The Bullwhip Effect manifests itself in the form of an increased order and inventory variability in the uppermost nodes of the supply chain. This dynamic phenomenon is not yet well understood in ...closed-loop supply chain settings, despite their growing importance in modern societies pursuing circular economy opportunities. Indeed, the problem-specific literature has provided somewhat conflicting findings. To better understand the Bullwhip Effect in closed-loop systems, we obtain expressions for the order and inventory variance amplification in four archetypes that differ in the structure of information transparency. Interestingly, we observe that the impact of return rates and lead times on the system performance strongly depend on the degree of supply chain visibility. This perspective allows us to revisit discrepancies in prior works. We later move the study from the operational to the economic prism. Here we prove the existence of an optimal return rate, and we derive its expression in the four closed-loop supply chain archetypes. We show that the optimal rate is dependent on the node’s cost structure, the lead times, and the variability of demand. Properties of the different closed-loop systems and relevant managerial implications are also discussed in our work.
We explore the value of information sharing for smoothing the dynamics of supply chains when some echelons do not collaborate. To this end, we study seven information sharing structures in a ...four-echelon supply chain using a system dynamics approach. We find that the overall propagation of the bullwhip effect in supply chains decreases as the number of echelons sharing information grows, but it is not dependent on their position. Nonetheless, the performance of the echelons strongly relies on the degree of downstream collaboration; therefore, information sharing in the lower nodes has a higher impact on the overall supply chain costs. We also investigate the benefits of adding new members to the collaborative strategy in different lead-time scenarios. Finally, we provide managerial recommendations for decentralised 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.
...the Operations Management community is currently rethinking on celebrated concepts and largely accepted ideas, with the aim of developing new theory that better captures the requirements of this ...organizational scene. ...the applications of these technologies for supply chain management have become a fruitful area of research, given its clear and strong managerial implications. ...this research paper concludes that deglobalization caused by 3D printing and globalization strengthening caused by trade cooperation will work together in this container system and lead to more complex changes. ...a relevant feature of this work is a valuable range of managerial implications, which would allow decision-makers to adjust product prices depending on the market fluctuations and sales requirements.
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.
•A closed-loop supply chain with remanufacturing lead time variability is analysed.•Through simulation, the dynamic performance is assessed under a variety of scenarios.•Different levels of ...information transparency are considered.•The variability of remanufacturing lead times seriously damage the dynamic behaviour.•Managerial implications are discussed.
Remanufacturing practices in closed-loop supply chains (CLSCs) are often characterised by highly variable lead times due to the uncertain quality of returns. However, the impact of such variability on the dynamic benefits derived from adopting circular economy models remains largely unknown in the closed-loop literature. To fill the gap, this work analyses the Bullwhip and inventory performance of a multi-echelon CLSC with variable remanufacturing lead times under different scenarios of return rate and information transparency in the remanufacturing process. Our results reveal that ignoring such variability generally leads to an overestimation of the dynamic performance of CLSCs. We observe that enabling information transparency generally reduces order and inventory variability, but it may have negative effects on average inventory if the duration of the remanufacturing process is highly variable. Our findings result in useful and innovative recommendations for companies wishing to mitigate the negative consequences of lead time variability in CLSCs.
•We propose a new approach to scheduling flexible manufacturing systems.•Knowledge about the system is obtained through ensemble methods.•Three different techniques are used: bagging, boosting, and ...stacking.•Stacking is deeply explored through two-level combinations of classical algorithms.•This dynamic approach proves to outperform existing alternatives.
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.
Environmental and demographic pressures have led to the current importance of Water Demand Management (WDM), where the concepts of efficiency and sustainability now play a key role. Water must be ...conveyed to where it is needed, in the right quantity, at the required pressure, and at the right time using the fewest resources. This paper shows how modern Artificial Intelligence (AI) techniques can be applied on this issue from a holistic perspective. More specifically, the multi-agent methodology has been used in order to design an Intelligent Decision Support System (IDSS) for real-time WDM. It determines the optimal pumping quantity from the storage reservoirs to the points-of-consumption in an hourly basis. This application integrates advanced forecasting techniques, such as Artificial Neural Networks (ANNs), and other components within the overall aim of minimizing WDM costs. In the tests we have performed, the system achieves a large reduction in these costs. Moreover, the multi-agent environment has demonstrated to propose an appropriate framework to tackle this issue.
Purpose: We expand a previous discussion in this journal by proposing a new solution concept, based on game theory, for profit allocation with the aim of aligning incentives in collaborative supply ...chains. Design/methodology/approach: Through the Gately’s notion of propensity to disrupt, we minimize the desire of the nodes to leave the grand coalition in the search of a self-enforcing allocation mechanism. Findings: We discuss the benefits and limitations of this solution in comparison with more established alternatives (e.g. nucleolus and Shapley value). We show that it considers the bargaining power of the nodes, but it may not belong to the core. Originality/value: Finding a fair and self-enforcing scheme for incentive alignment, and specifically profit allocation, is essential to ensure the long-term sustainability of collaborative supply chains.
PDF
