Abstract
Omnichannel retailing and digitalization result in considerable challenges for the management and optimization of retail operations. The continued demand of quantitative insights, their practical need, and the growing availability of data motivates an increasing number of scientists and practitioners to intensify research on demand and supply-related issues in retailing. This featured cluster provides the state-of-the art literature on forecasting and digitalization technologies, channel structures and delivery concepts as well as logistics in omnichannel and online retailing. The featured cluster contains 17 articles that deal with such topics. © 2021 Elsevier B.V.

Abstract
Big data offers great potential to improve organizational performance and generate competitive advantages. In this sense, knowing this phenomenon is relevant and this study aims to explore the role of big data on firm performance through a process of synthesis of several studies that have been published in recent years in different organizational contexts. This study adopts a mixed-methods approach. Initially, a systematic literature review is performed to characterize the studies that adopt structural equation modeling to determine the relationship between big data and firm performance. Additionally, a meta-analysis method is used to quantify the association between these two phenomena. The findings reveal a moderate positive relationship between the adoption of big data in the firm performance. This ratio is estimated at 0.38 with a confidence interval between 0.32 and 0.44 for a significance level of 0.05. The results of this study also allow us to conclude that the performance of organizations is also determined by other factors such as human capital, the data-driven organizational culture, or the learning capacity of the organization. This study offers mainly implications for companies that intend to invest in big data to know the potential value of this technology in organizational performance.

Abstract
The resilient smart distribution system is intended to cope with low probability, high-risk extreme events, including extreme natural disasters. In this regard, the flexible partitioning distribution system into supply-sufficient microgrids can be considered an interesting subject. This paper introduces a novel technique for partitioning a smart distribution system into supply-sufficient microgrids. In the presented model, the impact of demand response programs and energy storage has been considered in the proposed framework. Furthermore, besides the electricity aspects, the effects of gas network infrastructure on flexible portioning are also investigated to improve system resiliency facing natural disasters. The proposed model aims to maximize served load, which has been structured as a mixed integer linear programming (MILP) problem. The IEEE 34 bus standard test system is used to investigate the effectiveness of the proposed structure. The results of the study show that the proposed structure can increase the served load and the supply-sufficiently of the smart grid power system.

Abstract
The Real-Time Monitoring and Performance Management suite tool, known as UIL (User Interface Layer), was developed in the FASTEN project, a R&D initiative financed by the innovation and research program H2020 within a bilateral Europe-Brazil call. UIL was conceived and deployed in the IIoT architecture of the project. The goal was to provide a usercentered assistance to the human operator for both decision-responsibility and control loop, in a continuously updating information fashion, related to system's state. In order to have experimental results, a qualitative assessment was conducted in an industrial environment. The architecture proposed was based on the adoption of a Knowledge Engineering User Interface to support Operator 4.0. Our empirical experiments point out to a successful set of results. Copyright (C) 2021 The Authors.

Abstract
Unmanned Aerial Vehicles (UAVs) are a key ingredient in the industry and in warehouse logistics digital transformation process, providing the ability to perform automatic cyclic counting and real-time inventory, localize hard-to-find items and reach narrow storage areas. The use of UAVs poses new challenges, such as indoor autonomous localization and navigation, collision avoidance and automated UAV fleet management. This paper addresses the development of a vision-based Graph-SLAM approach for UAV indoor localization without predefined warehouse markers positions. A framework is proposed and developed to support different commercial UAV platforms, allowing the estimation in real-time of the UAV position and attitude. Indoor experimental tests were carried out in order to evaluate the performance of the developed method, comparing the results obtained with an approach based on the pre-mapped markers position indoor localization method.

Abstract
Researchers have to ensure that their projects comply with Research Data Management (RDM) requirements. Consequently, the main funding agencies require Data Management Plans (DMPs) for grant applications. So, institutions are investing in RDM tools and implementing RDM workflows in order to support their researchers. In this context, we propose a collaborative DMP-building method that involves researchers, data stewards and other parties if required. This method was applied as part of an RDM workflow in research groups across several scientific domains. We describe it as a systematic approach and illustrate it through a set of case studies. We also address the DMP monitoring process during the life cycle of projects. The feedback from the researchers highlighted the advantages of creating DMPs and their growing need. So, there is motivation to improve the DMP support process according to the machine-actionable DMPs concept and to the best practices in each scientific community.