A Bullwhip Type of Instability Induced by Time Varying Target Inventory in Production Chains (original) (raw)
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Reverse bullwhip effect: duality of a dynamic model of Supply Chain
Independent Journal of Management & Production
This study aims to investigate control strategies for the bullwhip effect based on a dynamic model of the linear supply chain, proposed by Helbing and Lammer (2005), which describes the inventory dynamics and production rates of productive units. We simulated the model for instability and stability conditions defined by mathematical analysis. Through these results, we verified both classical and reverse bullwhip effects associated with instability and stability conditions, respectively. The model revealed a duality once the control strategy proposed by Helbing and Lamer (2005) for the classical bullwhip effect ends up causing a reverse effect, which is equally troubling. In the reverse bullwhip effect, we observed amplification of the production rates in the network chains from the supplier to the customer in a way that the upstream chain was not able to meet the needs of the downstream chain. To withhold both effects, we suggest the dynamic control of the parameters that describe t...
The control of local stability and of the bullwhip effect in a supply chain
The bullwhip effect refers to the phenomenon of demand distortion in a supply chain. By eliminating or controlling this effect, it is possible to increase product profitability. The main focus of this work is to apply a recent developed control technique to reduce the bullwhip effect in a single-product serial supply chain in which an Order-Up-To order policy is applied. The control strategy is based on controlling the divergence of the supply chain considered as a non-linear dynamical system. First, the relationships between bullwhip, stability of the supply chain and total costs is studied, then, the divergence based control strategy is applied to stabilize the supply chain dynamic with a consequent reduction of the total costs and bullwhip effect.
Stability Control in a Supply Chain: Total Costs and Bullwhip Effect Reduction
The Open Operational Research Journal, 2008
The bullwhip effect refers to the phenomenon of demand distortion in a supply chain. By eliminating or controlling this effect, it is possible to increase product profitability. The main focus of this work is to apply a control technique, based on the divergence of system, to reduce the bullwhip effect in a single-product one echelon supply chain, in which an Order-Up-To (OUT) order policy is applied. First the relationships between bullwhip, stability of the supply chain and the total costs are analyzed. Second the divergence-based control strategy is applied to stabilize the supply chain dynamics with a considerable reduction of the total costs (> 30%) and, in relevant cases, of the bullwhip effect.
2006
Robust, multi-echelon dynamical models are proposed for better understanding of the bullwhip effect in supply chains and for testing of strategies that mitigate it. Enterprise-wide visibility through IT and Extranet data access between trading partners and is one such strategy. Other strategies include ordering policies that do not entail the immediate replacement of used safety stocks, expanded workweek to absorb the surges in production demand. Still other strategies are possible, such as adding additional supply lines for upstream supplies. The models presented build upon existing state-of-the-art models in system dynamics as presented in existing system dynamics literature. 1.0 INTRODUCTION Supply Chains are complex physical systems that behave badly when typical managerial practices are applied to them. For example, quantity discounts, promotional pricing, and media blitzes are examples of marketing ploys that raise havoc with the supply chain. Supply chains are the entire ente...
Exploring the Bullwhip Effect and Inventory Stability in a Seasonal Supply Chain
International Journal of Engineering Business Management, 2013
""The bullwhip effect is defined as the distortion of demand information as one moves upstream in the supply chain, causing severe inefficiencies in the whole supply chain. Although extensive research has been conducted to study the causes of the bullwhip effect and seek mitigation solutions with respect to several demand processes, less attention has been devoted to the impact of seasonal demand in multi‐echelon supply chains. This paper considers a simulation approach to study the effect of demand seasonality on the bullwhip effect and inventory stability in a four‐echelon supply chain that adopts a base stock ordering policy with a moving average method. The results show that high seasonality levels reduce the bullwhip effect ratio, inventory variance ratio, and average fill rate to a great extent; especially when the demand noise is low. In contrast, all the performance measures become less sensitive to the seasonality level when the noise is high. This performance indicates that using the ratios to measure seasonal supply chain dynamics is misleading, and that it is better to directly use the variance (without dividing by the demand variance) as the estimates for the bullwhip effect and inventory performance. The results also show that the supply chain performances are highly sensitive to forecasting and safety stock parameters, regardless of the seasonality level. Furthermore, the impact of information sharing quantification shows that all the performance measures are improved regardless of demand seasonality. With information sharing, the bullwhip effect and inventory variance ratios are consistent with average fill rate results.""
Exploring bullwhip effect and inventory stability in a seasonal supply chain
2013
The bullwhip effect is defined as the distortion of demand information as one moves upstream in the supply chain, causing severe inefficiencies in the whole supply chain. Although extensive research has been conducted to study the causes of the bullwhip effect and seek mitigation solutions with respect to several demand processes, less attention has been devoted to the impact of seasonal demand in multi-echelon supply chains. This paper considers a simulation approach to study the effect of demand seasonality on the bullwhip effect and inventory stability in a four-echelon supply chain that adopts a base stock ordering policy with a moving average method. The results show that high seasonality levels reduce the bullwhip effect ratio, inventory variance ratio, and average fill rate to a great extent; especially when the demand noise is low. In contrast, all the performance measures become less sensitive to the seasonality level when the noise is high. This performance indicates that using the ratios to measure seasonal supply chain dynamics is misleading, and that it is better to directly use the variance (without dividing by the demand variance) as the estimates for the bullwhip effect and inventory performance. The results also show that the supply chain performances are highly sensitive to forecasting and safety stock parameters, regardless of the seasonality level. Furthermore, the impact of information sharing quantification shows that all the performance measures are improved regardless of demand seasonality. With information sharing, the bullwhip effect and inventory variance ratios are consistent with average fill rate results.
SPC-based Inventory Control Policy to Im-prove Supply Chain Dynamics
2014
Inventory control policies have been recognized as a contributory factor to the bullwhip effect and inventory instability. Previous studies have indicated that there is a trade-off between bullwhip effect and inventory performance where the bullwhip effect reduction might increase inventory instability. Therefore, there is a need for inventory control policies that can cope with supply chain dynamics. This paper proposes an inventory control policy based on a statistical process control approach (SPC) to handle supply chain dynamics. The policy relies on applying individual control charts to control both the inventory position and the placed orders adequately. A simulation study has been conducted to evaluate and compare the proposed SPC policy with a traditional order-up-to in a multi-echelon supply chain. The comparison showed that the SPC policy outperforms the order-up-to in terms of bullwhip effect and inventory performances. The SPC succeeded to eliminate the bullwhip effect whilst keeping a competitive inventory performance. massimo.tronci @uniroma1.it Keyword-Supply Chain, Inventory Control, SPC, Control Chart, Bullwhip Effect, Inventory Variance, Simulation I. INTRODUCTION In supply chains, the variability in the ordering patterns often increases as demand information moves upstream in the supply chain, from the retailer towards the factory and the suppliers. This phenomenon of information distortion has been recognized as the bullwhip effect [1]. Fig. 1 depicts an example of the bullwhip effect in which the orders placed by four supply chain echelons over the same 100 periods are plotted side-by-side. The bullwhip effect has been observed in many industries such as Campbell Soup's [2], HP and Proctor & Gamble [1], fast moving consumer goods [3], and car manufacturing [4]
The effect of information sharing on supply chain stability and the bullwhip effect
European Journal of Operational Research, 2007
This paper analyzes the bullwhip effect in multi-stage supply chains operated with linear and time-invariant inventory management policies and shared supply chain information. Such information includes past order sequences and inventory records at all supplier stages. The paper characterizes the stream of orders placed at any stage of the chain when the customer demand process is known and ergodic, and gives an exact formula for the variance of the orders placed. The paper also derives robust analytical conditions, based only on inventory management policies, to predict the presence of the bullwhip effect and bound its magnitude. These results hold independently of the customer demand. The general framework proposed in this paper allows for any inventory replenishment policies, any ways of sharing and utilizing information, and any customer demand processes. It is also shown as a special case that sharing customer demand information across the chain significantly reduces, but does not completely eliminate, the bullwhip effect.
Transfer Function Models of Inventory Policies and Bullwhip Quantification in Supply Chain
Procedia Technology, 2016
Control theory approach to the supply chain is an analytical method to quantify the bullwhip effect. In this study, using control theory approach the bullwhip effect inducing nature of various inventory policies is analyzed. The inventory policies considered are forecast based, forecast + order smoothing, Order-Up-To (OUT), OUT + net stock smoothing + on-order inventory smoothing, and OUT + net stock smoothing + on-order inventory smoothing + order smoothing. The performance measures considered are order rate variance ratio and bullwhip slope. The forecast based policies cause the de-whip effect in the supply chain. The OUT policy and its variants cause bullwhip generation in the supply chain. It is found that the order variance amplification is more under OUT policy than its variants.
On the Bullwhip Avoidance Phase: The Synchronised Supply Chain
European Journal of Operational Research, 2012
The aim of this paper is to analyse the operational response of a Synchronised Supply Chain (SSC). To do so, first a new mathematical model of a SSC is presented. An exhaustive Latin Square design of experiments is adopted in order to perform a boundary variation analysis of the main three parameters of the periodic review smoothing (S, R) order-up-to policy: i.e., lead time, demand smoothing forecasting factor, and proportional controller of the replenishment rule. The model is then evaluated under a variety of performance measures based on internal process benefits and customer benefits. The main results of the analysis are: (I) SSC responds to violent changes in demand by resolving bullwhip effect and by creating stability in inventories under different parameter settings and (II) in a SSC, long productiondistribution lead times could significantly affect customer service level. Both results have important consequences for the design and operation of supply chains.