Editorial - Evolving Trends in Supply Chain Management: Complexity, New Technologies, and Innovative Methodological Approaches

Ministerio de Ciencia e Innovación DPI201680750

Building Resilience in Closed-Loop Supply Chains through Information-Sharing Mechanisms

In this paper we reflect on the role of information sharing on increasing the resilience of supply chains. Specifically, we highlight the lack of studies addressing this relevant topic in closed-loop supply chains. Then, we introduce the works covered by the Special Issue “Information Sharing on Sustainable and Resilient Supply Chains” to investigate the relationships between information sharing and resilience in sustainable supply chains.Universidad de Sevilla V PPIT-USDICAR-UniCT (Dpto. Ing. Civil y Arqu. Univ. Catania) Plan de investigación Departamental 2016-201

Effect of Industry 4.0 on Education Systems: An Outlook

Congreso Universitario de Innovación Educativa En las Enseñanzas Técnicas, CUIEET (26º. 2018. Gijón

The value of regulating returns for enhancing the dynamic behaviour of hybrid manufacturing-remanufacturing systems

Several studies have determined that product returns positively impact on the dynamics of hybrid manufacturing-remanufacturing systems, provided that they are perfectly correlated with demand. By considering imperfect correlation, we observe that intrinsic variations of returns may dramatically deteriorate the operational performance of these closed-loop supply chains. To cope with such added complexity, we propose a structure for controlling the reverse flow through the recoverable stock. The developed mechanism, in the form of a prefilter, is designed to leverage the known positive consequences of the deterministic component of the returns and to buffer the harmful impact of their stochastic component. We show that this outperforms both the benchmark push system and a baseline solution consisting of regulating all the returns. Consequently, we demonstrate that the operation of the production system is greatly smoothed and inventory is better managed. By developing a new framework for measuring the dynamics of closed-loop supply chains, we show that a significant reduction in the net stock, manufacturing, and remanufacturing variances can be achieved, which undoubtedly has implications both for stock reduction and production stabilization. Thus, the known benefits of circular economy models are strengthened, both economically and environmentally

The effect of returns volume uncertainty on the dynamic performance of closed-loop supply chains

We investigate the dynamics of a hybrid manufacturing/remanufacturing system (HMRS) by exploring the impact of the average return yield and uncertainty in returns volume. Through modelling and simulation techniques, we measure the long-term variability of end-product inventories and orders issued, given its negative impact on the operational performance of supply chains, as well as the average net stock and the average backlog, in order to consider the key trade-off between service level and holding requirements. In this regard, prior studies have observed that returns may positively impact the dynamic behaviour of the HMRS. We demonstrate that this occurs as long as the intrinsic uncertainty in the volume of returns is low —increasing the return yield results in decreased fluctuations in production, which enhances the operation of the closed-loop system. Interestingly, we observe a U-shaped relationship between the inventory performance and the return yield. However, the dynamics of the supply chain may significantly suffer from returns volume uncertainty through the damaging Bullwhip phenomenon. Under this scenario, the relationship between the average return yield and the intrinsic returns volume variability determines the operational performance of closed-loop supply chains in comparison with traditional (open-loop) systems. In this sense, this research adds to the still very limited literature on the dynamic behaviour of closed-loop supply chains, whose importance is enormously growing in the current production model evolving from a linear to a circular architecture

Learning-based scheduling of flexible manufacturing systems using ensemble methods

Dispatching rules are commonly applied to schedule jobs in Flexible Manufacturing Systems (FMSs). However, the suitability of these rules relies heavily on the state of the system; hence, there is no single rule that always outperforms the others. In this scenario, machine learning techniques, such as support vector machines (SVMs), inductive learning-based decision trees (DTs), backpropagation neural networks (BPNs), and case based-reasoning (CBR), offer a powerful approach for dynamic scheduling, as they help managers identify the most appropriate rule in each moment. Nonetheless, different machine learning algorithms may provide different recommendations. In this research, we take the analysis one step further by employing ensemble methods, which are designed to select the most reliable recommendations over time. Specifically, we compare the behaviour of the bagging, boosting, and stacking methods. Building on the aforementioned machine learning algorithms, our results reveal that ensemble methods enhance the dynamic performance of the FMS. Through a simulation study, we show that this new approach results in an improvement of key performance metrics (namely, mean tardiness and mean flow time) over existing dispatching rules and the individual use of each machine learning algorithm

On the dynamics of closed-loop supply chains with capacity constraints

In this paper, we investigate the dynamic behavior of a closed-loop supply chain with capacity restrictions both in the manufacturing and remanufacturing lines. We assume it operates in a context of a twofold uncertainty by considering stochastic demand and return processes. From a bullwhip perspective, we evaluate how the four relevant factors (specifically, two capacities and two sources of uncertainty) interact and determine the operational performance of the system by measuring the variability of the manufacturing and remanufacturing lines and the net stock. Interestingly, while the manufacturing capacity only impacts on the forward flow of materials, the remanufacturing capacity affects the dynamics of the whole system. From a managerial viewpoint, this work suggests that capacity constraints in both remanufacturing and manufacturing lines can be adopted as a fruitful bullwhip-dampening method, even if they need to be properly regulated for avoiding a reduction in the system capacity to fulfill customer demand in a cost-effective manner

Capacity restrictions and supply chain performance: Modelling and analysing load-dependent lead times

Several studies have reported that capacitated supply chains may benefit from an improved dynamic performance as compared to unconstrained ones. This occurs as a consequence of the capacity limit acting as a production smoothing filter. In this research, the relationship between capacity restrictions and the operational performance of supply chains is investigated from a novel perspective, i.e. we assume that the influence of capacity constraints on lead times depends on the supply chain's responsiveness. Under these circumstances, the experiments show that there is a negative effect of capacity constraints on supply chain performance, both in terms of process efficiency (internal) and customer satisfaction (external). Nonetheless, the magnitude of this impact greatly depends on the responsiveness of the firm, market conditions and adopted policies for inventory management. More specifically, the combination of tight capacity restrictions and low responsiveness significantly contributes to decrease supply chain performance, which may be very damaging for the dynamic behavior of the system. In this sense, efficient capacity planning proves to be essential to prevent the supply chain from entering into a vicious circle