Does the ripple effect influence the bullwhip effect? An integrated analysis of structural and operational dynamics in the supply chain†

The ripple effect refers to structural dynamics and describes a downstream propagation of the downscaling in demand fulfilment in the supply chain (SC) as a result of a severe disruption. The bullwhip effect refers to operational dynamics and amplifies in the upstream direction as ordering oscillations. Being interested in uncovering if the ripple effect can be a driver of the bullwhip effect, we performed a simulation-based study to investigate the interrelations of the structural and operational dynamics in the SC. The results advance our knowledge about both ripple and bullwhip effects and reveal, for the first time, that the ripple effect can be a bullwhip-effect driver, while the latter can be launched by a severe disruption even in the downstream direction. The findings show that the ripple effect influences the bullwhip effect through backlog accumulation over the disruption time as a consequence of non-coordinated ordering and production planning policies. To cope with this effect, a contingent production-inventory control policy is proposed that provides results in favour of information coordination in SC disruption management to mitigate both ripple and bullwhip effects. The SC managers need to take into account the risk of bullwhip effect during the capacity disruption and recovery periods.     

Serial vs. divergent supply chain networks: a comparative analysis of the bullwhip effect

The amplification of demand variation in a supply chain network (SCN) is a well-known phenomenon called the bullwhip effect, which creates inefficiencies due to high variation in the order quantities placed between companies, leading to a flow of a larger number of units than the actual need, increasing stock and generating stock-outs. Since this phenomenon has been recognised as one of the main obstacles for improving SCN performance, recently it has received a lot of attention by SCN managers and researchers. One of the most common simplifying assumptions in the literature is to assume that the SCN adopts a serial structure. The present work addresses a comparative analysis of the bullwhip effect between a serial SCN and a more complex divergent SCN. To do so, we analyse the response of both SCNs under two different input demands: a stationary demand and an impulse demand. The results reveal that there are not significant differences in terms of bullwhip effect between both SCNs for a stationary demand. Nevertheless, we show how for a violent disturbance in customer demand there is a great different between the two SCNs.     

The bullwhip effect in supply chains: a study of contingent and incomplete literature

The bullwhip effect (BWE) describes a phenomenon that involves the increasing amplification of demand variability along a supply chain (SC). The BWE has been a subject that has received continuous attention from researchers over the past 15 years and is a concern for SC managers because it is a major cause of efficiency and effectiveness loss in SCs. Information sharing between actors in an SC is usually considered to be one of the primary means to minimise the BWE. Approximately 50 articles published in major journals on these topics are studied in this article. An analytical framework is used to highlight the contingent character of the conclusions proposed by the authors. In this review, we identify the existence of significant gaps in the literature, especially concerning the BWE when it occurs in the productive part of the SC.     

The bullwhip effect on product orders and inventory: a perspective of demand forecasting techniques

Demand forecasting is one of the key causes of the bullwhip effect on product orders. Although this aspect of order oscillation is not ignored, the current study focuses on another critical aspect of oscillation: the bullwhip effect on inventory, i.e. the net inventory variance amplification. In particular, this paper studies a two-level supply chain in which the demand is price sensitive, while the price follows a first-order autoregressive pricing process. We derive the analytical expressions of the bullwhip effect on product orders and inventory using minimum mean-squared error, moving average and exponential smoothing forecasting techniques. We also propose the conditions under which the three forecasting techniques would be chosen by the retailer to minimise the sum of the bullwhip effect on product orders and inventory under different weightings. These observations are used to develop managerial insights regarding choosing an appropriate forecasting technique after considering certain distinct characteristics of the product.     

Revisiting rescheduling: MRP nervousness and the bullwhip effect

We study the material requirements planning (MRP) system nervousness problem from a dynamic, stochastic and economic perspective in a two-echelon supply chain under first-order auto-regressive demand. MRP nervousness is an effect where the future order forecasts, given to suppliers so that they may plan production and organise their affairs, exhibits extreme period-to-period variability. We develop a measure of nervousness that weights future forecast errors geometrically over time. Near-term forecast errors are weighted higher than distant forecast errors. Focusing on replenishment policies for high volume items, we investigate two methods of generating order call-offs and two methods of creating order forecasts. For order call-offs, we consider the traditional order-up-to (OUT) policy and the proportional OUT policy (POUT). For order forecasts, we study both minimum mean square error (MMSE) forecasts of the demand process and MMSE forecasts coupled with a procedure that accounts for the known future influence of the POUT policy. We show that when retailers use the POUT policy and account for its predictable future behaviour, they can reduce the bullwhip effect, supply chain inventory costs and the manufacturer’s MRP nervousness.     

Using simulation-based system dynamics and genetic algorithms to reduce the cash flow bullwhip in the supply chain

The bullwhip effect (BWE) is a phenomenon, which is caused by ineffective inventory decisions made by supply chain members. In addition to known inefficiencies caused by the bullwhip effect within a supply chain product flow, such as excessive inventory, it can also lead to inefficiencies in cash flow such as the cash flow bullwhip (CFB). The CFB reduces the efficiency of the supply chain (SC) through heterogeneous distribution of cash among supply chain members. This paper aims to decrease both the BWE and the CFB across a SC through applying a simulation-based optimisation approach, which integrates system dynamics (SD) simulation and genetic algorithms. For this purpose, cash flow modelling is incorporated into the SD structure of the beer distribution game (BG) to develop the CFB function. A multi objective optimisation model is then integrated with the SD-BG simulation model. Finally, a genetic algorithm (GA) is applied to determine the optimal values for the inventory, supply line, and financial decision parameters. Results show that the proposed integrated framework leads to efficient liquidity management in the SC in addition to cost management.     

Flexibility dimensions to control the bullwhip effect in a supply chain

In this paper, we analyse the consequences of two flexibility dimensions proposed in a previous approach—adjustment capability and responsiveness—on the bullwhip effect in a supply-chain model when a stochastic AR(1) demand process is considered. First modelling the manager's belief on forecasting in pull, push and hybrid ordering methods, it is revealed that high adjustment capability induces a robust reduction of the bullwhip effect. Secondly, it is found that maximal responsiveness is not always a necessary management strategy. Indeed, we show that these dimensions may be organised in a trade-off, always keeping amplification to acceptable values in the whole supply chain.     

Measure of the bullwhip effect considering the market competition between two retailers

In order to consider the market competition, a new supply chain with one supplier and two retailers is established in this paper. Two retailers employ different AR(1) demand processes, respectively, and an order-up-to inventory policy characterises the inventory decisions. The bullwhip effect in this supply chain is measured under the moving average forecasting technique. We investigate the effects of the lead time, the span of forecast, market competition and the consistency of demand volatility on the bullwhip effect using the algebraic analysis and numerical simulation. Conclusions indicate that different factors lead to the bullwhip effect following different patterns in the supply chain. Moreover, some suggestions are present to help managers to control parameters that yield the lower bullwhip effect.     

A study of the elongated replenishment frequency of TOC supply chain replenishment systems in plants

The TOC (theory of constraint) supply chain replenishment system (TOC-SCRS), a replenishment method of the TOC supply chain solution, is now being implemented by a growing number of companies. In the application of the TOC-SCRS in a node of a supply chain, the replenishment frequency (RF) and replenishment lead time are the required parameters. Generally, the RF of a node depends on the public transportation schedule such as ship schedules or its private conveyor schedule. If this node is a plant, however, the RF depends on the setup frequency in this plant. Basically, the RF in a plant is determined by its sales or production quantity; when sales significantly increase, the RF in a plant needs to be elongated from a higher frequency (i.e. once a day) to a lower frequency (i.e. once every two or more days) due to the limited capacity. However, during the RF conversion periods, some products will be confronted with an inventory shortage problem. An evaluation and enhancement model is then proposed to release the inventory shortage occurrence during the RF conversion periods. A numeric case is also utilised to evaluate the application of the proposed model. Employing this proposed methodology will facilitate a plant to successfully implement an effective TOC-SCRS.     

The internal bullwhip effect in car manufacturing

The paper describes variance amplification of orders in a car manufacturing context. Demand fluctuation in inter-company supply chains has been well described in literature over many years. The specific contribution of this paper is to examine the cyclical boom-and-bust behaviour, commonly known as the bullwhip effect, inside the plant boundaries of a single company with the help of system dynamics modelling. To gain insight into this phenomenon we will first perform an extensive literature review on the subject of factory relevant internal bullwhip. Based on these results we illustrate through a real-life car assembly operation the existence of internal bullwhip. Finally we conclude with a discussion of our results and future research opportunities.     

The bullwhip effect on inventory under different information sharing settings based on price-sensitive demand

Information sharing (IS) is proved to be a valid method to counter demand variability amplification along the supply chain, or bullwhip effect (BWE). Different from the traditional way of measuring the BWE based on order quantity, we measure the BWE on inventory in different IS settings and try to find the best IS approach. In this paper, the retailer will face the market demand which is price-sensitive, and the price follows a first-order autoregressive process. This demand model includes some indexes that can provide more useful managerial insights than previously studied parameters. Our study identifies the best IS setting under any conditions, and clarifies that the benefits of IS will be evident when the overall market product pricing process is highly correlated over time, the demand shocks to the retailer are high, the price sensitivity coefficient is small, the overall market shocks are low, the retailer’s lead-time is long and the manufacturer’s lead-time is short.     

The bullwhip effect under different information-sharing settings: a perspective on price-sensitive demand that incorporates price dynamics

Information sharing has been shown previously in the literature to be effective in reducing the magnitude of the bullwhip effect. Most of these studies have focused on a particular information-sharing setting that assumes demand follows an autoregressive process. In this paper, we contribute to the literature by presenting a price-sensitive demand model and a first-order autoregressive pricing process that is coupled to the optimal order-up-to inventory policy and the optimal minimum mean-squared error forecasting technique. We compare a no information-sharing setting – in which only the first stage of the supply chain observes end-customer demands and market prices, and upstream echelons must base their forecasts on downstream incoming orders – with two information-sharing settings, end-demand and order information and end-demand information. In the case of end-demand and order information, upstream echelons develop their forecasts and plan their inventories based on the end-customer demand, price information, and downstream orders. With end-demand information, upstream echelons use only end-customer demands and market prices to conduct their forecasting and planning. We derive the analytical expressions of the bullwhip effect with and without information sharing, quantify the impact of information sharing on the reduction of the bullwhip effect associated with end-demand and order information and end-demand information, and explore the optimal information setting that could most significantly restrain the bullwhip effect. Our analysis suggests that the value of these two information-sharing settings can be high, especially when the pricing process is highly correlated over time or when the product price sensitivity coefficient is small. Moreover, we find that the value of adopting end-demand and order information is always greater than that of end-demand information.     

Bullwhip and anti-bullwhip effects in a supply chain

The anti-bullwhip effect, coined by Li et al. in 2005 based on the findings in a simulation study, is the contrary effect of the well-known bullwhip effect. Although there is ample empirical evidence that suggests both effects exist, current literature has not yet provided an integrated framework to address how the two effects are related with each other. By extending the classic work of Lee, Padmanabhan, and Whang of 1997 to a multi-stage supply chain, we derive closed-form formula to analytically describe how the two effects originate initially and then evolve over time and space in the supply chain. Our results show both the bullwhip effect and the anti-bullwhip effect can occur when facing different end-customer demands. However, the magnitude of these effects gradually decreases when moving upstream. We also show the impact of long lead-time on increasing the magnitude of information transformation at the next stage and simultaneously decreasing the magnitude at higher stages. These analytical results provide a theoretical explanation to most simulation findings of Li et al.'s 2005 work and can be used by researchers and practitioners to examine the empirical data and design innovate marketing strategies to convert the unwelcome bullwhip effect into the anti-bullwhip effect.     

Applying machine learning to the dynamic selection of replenishment policies in fast-changing supply chain environments

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

Exploring nonlinear supply chains: the dynamics of capacity constraints

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

Simulation-based ripple effect modelling in the supply chain

In light of low-frequency/high-impact disruptions, the ripple effect has recently been introduced into academic literature on supply chain management. The ripple effect in the supply chain results from disruption propagation from the initial disruption point to the supply, production and distribution networks. While optimisation modelling dominates this research field, the potential of simulation modelling still remains under-explored. The objective of this study is to reveal research gaps that can be closed with the help of simulation modelling. First, recent literature on both optimisation and simulation modelling is analysed. Second, a simulation model for multi-stage supply chain design with consideration of capacity disruptions and experimental results is presented in order to depict major areas of simulation application to the ripple effect modelling. Based on both literature analysis and the modelling example, managerial insights and future research areas are identified in regard to simulation modelling application to the ripple effect analysis in the supply chain. The paper concludes by summarising the most important insights and outlining a future research agenda.     

Bullwhip effect and complexity analysis in a multi-channel supply chain considering price game with discount sensitivity

This paper constructs a multi-channel supply chain that includes a manufacturer, a dual-channel retailer and an online retailer. The dual-channel retailer has a traditional channel and an online channel, while the manufacturer cooperates with the retailers and opens a direct channel simultaneously. The horizontal and historical price discount sensitivities are taken into account to establish the price-sensitive demand functions. A price game model with the heterogeneous expectations is proposed and analysed with the methods of the stability domain, the bifurcation diagram and the maximum Lyapunov exponent. The complexity and bullwhip effect of each channel are investigated with respect to the price adjustment speed and historical price discount sensitivity. The results show that a moderate or low price discount sensitivity keeps the system stable and a high price discount sensitivity brings the system into the twofold cycle state or chaotic state. It is interesting that if both online channels increase the adjustment speeds with similar strategy the system will enter chaos through the Neimark–Sacker bifurcation. The bullwhip effect is affected by the price adjustment speed and the price discount sensitivity, especially in the double-period or chaotic state, where the bullwhip of the overall supply chain is increased significantly. The results show that controlling price discount sensitivity is useful for supply chain node companies.     

Risks and performance in supply chain: the push effect

A risk, when it occurs, causes negative effects on outputs. Typically risks are not independent, as multiple risks occur simultaneously. These risks have links, creating a ‘push’ effect, thus increasing the severity of each and all risk(s) on outputs. This paper aims to verify the mechanism of the push effect that is a new approach in the supply chain risk management literature. In this study, two models were compared: (1) only exists in direct effects of risks on supply chain performance, i.e. the competitive model. The other, (2), contains relationships among risks that show the mechanism of the push effect, i.e. the hypothesised model. Empirical evidence found in the Vietnam construction sector proved that the hypothesised model is better suited and has greater effect on supply chain performance in terms of each and all risk(s). Comparing 55% variance of the competitive model, the hypothesised one can explain up to 73% variance of supply chain performance. These results confirm our hypotheses of the push effect. Furthermore, findings achieved from this research can be used as ‘a guideline’ for reducing the impact of this mechanism.     

On measuring company performance within a supply chain

Emerging research strengthens the connection between supply chain performance and a company's financial performance (D’Avanzo, R., Von Lewinski, H. and Van Wassenhove, L. N., 2003. The link between supply chain and financial performance. Supply Chain Management Review, November/December, 40–47). The focus on integrating functional internal processes has expanded to include the need for integrating these with external processes of business partners (Edwards, P., Peters, M. and Sharman, G., 2001. The effectiveness of information systems in supporting the extended supply chain. Journal of Business Logistics, 22(1), 1–27). This need for enterprise efficiency is compelling companies to review, to identify, and to adopt supply chain initiatives. This research investigates the use of common measurement metrics in an attempt to determine which one(s) are most useful for measuring performance as companies implement SCM practices. For firms that were engaged in SCM we found inventory and cycle time to be the most significant metrics.     

Effective bullwhip metrics for multi-echelon distribution systems under order batching policies with cyclic demand

A large number of problems in a distribution supply chain require that decisions are made in the presence of the bullwhip effect phenomenon. The impact of the order batching policies on the bullwhip effect is analysed in this paper, when cycle demand on a multi-echelon supply chain operating is considered. While investigating which bullwhip effect metrics are more adequate to measure the bullwhip effect in these type of systems, the optimal reordering plan that minimises the operation costs of the overall system is calculated. A Mixed Integer Linear Programming (MILP) model is developed that takes into account an inventory and distribution system formed by multiple warehouses and retailers with lateral transshipments. The bullwhip effect is measured through four metrics: the echelon average inventory; the echelon inventory variance ratio; the echelon average order; and the echelon order rate variance ratio. As conclusion the inventory metrics suggest that (i) using batching policy reduces instability; (ii) batching may reduce in general order variance if using larger batches and (iii) cycle demand length has no major impact in the bullwhip effect. A motivational example and a real word case study are used and tested.