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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 propose a simulation model integrated with an empirical regression analysis to provide a new mathematical formulation for automated storage and retrieval system (AS/RS) travel time estimation under class-based storage and different input/output (I/O) point vertical levels.

A simulation approach is adopted to compute the travel time under different warehouse scenarios. Simulation runs with several I/O point levels and multiple shape factor values.

The proposed model is extremely precise for both single command (SC) and dual command (DC) cycles and very well fitted for a reliable computation of travel times.

The proposed mathematical formulation for estimating the AS/RS travel time advances widely applied methodologies existing in literature. As well as, it provides a practical implication by supporting faster and more accurate travel time computations for both SC and DC cycles. However, the regression analysis is conducted based on simulated data and can be refined by numerical values coming from real warehouses.

This work provides a new simulation model and a refined mathematical equation to estimate AS/RS travel time.

Driven by sustainable production, mobile robots are introduced as a new clean-energy material handling tool for mixed-model assembly lines (MMALs), which reduces energy consumption and lineside inventory of workstations (LSI). Nevertheless, the previous part feeding scheduling method was designed for conventional material handling tools without considering the flexible spatial layout of the robotic mobile fulfillment system (RMFS). To fill this gap, this paper focuses on a greening mobile robot part feeding scheduling problem with Just-In-Time (JIT) considerations, where the layout and number of pods can be adjusted.

A novel hybrid-load pod (HL-pod) and mobile robot are proposed to carry out part feeding tasks between material supermarkets and assembly lines. A bi-objective mixed-integer programming model is formulated to minimize both total energy consumption and LSI, aligning with environmental and sustainable JIT goals. Due to the NP-hard nature of the proposed problem, a chaotic differential evolution algorithm for multi-objective optimization based on iterated local search (CDEMIL) algorithm is presented. The effectiveness of the proposed algorithm is verified by dealing with the HL-pod-based greening part feeding scheduling problem in different problem scales and compared to two benchmark algorithms. Managerial insights analyses are conducted to implement the HL-pod strategy.

The CDEMIL algorithm's ability to produce Pareto fronts for different problem scales confirms its effectiveness and feasibility. Computational results show that the proposed algorithm outperforms the other two compared algorithms regarding solution quality and convergence speed. Additionally, the results indicate that the HL-pod performs better than adopting a single type of pod.

This study proposes an innovative solution to the scheduling problem for efficient JIT part feeding using RMFS and HL-pods in automobile MMALs. It considers both the layout and number of pods, ensuring a sustainable and environmental-friendly approach to production.

The purpose of this study is to systematically review available state-of-the-art literature on comparative studies on Quick Commerce (Q-commerce) and E-commerce and their greenhouse gas (GHG) emissions.

The literature survey methodology is based on the funneling approach of Kitchenham (2004), where results are obtained according to inclusion and exclusion criteria. The literature review methodology used for this study covers the period from 2016 to 2022. The areas considered for the survey are operations, logistics and supply chain network design for the distribution of goods in e-business. After deciding on the criteria, a total of 140 articles were extracted from 9 journal articles that study e-commerce and environmental emissions.

The result of this study reveals that GHG emissions from both modes of shopping depend on various parameters such as speed of delivery, last-mile depot locations, logistics and vehicle efficiency, customers’ order patterns and average basket size. Furthermore, the findings also highlight the difference between Q-commerce and E-commerce supply chain networks.

This study only accounts for GHG emissions from logistics activities, but there are other sources of GHG emissions in the overall supply chain that are not taken into consideration. Supply chain/business analysts in Q-commerce companies might refer the findings from this study to measure GHG emissions from their operations.

This is the first study in the Q-commerce field that uses a structured approach to find relevant literature from the years 2016 to 2022 and focuses on GHG emission measurement.

This paper addresses a gap in task performance research, with a focus on supply chain operations, by exploring the role that defensive pessimism (DP)—a phenomenon sparsely studied in supply chain literature—has in the workplace. It investigates the roles that task complexity, perceptions of control and employee situatedness in the workplace play as predictors of DP, as well as addresses the relationship between defensive pessimism and supply chain performance.

Five hypotheses are developed and empirically tested employing the data-generating method, Monte Carlo simulation and then applying factor analysis and structural equation modeling (SEM) to survey data from practitioner members of the Council of Supply Chain Management Professionals.

The results reveal that task complexity and external locus of control heighten perceptions among employees that task completion could be outside their locus of control. The increased tendency to be defensively pessimistic about workplace commitments negatively impacts supply chain performance. This study found that task complexity and external locus of control encourage DP, negatively impacting supply chain performance (SCP).

This study explored underlying causes of defensive pessimism, a self-limiting behavior among supply chain professionals. In understanding the role of DP, it is possible to enhance SCP by managing task complexity, external locus of control and job autonomy—predictors of defensive pessimism in work commitments.

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.

This study aims to enhance storage and distribution operations at a pharmaceutical supply center (PSC) in primary health care (PH) using lean health care (LH) tools. Supply centers for health products, medications and supplies have unique characteristics compared to centers for other goods due to complex processes, specific services, diverse stakeholders and multiple interactions. The authors adapt LH tools to address these complexities and meet industry-specific needs.

The investigation unit is a PSC in a large southern Brazilian city, and the processes analyzed are the storage and distribution of medications. The authors performed action research from June 2019 to February 2020. Data collection and problem diagnosis involved the development of a value stream mapping.

The authors adapted the overall equipment effectiveness calculation, efficiency analysis, and loss classification for PSC operations. Eighteen core issues were found: waiting, movement, transport, stock, inadequate processing, defects and human potential losses. The authors proposed waste reduction tools and practices. Inadequate storage conditions may compromise medicine quality, efficacy and safety. This can result from lacking physical structures or noncompliance with procedures. Next, the authors recommend simulating scenarios for validation before implementation.

The study explored ways to enhance layout and medicine distribution at the PSC, focusing on reducing loss and cost impact.

Originality lies in LH application in a PSC of PH, often applied in secondary or tertiary health levels like hospitals. The novelty necessitated adaptations of tools for future PSC applications.