Impact of Order Splitting on Bullwhip Effect in Supply Chain: Case of Identical Lead Time at Distributors-Retailer Links

2014 ◽  
Vol 931-932 ◽  
pp. 1652-1657
Author(s):  
Kittiwat Sirikasemsuk
Keyword(s):  
Supply Chain ◽  
Lead Time ◽  
Minimum Mean Square Error ◽  
Research Work ◽  
Bullwhip Effect ◽  
Detailed Comparison ◽  
Dual Sourcing ◽  
Effect Measure ◽  
Single Sourcing ◽  
Order Quantities

This research work attempts to establish the bullwhip effect measure under the dual sourcing environment in which the lead time periods of two distributors to fulfill the retailer's orders are identical. Our model was based on the simple three-echelon supply chain with one supplier, two distributors and one retailer for a stationary first-order autoregressive, i.e., AR(1), incoming demand process. It was assumed that the minimum mean-square error forecasting technique and the order-up-to inventory policy were employed in all stages. The impacts of the autoregressive coefficient, the replenishment lead time and the proportion of order quantities placed by the retailer with the two distributors were investigated. A detailed comparison of the bullwhip effect of dual sourcing and that of single sourcing was also provided.

Bullwhip Effect Analysis in a Supply Chain

2012 ◽  
pp. 646-665
Author(s):  
Mehdi Najafi ◽  
Reza Zanjirani Farahani
Keyword(s):  
Supply Chain ◽  
Statistical Method ◽  
Moving Average ◽  
Bullwhip Effect ◽  
Effect Analysis ◽  
Forecasting Methods ◽  
Effect Measure ◽  
Forecasting Method ◽  
Efficiency And Effectiveness ◽  
Main Factors

In today’s world, all enterprises in a supply chain are attempting to increase both their and the supply chain’s efficiency and effectiveness. Therefore, identification and consideration of factors that prevent enterprises to attain their expected/desired levels of effectiveness are very important. Since bullwhip effect is one of these main factors, being aware of its reasons help enterprises decrease the severity of bullwhip effect by opting proper decisions. Now that forecasting method is one of the most important factors in increasing or decreasing the bullwhip effect, this chapter considers and compares the effects of various forecasting methods on the bullwhip effect. In fact, in this chapter, the effects of various forecasting methods, such as Moving Average, Exponential Smoothing, and Regression, in terms of their associated bullwhip effect, in a four echelon supply chain- including retailer, wholesaler, manufacturer, and supplier- are considered. Then, the bullwhip effect measure is utilized to compare the ineffectiveness of various forecasting methods. Owing to this, the authors generate two sets of demands in the two cases where the demand is constant (no trend) and has an increasing trend, respectively. Then, the chapter ranks the forecasting methods in these two cases and utilizes a statistical method to ascertain the significance of differences among the effects of various methods.

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

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 Study of the Bullwhip Effect under Various Forecasting Methods in Electronics Supply Chain with Dual Retailers considering Market Share

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-19
Author(s):  
Junhai Ma ◽  
Jing Zhang ◽  
Liqing Zhu
Keyword(s):  
Supply Chain ◽  
Market Share ◽  
Moving Average ◽  
Bullwhip Effect ◽  
Consumer Electronics ◽  
Specific Reference ◽  
Chain Model ◽  
Forecasting Methods ◽  
Effect Measure ◽  
The Stability

We establish in this paper a new two-stage supply chain with one manufacturer and two retailers which have a fixed market share in the mature and stable market with specific reference to consumer electronics industry. This paper offers insights into how the three forecasting methods affect the bullwhip effect considering the market share under the ARMA(1,1) demand process and the order-up-to inventory policy. We also discuss the stability of the order with the theory of entropy. In particular, we derive the expressions of bullwhip effect measure under the MMSE, MA, and ES methods and compare them by numerical simulations. Results show that the MA is always better in contrast to the ES for reducing the bullwhip effect in our supply chain model. When moving average coefficient is lower than a certain value, the MMSE method is the best for reducing the bullwhip effect; otherwise, the MA method is the best. Moreover, the larger the market share of the retailer with a long lead time is, the greater the bullwhip effect is, no matter what the forecasting method is.

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.

Production Lots as Determinant of Paper Production Lead Time Performance

2009 ◽  
Vol 2 (4) ◽  
pp. 63-79
Author(s):  
Pekka Koskinen ◽  
Olli-Pekka Hilmola
Keyword(s):  
Supply Chain ◽  
Lead Time ◽  
A Priori ◽  
Research Work ◽  
Dynamics Simulation ◽  
Production Environment ◽  
Time Performance ◽  
Production Order ◽  
It Applications ◽  
Better Than

In this research work we are interested about connection between lead time performance, and production order size as well as in how many production lots this order was eventually produced. Based on the system dynamics simulation model, the authors got a priori assumption that production lots have in multiproduct environment better explanation power. Our empirical findings give support for this – number of production lots explain in production environment manufacturing lead time much better than production order size. Further support is gained from supply chain phases, which are analyzed similarly, but as surprise explanation power of production lots decreases, and seems to be significantly lower in more distant markets. It is interesting to note that currently used IT applications of analyzed global case company do not give real time snapshot regarding to the development of overall supply chain lead time.

scholarly journals Impact of Price-Sensitive Demand and Premium Payment Scheme on Bullwhip Effect

2022 ◽  
Vol 15 (1) ◽  
pp. 1-24
Author(s):  
Mona Verma ◽  
Reena Jain ◽  
Chandra K. Jaggi
Keyword(s):  
Supply Chain ◽  
Numerical Analysis ◽  
Lead Time ◽  
Bullwhip Effect ◽  
Capacity Constraints ◽  
Price Variation ◽  
Payment Scheme ◽  
Premium Payment ◽  
The Impact

Bullwhip effect reduces the efficiency, responsiveness, and value of the supply chain. There are some indirect causes like lead time, the number of echelons, and some direct causes of bullwhip effect such as rationing or price variation. Due to capacity constraints, retailers are forced to experience rationing of their demands. Fear of rationing usually gives rise to manipulable demand and hence increases the bullwhip effect. Moreover, if the retailer’s demand is price sensitive then it will cause price variation. The offerings of premium payment by retailers due to unfulfilled demand lure the supplier to extend his existing capacity and to allocate them more supply. In this paper, an attempt has been made to mitigate the impact of the bullwhip effect using a premium payment scheme. A technique has been coined that will help in reducing the bullwhip effect. The increased value of the supply chain on using a premium payment scheme is proof of the reduction of the bullwhip effect. Results are validated through numerical analysis.

Production Lots as Determinant of Paper Production Lead Time Performance

2011 ◽  
pp. 310-325
Author(s):  
Pekka Koskinen ◽  
Olli-Pekka Hilmola
Keyword(s):  
Supply Chain ◽  
Lead Time ◽  
A Priori ◽  
Research Work ◽  
Dynamics Simulation ◽  
Production Environment ◽  
Paper Production ◽  
Time Performance ◽  
Production Order ◽  
It Applications

In this research work we are interested about connection between lead time performance, and production order size as well as in how many production lots this order was eventually produced. Based on the system dynamics simulation model, the authors got a priori assumption that production lots have in multiproduct environment better explanation power. Our empirical findings give support for this – number of production lots explain in production environment manufacturing lead time much better than production order size. Further support is gained from supply chain phases, which are analyzed similarly, but as surprise explanation power of production lots decreases, and seems to be significantly lower in more distant markets. It is interesting to note that currently used IT applications of analyzed global case company do not give real time snapshot regarding to the development of overall supply chain lead time.

scholarly journals Inherent Complexity Research on the Bullwhip Effect in Supply Chains with Two Retailers: The Impact of Three Forecasting Methods Considering Market Share

2014 ◽  
Vol 2014 ◽  
pp. 1-13
Author(s):  
Junhai Ma ◽  
Binshuo Bao ◽  
Xiaogang Ma
Keyword(s):  
Supply Chain ◽  
Market Share ◽  
Mean Squared Error ◽  
Moving Average ◽  
Bullwhip Effect ◽  
Minimum Mean Squared Error ◽  
Forecasting Methods ◽  
Effect Measure ◽  
Demand Variability ◽  
The Impact

An important phenomenon in supply chain management which is known as the bullwhip effect suggests that demand variability increases as one moves up a supply chain. This paper contrasts the bullwhip effect for a two-stage supply chain consisting of one supplier and two retailers under three forecasting methods based on the market share. We can quantify the correlation coefficient between the two retailers clearly, in consideration of market share. The two retailers both employ the order-up-to inventory policy for replenishments. The bullwhip effect is measured, respectively, under the minimum mean squared error (MMSE), moving average (MA), and exponential smoothing (ES) forecasting methods. The effect of autoregressive coefficient, lead time, and the market share on a bullwhip effect measure is investigated by using algebraic analysis and numerical simulation. And the comparison of the bullwhip effect under three forecasting methods is conducted. The conclusion suggests that different forecasting methods and various parameters lead to different bullwhip effects. Hence, the corresponding forecasting method should be chosen by the managers under different parameters in practice.

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