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The impact of replenishment parameters and information sharing on the bullwhip effect: A computational study
Abstract
Demand variability amplification across the supply chain, known as the bullwhip effect, results in serious inefficiencies across the chain. Managers are expected to minimize this phenomenon in their chain in order to reduce costs and increase customer satisfaction by making critical decisions on replenishment policy. We study how specific replenishment parameters affect order variability amplification, product fill rates and inventory levels across the chain. Furthermore, we study how demand information sharing can help towards reducing order oscillations and inventory levels in upper nodes of a supply chain. A two-stage supply chain consisting of a warehouse and stores that face customer demand is modeled. Real demand data are used as the underlying customer demand during the experiments.
... Regarding the information exchanged, Lee and Whang [63] describe the different types of shared information as inventory level, sales data, order status, sales forecast or others. When analyzing the articles that implement an information sharing collaborative scheme, the most frequent piece of information shared is the customer sales data [59,[68][69][70][71][72][73][74]. Essentially, when information sharing is active, upward members of the supply chain have two sources of information available: orders from right downward echelons and the end customer data. ...
... The way to deal with both sources of data is not unique. Some articles integrate both, modifying the previous forecasting models [42, 59,72] while others simply replace orders by the end customer data without changing the forecasting technique [68][69][70]. The implications of integrating versus replacing still remains as an open question. ...
... This toolbox would allow researchers to either reproduce or use previous models, which have been tested, so that they do not have to implement it from scratch and avoid mistakes in the implementation; ii) there exists a serious lack of empirical works at various levels. For example, fully empirical works as [59,87] and semi-empirical works where real demand data is employed within simulations to verify theoretical developments as [68,121]; iii) the integration versus substitution issue of end demand as well as other demand drivers in relation to the forecasting model still remains an open question and further research is needed about the adequacy of each option; iv) the influence of shared information on the accuracy of judgmental forecasting is completely overlooked; v) more comparisons between machine learning and traditional statistical forecasting methods is also required; and finally, vi) despite the fact that lost sales case is frequent in some sectors as retailing [122], most of the simulations are based on the total backordering assumption and more research is needed assuming the lost sales case. ...
Throughout the last decades, collaborative schemes, under an amalgam of different acronyms (ECR, CPFR, VMR, etc.), have been developed to mitigate the problematic Bullwhip effect. Essentially, companies work together by either sharing information, making joint decisions, or sharing benefits to reach potential synergies. This work aims at reviewing these works through a systematic literature review process to investigate the different collaborative models from an operational perspective. A total of 92 articles have been classified into 3 categories: Information Exchange; Vendor Managed Replenishment; and Synchronized Supply Chain. For each category, we have identified the type of research, supply chain structures, forecasting models, demand characteristics, replenishment policies and assumptions employed in the considered articles. This article identifies the main results achieved and the gaps and opportunities to be developed as further research.
... Most of this research has been attempting to characterize and quantify the impact of the operational causes of the bullwhip effect such as forecasting method (Li et al., 2014;Costantino et al., 2015b,c), inventory replenishment policy (Kelepouris et al., 2008;Jakšič and Rusjan, 2008;Costantino et al., 2015b;Keshari et al., 2017), lead-time (Chatfield et al., 2004;Duc et al., 2008), supply chain structure (Dominguez et al., 2014) and demand process (Duc et al., 2008;Costantino et al., 2016;Gao et al.;. The results of those studies have provided a significant contribution and useful conclusions regarding both the prediction of the bullwhip effect and the directions to limit the bullwhip effect under various operational conditions. ...
... In particular, demand forecast updating and inventory replenishment policies are shown to contribute significantly to the variance amplification problem. Many researches focus on characterizing the impact of different ordering policies and their parameters on the bullwhip effect (Dejonckheere et al., 2003(Dejonckheere et al., , 2004Kelepouris et al., 2008;Jakšič and Rusjan, 2008;Costantino et al., 2015b;Keshari et al., 2017). Specifically, the periodic-review order-up-to (OUT) ordering policy (integrated with different forecasting methods) is the most commonly used in the bullwhip effect research. ...
... Inventory replenishment policies have also been recognized as one of the main causes of variance amplification in supply chains. It received a significant attention in the literature (Jakšič and Rusjan, 2008;Kelepouris et al., 2008;Keshari et al., 2017). They showed that inventory replenishment policies contribute not only to the bullwhip effect but also to the inventory variability. ...
Supply chains experience variance amplification in replenishment orders and inventory levels, leading to severe inefficiencies. Extensive studies are conducted while focusing mainly on the demand variance amplification (also known as bullwhip effect), but limited research is undertaken to optimize the variance amplification that considers both the orders and net stock variability. A single-echelon supply chain with a stationary demand process, a generalized periodic-review order-up-to (OUT) policy, and an exponential smoothing forecasting model are assumed. Hence, this paper seeks to optimize the best values of the exponential smoothing and OUT policy parameters that minimize the sum of demand and inventory variances. A hybrid approach that integrates simulation modeling and response surface methodology is proposed. The algorithm is iterative in nature, where at each iteration simulation runs are conducted to generate a response surface for the variance amplification, and a gradient search is applied to locate a new incumbent solution. Several experiments are conducted to demonstrate the applicability of the approach, and to validate its results with previous researches. The proposed RSM-Simulation based algorithm produces comparable results to existing methods and thus having a good potential to accommodate more supply chain complexities. It can be used to model and optimize nonlinear supply chains, supply chain with stochastic lead-time, supply chains with correlated demand, and supply chains with capacity constraints.
... For the literature of bullwhip effect, many scholars obtained some useful results by using simulation methods. The information sharing and information quality had a significant effect on bullwhip effect (Chatfield et al., 2004;Kelepouris et al., 2008). Croson and Donohue (2006) found that the bullwhip effect still existed when normal operational causes were removed with experiments. ...
... One aim of this study is to measure the bullwhip effect on market competition with copulas. In general, the information is an important factor in a supply chain (e.g., Chen et al., 2000;Kelepouris et al., 2008;Dominguez et al., 2018a). However, many articles ignored the effect of the information of residuals on the bullwhip effect and the independent and identically distributed residuals of retailers was a regular assumption in the bullwhip effect literature (e.g., Lee et al., 1997a;Chen et al., 2000). ...
By considering the market competition among retailers, the object of this study is to measure the bullwhip effect in a two-stage supply chain with one supplier and multiple retailers. A model is detailed for measuring the bullwhip effect in which multiple retailers exhibit AR(1) demand processes and the degree of market competition is captured with copula. Through the model developed in this paper, we use simulation methods to study the influence of market competition on the bullwhip effect. The simulation results show that the market competition has a significant impact on bullwhip effect. The influence of the types of copulas on the bullwhip effect remains stable as the lead time increases. The supply chain structure has an impact on the bullwhip effect. The results of the empirical application show that a suitable copula helps to improve prediction accuracy for the bullwhip effect. Moreover, we discuss how our study can be applied to bullwhip effect management.
... This naturally implies that the risk bearers are not only the chief information officer (CIO) and the chief information security officers but also the general supply chain leaders. The modest increase in the number of access points that are processed by the internet of things (IoT) (Kelepouris et al. 2008) and cloud-based systems is another big cyber challenge for the supply chain communities. The many disparate access points create difficulties for standardizing and controlling distributed supply chains. ...
... In quantifying the benefits of information sharing, some modelbased studies have concluded that information sharing works only if flexible measures are implemented in the supply chain system in order to manage the information and material flows Simchi-Levi and Zhao 2003); in other words, information sharing yields benefits through operational improvements rather than through the mere acquisition of information, although it facilitates information flows (Baihaqi and Sohal 2013). Such operational improvements could include perfect forecasting mechanisms (Williams and Waller 2011;Weber and Kantamneni 2002), lower inventory levels and reduced costs (Wu and Cheng 2008), shortened delivery times, a mitigated bullwhip effect (Kelepouris et al. 2008;Agrawal et al. 2009;Croson and Donohue 2003), and controllable risk. ...
Globalization and growing supply chain interconnectivity have led to greater complexity, uncertainty, and vulnerability in supply chains. Concequently, supply chains must become smarter to confront these challenges. The smarter supply chain has shown great promise; however, the business, policy, and technical challenges must be addressed before changes can be made. A literature review was performed to synthesize the studies on smarter supply chain management. The proior literature has been categoried into four aspect, including information sharing and supply–demand forecasting, smarter supply chain process integration and smarter decision-making, smarter supply chain risk management, and smarter supply chain collaboration. The successful practices and existing solutions for smarter supply chain management are also presented, which could serve as references for enterprises. The review concludes with a discussion of several research topics for futher work on smarter supply chain management.
... Order rate variance ratio [43,48,61,64,[69][70][71][72][73][74][75][76][77][78][79] Amplitude rate cost ratio [80] Amplification ratio [81] Ratio inventory [71,75] Ratio backlog inventory [64] Variance ratio fill rate [75,77] Ratio inventory integrated squared error [69] Ratio root mean square costs [61] Fill rate [82] Costs order rate variance ratio [43,73,83] Costs [84] Ratio inventory stock [70,74] Out size stock out number [70] As Figure 1 illustrates, the most common performance metric suggested in the literature is the order rate variance ratio [85], which was proposed by Chen et al. [48]. This indicator is given by the following Equation (1) [29]: ...
... Order rate variance ratio [43,48,61,64,[69][70][71][72][73][74][75][76][77][78][79] Amplitude rate cost ratio [80] Amplification ratio [81] Ratio inventory [71,75] Ratio backlog inventory [64] Variance ratio fill rate [75,77] Ratio inventory integrated squared error [69] Ratio root mean square costs [61] Fill rate [82] Costs order rate variance ratio [43,73,83] Costs [84] Ratio inventory stock [70,74] Out size stock out number [70] As Figure 1 illustrates, the most common performance metric suggested in the literature is the order rate variance ratio [85], which was proposed by Chen et al. [48]. This indicator is given by the following Equation (1) [29]: ...
Several suppliers of oil and gas (O & G) equipment and services have reported the necessity of making frequent resources planning adjustments due to the variability of demand, which originates in unbalanced production levels. The occurrence of these specific problems for the suppliers and operators is often related to the bullwhip effect. For studying such a problem, a research proposal is herein presented. Studying the bullwhip effect in the O & G industry requires collecting data from different levels of the supply chain, namely: services, upstream and midstream suppliers, and downstream clients. The first phase of the proposed research consists of gathering the available production and financial data. A second phase will be the statistical treatment of the data in order to evaluate the importance of the bullwhip effect in the oil and gas industry. The third phase of the program involves applying artificial neural networks (ANN) to forecast the demand. At this stage, ANN based on different training methods will be used. Further on, the attained mathematical model will be used to simulate the effects of demand fluctuations and assess the bullwhip effect in an oil and gas supply chain.
... A large number of papers have been published over the last two decades, and we only provide a glimpse of the available literature. Key examples are the studies by Gavirneni et al. [4] , Kelepouris et al. [5] , Cannella [6] , Bian et al. [7] , Ha et al. [8] , and Zhou et al. [9] that considered the value of demand information sharing; the studies by Cachon and Fisher [10] and Ouyang [11] that considered both demand and inventory information sharing; the studies by Zipkin [12] , Gavirneni [13] , Armony and Plambeck [14] , Li and Liu [15] , Choudhary et al. [16] , and Srivathsan and Kamath [17] that focused solely on inventory information sharing; and the study by Zhou and Li [18] that considered demand and sales price information sharing. For a broad overview of the studies on the value of information sharing, the reader is referred to Chen [19] , Li et al. [3] , and Kembro et al. [20] . ...
... Gavirneni et al. [4] Studied the periodic review inventory control problem in a two-echelon supply chain with a retail store and a supplier with three levels of information sharing. Kelepouris [5] Studied the impact of replenishment parameters and information sharing on mitigating bullwhip effect in a SCN with retail stores and a warehouse. Cannella [6] Studied how the performance of an Information Exchange SC improved while implementing periodic review Order-Up-To policy. ...
Global competition has caused a paradigm shift in a firm’s outlook from a product-centric to a customer-centric view. With this shift, the availability of inventory at a store is a prime concern for firms in a supply chain network (SCN) as it affects customer goodwill and market share. Information sharing among the SCN partners is a key strategy to address this issue. In this study, we focus on the impact of sharing upstream inventory information in a SCN on its overall performance. A two-echelon SCN configuration with one retail store and two production facilities was used as an experimental test bed. To assess the marginal benefits of sharing additional information, three levels of information sharing were considered along with the base case of no information sharing. The information shared ranged from
stock-out information at the lowest level to inventory and backorder levels at the highest level. Continuous Time Markov Chain models of the SCN were developed and analyzed to gain insights into the value of inventory information sharing. Numerical experiments were
conducted to show that inventory information can be an effective substitute for physical inventory and to assess the impact of backorder limits on the SCN performance.
... A stable and concentrated customer base can facilitate suppliers to consolidate resources and share information along the supply chain, thus improving inventory management and operational efficiency [31,32]. However, interdependence among supply chain participants is not always beneficial but may instead lead to severe squeezes. ...
Digital transformation is increasingly recognized as a key driver of sustainable development, enabling suppliers to improve efficiency, reduce resource consumption, and adapt to changing market demands. However, it remains a challenging process for suppliers, often hindered by resource and capacity constraints. This study investigates how government subsidies can facilitate supplier digital transformation, considering supply chain diffusion and local government competition dynamics. Using data from A-share listed companies in China between 2010 and 2021, our analysis reveals that government subsidies significantly promote supplier digital transformation by encouraging a more diversified downstream customer base. Moreover, customer digital transformation can facilitate supplier digital transformation, but spillover effects are higher within the same jurisdiction than across different jurisdictions. This study further identifies that the impact of government subsidies is more pronounced under higher opportunistic risk but is constrained by systemic risk. Additionally, suppliers with higher human capital and a smaller digital divide with customers exhibit greater effectiveness in adopting innovation diffusion. These findings provide valuable insights into optimizing local government subsidies policies to enhance supplier digital transformation and contribute to the broader goal of sustainable development.
... Instead of being a vital asset or mechanism, high customer concentration hinders firms from alleviating the bullwhip effect by adopting DT. Therefore, our research contributes to the bullwhip effect literature by explaining the inconsistent findings from customer concentration's role in shaping the bullwhip effect (Kelepouris et al., 2008;Zhao et al., 2019). Using theory and empirical computations, we demonstrated how customer concentration explains the moderation observed in the negative relationship between DT and the bullwhip effect. ...
The bullwhip effect in supply chains causes mismatches between demand and production, and the emergence of digital transformation offers a potential prominent avenue for firms to address this uncertainty. This study draws on the organizational information processing theory to investigate the impact of digital transformation on the firm-level bullwhip effect and how this relationship may be contingent on customer concentration. We empirically examined our hypotheses by using hierarchical linear modeling with multiple high-dimensional fixed effects on a large panel dataset of 2,159 listed Chinese manufacturing firms (8,540 firm–year observations) from 2016 to 2020. The results show that digital transformation significantly mitigates firms’ bullwhip effect. Moreover, customer concentration weakens the negative relationship between digital transformation and the bullwhip effect. Specifically, the negative effect of digital transformation on the bullwhip effect will be stronger in firms with a diffuse customer base. The present research findings provide important theoretical contributions to the supply chain and technology management literature and offer valuable practical implications for managers to minimize the bullwhip effect.
... This results in prompt synergy and superior performance. Moreover, effective information sharing between supply chain members has also been identified as a key antecedent in the efforts to mitigate the negative effect of the Bullwhip effect in organisations (Kelepouris, Miliotis and Pramatari, 2008). The Bullwhip effect refers to increasing swings in inventory as a response to various shifts in customer demand as one moves further up the supply chain (Cannella and Ciancimino, 2010). ...
Supply chain relationships and performance as found in large organisations can be applied to SMEs to
increase competitiveness. This study explored perceptions of South African SMEs regarding supply chain
management practices such as information sharing, trust, synergies and performance of suppliers. The
study was conducted using a systematically drawn sample consisting of 309 owners and managers of
SMEs based in Gauteng Province. Perceptions of respondents were analysed using simple descriptive
statistics and the analysis of means. There was general satisfaction with the quantity and quality of
information exchanged, the degree of trust and the extent of synergies existing between SMEs and their
suppliers. Supplier performance was deemed to be satisfactory. Owners and managers were more satisfied
with the degree of supplier trust than with the other factors. Based on these results, conclusions were
drawn and managerial implications for improvements in the dimensions considered in this study were put
forward.
... Machuca and Barajas [18] also studied the effect of electronic data exchange on reducing the bullwhip effect and the average cost of inventory using Internet simulation software. Kelepouris et al. [19] investigated the effect of replenishment and information sharing parameters on bullwhip effect. Marko and Rusjan [20] also studied the effect of replenishment policies on this effect. ...
Nowadays, regarding the technology development and communication means, supply chain management has gained special significance among different industries. The impact of bullwhip is one of the factors that could lessn the supply chain efficiency and increase the cost and delivery time of products and services. In this study, we explored the demand forecasting in supply chain, a four-level chain of retailers, wholesalers, manufacturers, and suppliers. Each level of the chain forecasted demand by moving average method, exponential smoothing, multilayer perceptron artificial neural network, and regression. Also, we provide a hybrid model based on statistics and mathematics to reduce the effect of bullwhip. For this purpose, at first, the supply chain simulation was performed. The results were then evaluated applying analysis of variance and the best combined model to reduce the amount of bullwhip effect was introduced. The model of this research could be useful for other studies. Finally, forecast for retail demand using the regression model; wholesale demand using the exponential smoothing model; manufacture demand using the neural network; and supplier demand using the moving average method have been done.
... Based on transaction cost theory, a set of the literature encouraged firms to enhancing customer concentration, given that a high level of customer concentration generally corresponds to low marginal cost of production and transaction (Krolikowski & Yuan, 2017;Patatoukas, 2012;Pan et al., 2020). Holding a more concentrated customer base can facilitate a firm's resources integration and information sharing along the supplier chain, improving inventory management and operational efficiency and avoiding the bullwhip effect (Irvine et al., 2016;Kelepouris et al., 2008;Krolikowski & Yuan, 2017). While other scholars objected to it because that customer concentration may have negative consequences. ...
A great deal of research attention has been devoted to studying the effects of customer concentration on firm strategic acts. Scholars have also investigated the relationship between customer concentration and firm innovation, but concluded inconsistent findings of such relationship. Furthermore, the underlying mechanism remains unclear. To address these concerns, this study decouples exploratory innovation from firm innovation and introduce performance-reducing threats perceived by the executives as the mediator. Based on the observations of China high-tech listed firms from 2011 to 2018, empirical results show that customer concentration has a U-shaped relationship with exploratory innovation, via the mediating effect of performance-reducing threats perceived by the executives.
... The information that provides insight into inventory levels and demand, improves replenishment decision, and potentially increases product availability is often shared with the help of automated replenishment programs (ARP) and supported by automated replenishment systems (ARS). Theory demonstrated that different types of automated replenishment programs and shared information can result in reduced uncertainty, reduced inventory levels, reduced bullwhip, and increased accuracy [9][10][11]. ...
Maintaining product availability is one of the biggest challenges in retail business because it directly relates to lost sale and decrease in customer loyalty. A solution that ensures a more accurate prediction and eliminates costly stock-outs and wasteful overstocks is an automatic replenishment system. The goal of this paper is to measure the impact that the automatic replenishment system can have on product availability in retail business, especially when it comes to specific product and store related risk factors. A large quantitative study measured the performance of manual and automatic replenishment processes in a sample of 85 stores and 95 products of a major retail chain in Serbia. The study concluded that utilization of an automatic replenishment system can reduce stock-outs for the retail chains up to 60%. Specifically, when ordered through an automatic replenishment system, fast-selling products recorded 40% greater availability, products on promotion 48% higher availability, and products in a high-density retail stores 59% higher availability. The findings extend current understanding of automatic replenishment systems, and especially their performance related to high-risk retail conditions.
... For S-shaped demand patterns (with or without error), sharing of POS data hurts performance. Studies of Kelepouris et al. [20] and Agrawal et al. [21] also indicated that POS data sharing, reduce BWE. ...
... Similarly, it should be linked to suppliers and retailers as well to ensure the inventory is efficient. Thus, the management of information sharing can increase inventory efficiency in supply chain networks (Kelepouris et al., 2008). ...
Abstract
Purpose
This paper aims to examine the effect of inventory information sharing on inventory efficiency and its intervening effect of information technology (IT) capability in manufacturing firms.
Design/methodology/approach
Stratified random sampling and filter questions selected targeted respondents, and an online survey collected 124 completed questionnaires from Malaysian manufacturing firms. partial least squares structural equation modeling (PLS-SEM) examined the structural model and hypothesis statement. An analysis of importance-performance map analysis (IPMA) test identified the relative importance drivers of inventory efficiency.
Findings
The findings showed that enhanced IT capabilities in manufacturing firms mediate a positive relationship between inventory sharing and inventory efficiency.
Research limitations/implications
This study portrays the relationship between inventory level, demand and information sharing. The research was carried out only within Malaysian manufacturing firms.
Practical implications
These findings will enable the management of manufacturing firms to design and visualise their inventory levels and share best practices across supply chain networks to achieve effective and optimised inventory planning.
Social implications
This study illustrates an intervention model that offers a direct and indirect impact of IT capabilities that allow scholars to close inventories productivity gaps in research.
Originality/value
This paper extends the limited literature on the sharing of inventory information and inventory productivity, notably from a strategic management perspective. The findings help scholars clearly understand the information systems capability and its mediating impact on information sharing and inventory efficiency’s relationship in the manufacturing sector. Moreover, demand information sharing affected the dynamic supply chain.
... Some researchers reported that longer the mean lead time, stronger the bullwhip effect (Chen et al., 2000;Agrawal et al., 2009;Hosoda et al., 2015). Kelepouris et al. (2008) demonstrated that an increase in mean lead times tended to result in a decreased customer satisfaction. Moreover, some researchers emphasised the effect of lead time uncertainty on supply chains management (Song et al., 2010;Kouvelis and Tang, 2011;Isotupa and Samanta, 2013;Bandaly et al., 2016;Chung et al., 2018;Ponte et al., 2018). ...
Although lead time variation is common in practice, integrated single-manufacturer multi-buyer model considering this factor is unavailable in the extant literature. This article considers generic distribution of lead times of delivering equal and/or unequal batch (sub-lot) sizes of a lot in developing a synchronised integrated single-manufacturer multi-buyer model. The batch sizes are assumed to be in geometric series. The variables considered in the model are the smallest batch size, total number of batches and number of unequal batch sizes delivered from the manufacturer to buyers. The smallest batch sizes delivered to the buyers are bounded below by 1 and above by the capacity of the transport vehicle. The minimal total cost solution technique to the model is derived by the method of differentiation. Significant minimal total cost reductions by the synchronised flow is illustrated through solutions to some numerical example problems. Sensitivity analyses on increasing costs of transportation, shortage, inventory and increasing mean lead times upon the optimal solution have been performed.
... They show that significant benefits can be achieved by exploiting POS data. In a similar setting, also Kelepouris, Miliotis, and Pramatari (2008) find that information sharing enabled the upstream supply chain member to respond more effectively and efficiently to demand variations, to produce more accurate forecasts, and therefore to reduce the impact of the BWE and inventory levels. Williams and Waller (2010) come to similar conclusions but emphasize that, although the use of POS data as forecasting input produces a lower forecast error on average and in the majority of the cases, it does not always outperform the use of traditional data in terms of forecast accuracy. ...
Operational forecasting in supply chain management supports a variety of short-term planning decisions, such as production scheduling and inventory management. In this respect, improving short-term forecast accuracy is a way to build a more agile supply chain for manufacturing companies. Demand forecasting often relies on well-established univariate forecasting methods to extrapolate historical demand. Collaboration across the supply chain, including information sharing, is suggested in the literature to improve upon the forecast accuracy of such traditional methods. In this paper, we review empirical studies considering the use of downstream information in demand forecasting and investigate different modeling approaches and forecasting methods to incorporate such data. Where empirical findings on information sharing mainly focus on point-of-sale data in two-level supply chains, this research empirically investigates the added value of using sell-through data originating from intermediaries, next to historical demand figures, in a multi-echelon supply chain. In a case study concerning a US drug manufacturer, we evaluate different methods to incorporate this data and consider both time series methods and machine learning techniques to produce multi-step ahead weekly forecasts. The results show that the manufacturer can effectively improve its short-term forecast accuracy by integrating sell-through data into the forecasting process and provide useful insights as to the different modeling approaches used. The conclusion holds for all forecast horizons considered, though it is most pronounced for one-step ahead forecasts. Therefore, our research provides a clear incentive for manufacturers to assess the forecast accuracy that can be achieved by using sell-through data.
... Examples of these type of policies are Average Demand Strategy (ADS), Fixed Order Quantity (FOQ), Demand Flow Strategy (DFS) [19] [20], ] and EOQ-based policy [23]. The supply chain performance can be influenced by many factors such as review period and the lead time [24], also the customer demand pattern affects the performance [3] [25]. Lead time variability has a bigger effect on the supply chain performance more than the effect of variations in the lead time mean value [26]. ...
... The replenishment parameters such as lead time and review period can also affect the performance of SC [46]. The time gap between the receipts of the order to delivery of the product is referred as lead time, which is the sum of order lead time and delivery lead time. ...
In the present global market, the competition is not between the companies but between the supply chains. The comparison of performance measures of supply chains helps to identify a good supply chain. The best performing supply chains may survive over a long period. The performance of a supply chain is affected by various internal and external factors. The objective of this paper is to identify and conduct a detailed study of the major factors affecting the supply chain performance. The factors are identified by reviewing various literature in the supply chain field. In this respect, a total of 54 literature are reviewed. The major factors identified as supply chain structure, inventory control policy, information sharing, customer demand, forecasting method, lead time and review period length. The optimum selection of parameters of these factors improves the supply chain performance.
... The impact of lead times on the external performance of supply chains has often been analyzed through the balance between customer satisfaction and safety stock required. For instance, Kelepouris et al. (2008) demonstrated that an increase in mean lead times tends to result in a decreased customer satisfaction-thus, more safety stock is required to achieve a target CSL. This positive relationship between the lead time and the safety stock can also be observed in classic safety stock models, e.g. ...
This work quantifies the financial impact of the mean and the variability of production and shipping lead times on multi-echelon supply chains. We combine agent-based modelling and Taguchi methods, through which we develop a framework for supporting entrepreneurial investment decisions. A throughput-based analysis reveals that decreasing mean lead times improves the internal operation of production and distribution systems, while reducing lead time variability enhances the satisfaction of consumers. In this regard, we contrast traditional and collaborative supply chains. We find that the latter are not only more profitable than the former, but also more robust to variations in lead times.
... Many studies (e.g. [23][24][25][26][27] ) have found that time delays increase the inventory cost and aggravate the bullwhip effect, with a consequent major effect on inventory policy. Existing studies have derived the mathematical relationship between order quantity and arbitrary lead time under a linear model [16] , and recently some studies [28][29] have devoted to the relationship between system stability and delay time under the nonlinear model. ...
Considering the non-negative constraint of order quantity, this study explored inventory system performance, including system stability, service level, inventory cost, and the effect of transportation delay time. Both the non-negative constraint and delay time render the system nonlinear and complicated, which makes it difficult to identify optimal order policy regions that combine system stability with a high service level and low cost. The purpose of this study is to systematically reflect the impact of order policies on inventory system performance from three aspects, including system stability, service level, and cost. The results of the simulation revealed the existence of public optimal order policies for different transportation delay times. Although these optimal order policies are similar when the target inventory parameter changes, lowering the target inventory parameter can also lower the inventory cost. If an appropriate order policy can be adopted, a low target inventory reduces inventory cost while maintaining system stability and a high service level, opening up new options for decision makers in supply chain management.
... However, most studies assume that when companies have access to better demand, inventory, or process data in the supply chain, the operational performance of the chain improves (Barratt and Oke, 2007;Gavirneni et al., 1999;Nagashima et al., 2015) without considering that there may be contexts where benefits cannot be realized. In many studies, operational improvements such as more accurate forecasting (Weber and Kantamneni, 2002;Williams and Waller, 2011), lower inventory levels or costs (Wu and Cheng, 2008), shorter lead times and a reduced bullwhip effect (Agrawal et al., 2009;Croson and Donohue, 2003;Kelepouris et al., 2008), and reduced risk (Zhao et al., 2013) are found. Thus, information sharing in modelling studies recognize that information sharing does not lead directly to improved performance, but assume improved performance when information sharing is used in operational processes within and between the participating companies (Baihaqi and Sohal, 2013). ...
We develop actionable design propositions for collaborative sales and operations planning (S&OP) based on the observation of contexts in which benefits are generated — or are absent — from retail information sharing. An information sharing pilot project in a real-life setting of two product manufacturers and one retailer was designed. The project resulted in one manufacturer, serving a retailer from its local factory, developing a process for collaborative S&OP, while the other manufacturer serving a retailer from more distant regional factories abandoned the process. The evaluation of the outcomes experienced by the two manufacturers allows us to examine contexts in fine-grained detail and explain why introducing information sharing in the S&OP processes produce — or fail to produce — benefits. The paper contributes to the supply chain information sharing literature by presenting a field tested and evolved S&OP design for non-standard demand situations, and by a contextual analysis of the mechanisms that produce the benefits of retailer collaboration and information sharing in the S&OP process.
... where 0 < γ < 1 order variance ratio 9 dejonckheere et al. [9], disney and towill [30], disney et al. [31] aPIoBPcs Periodic Yes average age of exponential smoothing forecast (T a ), time to adjust errors in net stock (T n ), time to adjust errors in The factors such as lead time and review period, [34] information sharing, forecasting models, supply chain configuration features (such as structure, number of stages, and number of members) and the nature of customer demand pattern [3,35] can influence the performance of supply chain. The effect of lead time variance on the performance of supply chain is much greater than the effect of changes in the mean level of lead time. ...
This paper proposes an inventory position-based policy, and its performance is assessed in terms of supply chain fill rate, bullwhip effect and total cost of supply chain under different scenarios. The scenarios are developed considering the different levels of supply chain structures, demand patterns, and information sharing strategies. Under each scenario, the performance of the proposed policy is compared with the other inventory position-based policies such as Order-Up-To, Order-Up-To smoothing, (s, S) and (s, Q) using spreadsheet-based simulation methodology. The multi-attribute performance comparison, grey relational analysis, shows that the proposed policy is the best compared to other policies tested because of the better performance in supply chain fill rate and order smoothing. The simulation study provides sufficient insights to managers in selecting a suitable inventory policy based on the operating conditions of a supply chain and multiple performance measures.
... local information in each echelon of the supply chains leads to a propagation of variance moving in the upstream levels. This amplification is affected by the forecasting process used in each echelon (Graves, 1999;Chen et al., 2000;Khosroshahi et al., 2016;Yuan and Zhu, 2016;Ma and Ma, 2017), the adopted inventory policy ( Dejonckheere et al., 2003;Kelepouris et al., 2008;Cheng, 2009) and the lead time (both its length ( Chen et al., 2000) and its variability ( Duc et al., 2008)). ...
The increase of demand variability along the supply chain has a huge impact in companies, where demand fluctuations make the forecasting and planning processes harder, especially in the upstream levels. We present an empirical analysis on the Bullwhip Effect (BE) identification on more than 30,000 SKUs of a three-echelon European automotive spare parts supply chain. With our analyses, we aim at shedding some light on the incentives that lead to the smoothing/amplification of demand variability.
We analyse the bullwhip effect on two different aggregation levels. First, we evaluate the global BE along the supply chain, calculated on the total demand series. We aim at identifying the intensity of the BE in each level and at investigating if the empirical context reflects the literature results concerning a monotonic demand variability amplification through the echelons. Secondly, we increase the level of detail and we study the BE on the single SKUs. The objective is to measure how the intensity of the BE varies across products. Also we want to investigate what product characteristics tend to make the bullwhip effect stronger.
Preliminary analyses confirm the presence of bullwhip when considering the total demand series. Demand variability amplifies through each echelon, especially moving from dealers to local warehouses. This first outcome provides us with insights on the incentives structure of the company. In fact, dealers are independent entities. The strong increase of demand variability from independent nodes to the distribution company is probably due to the incentive structures given to dealers. In particular, demand at the top level is found to be more than twice variable than the final sell-out. Further analyses will be devoted to understanding the final effect of such a great demand variability on the suppliers’ behaviour. Also, the comparison of the BE distribution among different demand classes calculated on single SKUs gives us a very interesting perspective. The BE is higher for fast moving products than for slow movers. The combination of the two results suggests that dealers tend to decouple demand and supply for fast movers. Thus, when they are given an incentive to forward buy, they prefer to forward buy fast moving items. Further research will be devoted to prove the validity of our hypothesis on dealers’ behaviour and investigate the contribution of other product characteristics on the BE.
... Step " Dejonckheere et al. [33] OVR Traditional production inventory system -Sinusoidal " -Step -i.i.d Chatfield et al. [34] " Two four-echelon supply chains -i.i.d " Dejonckheere et al [35] " Two four-echelon supply chains " " Disney et al. [36] -OVR Traditional production inventory system " " -IV [37] BW " S t e p " Disney et al [38] -OVR " -i.i.d " -Auto Regressive -IV -Moving Average Kim et al. [39] OVR Two five-layer supply chains i.i.d " Chen and Disney [40] -OVR Traditional production inventory system -Auto Regressive " -IV -Moving Average Boute et al. [41] OVR " i.i.d " Hosoda et al. [42] " Two-echelon Epos supply chain Real life data set " Jakši č and Rusjan [43] " Two-echelon traditional supply chains Sinusoidal " Kelepouris et al [44] " " Real life data set " Bayraktar et al. [45] BW " Online linear demand forecast with seasonal swings " Haughton [46] BW Two-echelon traditional supply chains Multiplicative Seasonal " Agrawal et al. [47] -OVR " Auto Regressive " -IV Sucky [23] BW Three-echelon traditional supply chains Moving Average " Xie [48] BW Two-echelon traditional supply chains Fuzzy forecasting " Coppini et al [49] BW " Exponential Smoothing " Cannella and Ciancimino [21] -OVR Multi-echelon traditional supply chains Smoothing " -IV -BW Cho and Lee [50] BW Two-echelon traditional supply chains -Moving Average " -Seasonal Auto Regressive Li et al. [51] BW " Moving Average " Syntetos et al. [52] BW Three-echelon traditional supply chains Different methods " Cantor and Katok [53] BW Two-echelon traditional supply chains Seasonal " Ciancimino et al [54] BW Three-echelon traditional supply chains Exponential Smoothing " Nepal et al [14] BW " " " [12] BW " " " Chatfield and Pritchard [55] BW Multi-echelon traditional supply chains Moving Average " Buchmeister et al. [56] BW " Seasonal " Li et al. [57] BW Two-echelon traditional supply chains Damped Trend forecasting " Costantino et al. [58] -OVR Multi-echelon traditional supply chains -Moving Average " -IV -Exponential Smoothing Nagaraja et al [59] BW Two-echelon traditional supply chains SARMA " Dominguez et al [11] BW Multi-echelon traditional supply chains Moving Average " OVR and IV. Dominguez et al. [29] used two different strategies in a serial linked supply chain network to mitigate the bullwhip effect. ...
The bullwhip effect is an undeniable phenomenon in supply chains that has a negative effect on their performance and efficiency. There are a variety of causes for the appearance of the bullwhip effect, one most important of which is the existence of uncertain demand. Due to the variety of causes as well as in order to recognize relevant factors, it is important to be able to quantify the bullwhip effect in a supply-chain environment. In this study, we aim to quantify the bullwhip effect in a 3-stage supply chain with multiple retailers. First, we quantify the bullwhip effect, order rate variance ratio (OVR) and inventory variance ratio (IV) in a pipeline supply chain, for which we develop a relation to calculate the bullwhip effect. Then we extend it to a supply chain with multiple retailers. We analyze the impact of service levels on the bullwhip effect for both defined supply chain situations. This analysis, which highlights the importance of the impact of service levels on the bullwhip effect, can be considered the main contribution of this paper. In addition, we survey the influence of the correlation coefficient on the bullwhip effect.
... Previous studies on supply chain management (SCM) have mainly focused on the effects on the supply chain of information sharing [1], inventory policy [2], and/or demand prediction [3]. In other words, the mainly addressed research issues were all related to goods and information flows involved in the supply chain [4]. ...
... Therefore, how to efficiently transport materials in emergency disasters to the demand point becomes a key problem in the disaster relief process. Emergency logistics are special logistics activities to ensure that the demand of materials, personnel, and money are allocated to public health emergencies, major accidents and other emergencies which are sudden, uncertain, unconventional, and with urgency based on time constraints that benefit from a maximum of time management with an objective of minimum disaster losses [2][3][4][5][6]. ...
Currently, the sweet corn marketing competition has led to an increasing awareness of supply chain management. Information sharing is one of the many ways which has been interested by researchers and supply chain members. Because it can increase supply chain efficiency and reduce costs. Nevertheless, motivating to information sharing is still a challenge. The purpose of this paper is to examine how trust, commitment, strategic alliance, opportunistic behavior and power influence on information sharing in sweet corn supply chain. In this study was analyzed using PLS-SEM. Drawing upon transaction cost theory and social exchange theory. Data were collected from 300 respondents comprise of growers, coordinators, integrators and producers, located in Chiang Mai. The results show that all paths have significant positive effects. Strategic alliance is crucial factor to information sharing that brings supply chain collaboration based on trust and commitment. Subsequently it improves a competitive advantage and has positive effect on long-term relationships.
Due to the competition in the market, sweet corn supply chain members are more aware of supply chain management. Information sharing in the supply chain is an approach that scholars and practitioners accept in terms of improving supply chain management efficiency. However, participating in a supply chain that promotes information sharing is still a challenge. This paper aims to investigate the association between related resources and information sharing and to evaluate the possibility of information sharing in sweet corn supply chain. This paper used the resource-advantage theory as the theoretical foundation and modeling binary logistic regression analysis, consisting of human, relational, and physical resources. Data was collected through semi-structured interviews with growers, coordinators, integrators, and factory managers. The results revealed that human resources (e.g., education level, work experience), relational resources (e.g., membership, trading channel), and physical resources (e.g., sweet corn variety) have statistical significance with information sharing. This confirms the resource as a potential source of opportunities to share information. The study helps supply chain members select a supply chain with better information sharing and provides guidelines to improve the efficiency of information sharing.
Purpose
An important phenomenon often observed in supply chain, known as the bullwhip effect, implies that demand variability increases as we move up in the supply chain. On the other hand, the cross-docking is a distribution strategy that eliminates the inventory holding function of the retailer distribution center, where this latter functions as a transfer point rather than a storage point. The purpose of this paper is to analyze the impact of cross-docking strategy compared to traditional warehousing on the bullwhip effect.
Design/methodology/approach
The authors quantify this effect in a three-echelon supply chain consisting of stores, retailer and supplier. They assume that each participant adopts an order up to level policy with an exponential smoothing forecasting scheme. This paper demonstrates mathematically the lower bound of the bullwhip effect reduction in the cross-docking strategy compared to traditional warehousing.
Findings
By simulation, this paper demonstrates that cross-docking reduces the bullwhip effect upstream the chain. This reduction depends on the lead-times, the review periods and the smoothing factor.
Research limitations/implications
A mathematical demonstration cannot be highly generalizable, and this paper should be extended to an empirical investigation where real data can be incorporated in the model. However, the findings of this paper form a foundation for further understanding of the cross-docking strategy and its impact on the bullwhip effect.
Originality/value
This paper fills a gap by proposing a mathematical demonstration and a simulation, to investigate the benefits of implementing cross-docking strategy on the bullwhip effect. This impact has not been studied in the literature.
Information sharing has been regarded as a way to coordinate or to optimize the whole supply chain. Prior literature focuses on unilateral information sharing in a supply chain with single or substitutable products. This paper explores bilateral demand information sharing in a supply chain, in which two competing manufacturers provide substitutable products to a common retailer. All supply chain members have partial forecast information about the market demand. We establish three pricing decision models and derive the corresponding results by backward induction to study the effects of bilateral information sharing on pricing and expected profits of supply chain members under three information sharing strategies. The results show that (i) information sharing decreases the positive effect of manufacturer’s demand forecast on the optimal price, but increases the positive effect of retailer’s demand forecast; (ii) information sharing benefits two manufacturers and hurts the retailer, whereas benefits the whole supply chain under certain conditions; (iii) the retailer prefers to share information with one of manufacturers rather than both of manufacturers. Finally, we also provide some managerial insights with the help of computational studies.
This chapter is developed to investigate the effect of supply chain dynamics on a number of aspects related to different dimensions of the quality of the information acquired by each node in the supply chain. First, how sharing node information across the supply chain can improve inventory variability is studied for different scenarios. Next, different dimensions of the quality of the information, such as its timeliness, accuracy or availability in advance are discussed. The chapter concludes with a summary of the topics discussed and with a list of annotated references, so that the reader can further study the issues presented.
Les structures des chaînes d’approvisionnement connaissent de plus en plus une complexité croissante, notamment sous l'effet de la mondialisation et des manœuvres de décentralisation. Plusieurs inhibiteurs accentuent le caractère décentralisé et empêchent l’amélioration des performances des chaînes décentralisées. Ce travail de thèse s’intéresse à une approche de prévision collaborative qui a pour objectif l’amélioration des performances des chaînes décentralisés. Cette approche traite le non-partage de l’information de la demande dans une chaîne d’approvisionnement en série et montre qu’une inférence de cette demande est possible sous certaines conditions. Cette inférence permet de rapprocher les performances d’une chaîne décentralisée à celles d’une chaîne centralisée. Une relaxation de l’hypothèse du non-partage d’informations, permet encore d’augmenter les performances opérationnelles du système décentralisé. Cette recherche enrichit la notion de performance des chaînes d’approvisionnement en démontrant que la performance opérationnelle des chaînes décentralisées peut être considérablement augmentée grâce à une source d’amélioration collaborative de prévision.
The aim of this chapter is to review various perspectives on supply chain (SC) coordination issues and appreciate a range of coordination categories and mechanisms. The application of information technology in supply chain coordination (SCC) and the related cases are also studied in this chapter.
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.
Background
The increased variability in production or procurement with respect to less increase of variability in demand or sales is considered as bullwhip effect. Bullwhip effect is considered as an encumbrance in optimization of supply chain as it causes inadequacy in the supply chain. Various operations and supply chain management consultants, managers and researchers are doing a rigorous study to find the causes behind the dynamic nature of the supply chain management and have listed shorter product life cycle, change in technology, change in consumer preference and era of globalization, to name a few. Most of the literature that explored bullwhip effect is found to be based on simulations and mathematical models. Exploring bullwhip effect using machine learning is the novel approach of the present study.
Methods
Present study explores the operational and financial variables affecting the bullwhip effect on the basis of secondary data. Data mining and machine learning techniques are used to explore the variables affecting bullwhip effect in Indian sectors. Rapid Miner tool has been used for data mining and 10-fold cross validation has been performed. Weka Alternating Decision Tree (w-ADT) has been built for decision makers to mitigate bullwhip effect after the classification.
Results
Out of the 19 selected variables affecting bullwhip effect 7 variables have been selected which have highest accuracy level with minimum deviation.
Conclusion
Classification technique using machine learning provides an effective tool and techniques to explore bullwhip effect in supply chain management.
The complexity in which the interactions of the supply chain in framed is one of the main difficulties for the organizational management. The amount of structured and unstructured data emerging from their own interactions increases exponentially, it is constantly and rapidly transformed; its usefulness will lie on the integrity in handling and speed for processing the mentioned data. This Article proposes a new archetype for the supply chain based on the decentralized information management which operation is based on the management of Big Data from the application of Blockchain technologies. It is concluded that the technological gap to reach this new archetype is only broken through the redefinition of the way in which transactions are carried out, integrating Big Data management with the Blockchain application.
We examine the impact of increasing product variety on two measures of a firm’s forecast performance – forecast accuracy and forecast bias – and test whether shared information mitigates this impact. With companies under pressure to expand product variety yet maintain good forecast accuracy understanding this relationship is critical. We use data gathered pre and post a vertical integration event, where some information forecasted prior to the merger was now available. We show that increasing product variety, and thus the number of forecasts, indeed deteriorates both forecast accuracy and bias. The vertical integration event, providing information sharing, results in improved forecast performance. Further, different product variety attributes (e.g. brand variety and pack variety) are found to have differing impacts. Increasing brand variety is found to have a significantly greater impact on forecast accuracy than pack variety. Using the vertical integration event as a natural experiment we document that expanding product variety negatively impacts forecasts and that information can help mitigated the impact. This is an important contribution as it tests the value of ‘truthful’ information given the elimination of the firm boundary post merger. Further, we show that a firm’s decision to expand product variety should include product variety attributes given their differential impact.
The aim of this study is to empirically investigate the impact of automatic replenishment on food waste metrics in grocery stores. The work has been designed as a case study focusing on the replenishment process among various stores and a single warehouse. Food waste metrics of products ordered through an automatic replenishment program are compared against products ordered manually. Specifically we contrast food waste, remaining shelf life and availability at the stores for a variety of products with different shelf life. The study suggests that by utilising an automatic replenishment program the stores can reduce their level of food waste by up to 20% and their products have a longer remaining shelf life without compromising on-shelf availability. The study also indicates that the impact of the automatic replenishment program is dependent on the product’s shelf life. Those products with a shelf life of between 51 and 110 days experience the highest impact, while products with a shelf life below 30 days experience a low or even negative impact. The study extends the current understanding of automatic replenishment programs. The key point for practitioners is to apply appropriate replenishment programs according to the product characteristics and especially the shelf life.
Supply chain effectiveness and costs are affected by the demand variability, especially in the upstream echelons. The propagation of demand variability moving upstream in the supply chain has been widely studied in the literature and it is known as the Bullwhip Effect phenomenon. In this paper, the bullwhip effect in a European automotive spare parts chain is identified, with the aim of shedding some light on how demand variability propagates in different groups of products. We considered more than 30,000 products, characterised by different technical characteristics, demand classes and planning parameters.The results showed that the considered supply chain is affected by the bullwhip effect. Additional analyses suggested that the bullwhip effect is larger for fast moving products rather than for slow movers. Hence, dealers tends to decouple supply and demand and, when they are given incentives to forward-buy, they may prefer to forward-buy fast moving items. Moreover, as the dealers tend to exploit the promotional benefits by forward-buying in the final period of promotions, frequent switches from promotional to non promotional periods tend to increase the propagation of the demand variability.
Bullwhip effect (BWE), which refers to the phenomenon wherein the variance in demand orders increases as one moves up the supply chain (SC), has been a source of concern for most of the SC managers as it has serious implications in the SC. Problems can vary from inefficient ordering and inventory policies (IP) to higher total cost of SC with extreme cases of stock-out and disruptions in the SC. The literature on this topic suggests various factors which cause BWE and these have further been divided into two broad groups, namely, “operational causes” and “behavioural causes.” This study focuses on the operational factors identified through the survey of literature from 1990 to 2013 and suggests 18 factors which contribute to the BWE. Further, from an analysis of literature, based on a number of articles and their citations, and responses from field experts through a questionnaire, a refined list of seven factors has been taken up for the study. These factors have been ranked using the “analytical hierarchy process” (AHP). The ranking is based on pairwise comparisons based on the perception of the SC managers in the manufacturing sector in north India.
The article is divided into five sections. The first section deals with the introduction and identification of the BWE. The second section identifies the various factors which cause this effect under the two broad categories, namely, the operational and behavioural factors. The third section discusses AHP and the methodology used in the article to rank the operational factors. The fourth section presents the results in a tabular and graphical form. Order batching (OB) is found to be the biggest contributor of BWE in manufacturing sector in the aforementioned region followed by demand signal processing (DSP), lead time (LT), IP, price fluctuation (PF), lack of trust (LOT), and number of echelons (NOE). The fifth and final section undertakes a discussion and presents guidelines for future research.
Resumo
The aim of the paper is to develop a theoretical construct about the mitigation of the bullwhip effect, considering trust and collaboration in managing the supply chain. The study presents a qualitative research based on the systematic literature review, which is tested through field research, involving companies in the medical and hospital area belonging to the same supply chain. The bullwhip effect has been observed throughout the industry for many years. Several academic studies have assigned to operating causes the reason for its occurrence. Few studies have focused behavioral causes. Through this study, it appears that affective trust (honesty, mutual understanding, credibility, respect and compliance) and trust in the competence (knowledge/technique, commitment in the relationship) are both necessary for keeping the relationship, but without affective trust, the relationship does not develop. Moreover, an organizational culture based on trust and collaboration exchange, and knowledge related to processes and technology among businesses, contributing to the joint planning and collaboration in the information sharing occurs. Thus, aspects of behavior toward partners of supply chain companies can mitigate the operational causes of the bullwhip effect by improving information and knowledge sharing, demand forecasting, replenishment policy, and reducing the risk coordination among the chain participants.
The Bullwhip Effect, which refers to the increasing variability of orders traveling upstream the supply chain, has shown to be a severe problem for many industries. The inventory policy of the various nodes is an important contributory factor to this phenomenon, and hence it significantly impacts on their financial performance. This fact has led to a large amount of research on replenishment and forecasting methods aimed at exploring their suitability depending on a range of environmental factors, e.g. the demand pattern and the lead time. This research work approaches this issue by seeing the whole picture of the supply chain. We study the interaction between four widely used inventory models in five different contexts depending on the customer demand variability and the safety stock. We show that the concurrence of distinct inventory models in the supply chain, which is a common situation in practice, may alleviate the generation of inefficiencies derived from the Bullwhip Effect. In this sense, we show that the performance of each policy depends not only upon the external environment but also upon the position within the system and upon the decisions of the other nodes. The experiments have been carried out via an agent-based system whose agents simulate the behavior of the different supply chain actors. This technique proves to offer a powerful and risk-free approach for business exploration and transformation.
This study introduces the methods of supply chain inventory management into the cluster supply chains and proposes the implementation of supply chain inventory management strategies under this circumstance. First, we analyze the system behavior patterns of the co-operation planning, forecasting and replenishment (CPFR), vendor-managed inventory (VMI), and jointly managed inventory (JMI) models of cluster supply chains. Therefore, we establish the inventory management models of CPFR, VMI and JMI in cluster supply chains. These models are simulated by VENSIM software. The simulation results show that compared with those in the VMI and JMI models, the inventory fluctuations of manufacturers, wholesalers and retailers in the CPFR model correspond; the total inventory is reduced while its stability is greatly improved. Therefore, the application of CPFR in cluster supply chains can effectively restrain the bullwhip effect, reduce the inventory and improve the efficiency of the entire supply chain.
Bullwhip effect is a threat observed in multi-echelon supply chains, which is one of the prominent indicators of inefficiencies in a supply chain. Primarily, bullwhip effect occurs as a result of disruptions in information and materials flow, lead-time delays, lack of coordination, and panic stocking amidst visibility into local risk factors. When bullwhip effect occurs, the demand variations entering the supply chain from the customer end amplifies gradually as it flows upstream towards the supplier ends. This may cause unused inventory and may later lead to wastage and obsolescence. Bullwhip effect can be curbed through many approaches. This study has focused on control theory approach that promotes small-scale control behaviors throughout the supply chain to dampen the bullwhip tidal waves. The approach investigated in this research is a combination of control system modeling and systems dynamics modeling, which is not researched adequately by bullwhip academics. Based on the investigations, a six-step approach for reducing Bullwhip effect is proposed in this research and illustrated with examples. The six-step approach comprises of first-level multi-echelon survey to derive the initial system dynamics model, second-level survey to collect primary data for all the variables and relationships formed, principal component analysis and Cronbach Alpha / split-half testing for reliability, verification, and validity testing and exploring the best optimal construct using structural equation modeling, and finally, applying controllers to the optimal systems dynamics model through interpretive analysis of the model.
In this paper, we consider an adaptive base-stock policy for a single-item inventory system, where the demand process is nonstationary. In particular, the demand process is an integrated moving average process of order (0, 1, 1), for which an exponential-weighted moving average provides the optimal forecast. For the assumed control policy we characterize the inventory random variable and use this to find the safety stock requirements for the system. From this characterization, we see that the required inventory, both in absolute terms and as it depends on the replenishment lead-time, behaves much differently for this case of nonstationary demand compared with stationary demand. We then show how the single-item model extends to a multi-stage, or supply-chain context; in particular we see that the demand process for the upstream stage is not only nonstationary but also more variable than that for the downstream stage. We also show that for this model there is no value from letting the upstream stages see the exogenous demand. The paper concludes with some observations about the practical implications of this work.
Compares the bullwhip properties of a vendor managed inventory (VMI) supply chain with those of a traditional “serially-linked” supply chain. The emphasis of this investigation is the comparative impact the two structures have on the “bullwhip effect” generated. Particular attention is paid to the manufacturer's production ordering activities as demonstrated using a simulation model based on difference equations. Documents and considers each of the four important sources of the bullwhip effect in turn. The analysis shows that with VMI implementation two sources of the bullwhip effect may be completely eliminated, i.e. rationing and gaming or the Houlihan effect, and the order batching effect or the Burbidge effect. VMI is also significantly better at responding to rogue changes in demand due to the promotion effect or to price induced variations. However, the effect of VMI on demand signal processing induced bullwhip or the Forrester effect is less clear cut. Concludes that on balance VMI offers a significant opportunity to reduce the bullwhip effect in real-world supply chains.
In preparing a review I recently discovered an important reference for a key result in Graves (1999). Wecker (1979) had previously derived the variance for demand over a deterministic lead-time for an IMA (0, 1, 1) demand process. I develop effectively the same result, given as Equation (8) in Graves (1999). I state this as the variance of the inventory random variable for an inventory system that is subject to an IMA (0, 1, 1) demand process, a deterministic replenishment lead-time and an adaptive base-stock control policy given by (7). But given these assumptions, the variance of the inventory is the same as the variance of the demand over the lead-time.
Although the Wecker manuscript has not been published, Eppen and Martin (1988) reference it and use the key result as part of their research.
(This article originally appeared in Management Science, April 1997, Volume 43, Number 4, pp. 546--558, published by The Institute of Management Sciences.) Consider a series of companies in a supply chain, each of whom orders from its immediate upstream member. In this setting, inbound orders from a downstream member serve as a valuable informational input to upstream production and inventory decisions. This paper claims that the information transferred in the form of ÜordersÝ tends to be distorted and can misguide upstream members in their inventory and production decisions. In particular, the variance of orders may be larger than that of sales, and distortion tends to increase as one moves upstreamÔa phenomenon termed Übullwhip effect.Ý This paper analyzes four sources of the bullwhip effect: demand signal processing, rationing game, order batching, and price variations. Actions that can be taken to mitigate the detrimental impact of this distortion are also discussed.
Many companies have embarked on initiatives that enable more demand information sharing between retailers and their upstream suppliers. While the literature on such initiatives in the business press is proliferating, it is not clear how one can quantify the benefits of these initiatives and how one can identify the drivers of the magnitudes of these benefits. Using analytical models, this paper aims at addressing these questions for a simple two-level supply chain with nonstationary end demands. Our analysis suggests that the value of demand information sharing can be quite high, especially when demands are significantly correlated over time.
Studies in the psychology of individual choice have identified numerous cognitive and other bounds on human rationality, often producing systematic errors and biases. Yet for the most part models of aggregate phenomena in management science and economics are not consistent with such micro-empirical knowledge of individual decision-making. One explanation has been the difficulty of extending the experimental methods used to study individual decisions to aggregate, dynamic settings. This paper reports an experiment on the generation of macrodynamics from microstructure in a common managerial context. Subjects manage a simulated inventory distribution system which contains multiple actors, feedbacks, nonlinearities, and time delays. The interaction of individual decisions with the structure of the simulated firm produces aggregate dynamics which systematically diverge from optimal behavior. An anchoring and adjustment heuristic for stock management is proposed as a model of the subjects' decision processes. Econometric tests show the rule explains the subjects' behavior well. The estimation results identify several `misperceptions of feedback' which account for the poor performance of the subjects. In particular, subjects are shown to be insensitive to the feedbacks from their decisions to the environment. Finally, the generality of the results is considered and implications for behavioral theories of aggregate social and economic dynamics are explored.
The tendency of orders to increase in variability as one moves up a supply chain is commonly known as the 'bullwhip effect.' We study this phenomenon from a behavioral perspective in the context of a simple, serial, supply chain subject to information lags and stochastic demand. We conduct two experiments on two different sets of participants. In the first we find the bullwhip effect still exists when normal operational causes (e.g., batching, price fluctuations, demand estimation, etc.) are removed. The persistence of the bullwhip effect is explained, to some extent, by evidence that decision-makers consistently under-weight the supply line when making order decisions. In the second experiment we find that the bullwhip, and the underlying tendency of underweighting, remains when information on inventory levels is shared. However, we observe that inventory information helps somewhat to alleviate the bullwhip effect by helping upstream chain members better anticipate and prepare for fluctuations in inventory needs downstream. These experimental results support the theoretically suggested notion that upstream chain members stand to gain the most from information sharing initiatives.
En la UE se ha estimado que los costes de la congesti�n representan el 2% de su PIB y que el coste de la poluci�n del aire y ruido supera el 0,6% del PIB, siendo alrededor del 90% de los mismos ocasionados por el transporte terrestre. Ante este hecho y el continuo aumento de la demanda del transporte privado frente al p�blico para los desplazamientos, muchos abogan por una conjunci�n de medidas tanto restrictivas como alternativas al uso del coche. Dentro de las primeras se encuentra el establecimiento de un peaje o una tarifa por el uso de las carreteras, medida que aunque desde el punto de vista de la Teor�a Econ�mica es la manera m�s eficiente para corregir el fallo de mercado que supone la congesti�n, desde la visi�n de pol�ticos y del p�blico no goza de gran aceptaci�n. En este trabajo se pretende hacer una simulaci�n de los efectos que tendr�a sobre el bienestar social de la implantaci�n de una medida de este tipo en la Bah�a de C�diz. In the European Union it has been estimated that the congestion cost are the 2% of the gross domestic product and the cost of pollution and noise is over 0,6%, olso it is known that the 90% of this cost are caused by overland transport. For this reason and for the always increasing demand of private transport, there are professionals who thinks that the solution have to be restrictive measures added to alternatives to the car. road pricing is a restrictive measures that for the economic theory is the most efficient way to solve congestion cost but for politicians and user of transport is not always accepted. In this study we are going to simulate road pricing for commuters in the Bah�a of C�diz and then it will be estimated welfare effects.
We incorporate information flow between a supplier and a customer in a two-echelon model that captures the capacitated setting of a typical supply chain. We consider three situations: (1) a traditional model where there is no information to the supplier prior to a demand to him except from past data; (2) the supplier has the information of the (s,S) policy used by the customer as well as the end-product demand distribution; and (3) the supplier has full information about the state of the customer. We show that order up-to policies continue to be optimal for models with information flow for the finite horizon, the infinite horizon discounted and the infinite horizon average cost cases. We develop solution procedures to compute the optimal parameters. Study of these three models enables us to understand the relationships between capacity, inventory and information at the supplier level and how they are affected by customer S Gamma s values and end-item demand distribution. We...
The paper examines the problem of forecasting ongoing factory orders and monitoring retail demand, with specific reference to high-technology consumer durables. We present evidence of the managerial importance of the problem and, using a case study of a computer peripheral manufacturer, we describe how different data sources and models can be used to increase prediction accuracy. First we examine the order placement and retail demand process using extrapolative methods that focus on identifying short- versus long-run movements in orders. We then introduce marketing-mix data for improved retail demand tracking and forecasting, and we propose the use of conjoint measurement data to simulate a product's utility over time and include that information in the demand model. Lastly, we describe the forecasting and marketing planning use of these models and discuss their implications. (C) 1998 John Wiley & Sons, Ltd.
The paper examines the problem of forecasting ongoing factory orders and monitoring retail demand, with specific reference to high-technology consumer durables. We present evidence of the managerial importance of the problem and, using a case study of a computer peripheral manufacturer, we describe how different data sources and models can be used to increase prediction accuracy. First we examine the order placement and retail demand process using extrapolative methods that focus on identifying short- versus long-run movements in orders. We then introduce marketing-mix data for improved retail demand tracking and forecasting, and we propose the use of conjoint measurement data to simulate a products utility over time and include that information in the demand model. Lastly, we describe the forecasting and marketing planning use of these models and discuss their implications.
This paper studies supply chain demand variability in a model with one supplier and N retailers that face stochastic demand. Retailers implement scheduled ordering policies: Orders occur at fixed intervals and are equal to some multiple of a fixed batch size. A method is presented that exactly evaluates costs. Previous research demonstrates that the supplier's demand variance declines as the retailers' order intervals are balanced, i.e., the same number of retailers order each period. This research shows that the supplier's demand variance will (generally) decline as the retailers' order interval is lengthened or as their batch size is increased. Lower supplier demand variance can certainly lead to lower inventory at the supplier. This paper finds that reducing supplier demand variance with scheduled ordering policies can also lower total supply chain costs.
An important phenomenon often observed in supply chain management, known as the bullwhip effect, implies that demand variability increases as one moves up the supply chain, i.e., as one moves away from customer demand. In this paper we quantify this effect for simple, two-stage, supply chains consisting of a single retailer and a single manufacturer. We demonstrate that the use of an exponential smoothing forecast by the retailer can cause the bullwhip effect and contrast these results with the increase in variability due to the use of a moving average forecast. We consider two types of demand processes, a correlated demand process and a demand process with a linear trend. We then discuss several important managerial insights that can be drawn from this research. © 2000 John Wiley & Sons, Inc. Naval Research Logistics 47: 269–286, 2000
Advances in information system technology have had a huge impact on the evolution of supply chain management. As a result of such technological advances, supply chain partners can now work in tight coordination to optimise the chain-wide performance, and the realised return may be shared among the partners. A basic enabler for tight coordination is information sharing, which has been greatly facilitated by the advances in information technology. This paper describes the types of information shared inventory, sales, demand forecast, order status, and production schedule. We discuss how and why this information is shared using industry examples and relating them to academic research. We also discuss three alternative system models of information sharing - the information transfer model, the third party model and the information hub model.
There are several ways for a manufacturer to cope with demand uncertainty, e.g. inventories, capacity and cash. Among these, this study focuses on the second one, the capacity, especially on the problem of investing in flexible facilities and enhancing their utilization via demand management. In a supply chain, demands that an upstream firm (supplier) faces are the purchase orders from the downstream members (buyers). We analyse the impacts of buyers' order batching on the supplier's demand correlation and capacity utilization in a simple branching supply chain, where a supplier does business with two buyers whose market demands are correlated. Our results show that: (i) a supplier whofacesa smaller demand correlation coefficient (i.e. closer to-1) would invest more in flexible facilities; (ii) an increase in order lot size mitigates the correlation of purchase orders; and (iii) a supplier whose facilities are flexible would prefer frequent orders with smaller lots only when market demands are highly negatively correlated. This means that even suppliers whose facilities are flexible would rather prefer infrequent orders with larger lots in the presence of positively correlated demands. Additionally, some managerial implications are discussed.
We examine the impact of point of sale (POS) data sharing on ordering decisions in a multi-echelon supply chain. In particular, we focus on how exposure to POS data may help reduce the “bullwhip effect,” the tendency of orders to increase in variability as one moves up a supply chain. Theoretical studies have shown that exposure to POS data can lead to a reduction in the bullwhip effect when suppliers have no prior knowledge of the demand distribution. The benefit of sharing POS data in stable industries, where the demand distribution is commonly known, is less clear. We study this phenomenon from a behavioral perspective in the context of a simple, serial, supply chain subject to information lags and stochastic demand. We find, using a controlled simulation experiment, that sharing POS information does help reduce some components of the bullwhip effect in a stable demand setting, namely the order oscillation of upstream members. We offer one possible explanation for this improvement by examining the relationship between order decisions and demand line information.
We use a simulation model called ‘SISCO’ to examine the effects in supply chains of stochastic lead times and of information sharing and quality of that information in a periodic order-up-to level inventory system. We test the accuracy of the simulation by verifying the results in Chen et al. (2000a) and Dejonckheere et al. (2004). We find that lead-time variability exacerbates variance amplification in a supply chain, and that information sharing and information quality are highly significant. For example, using the assumptions in Chen et al. (2000a) and Dejonckheere et al. (2004), we find in a numerical experiment of a customer-retailer-wholesaler-distributor-factory supply chain that variance amplification is attenuated by nearly 50 percent at the factory due to information sharing. Other assumptions we make are based on interviews or conversations with managers at large supply chains.
In traditional supply chain inventory management, orders are the only information firms exchange, but information technology now allows firms to share demand and inventory data quickly and inexpensively. We study the value of sharing these data in a model with one supplier, N identical retailers, and stationary stochastic consumer demand. There are inventory holding costs and back-order penalty costs. We compare a traditional information policy that does not use shared information with a full information policy that does exploit shared information. In a numerical study we find that supply chain costs are 2.2% lower on average with the full information policy than with the traditional information policy, and the maximum difference is 12.1%. We also develop a simulation-based lower bound over all feasible policies. The cost difference between the traditional information policy and the lower bound is an upper bound on the value of information sharing: In the same study, that difference is 3.4% on average, and no more than 13.8%. We contrast the value of information sharing with two other benefits of information technology, faster and cheaper order processing, which lead to shorter lead times and smaller batch sizes, respectively. In our sample, cutting lead times nearly in half reduces costs by 21% on average, and cutting batches in half reduces costs by 22% on average. For the settings we study, we conclude that implementing information technology to accelerate and smooth the physical flow of goods through a supply chain is significantly more valuable than using information technology to expand the flow of information.
We consider a simple supply chain in which a single supplier sells to several downstream retailers. The supplier has limited capacity, and retailers are privately informed of their optimal stocking levels. If retailer orders exceed available capacity, the supplier allocates capacity using a publicly known allocation mechanism, a mapping from retailer orders to capacity assignments. We show that a broad class of mechanisms are prone to manipulation: Retailers will order more than they need to gain a more favorable allocation. Another class of mechanisms induces the retailers to order exactly their needs, thereby revealing their private information. However, there does not exist a truth-inducing mechanism that maximizes total retailer profits.
We also consider the supplier's capacity choice. We show that a manipulable mechanism may lead the supplier to choose a higher level of capacity than she would under a truth-inducing mechanism. Nevertheless, her choice will appear excessively restrictive relative to the prevailing distribution of orders. Furthermore, switching to a truth-inducing mechanism can lower profits for the supplier, the supply chain, and even her retailers. Hence, truth-telling is not a universally desirable goal.
Consider a supplier selling to multiple retailers. Demand varies across periods, but the supplier's capacity and wholesale price are fixed. If demand is high, the retailers' needs exceed capacity, and the supplier must implement an allocation mechanism to dole out production. We examine how the choice of mechanism impacts retailer actions and supply chain performance. In particular, we analyze turn-and-earn allocation, a method commonly used in the automobile industry. This scheme bases current allocations on past sales and thus enables retailers to influence their future allocations; they compete for scarce capacity even if they do not compete for customers.
We show that turn-and-earn induces the retailers to increase their sales when demand is low and the supplier's capacity is otherwise underutilized. Supplier profits thus increase. The impact on the supply chain depends on how restrictive capacity is. With mildly tight capacity, the retailers' higher sales rate does not significantly lower their profits but does reduce the cost of idle capacity. Supply chain performance improves. With extremely tight capacity, the retailers' intense competition dissipates more profits than the supplier gains, and supply chain performance suffers. Consequently, turn-and-earn does not generally coordinate the system. It is best characterized as a means for the supplier to increase her profits at the expense of the retailers and potentially even the supply chain. Furthermore, these results hold even if the retailers can hold inventory in anticipation of scarce capacity.
A three-echelon Forrester production distribution system is used as a supply chain reference model for comparing various methods of improving total dynamic performance. Many authors have exploited the original simulation results for the nominal system, especially when describing problems associated with supply chain behaviour. However, few of these authors have attempted to produce a dynamically superior supply chain as distinct from offering detailed organisational and attitudinal changes needed to achieve any improvement. As the starting point of this paper, the production-distribution system has been transformed into a block diagram representation capable of considerable simplification. A combination of analysis and simulation can then be used to gain a far deeper understanding of the system dynamics than has so far been published. Thus, although the Forrester model is far from optimal, it does provide a well established benchmark against which proposals may be evaluated. For the purpose of illustration, five different approaches are then used to improve the supply chain dynamics. These are -“fine tuning” the existing ordering policy parameters;-reducing system delays;-removal of the distribution echelon;-changing the individual echelon decision rules;-better use of information flow throughout the supply chain.It is shown that by better utilisation of the information flow, significant reductions in the demand amplification can be achieved without substantial expenditure. This is because it is only necessary to separate out the flow of “real” orders from “system” orders as they are passed up the chain. Such collaboration does, however, correspond to the establishment of an integrated supply chain in which the concept of “total system stocks” is accepted.
Consider multiple companies operating as a serial supply chain. Within this environment, end users form the demand for the last company in the supply chain, but the demand for upstream companies is formed by the companies in the immediate downstream supply chain link. It has been shown that demand seasonality and forecast error can increase as we proceed up the supply chain. These demand distortions, called the “bullwhip” effect, create inefficiencies for upstream firms. This work seeks to identify the magnitude of the problem by establishing an empirical lower bound on the profitability impact of the bullwhip effect. Results indicate that the importance of the bullwhip effect to a firm differs greatly depending on the specific business environment. Given appropriate conditions, however, eliminating the bullwhip effect can increase product profitability by 10–30%.
Supply chain phenomena such as the bullwhip effect and boom and bust have been widely studied. However, their interaction with other factors has not been elaborated. We use scenario-based dynamic simulations to study the short product life cycle case, exemplified by TamagotchiTM, which was the first of the virtual pet toys. Our model has three components, market, retail and factory. To simulate the supply chain dynamics, all parts consist of scenarios based on the TamagotchiTM case and are integrated into a dynamic model. Our model should be helpful to decision makers and planners faced with similar short life cycle product introductions.
We study the information transformation by simulating a multi-stage supply chain when the end customer's demand is a general autoregressive integrated moving average (ARIMA) process, and the information, represented in the form of orders, is propagated from downstream to upstream in the supply chain. Our simulation results indicate several important and novel phenomena that need further theoretical analysis: (1) the anti-bullwhip effect and the transition from the regular bullwhip effect; (2) the trend of information transformation at higher stages of a supply chain; (3) the impact of lead-time on information transformation and the so-called ‘lead-time paradox’. In this paper, we will demonstrate these aspects via extensive computational experiments.
Advances in information system technology have had a huge impact on the evolution of supply chain management. As a result of such technological advances, supply chain partners can now work in tight coordination to optimize the chain-wide performance, and the realized return may be shared among the partners. A basic enabler for tight coordination is information sharing, which has been greatly facilitated by the advances in information technology. This paper describes the types of information shared: inventory, sales, demand forecast, order status, and production schedule. We discuss how and why this information is shared using industry examples and relating them to academic research. We also discuss three alternative system models of information sharing - the information transfer model, the third party model and the information hub model.
http://deepblue.lib.umich.edu/bitstream/2027.42/35663/2/b1738136.0001.001.pdf http://deepblue.lib.umich.edu/bitstream/2027.42/35663/1/b1738136.0001.001.txt
An important observation in supply chain management, known as the bullwhip effect, suggests that demand variability increases as one moves up a supply chain. In this paper we quantify this effect for simple, two-stage, supply chains consisting of a single retailer and a single manufacturer. Our model includes two of the factors commonly assumed to cause the bullwhip effect: demand forecasting and order lead times. We extend these results to multiple stage supply chains with and without centralized customer demand information and demonstrate that the bullwhip effect can be reduced, but not completely eliminated, by centralizing demand information.
The impact of information sharing on forecasting accuracy in a multy stage distribution system with stationary demand. Unpublished manuscript
- D Simchi-Levi
- Y Zhao
Simchi-Levi D, Zhao Y. The impact of information sharing on forecasting accuracy in a multy stage distribution system with stationary demand. Unpublished manuscript; 2003.
Value pricing at Stanford University
- M Kristofferson
Kristofferson M, Lal R. Value pricing at Procter & Gamble (A,B). Stanford University; 1996.
Modeling the supply chain. California, CA: Duxbury, Thomson Learning
- Jf Shapiro
Shapiro JF. Modeling the supply chain. California, CA: Duxbury, Thomson Learning; 2001.
The bullwhip effect in supply chains. Sloan Management Review
- H L Lee
- V Padmanabhan
- S Whang
Value pricing at Procter & Gamble (A,B)
- M Kristofferson
- R Lal
Value pricing at procter &
- M Kristofferson
- R Lal
The impact of exponential smoothing forecasts on the bullwhip effect
- Chen