The circular economy (CE) is arising as a novel economic system that is restorative by design. In light of its capability to boost sustainable economic development and to cope with environmental challenges, it has recently attracted increasing attention from academics, practitioners, policymakers, and intergovernmental organizations. Despite the wide speculation on this issue, the scientific literature lacks a wide-ranging, systematic, and updated identification and classification of the main drivers and Critical Success Factors of CE initiatives, which appears increasingly necessary to facilitate future scientific work, practical implementations, and policy guidelines. With this aim, this paper develops a systematic literature review by starting with over 400 manuscripts. A final set of 55 selected papers was selected for singling out and classifying drivers and Critical Success Factors in the CE context. The results may provide clear indications for further research, may help business organizations in evaluating CE initiatives, and may guide policy makers in developing and refining CE normative frameworks.

Purpose: This paper aims to investigate the process performances in Emergency Departments (EDs) with a novel data-driven approach, permitting to discover the entire patient-flow, deploy the performances in term of time and resources on the activities and flows and identify process deviations and critical bottlenecks. Moreover, the use of this methodology in real time might dynamically provide a picture of the current situation inside the ED in term of waiting times, crowding, resources, etc., supporting the management of patient demand and resources in real time.Design/methodology/approach: The proposed methodology exploits the process-mining techniques. Starting from the event data inside the hospital information systems, it permits automatically to extract the patient-flows, to evaluate the process performances, to detect process exceptions and to identify the deviations between the expected and the actual results.Findings: The application of the proposed method to a real ED revealed being valuable to discover the actual patient-flow, measure the performances of each activity with respect to the predefined targets and compare different operating situations.Practical implications: Starting from the results provided by this system, hospital managers may explore the root causes of deviations, identify areas for improvements and hypothesize improvement actions. Finally, process-mining outputs may provide useful information for creating simulation models to test and compare alternative ED operational scenarios.Originality/value: This study responds to the need of novel approaches for monitoring and evaluating processes performances in the EDs. The novelty of this data-driven approach is the opportunity to timely connect performances, patient-flows and activities.

The current digitalization trend, the increased attention towards sustainability, and the spread of the business analytics call for higher efficiency in port operations and for investigating the quantitative approaches for maritime logistics and freight transport systems. Thus, this manuscript aims at enabling analytics-driven improvements in the port transportation processes efficiency by streamlining the related information flow, i.e., by attaining shorter time frames of the information and document sharing among the export stakeholders. We developed a case study in a mid-sized European port, in which we applied Process Mining (PM)—an emerging type of business analytics—to a seven-month dataset from the freight export process. Four process inefficiencies and an issue that can jeopardize the reliability of the time performance measurements were detected, and we proposed a draft of solutions to cope with them. PM enabled enhancements in the overall export time length, which might improve the vessels’ turnover and reduce the corresponding operational costs, and supported the potential re-design of performance indicators in process control and monitoring. The results answer the above-mentioned calls and they offer a valuable, analytics-based alternative to the extant approaches for improving port performance, because it focuses on the port information flow, which is often related to sustainability issues, rather than the physical one.

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.