This paper studies that the bullwhip effect of order releases and the amplifications of safety stock arise within the supply chain even when the demand model is ARIMA(0, 1, 1) and the forecast method used is a simple ...This paper studies that the bullwhip effect of order releases and the amplifications of safety stock arise within the supply chain even when the demand model is ARIMA(0, 1, 1) and the forecast method used is a simple exponentially weighted moving average. It also examines a vendor managed inventory (VMI) program to determine how it can help alleviate such negative effects, and gives the theoretical proofs and numerical illustrations. The results show that the effects with VMI are better than the effect without VMI in demand forecasting and safety stock levels, etc.展开更多
Conflicts between supply chain members emerge because individual strategic actions may not be jointly optimal.Efforts to forecast consumer demand represent a source of conflict.The coordination of forecasts requires a...Conflicts between supply chain members emerge because individual strategic actions may not be jointly optimal.Efforts to forecast consumer demand represent a source of conflict.The coordination of forecasts requires a powerful incentive alignment approach.This work proposes a smart contract equipped consortium blockchain system that creates an incentive structure that makes coordination with respect to forecasts economically appealing.Distortions of demand information due to uncoordinated forecasting are captured by a bullwhip measure that factors both forecast error and variance.Cooperation under the system is shown to help minimize this bullwhip measure,thus generating new outcomes for the participants that allow for a higher reward.Under a fixed payout structure,the system achieves credibility of continued cooperation,thus promoting an optimally coordinated equilibrium between the retailer and supplier.Blockchain technology represents a novel information system and consensus formation mechanism that can intermediate the behavior of a supply chain network.展开更多
Based on the two-level supply chain composed of suppliers and retailers, we assume that market demand is subject to an ARIMA(1, 1, 1). The supplier uses the minimum mean square error method (MMSE), the simple moving a...Based on the two-level supply chain composed of suppliers and retailers, we assume that market demand is subject to an ARIMA(1, 1, 1). The supplier uses the minimum mean square error method (MMSE), the simple moving average method (SMA) and the weighted moving average method (WMA) respectively to forecast the market demand. According to the statistical properties of stationary time series, we calculate the mean square error between supplier forecast demand and market demand. Through the simulation, we compare the forecasting effects of the three methods and analyse the influence of the lead-time L and the moving average parameter N on prediction. The results show that the forecasting effect of the MMSE method is the best, of the WMA method is the second, and of the SMA method is the last. The results also show that reducing the lead-time and increasing the moving average parameter improve the prediction accuracy and reduce the supplier inventory level.展开更多
文摘This paper studies that the bullwhip effect of order releases and the amplifications of safety stock arise within the supply chain even when the demand model is ARIMA(0, 1, 1) and the forecast method used is a simple exponentially weighted moving average. It also examines a vendor managed inventory (VMI) program to determine how it can help alleviate such negative effects, and gives the theoretical proofs and numerical illustrations. The results show that the effects with VMI are better than the effect without VMI in demand forecasting and safety stock levels, etc.
文摘Conflicts between supply chain members emerge because individual strategic actions may not be jointly optimal.Efforts to forecast consumer demand represent a source of conflict.The coordination of forecasts requires a powerful incentive alignment approach.This work proposes a smart contract equipped consortium blockchain system that creates an incentive structure that makes coordination with respect to forecasts economically appealing.Distortions of demand information due to uncoordinated forecasting are captured by a bullwhip measure that factors both forecast error and variance.Cooperation under the system is shown to help minimize this bullwhip measure,thus generating new outcomes for the participants that allow for a higher reward.Under a fixed payout structure,the system achieves credibility of continued cooperation,thus promoting an optimally coordinated equilibrium between the retailer and supplier.Blockchain technology represents a novel information system and consensus formation mechanism that can intermediate the behavior of a supply chain network.
文摘Based on the two-level supply chain composed of suppliers and retailers, we assume that market demand is subject to an ARIMA(1, 1, 1). The supplier uses the minimum mean square error method (MMSE), the simple moving average method (SMA) and the weighted moving average method (WMA) respectively to forecast the market demand. According to the statistical properties of stationary time series, we calculate the mean square error between supplier forecast demand and market demand. Through the simulation, we compare the forecasting effects of the three methods and analyse the influence of the lead-time L and the moving average parameter N on prediction. The results show that the forecasting effect of the MMSE method is the best, of the WMA method is the second, and of the SMA method is the last. The results also show that reducing the lead-time and increasing the moving average parameter improve the prediction accuracy and reduce the supplier inventory level.
