Quantifying the bullwhip effect in a supply chain with stochastic lead time

2006 ◽  
Vol 173 (2) ◽  
pp. 617-636 ◽  
Author(s):  
Jeon G. Kim ◽  
Dean Chatfield ◽  
Terry P. Harrison ◽  
Jack C. Hayya
Keyword(s):  
Supply Chain ◽  
Lead Time ◽  
Bullwhip Effect ◽  
Stochastic Lead Time

scholarly journals A Discrete Event Simulation Analysis of the Bullwhip Effect in a Multi-Product and Multi-Echelon Supply Chain of Fast Moving Consumer Goods

2020 ◽  
pp. 561-576
Author(s):  
Ramsha Ali ◽  
Ruzelan Khalid ◽  
Shahzad Qaiser
Keyword(s):  
Supply Chain ◽  
Information Sharing ◽  
Discrete Event Simulation ◽  
Fast Moving ◽  
Lead Time ◽  
Discrete Event ◽  
Bullwhip Effect ◽  
Event Simulation ◽  
Stochastic Lead Time ◽  
The Impact

Timely delivery is the major issue in Fast Moving Consumer Good (FMCG) since it depends on the lead time which is stochastic and long due to several reasons; e.g., delay in processing orders and transportation. Stochastic lead time can cause inventory inaccuracy where echelons have to keep high product stocks. Such performance inefficiency reflects the existence of the bullwhip effect (BWE), which is a common challenge in supply chain networks. Thus, this paper studies the impact of stochastic lead time on the BWE in a multi-product and multi-echelon supply chain of FMCG industries under two information-sharing strategies; i.e., decentralized and centralized. The impact was measured using a discrete event simulation approach, where a simulation model of a four-tier supply chain whose echelons adopt the same lead time distribution and continuous review inventory policy was developed and simulated. Different lead time cases under the information-sharing strategies were experimented and the BWE was measured using the standard deviation of demand ratios between echelons. The results show that the BWE cannot be eliminated but can be reduced under centralized information sharing. All the research analyses help the practitioners in FMCG industries get insight into the impact of sharing demand information on the performance of a supply chain when lead time is stochastic.

Analysis and Verification of Bullwhip Effect Based on System Dynamics

2013 ◽  
Vol 340 ◽  
pp. 312-319
Author(s):  
Fu Xin Yang ◽  
Bai Lan Zhang ◽  
Zhi Yuan Su
Keyword(s):  
Supply Chain ◽  
System Dynamics ◽  
Correlation Coefficient ◽  
Lead Time ◽  
Process Model ◽  
Autoregressive Process ◽  
Bullwhip Effect ◽  
Simulation Software ◽  
Simulation Results ◽  
The Impact

To study the bullwhip effect (BWE) in supply chain (SC), this paper built two system dynamics (SD) models strictly referring to the AR(1) (autoregressive process) model constructed by Frank Chen. Using Vensim simulation software, it analyzed the impact of the correlation coefficient of demand, lead time, smoothing time of demand and information to BWE, and then put forward some proposals on how to reduce BWE. By contrasting the simulation results of SD models with the AR(1) models', it verifies the validity of the AR(1) model of Frank Chen from a simulation perspective. It also shows SD model combined with AR(1) model can analyze BWE in SC reliably and powerfully.

scholarly journals An Empirical Study of Supply Chain Frangibility and Risk Management in Indian Automobile Industry

2021 ◽  
Author(s):  
Arora Ankit ◽  
Rajagopal Rajesh
Keyword(s):  
Risk Factors ◽  
Supply Chain ◽  
Automobile Industry ◽  
Lead Time ◽  
Risk Mitigation ◽  
Transportation Network ◽  
Demand Forecasting ◽  
Bullwhip Effect ◽  
Effect Analysis ◽  
Supply Chain Efficiency

Abstract The automobile sector in India is one the key segment of Indian economy as it contributes to 4% of India’s GDP and 5% of India’s Industrial production. The supply chain of any firm is generally dependent on six driving factors out of which three are functional (information, inventory, and facilities) and 3 are logistic (sourcing, pricing, and transportation). The risk causing factors in supply chains consists of various levels of sub-factors under them. Say for instance, under supply risk, the sub-factors can be poor logistics at supplier end, poor material quality etc., under demand risk, the sub-factors can be inaccurate demand forecasting, fluctuating demand, bullwhip effect, and under logistics risk, the sub-factors can be poor transportation network, shorter lead time, stock outs. Through this study, we observe to find the effect of these factors in the supply chain. We use Failure Mode and Effect Analysis (FMEA) technique to prioritize the various types of risk into zones namely high, medium and low risk factors. Also, we use the Best Worst Method (BWM), a multi-criteria decision-making technique to find out the overall weightings of different risk factors. The combination of these methods can help an organization to prioritize various risk factors and proposing a proper risk mitigation strategy leading to increase in overall supply chain efficiency and responsiveness.

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