Quantifying the bullwhip effect in supply chains

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.

Purpose Triggered by the measures taken to manage Covid-19, the increase in supply uncertainty and demand distortion in the market caused information asymmetry that led to the bullwhip effect of supply chain. We study the factors that affect the bullwhip effect caused by Covid-19 as well as its aftermaths. Methods We designed a stock flow diagram to analyze the bullwhip effect based on a supply chain simulation using system dynamics and also perform a causal analysis on variables affecting the supply. Results The results show that the increase in safty inventory, reduction of ship delays, and information sharing can reduce the bullwhip effect while dampening the amplification of orders in the global supply chain consisting of a retailer, a RDC (Regional Distribution Center) and a manufacturer. Conclusion A research model that analyzes the bullwhip effect of the covid-19 logistic crisis using system dynamics is proposed. The model is expected to serve as a useful tool to analyze and manage potentially uncertain global supply chain risk scenarios caused by disruptive events such as Covid-19.

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

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.

Free AccessAboutSectionsView PDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked InEmail Go to SectionFree Access HomeManufacturing & Service Operations ManagementVol. 12, No. 4 2010 M&SOM Best Paper AwardTava Lennon OlsenTava Lennon OlsenPublished Online:1 Oct 2010https://doi.org/10.1287/msom.1100.0313"2010 M&SOM Best Paper Award." Manufacturing & Service Operations Management, 12(4), p. 708 Previous Back to Top FiguresReferencesRelatedInformation Volume 12, Issue 4Fall 2010Pages 547-708 Article Information Metrics Downloaded 50 times in the past 12 months Information Published Online:October 01, 2010 Copyright © 2010, INFORMSCite asTava Lennon Olsen, (2010) 2010 M&SOM Best Paper Award. Manufacturing & Service Operations Management 12(4):708-708. https://doi.org/10.1287/msom.1100.0313 PDF download

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

Production smoothing in a serial supply chain: A laboratory investigation

Order variability in perishable product supply chains

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.