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The effective operations of closed-loop supply chains (CLSCs) can help companies achieve sustainability goals and boost economic performance. In practice, CLSCs must collect used products, a complex process that is often constrained by collection station capacity. However, how collection station capacity influences the bullwhip effect and the dynamic performance of CLSCs remains unclear. Here, we develop a system dynamics model for CLSCs that integrates traditional manufacturing with remanufacturing and explore the effects of the stochastic capacity constraint of the collection station on the bullwhip effect of CLSCs. We find that, generally speaking, a collection station with looser or more stable capacity constraints tends to reduce bullwhip of CLSCs. However, pertinent interactions emerge between the relevant parameters; in some situations, reducing the capacity level of a collection station may be reasonable and beneficial when the stochastic capacity constraint is very stable, or when customer demand is highly variable. We also consider the partial backlog in return collection, a phenomenon associated with the stochastic capacity constraint of a collection station, and identify a new trade-off between CLSC sustainability and economic performance. Ultimately, our findings provide evidence that will guide managers' plans for the capacity management of return collection in CLSCs. [ABSTRACT FROM AUTHOR]

With most inventory theory investigating linear models, the dynamics of nonlinear inventory systems is not well understood. We explore the dynamics of the order-up-to policy under lost-sales for the case of i.i.d. normally distributed demand and unit lead times. We consider the ideal minimum mean-squared error forecast and two alternative scenarios: partial demand observation and dynamic demand forecasting, providing a broad understanding of the operational performance of lost-sales systems. In each scenario, we obtain analytical expressions for the order, inventory, and satisfied demand distributions. This allows us to quantify the Bullwhip and inventory variance amplification ratios as well as the fill rate and the inventory cover. We show the lost sales nonlinearity induces complex behaviours in inventory systems. Interestingly, lost sales smooth supply chain dynamics, significantly affecting the trade-off between service level and average inventory holding. We also reveal the inventory downsides of demand censoring and the production damages induced by dynamic forecasts. We identify a key parameter, the relative safety margin, that characterises the performance of lost-sales systems. We finish by offering some prescriptive results for the optimal safety stock and capacity level in both a retail and a manufacturing lost-sales setting. [ABSTRACT FROM AUTHOR]

Increasing the understanding of the management of closed-loop supply chains (CLSCs) is fundamental to accelerate the much-desired transition towards the circular economy. From this perspective, we investigate the value of proportional order-up-to policies (POUT) policies and the adjustment of their inventory controllers in these systems. These policies are often used to improve the performance of traditional supply chains due to their ability to cope with the damaging bullwhip effect; however, they have not been sufficiently studied in CLSCs. Through a difference equation modelling approach, we show that POUT policies are also a valuable instrument for enhancing the CLSC dynamics. Specifically, we find that the POUT model outperforms the traditional order-up-to policy in a hybrid manufacturing/remanufacturing system, yielding significant cost savings. To optimise the key trade-off between order and inventory variability, the tuning of the inventory controllers needs to consider not only the cost structure of the CLSC but also the average return rate. Specifically, managers should react to increasing levels of circularity by lowering the setting of the controllers' time constant. In the light of our findings, we suggest two strategies for aligning the calibration of the POUT controllers and the forecasting methods to increase the economic performance of CLSCs. [ABSTRACT FROM AUTHOR]

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 well-developed 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. [ABSTRACT FROM AUTHOR]

Firms currently operate in highly competitive scenarios, where the environmental conditions evolve over time. Many factors intervene simultaneously and their hard-to-interpret interactions throughout the supply chain greatly complicate decision-making. The complexity clearly manifests itself in the field of inventory management, in which determining the optimal replenishment rule often becomes an intractable problem. This paper applies machine learning to help managers understand these complex scenarios and better manage the inventory flow. Building on a dynamic framework, we employ an inductive learning algorithm for setting the most appropriate replenishment policy over time by reacting to the environmental changes. This approach proves to be effective in a three-echelon supply chain where the scenario is defined by seven variables (cost structure, demand variability, three lead times, and two partners' inventory policy). Considering four alternatives, the algorithm determines the best replenishment rule around 88% of the time. This leads to a noticeable reduction of operating costs against static alternatives. Interestingly, we observe that the nodes are much more sensitive to inventory decisions in the lower echelons than in the upper echelons of the supply chain. [ABSTRACT FROM AUTHOR]

While most supply chain models assume linearity, real production and distribution systems often operate in constrained contexts. This article aims to analyse the consequences of capacity limits in the order-up-to replenishment policy with minimum mean squared error forecasting under independently and identically distributed random demand. Our study shows that the impact of this nonlinearity is often significant and should not be ignored. In this regard, we introduce the concept of asettling capacity, which informs when our knowledge from a linear analysis is a reasonable approximation in a nonlinear context. If the available capacity is less than the settling capacity, the nonlinear effects can have a significant impact. We compare the Bullwhip Effect and Fill Rate in constrained contexts to well-established results for linear supply chains. We reveal the capacity limit acts as a production smoothing mechanism, at the expense of increasing inventory variability. We proceed to analyse the economic consequences of the capacity constraint and show that it can actually reduce costs. We provide an approximate solution for determining the optimal capacity depending on the demand, the unit costs and the lead time. [ABSTRACT FROM AUTHOR]