Purpose: The purpose of the research is to conduct an exploratory investigation of the material handling activities of an Italian logistics hub. Wearable sensors and other smart tools were used for collecting human and environmental features during working activities. These factors were correlated with workers' performance and well-being.Design/methodology/approach: Human and environmental factors play an important role in operations management activities since they significantly influence employees' performance, well-being and safety. Surprisingly, empirical studies about the impact of such aspects on logistics operations are still very limited. Trying to fill this gap, the research empirically explores human and environmental factors affecting the performance of logistics workers exploiting smart tools.Findings: Results suggest that human attitudes, interactions, emotions and environmental conditions remarkably influence workers' performance and well-being, however, showing different relationships depending on individual characteristics of each worker.Practical implications: The authors' research opens up new avenues for profiling employees and adopting an individualized human resource management, providing managers with an operational system capable to potentially check and improve workers' well-being and performance.Originality/value: The originality of the study comes from the in-depth exploration of human and environmental factors using body-worn sensors during work activities, by recording individual, collaborative and environmental data in real-time. To the best of the authors' knowledge, the current paper is the first time that such a detailed analysis has been carried out in real-world logistics operations.

With the rise of a consciousness in warehousing sustainability, an increasing number of autonomous vehicle storage and retrieval systems (AVS/RS) is diffusing among automated warehouses. Moreover, manufacturers are offering the option of equipping machines with energy recovery systems. This study analyzed a deep-lane AVS/RS provided with an energy recovery system in order to make an energy evaluation for such a system. A simulator able to emulate the operation of the warehouse has been developed, including a travel-time and an energy model to consider the real operating characteristics of lifts, shuttles and satellites. Referring to a single command cycle with a basic storing and picking algorithm for multiple-depth channels, energy balance and recovery measurements have been presented and compared to those of a traditional crane-based system. Results show significant savings in energy consumption with the use of a deep-lane AVS/RS.

Warehouses are essential elements of almost every Supply Chain and have a significant impact on its performance. However, existing research on warehouse operations mainly aims at maximizing operational performance, neglecting their effect on downstream nodes. In this paper, we propose the use of a digital twin (DT) to support warehouse managers to identify the picking policy that most effectively balances picking and outbound loading efficiencies in an SBS/RS, with the aim of providing both a cost-effective and timely delivery to the subsequent nodes. The problem is set referring to a real case study of the logistics hub of a tire distributor company. The DT was built and validated based on real data from plant sensors and information systems. Afterwards, the DT was used to define three picking strategies that differently impact on both picking and outbound loading efficiency. The DT was then employed on a daily basis and fed with real orders, machine and rack availability to replicate stocking and picking operations and to directly communicate the recommended picking strategy to the warehouse PLC. Several demand scenarios have been considered to extend managerial inferences. Results show that the DT is a valuable tool to support the balancing of picking and outbound loading performance.

Governments are promoting energy community (EC) policies to encourage joint investment and the operation of shared energy assets by citizens, industries, and public authorities, with the aim of promoting economic, social, and environmental benefits. However, ECs require appropriate planning and energy management strategies, which require data that are rarely clean and well-structured. Data providers rarely adhere to a common format for data sharing, which hinders the development of ECs. As the number of ECs is expected to grow significantly, this poses significant issues for stakeholders to quickly and efficiently develop projects. To address this issue, in this paper we propose a literature-based analysis and classification to derive the major data needs for EC planning, as well as a template format for data sharing. Our literature review on ECs successfully identifies the main data required to properly describe this system and its components. Their classification further clarifies that data structures shall account for tabular-like data of various types and flexible dimentionality, or cardinality. A public release of an open dataset for a case study in Pisa, Italy is also provided, supported by realistic or real data for testing the sizing and operation of ECs. The results suggest that data standard practices are needed, and this paper can lay the foundation for their standardization for ECs to fast-forward their deployment as support policy and technical decision-making.