Exploring the Bullwhip Effect by Means of Spreadsheet Simulation (original) (raw)
Related papers
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...
A SIMULATION STUDY ON THE BULLWHIP EFFECT IN SUPPLY CHAIN
Under the competition among the global market place, enterprises must have skills of dealing with uncertainty. It is known that the lack of information sharing causes the uncertainty. The order variability increase as we move up the supply chain. In this study the bullwhip effect in supply chain is studied. The increase in inventory costs under the bullwhip effect is examined using simulation method. The effect of choosing the right forecasting technique for the demand pattern is taken into account to show its impact on the bullwhip effect.
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.
A n 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.
PROMET - Traffic&Transportation, 1970
The research of the Bullwhip effect has given rise to many papers, aimed at both analysing its causes and correcting it by means of various management strategies because it has been considered as one of the critical problems in a supply chain. This study is dealing with one of its principal causes, demand forecasting. Using different simulated demand patterns, alternative forecasting methods are proposed, that can reduce the Bullwhip effect in a supply chain in comparison to the traditional forecasting techniques (moving average, simple exponential smoothing, and ARMA processes). Our main findings show that kernel regression is a good alternative in order to improve important features in the supply chain, such as the Bullwhip, NSAmp, and FillRate.
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]
SPC forecasting system to mitigate the bullwhip effect and inventory variance in supply chains
Demand signal processing contributes significantly to the bullwhip effect and inventory instability in supply chains. Most previous studies have been attempting to evaluate the impact of available traditional forecasting methods on the bullwhip effect. Recently, some researchers have employed SPC control charts for developing forecasting and inventory control systems that can regulate the reaction to short-run fluctuations in demand. This paper evaluates a SPC forecasting system denoted as SPC-FS that utilizes a control chart approach integrated with a set of simple decision rules to counteract the bullwhip effect whilst keeping a competitive inventory performance. The performance of SPC-FS is evaluated and compared with moving average and exponential smoothing in a four-echelon supply chain employs the order-up-to (OUT) inventory policy, through a simulation study. The results show that SPC-FS is superior to the other traditional forecasting methods in terms of bullwhip effect and inventory variance under different operational settings. The results confirm the previous researches that the moving average achieves a lower bullwhip effect than the exponential smoothing, and we further extend this conclusion to the inventory variance.