Reducing the bullwhip effect in supply chains with control-based forecasting
Ricki G. Ingalls, Bobbie L. Foote, Ananth Krishnamoorthy
Abstract
Ricki G. Ingalls, Bobbie L. Foote, Ananth Krishnamoorthy
Abstract
The bullwhip effect in supply chains occurs when the variance in the demand forecast magnifies itself as it moves through the supply chain from the distributors to the material suppliers. It is understood that demand forecast variance contributes to the bullwhip effect in the supply chain. With this understanding, the authors experimented with a forecasting technique called control-based forecasting to see if the implementation of control-based forecasting could reduce the bullwhip effect. Through the use of a simulation, the authors show that using control-based forecasting techniques can drastically reduce the bullwhip effect, and thus the demand variance and production variance, for all of the participants in a supply chain. The authors also show that, in a simple two-tier supply chain, the cause of the bullwhip effect in supply chains is not related to demand variance, lead-time variance or inventory policy. It is because the demand forecast is constantly changing, which causes changes in inventory policy and order quantities.
OpenAlex reports 21 citations for this work. Citation counts describe recorded attention and do not establish research quality.
A contribution statement is not available in the OpenAlex record.
Method details are not available in the OpenAlex metadata.
Findings are not separately available in the OpenAlex metadata.
Limitations are not available in the OpenAlex metadata.
Application details are not available in the OpenAlex metadata.
The bullwhip effect in supply chains occurs when the variance in the demand forecast magnifies itself as it moves through the supply chain from the distributors to the material suppliers. It is understood that demand forecast variance contributes to the bullwhip effect in the supply chain. With this understanding, the authors experimented with a forecasting technique called control-based forecasting to see if the implementation of control-based forecasting could reduce the bullwhip effect. Through the use of a simulation, the authors show that using control-based forecasting techniques can drastically reduce the bullwhip effect, and thus the demand variance and production variance, for all of the participants in a supply chain. The authors also show that, in a simple two-tier supply chain, the cause of the bullwhip effect in supply chains is not related to demand variance, lead-time variance or inventory policy. It is because the demand forecast is constantly changing, which causes changes in inventory policy and order quantities.
Key concepts: Bullwhip effect, Supply chain, Variance (accounting), Demand forecasting, Inventory control, Supply chain management, Economics, Econometrics