challenges to channel integration. The authors categorized these challenges into two groups: inter-channel communication and inter-channel competition. Inter-channel competition carries more weight at the latter stage of integration. The authors also identified two antecedents that affect the seriousness of these challenges: heterogeneity among channels in business operation and external competitive pressure. In the second part, the authors found that both inter-channel communication and inter-channel competition were improved because of the external competitive pressure exerted by the COVID-19 pandemic. However, the heterogeneity of offline channels against online channels in business operation is a double-edged sword.

The study identifies the changing effects of the challenges of channel integration and their antecedents in the midst of integration. The positive influence of a specific dimension of channel heterogeneity against other channels increases and then decreases along channel integration. The identification of the changing effects lays the foundation for a finer stage model of channel integration.

The purpose of this paper is to explore and disseminate knowledge about quantum-inspired computing technology's potential to solve complex challenges faced by the operational agility capability in Industry 4.0 manufacturing and logistics operations.

A multi-case study approach is used to determine the impact of quantum-inspired computing technology in manufacturing and logistics processes from the supplier perspective. A literature review provides the basis for a framework to identify a set of flexibility and agility operational capabilities enabled by Industry 4.0 Information and Digital Technologies. The use cases are analyzed in depth, first individually and then jointly.

Study results suggest that quantum-inspired computing technology has the potential to harness and boost companies' operational flexibility to enhance operational agility in manufacturing and logistics operations management, particularly in the Industry 4.0 context. An exploratory model is proposed to explain the relationships between quantum-inspired computing technology and the deployment of operational agility capabilities.

This is study explores the use of quantum-inspired computing technology in Industry 4.0 operations management and contributes to understanding its potential to enable operational agility capability in manufacturing and logistics operations.

This paper investigates the buy online and pick up in-store cooperation (BOPSC) of online and offline retailers. Specifically, this study solves the following questions: (1) What is the impact of BOPSC on their optimal price and sales volume of products? (2) When should an online retailer and an offline retailer conduct the BOPSC strategy with each other?

The paper first establishes two game models to explore the equilibriums of online and offline retailers in non-BOPSC and BOPSC. Then the condition for online and offline retailers to implement BOPSC strategy are determined. Furthermore, the applicability of the BOPSC strategy is enhanced by incorporating numerical analysis.

The study’s findings reveal that BOPSC strategy will not always beneficial to online and offline retailers, which depends on the total cost of online shopping and the product valuation of consumers. BOPSC strategy leads to the increase of prices and online orders, and the demand of offline retailer is eroded. Moreover, BOPS cooperation between different retailers is easier to achieve than omni-channel integration strategy. When the convenience difference between offline shopping and BOPSC pick-up is moderate, the effectiveness of BOPSC strategy can be improved.

This study has the following two main contributions: Firstly, the authors investigate the effects of BOPSC strategy on the prices of online and offline retailers. The study results show that the BOPSC strategy alleviates price competition and promotes a win–win situation between online retailers and offline retailers. Secondly, this paper mainly studies the cooperative behavior between online and offline retailers and reveals the optimal conditions for online and offline retailers to adopt BOPSC strategy. It can help small- and medium-sized online and offline retailers to choose suitable products for BOPSC strategy, so as to achieve the purpose of increasing profit.

Online food delivery service has evolved swiftly and stretched the bounds of the catering business. In the gig economy, being a food delivery rider draws employees with the promise of flexibility and independence. To this end, the purpose of this paper is to explore the main themes shared in online reviews by food delivery riders and which of these themes are linked to positive and negative ratings.

The authors used 729 employee reviews posted on the Glassdoor website. In addition, they used content analysis to examine reviews of Uber Eats online food ordering company shared by food delivery riders on an online platform.

The results of this study include seven main themes: “earning,” “customers,” “orders,” “tips,” “car,” “flexible schedule” and “app” (navigation). Positive concepts are associated with “earning,” “orders,” “tips” and “flexible schedule.” Negative themes are linked to the “app” (navigation), “car” and “customers.”

Management should consider online reviews as employees’ opinions and voices. Specifically, management should provide financial support to employees for car maintenance, offer insurance for income stability and arrange training programs to enable them to use several tip-enhancing behaviors.

No research has been conducted using online reviews from an employment search engine to investigate employees’ experiences of online food delivery. To the best of the authors’ knowledge, this study is one of the first attempts using user-generated content from an employment search engine to explore employees’ experiences.

The digital warehouse management system is an emergence that forms a critical part of the transformation of economic structure in Industry 4.0. In the present business scenario, the warehouse management system encounters a messy layout, poor damage control, unsatisfactory order management, lack of visibility and lack of technological interventions. Digital twin (DT) based warehouse system shows the ontology and knowledge graphs for competitive advantage by consolidating and transferring goods directly from an inbound supplier to an outbound customer on short notice and with no or limited storage. There remains a lack of clarity on how the DT can be implemented successfully in warehouse management.

The current literature remains largely unstructured and scattered due to a lack of a systematic approach to integrating the research implications and analysis. This paper probes the conceptualization of the DT with the help of theoretical analysis using the systematic literature analysis method.

The study explores essential concepts such as interoperability and integrability in implementing DT. Further, it analyzes the role of a supply chain control tower (SCCT) in modern supply chain management. A research framework is proposed for practitioners and academicians by incorporating the opportunities and challenges associated with DT implementation. The research findings are mainly threefold: Conceptualization of DT, Featuring SCCT and Exploration of cross-computer platform interfaces, scalability and maintenance strategies.

This study is among the first to analyze and review DT applications in warehouse management. Moreover, the study proposes a theoretical toolbox for the practitioners to successfully implement the DT in warehouse DT-based warehouse management system: A theoretical toolbox for future research and applications.