시스템 다이내믹스를 활용한 Covid-19 물류대란에서의 채찍현상에 관한 연구

A Fuzzy agent-based model for reduction of bullwhip effect in supply chain systems

In a supply chain, the distorted demand information when it goes upstream is commonly known as the bullwhip effect. In this paper, the impact of demand substitution on the bullwhip effect in a two-stage supply chain is investigated. In our model, a single retailer observes inventory levels for two products, among which product 1 can be used to substitute product 2. The retailer places orders to a single manufacturer following an order-up-to inventory policy and uses a simple moving average forecasting method to estimate the lead-time demand. The customers' demands are modeled by an autoregressive process. By analyzing the bullwhip effect in such settings, quantitative relations between the bullwhip effect and the forecasting method, lead time, demand process, and the product substitution are obtained. Numerical results show that demand substitution can reduce the bullwhip effect in most cases.

In this paper, the bullwhip effect in a multistage supply chain has been analyzed using double moving average and double exponential smoothing forecasts. We find out that double exponential smoothing and double moving average forecasts can sometimes have a variance reducing effect on the supply chain. In particular, the bullwhip effect exists for positively correlated or independent and identically distributed (i.i.d) demands. However, for negatively correlated demands, bullwhip effect does not necessarily exist. In addition, our analysis shows that the order variance based on simple moving average forecast is always larger than the order variance based on double moving average forecast, and the order variance based on simple exponential smoothing forecast is always larger than the order variance based on double exponential smoothing forecast. For the independent and identically distributed (i.i.d) demands , it can be shown that the order quantity fluctuation range for double exponential smoothing is greater than that for double moving average.

Quantifying the bullwhip effect in supply chains

Measure of bullwhip effect in supply chains with first-order bivariate vector autoregression time-series demand model

Information blackouts, defined as sudden and short-duration failures of the information flow in a supply chain, amplify the bullwhip effect in supply chains. We develop a discrete-event simulation of a multi-echelon supply chain, utilizing Rockwell Automation's Arena software tool, to investigate this phenomenon. We investigate inventory order history blackouts of three different durations (1, 2, and 3 time periods). Based on the increased bullwhip effect observed as the result of an information blackout, managers may decide to "wait out" the amplification or to use an estimate to replace the missing inventory order history by utilizing the last known value. The latter choice employs the common manager's heuristic of trusting the recent past to be the best predictor of the future. Our results provide supporting evidence for such managerial decisions.

A robust optimization approach to reduce the bullwhip effect of supply chains with vendor order placement lead time delays in an uncertain environment

The impact of customers’ MD&A sentiment on the bullwhip effect: A dyadic buyer-supplier perspective

Nonlinear goal programming models quantifying the bullwhip effect in supply chain based on ARIMA parameters

The Bullwhip Effect (BWE) occurs when orders made to suppliers have a bigger variation than sales to the buyer. This is a major concern that businesses are working on eliminating, as it has numerous side effects, such as excessive inventory, stock-outs, insufficient production, increased costs, etc. The new study adds to the literature by demonstrating how to quantify and assess the bullwhip impact on any supply chain. The results show that when consumer demand is unstable, the BWE is magnified. This was achieved by using the proposed formula which was based on an explanation and graph of the traditional BWE. A stochastic simulation based on a case study that replicates the behavior of a generic supply chain in a real-world market was used to evaluate the formula.

This paper is concerned with the comparison of a factory managed inventory (FMI) supply chain to a traditional forecast-based orders supply chain. The particular emphasis of this paper is the impact the FMI structures have on the ''Bullwhip Effect'' generated within the supply chain. We especially focused on the relationship between the sales site and the factory site, and the developed FMI supply chain that the factory manages each sales site's inventory level, the production site inventory level and actual customer demand as a unified integrated system. A comparison of the performance of the FMI system and conventional supply chain system of a global electronics parts company was investigated by present value (PV) analysis using the value of inventories as a variable. The results revealed that FMI greatly contributed to both reduction of the Bullwhip Effect and improvements of the cash flow PV Index.

In this paper, the supply chain of manufacturing enterprises under service transformation is analyzed first in detail. Second, the supply chain model is constructed by means of system dynamics simulation. Then, the characteristics of bullwhip effect of supply chain in service transformation are studied through simulation. Finally, on the basis of simulation analysis, this paper further proposed the countermeasures of bullwhip effect in supply chain under service transformation.

Systematic Risk and the Bullwhip Effect in Supply Chains

At most time, supply chain is a hybrid dynamic system composed of continuous material flow, discrete information flow and their interaction. Traditional method of modeling and simulation of supply chain looking supply chain as discrete event system can’t reflect systemic dynamic complexity. In this paper, two kinds of order decision-making simulation model of supply chain, discrete model and discrete-continuous hybrid model are built with system dynamics and discrete state chart using the software AnyLogic. Inventory levels of manufacturer and distributor under traditional inventory strategy and VMI strategy are studied. Simulation results show that hybrid model describes bullwhip effect of supply chain different from discrete model, that is the bullwhip effect exhibiting higher inventory level of supply chain upstream enterprise in discrete-continuous combined modeling while exhibiting higher inventory fluctuation of supply chain upstream enterprise in discrete model and can simulates the process of system evolvement caused by adaptive agent’s evolutionary learning greatly.

Order variability in perishable product supply chains