The Experts below are selected from a list of 41793 Experts worldwide ranked by ideXlab platform
Seungjin Whang - One of the best experts on this subject based on the ideXlab platform.
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information distortion in a supply chain the bullwhip effect
Management Science, 1997Co-Authors: Hau L Lee, Vineet Padmanabhan, Seungjin WhangAbstract:(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 "orders" 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 upstream-a phenomenon termed "bullwhip effect." 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.
Massimo Tronci - One of the best experts on this subject based on the ideXlab platform.
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spc forecasting system to mitigate the bullwhip effect and inventory variance in supply chains
Expert Systems With Applications, 2015Co-Authors: Francesco Costantino, Ahmed Shaban, Giulio Di Gravio, Massimo TronciAbstract:We study the impact of forecasting on the bullwhip effect and inventory variance.A forecasting system (SPC-FS) based on control charts is presented and evaluated.Simulation is adopted to evaluate SPC-FS in a supply chain employs OUT policy.SPC-FS leads to lower bullwhip effect and inventory variance than MA and ES.SPC-FS has less sensitivity to lead-time compared to the other methods. 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.
Zongbao Zou - One of the best experts on this subject based on the ideXlab platform.
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better Demand Signal better decisions evaluation of big data in a licensed remanufacturing supply chain with environmental risk considerations
Risk Analysis, 2017Co-Authors: Baozhuang Niu, Zongbao ZouAbstract:Big data ability helps obtain more accurate Demand Signal. However, is better Demand Signal always beneficial for the supply chain parties? To answer this question, we investigate a remanufacturing supply chain (RSC), where Demand uncertainty is significant, and the value to reduce environmental risk is large. Specifically, we focus on a licensed RSC comprising an original equipment manufacturer (OEM) and a third‐party remanufacturer (3PR). The latter pays a unit license fee to the former, and can be risk averse to the Demand of remanufactured products. We show that the OEM and the risk‐neutral 3PR always have incentives to improve their big data abilities to increase their profits. However, when the 3PR is risk averse, big data might hurt its profit: the value of big data is positive if its Demand Signal accuracy is sufficiently low. Interestingly, we find that while information sharing hurts the 3PR, it benefits the OEM as well as the supply chain. Thus, if costly information sharing is allowed, a win–win situation can be achieved. We also find that information sharing generates more valuation when the 3PR is risk averse than that when the 3PR is risk neutral. More importantly, we find that the 3PR's risk attitude and Demand Signal accuracy can significantly mitigate the negative environmental impact (measured by the amount of the waste): (1) the more risk neutral the 3PR is, the better the environment is; (2) the more accurate Demand Signal is, the better the environment is.
Hau L Lee - One of the best experts on this subject based on the ideXlab platform.
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information distortion in a supply chain the bullwhip effect
Management Science, 1997Co-Authors: Hau L Lee, Vineet Padmanabhan, Seungjin WhangAbstract:(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 "orders" 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 upstream-a phenomenon termed "bullwhip effect." 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.
Francesco Costantino - One of the best experts on this subject based on the ideXlab platform.
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spc forecasting system to mitigate the bullwhip effect and inventory variance in supply chains
Expert Systems With Applications, 2015Co-Authors: Francesco Costantino, Ahmed Shaban, Giulio Di Gravio, Massimo TronciAbstract:We study the impact of forecasting on the bullwhip effect and inventory variance.A forecasting system (SPC-FS) based on control charts is presented and evaluated.Simulation is adopted to evaluate SPC-FS in a supply chain employs OUT policy.SPC-FS leads to lower bullwhip effect and inventory variance than MA and ES.SPC-FS has less sensitivity to lead-time compared to the other methods. 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.