Recent years have witnessed companies abandon traditional open-loop supply chain structures in favour of closed-loop variants, in a bid to mitigate environmental impacts and exploit economic opportunities. Central to the closed-loop paradigm is remanufacturing: the restoration of used products to useful life. While this operational model has huge potential to extend product life-cycles, the collection and recovery processes diminish the effectiveness of existing control mechanisms for open-loop systems. We systematically review the literature in the field of closed-loop supply chain dynamics, which explores the time-varying interactions of material and information flows in the different elements of remanufacturing supply chains. We supplement this with further reviews of what we call the three 'pillars' of such systems, i.e. forecasting, collection, and inventory and production control. This provides us with an interdisciplinary lens to investigate how a 'boomerang' effect (i.e. sale, consumption, and return processes) impacts on the behaviour of the closed-loop system and to understand how it can be controlled. To facilitate this, we contrast closed-loop supply chain dynamics research to the welldeveloped research in each pillar; explore how different disciplines have accommodated the supply, process, demand, and control uncertainties; and provide insights for future research on the dynamics of remanufacturing systems.

Remanufacturing operations rely upon accurate forecasts of demand and returned items. Return timing and quantity forecasts help estimate net demand (demand minus returns) requirements. Based on a unique data set of serialized transactional issues and returns from the Excelitas Group and one of their defense contractors, Qioptiq, we assess the empirical performance of some key methods in the area of returns forecasting. We extend their application (for net demand forecasting), by considering that demand is also subject to uncertainty and thus needs to be forecast. Information on remanufacturing costs allows for an evaluation of the inventory implications of such forecasts under various settings. A foray into the literature on information technologies enables a discussion on the interface between information availability and forecast accuracy and utility. We find that serialization accounts for considerable forecast accuracy benefits, and that the accuracy of demand forecasts is as important as that of returns. Further, we show how the combined returns and demand forecast uncertainty affects the inventory performance. Finally, we identify opportunities for further improvements for the operations of Qioptiq, and for remanufacturing operations in general.

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Accurate forecasts of daily COVID-19 admissions are critical for healthcare planners and decision-makers to better manage scarce resources during and around infection peaks. Numerous studies have focused on forecasting COVID-19 admissions at the national or global levels. Localised predictions are vital, as they allow for resource planning redistribution, but also scarce and harder to get right. Several possible indicators can be used to predict COVID-19 admissions. The inherent variability in the admissions necessitates the generation and evaluation of the forecast distribution of admissions, as opposed to producing only a point forecast. In this study, we propose a quantile regression forest (QRF) model for probabilistic forecasting of daily COVID-19 admissions for a local hospital trust (aggregation of 3 hospitals), up to 7-days ahead, using a multitude of different predictors. We evaluate point forecast accuracy as well as the accuracy of the forecast distribution using appropriate measures. We provide evidence that QRF outperforms univariate time series methods and other more sophisticated benchmarks. Our findings also show that lagged admissions, total positive cases, daily tests performed, and Google grocery and Apple driving are the most salient predictors. Finally, we highlight areas where further research is needed.

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