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This article investigates the impact of information sharing on supply chain performance when considering the product loss in e-commerce. By characterizing four information sharing mechanisms, we analyze the optimal information sharing decisions on bullwhip effects and inventory costs in near/far-distance e-commerce contexts. The analytical results reveal the vital significance of product loss information to bullwhip and cost reductions. Particularly, in far-distance e-commerce, if the online retailer only shares the demand information, the wholesaler may suffer an even worse bullwhip effect than that without information sharing. Moreover, the impacts of product loss reveal the supply chain characteristic difference between fragile products and ordinary products in e-commerce. Our research generates important managerial insights regarding supply chain management and provides practical guidelines for online retailers and wholesalers optimizing their information sharing decisions according to different e-commerce contexts.

Purpose This study aims to answer the question of how firms can deal with the great bullwhip effects caused by COVID-19? Design/methodology/approach An exploratory research method has been adopted and evidence was collected based on 41 online interviews. Findings The study finds that the bullwhip effect is caused by the sudden changes in customers purchasing behaviour during the pandemic and the businesses’ inaccurate anticipation of the situation. Managing the bullwhip effects caused by COVID-19 requires situation awareness, localisation and an intelligent supply chain. Situation awareness is a vital concept in emergency response, knowing what is going to figure out what should be done. Furthermore, reducing the geographical distances between the firm and other parties in the supply chain, which equates to supply chain localisation, enforces just-in-time inventory. Finally, supply chain digitalisation is no longer an option; implementing such a solution enables end-to-end visibility, collaboration, flexibility and optimisation of orchestration of the supply chain. Research limitations/implications This study presents indicators explaining how organisations can deal with the great bullwhip effects caused by COVID-19. Originality/value The ongoing outbreak of the COVID-19 pandemic has brought about significant challenges for supply chain management, and this study contributes to the body of knowledge and proposes a model of reducing the bullwhip effects.

Information distortion tends to inflate from downstream to upstream, and results in demand planning errors and this is followed by inaccurate forecasting. This situation is referred to as the bullwhip effect phenomenon. Demand management is also determined based on either a centralized or decentralized distribution strategy. Both strategies will influence the accuracy of demand planning and its effect on the bullwhip phenomenon. Unfortunately, research investigating the relationship between centralization and decentralization strategies for demand and the bullwhip effect is still limited. To answer this shortcoming, this paper has two scenarios for its analysis. First, forecasting is done to determine the accuracy of demand planning, indicated by the smallest forecast error value. Based on the results of the analysis, it is known that single exponential smoothing with alpha 0.5 is the best forecasting method. The second step is to measure the bullwhip effect; the results show that the coefficient is less than one. This coefficient indicated that the company underperformed in the fulfillment of its customers’ needs. Theoretically, this paper extends the literature on demand management in the supply chain by considering centralized and decentralized strategies.

We study the dynamic behaviour of a hybrid system where manufacturing and remanufacturing operations occur simultaneously to produce the same serviceable inventory for order fulfilment. Such a hybrid system, commonly found in the photocopier and personal computer industries, has received considerable attention in the literature. However, its dynamic performance and resulting bullwhip effect, under push and pull remanufacturing policies, remain unexplored. Relevant analysis would allow considering the adoption of appropriate control strategies, as some of the governing rules in a push-based environment may break down in pull-driven systems, and vice versa. Using nonlinear control theory and discrete-time simulation, we develop and linearise a nonlinear stylised model, and analytically assess bullwhip performance of push- and pull-controlled hybrid systems. We find the product return rate to be the key influencing factor of the order variance performance of pull-controlled hybrid systems, and thus, to play an important role towards push or pull policy selection. Product demand frequency is another important factor, since order variance has a U-shaped relation to it. Moreover, the product return delay shows a supplementary impact on the system's dynamics. In particular, the traditional push-controlled hybrid system may be significantly influenced by this factor if the return rate is high. The results highlight the importance of jointly considering ordering structure and product demand characteristics for bullwhip avoidance.

This paper investigates whether the bullwhip has economic consequences at the firm level. In particular, we examine the relation between the bullwhip and various accounting/financial performance measures including equity returns, cash flows, earnings, and earnings attributes such as earnings quality and earnings persistence, using a large panel of cross-sectional firm-level data. Performance is measured both in terms of first-moment mean effects and second-moment volatility effects. Contrary to the maintained assumption of economics and operations management scholars, our analysis yields results that are inconsistent with the notion that the bullwhip has significant negative economic consequences at the firm level. In particular, we find almost no significant statistical/economic relations between accounting/financial measures of profitability and the bullwhip, both with and without covariate controls.

With the development of economic globalization, the competition among enterprises is increasingly fierce. Therefore, companies need close information sharing to realize the integration of supply chain. This article aims to study the collaborative management and information sharing mechanism of supply chain inventory based on cloud computing and 5G Internet of Things. This article first introduces the theory and methods of collaborative supply chain management and the information exchange mechanism and then discusses the problem of information sharing in the supply chain, that is, the bullwhip phenomenon, and then from the demand forecast, supply chain structure, time lag, and shortage game, six aspects are analyzed. The cause of the bullwhip phenomenon is analyzed. Secondly, this article proposes a quantitative analysis of the bullwhip effect, establishes a mathematical model of the bullwhip effect in the supply chain, and uses quantitative analysis to analyze the value of information sharing in the supply chain. Finally, this article uses cloud computing technology to build a supply chain information collaboration system architecture and uses EPC Internet of Things to build a supply chain information sharing model and describes the entire operation process of the supply chain. The experimental results of this paper show that the application of cloud computing technology to supply chain management establishes a system platform for supply chain information sharing, improves the overall operational efficiency of supply chain management, and realizes supply chain information sharing and business collaboration. In addition, the operating costs and risks of each node enterprise in the supply chain are reduced by 12% compared with the nonsharing situation, which also shows that the overall benefits of the supply chain have been correspondingly improved and market competitiveness has been enhanced.

Narwastu Firm is a company in Surabaya which produces traditional body care and aromatherapy. During the product distribution, this company encounters many mistakes such as inappropriateness between Purcashing Order (PO) from distributors/consumers and the goods delivered due to bullwhip effects. Therefore, this research aimed at reducing the bullwhip effects by implementing Collaborative, Planning, Forecasting, and Replenishment (CPFR) method and investigating a lot sizing technique for producing minimum distribution cost by Distribution Requirement Planning (DRP) method in order to regulate order and plan a certain distribution activity. The research results demonstrated that the bullwhip effect values after CPFR improvement of two products that could be repaired were 1.0 for Body Scrub Goats Milk Balibloom (BSC GM BB) 200 gr and 0.96 for Body Mist Sparkling White (BM SW) 60 ml. Meanwhile, the calculations by Distribution Requirement Planning (DRP) method with Economic Order Quantity (EOQ) method got the plans for ordering Body Scrub Lightening (BSC LIGHT) 200 gr by 182 times with total cost IDR 96,886,476 and Body Lotion Lightening (BL LIGHT) 250 ml by 235 times with total cost IDR 125,873,816.

Supply chain network structure has been mentioned as one of the contributors to the Bullwhip Effect in the supply chain. With the incorporation of reverse logistics into most of the supply chains, the structure of the supply chain changes especially if there are more than one player in the reverse chain. In this research, systems dynamics models were developed to compare the Bullwhip Effect between serial and divergent supply chain networks with different structures in closed-loop supply chains. The scenario experimentation revealed that increasing the number of collectors in the reverse chain increased the Bullwhip suffered, Bullwhip generated and the Bullwhip slope in all supply chain configurations. Therefore, it seems to be more beneficial for a closed-loop supply chain to have one reliable collector supplying used products than more than one collector with uncertainties in the quantity of products returned. Findings also reveal that a serial network is more sensitive to changes in the reverse chain in terms of the number of parties involved compared to a divergent supply chain. This means that the serial network assumption overestimates Bullwhip measures. Whilst it may be easy to measure the Bullwhip Effect by assuming a serial supply chain structure, this value might be overestimated especially for supply chains that are involved with more than one player at each echelon. These findings provided some useful insights on modelling supply chain networks to reduce the Bullwhip Effect in closed-loop supply chains.

Purpose The operational aspects of supply chain, when handled correctly, results in diminishing the impact of the bullwhip effect. The purpose of this study is to analyze the impact of operational and financial variables on the bullwhip effect. Various operational factors that contribute to the bullwhip effect in a supply chain are identified and their impact on variability in production is measured at manufacturer’s end in the supply chain. Design/methodology/approach Ten different sectors of the Indian economy are identified and analyzed on the basis of bullwhip effect. The ratio of change in production with respect to change in demand is taken as a metric to measure the bullwhip effect. Initially, the impact of identified variables on bullwhip effect is analyzed using the linear regression analysis and then to gain more insights, the threshold regression model is applied according to the change in bullwhip ratio. Findings The study identifies four threshold regions in which bullwhip ratio is changing its slope considerably. The operational and financial variables impacting bullwhip effect differently in these four regions provide useful insights about how the variables are impacting the bullwhip effect. Research limitations/implications Past 11 years of observations on identified operational and financial variables are studied for ten different sectors. The operational and financial variables are identified on basis of available literature but may not be exhaustive in nature. Practical implications The present study implies that the emphasis must be given to the magnitude of the bullwhip ratio. Strategies must be adopted that result in mitigation of bullwhip effect. Such mitigation strategies must not only be restricted on the basis of type of product or sector, perhaps they must be on the basis of threshold region of bullwhip ratio. Originality/value The study suggests a novel approach to study the bullwhip effect in supply chain management using the application of threshold regression considering the bullwhip ratio as a threshold variable.

Batching is a well-known cause of the bullwhip effect. Despite being very common in many industries to leverage economies of scale, it has been little explored due to its nonlinear complexity. This work examines how order-quantity batching affects the performance of closed-loop supply chains, which are gaining importance as a result of their environmental and economic value. Specifically, we analyse a hybrid system with both manufacturing and remanufacturing operations. We observe that, when an order-up-to policy is used in the serviceable inventory, bullwhip is always an increasing function of the batch size. Nevertheless, when a smoothing replenishment rule is used, the closed-loop supply chain behaves differently for low and high volumes of returns due to the different degrees of uncertainty they convey. In the high-volume case, batches should also be as small as possible. In contrast, in the low-volume case, bullwhip can be mitigated by setting the batch size to a divisor of the mean production rate. However, this may lower the customer service level achieved. We also find that reducing manufacturing batch sizes should be prioritised over remanufacturing ones when both are large. In the light of our results, we finally provide professionals with specific suggestions on how to better manage closed-loop supply chains where goods are produced and delivered in batches. Ministerio de Ciencia e Innovación PID2019-108756RB-I00 y PID2020-117021GB-I00 Junta de Andalucía P18-FR-1149 y US-126451 Universidad de Sevilla V PPIT-US

Bullwhip effect is a distribution channel phenomenon caused by the unmeasured inflated ordering details that move upstream at every level of supply chain. If Bullwhip effect oppressed supply chain management a lot, then it results business loss. Nowadays, due to incorporating new business plans in online retailing, uncertain and fluctuating business growths are noticed very frequently. Fluctuating business growth should address quickly, otherwise there is a chances for generating erroneous ordering that in turn could increase the effect of bullwhip. Another reason for increasing bullwhip effect is poor forecasting of sale. Future business progress depends not only upon present sale but also upon customers’ feedback about that product. Specially, in case of online retailing, customers’ feedback is one of the important factors for future business growth. In this research, we have proposed a modified Prophet forecasting model for emphasizing more upon customers’ feedback. Fluctuating business growths are handled through the concept of virtual data warehouse. Experimental result shows the efficiency of proposed model.

Closed-loop supply chains are complex systems as they involve the seamless backward and forward flow of products and information. With the advent of e-commerce and online shopping, there has been a growing interest in product returns and the associated impact on inventory variance and the bullwhip effect. In this paper, a novel four-echelon closed-loop supply chain model is presented, where base-stock replenishment policies are modelled by means of a proportional controller. A stochastic state-space model is implemented, initially to capture the supply chain dynamics while the model is analysed under stationarity conditions with the aid of a covariance matrix. This allows the bullwhip effect to be expressed as a function of replenishment policies and product return rates. Next, an optimisation method is introduced to study the impact of the Internet of Things on inventory variance and the bullwhip effect. The results show that the Internet of Things can reduce costs associated with inventory fluctuations and eliminate the bullwhip effect in closed-loop supply chains.

This paper is extensions of the Tesfay (2014) paper on the Bullwhip effects and Tesfay (2015) paper on the foundations of the Bullwhip effect and its implications on the theory of organizational coordination. The major outcome of the Tesfay (2014) paper suggests that one way or another Bullwhip effect attacks all the business collaborates in supply chain. The major outcome of the Tesfay (2015) paper suggests that the solutions from the transaction cost economics are still insufficient to produce the most efficient organizational coordination. This paper extends stochastic analysis by applying various panel data regression models and structural equations of seemingly unrelated regression (SUR) models on the experimental data from Beer game. Recursive autoregressive-SUR model estimation result confirmed that the Bullwhip effects can be cause by both intra-organizational coordination and inter-organizational coordination of the business collaborates of supply chain. In order to control the effect of intra-organizational coordination on the Bullwhip effects, the author outline a new coordination type known as the hyper-hybrid coordination. As a final point, the author has shown important applications of the hyper-hybrid coordination in the airline industry. Keywords: Airline industry, Bullwhip effect, Stochastic model, Tesfay coordination

Purpose The purpose of this paper is to conduct a large-sample empirical investigation of how relational capital impacts bullwhip at the supplier. Design/methodology/approach The study uses mandatory disclosures in regulatory filings of US firms to identify a supplier’s major customers and constructs empirical proxies of supply chain relational capital, i.e., length of the relationship between suppliers and customers and partner interdependence. Multivariate regression analyses are performed to examine the effects of relational capital on bullwhip at the supplier. Findings The findings show that bullwhip at the supplier is greater when customers are more dependent on their suppliers, but is reduced when suppliers share longer relationships with their customers. The results also provide additional insights on several firm characteristics that impact supplier bullwhip, including shocks in order backlog, selling intensity and variations in profit margins. Furthermore, the authors document that the effect of supply chain relationships on bullwhip tends to vary across industries and over time. Originality/value The study employs a novel data set that is constructed using firms’ financial disclosures. This large panel data set consisting of 13,993 observations over 36 years enables thorough and robust analyses to characterize supply chain relationships and gain a deeper understanding of their impact on bullwhip.

Extensive research has shown that collaboration has substantial impact on supply chains performance. However, most of previous research has focused on information sharing-based collaboration models that require considerable effort to be implemented, such as information exchange supply chain (IESC). This paper introduces a new efficient collaboration model (IS-OUT) for multi-echelon supply chains. The IS-OUT model relies upon the ordering mechanism of the classical order-up-to policy (OUT). In traditional supply chains (TSC), although OUT replenishment orders include two pieces of information: demand forecast and inventory position balance, they are transferred to upstream echelons as single-quantity orders. In IS-OUT, the order information is transferred to the upstream echelons as two component parts of information to provide better coordination in supply chains. In this paper, the mathematical formulation of IS-OUT is presented, and simulation is adopted to compare the performance of TSC, IS-OUT, and IESC under special conditions and assumptions, considering various performance metrics. Although the results indicate that IESC model offers higher performance mostly than IS-OUT, IS-OUT outperforms or at least is comparable with IESC under a few conditions. Since IS-OUT model is easier to implement than IESC, IS-OUT provides a compromise between extent and information sharing requirements, and performance efficiency. The results should help a decision maker to select the model that optimizes its operating environment.

The present study is an attempt to quantify the Bullwhip Effect (BWE) -the phenomenon in which information on demand is distorted in moving up a supply chain. Assuming that the retailer employs an order-up-to level policy with auto-regressive process (AR), the paper investigates the influence of forecasting methods on bullwhip effect. Determining the order-up-to levels and the orders for the retailers’ demands in an isolated manner neglects the correlation of the demands and the relevant risk pooling effects associated with the network structure of the supply chains are disregarded. It is illustrated that the bullwhip effects are significantly reduced with consideration of potential correlation between the retailers’ demand.

Information communication technologies (ICTs) have been extensively applied in smart destinations in recent years so as to improve the information transfer and information sharing amongst individual actors involving in the destination network ecosystem. This paper attempts to explore the influence of information sharing on the bullwhip effect throughout the tourism supply chain (SC) network, which has been considered as a major problem faces the destination management and all individual actors. Specifically, two mathematic models have been built to analyse the following different situations respectively; first, ICTs have not been well applied in destinations and there is no information sharing amongst individual actors, second, ICTs have been well applied in destinations and there exists information sharing amongst individual actors. Further, this paper makes a comparison of the bullwhip effects under these two situations. What's more, the data collected from JIUZHAI valley has been used to verify the correction of the mathematic analysis. Overall, this paper proves that the application of ICTs will contribute to the reduction of bullwhip effect throughout the tourism SC network by promoting the information sharing amongst individual actors, and some parameters (e.g., autocorrelation coefficient, the number of demand forecasting, and the lead time) will play a crucial role on the acting process. The results have both theoretical significance to the development of tourism SC management theory and managerial implication to tourism practices of individual actors and management departments.

Given the input/output constraints and cross couplings of supply chain (SC) nodes, model predictive control (MPC) is efficient to seek the optimal solutions to the problems posed by interacting nodes to satisfy customer demands. In supply chain applications, due to the growing spatial distribution and interactions between the supply network elements, the information flow management becomes a challenging yet significant task. To reduce numerical complexity while maintaining implementability, a distributed MPC strategy is proposed. The scheme aims at finding the Nash equilibrium where the controller of each subsystem communicates with other ones in the presence of noncooperative interaction and strong coupled inputs due to the ordering decisions. Extensive numerical simulations verify that the strategy outperforms conventional policies in terms of substantially reduced SC operating cost.

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. Ministerio de Ciencia e Innovación DPI201680750P

Generally, there are various elements of uncertainty in a supply chain. In particular, uncertainties in lead time, demand, and yield are very important in the semiconductor industry. Higher uncertainty can lead to bullwhip effects that can undermine the performance of the entire supply chain. This study examines the relationship between uncertainty in the supply chain and the outcome of inventory replenishment policies. Specifically, we analyze the effects of well-known uncertainties on manufacturer production quantity and retailer order quantity decisions in a decentralized supply chain. In addition, we also analyze and compare the effects of these uncertainties for the retailer-managed inventory and the vendor-managed inventory policies. Using numerical experiments, a comparative analysis of the two alternatives is conducted to determine suitable options for improving supply chain performance. In general, the performance of vendor-managed inventory is better than that of retailer-managed inventory, but we observe from the numerical experiments that there exist circumstances under which retailer-managed inventory shows better supply chain performance.

Article number 108129 We consider a hybrid manufacturing/remanufacturing system where the returned products (cores) are classified into different quality grades. Each grade requires different remanufacturing operations and thus lead times. We examine the implications of the quality-grading scheme on the dynamic behavior of closed-loop supply chains, benchmarking this against a typical system where all the returns undergo the same remanufacturing process. Through control engineering techniques, we evaluate the Bullwhip and inventory performance of the supply chain by observing the step response of the orders and net stocks (the shock lens), analyzing the frequency behavior of these signals (the filter lens), and measuring their dynamics due to stochastic demand (the variance lens). Subsequently, we discuss the operational savings and additional costs derived from quality grading. We find that the pre-sorting mechanism allows for smoothing the supply chain operations; however, its impact on customer satisfaction is ambivalent. Indeed, we observe that the documented ‘lead-time paradox’ of the remanufacturing process in hybrid systems results here in a ‘quality paradox’: lower quality returns may increase the performance of inventories. This affects particularly low-frequency demands. Importantly, we analytically derive the optimal setting of the closed-loop pipeline estimation in order-up-to policies for avoiding long-term inventory drifts. This analysis reveals key potential benefits of information transparency for improving the operational performance, and thus the environmental and economic sustainability, of closed-loop supply chains. Universidad de Sevilla V PPIT-US Junta de Andalucía US-1264511 Instituto Universitario de Tecnología Industrial de Asturias IUTA-20- GIJÓN-20 Consejo de Investigaciones en Ingeniería y Ciencias Físicas (Reino Unido) EP/P008925/1

This chapter is the extension of Chap. 10 on the Bullwhip effects. We have seen that Chap. 10 gives a detailed emphasis on the foundations of the Bullwhip effect and its implications on the theory of organizational coordination. In Chap. 10, we have seen that one way, or another the Bullwhip effect attacks all the businesses collaborate in the supply chain. Furthermore, the chapter suggested that transaction cost economics solutions are insufficient to produce the most efficient organizational coordination. This chapter extends the stochastic analysis by applying various panel data regression models and structural equations of seemingly unrelated regression (SUR) models on the Beer game’s experimental data. Recursive autoregressive-SUR model estimation result confirmed that both the intra- and interorganizational coordination of the business partners in the supply chain could cause Bullwhip effects. To control the impact of intra-organizational coordination on the Bullwhip effects, the author outlines a new coordination type known as the Tesfay (also called hyper-hybrid) coordination. As a final point, the author has shown some essential hyper-hybrid coordination applications in the airline industry.

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 behaviors 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 characterizes 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.

Due to the new possibilities offered by digital technologies, more and more companies are embarking on a process of digitizing their supply chains. This dynamic seems to be the opportunity to analyse the impact that digital technologies may have on one of the phenomena that disrupt financial flows within supply chains, and that can alter the companies’ treasury, namely that of cash flow bullwhip (CFB). The results of the systematic literature review that was carried out allow to affirm that several technologies can contribute positively to limiting this phenomenon and this by acting on these operational causes, which are the reliability of forecasts, batch orders, the fluctuation in sales prices, rationing games, and lead times.

In this study, we examine the joint effects on cost structure of two major sources of supply chain risks: the internal firm-level bullwhip effect and external demand uncertainty. The firm-level bullwhip effect is a phenomenon in which fluctuations in production (for manufacturing firms) or purchases from suppliers (for retailers or wholesalers) in a firm tend to be greater than its demand fluctuations. While prior accounting literature focuses on the impact of external demand uncertainty on cost structure, the internal bullwhip effect in firms plays a critical role because the bullwhip effect determines the extent to which the impact of external demand uncertainty is transmitted within the firm. Using data from 7,179 public U.S. firms over the 1990–2019 period, we show that the firm-level bullwhip effect amplifies the impact of demand uncertainty on cost structure. Specifically, regardless of whether demand uncertainty leads to a more rigid or elastic cost structure in a particular setting, we find that such effect is more pronounced in firms with a higher bullwhip ratio than in firms with a lower bullwhip ratio. Drawing on both management accounting and supply chain management literatures, our study highlights the interactive effect of the firm-level bullwhip effect and demand uncertainty on firms’ cost structure decisions.

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.