A Review on the Application of Neural Networks for Decreasing Bullwhip Effect in Supply Chain

2015 ◽  
Vol 9 (5) ◽  
pp. 438
Author(s):  
Milad Yousefi ◽  
Moslem Yousefi ◽  
Ricardo Poley Martins Ferreira
Keyword(s):  
Neural Networks ◽  
Supply Chain ◽  
Bullwhip Effect

Operations-based classification of the bullwhip effect

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sachin Gupta ◽  
Anurag Saxena
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Logistic Regression ◽  
Supply Chain ◽  
Discriminant Analysis ◽  
Bullwhip Effect ◽  
Content Type ◽  
Operational Variables ◽  
Artificial Neural ◽  
The Impact

Purpose Present study deals with the most discussed rather than addressed yet still an unsolved problem of supply chain known as the bullwhip effect. Operational variables affecting the bullwhip effect are identified and their role in causing the bullwhip effect has been explored using artificial neural networks. The purpose of this study is to analyze the impact of identified operational reasons that affect the bullwhip effect and to analyze the bunch of variables that are more prominent in explaining the phenomenon of the bullwhip effect. Design/methodology/approach Ten major sectors of the Indian economy are analyzed for the bullwhip effect in the present study, and the operational variables affecting the bullwhip effect in these sectors are identified. The bullwhip metric is developed as the ratio of variance in production to the variance in the demand. The impact of identified operation variables on the bullwhip effect has been discussed using the artificial neural network technique known as multilayer perceptron. The classification is also performed using neural network, logistic regression and discriminant analysis. Findings The operation variables are found to be varying with respect to sectors. The study emphasizes that analyzing the right set of operation variables with respect to the sector is required to deal with the complex problem, the bullwhip effect. The operational variables affecting the bullwhip effect are identified. The classification result of the neural network is compared with those of the logistic regression and discriminant analysis, and it is found that the dynamism present in the bullwhip effect is better classified by neural network. Research limitations/implications The study used 11 years of observations to analyze the bullwhip effect on the basis of operational variables. The bullwhip effect is a complex phenomenon, and it is explained on the basis of an extensive set of operational variables which is not exhaustive. Further, the behavioral aspect (bullwhip because of decision-making) is not explored in the present study. Practical implications The operational aspect plays a gigantic role to explain and deal with the bullwhip effect. Strategies to mitigate the bullwhip effect must be in accordance with the operational variables impacting the sector. Originality/value The study suggests a novel approach to study the bullwhip effect in supply chain management using the application of neural networks in which operational variables are taken as predictor variables.

Classification of Operational and Financial Variables Affecting the Bullwhip Effect in Indian Sectors: A Machine Learning Approach

2019 ◽  
Vol 12 (3) ◽  
pp. 171-179 ◽  
Author(s):  
Sachin Gupta ◽  
Anurag Saxena
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Product Life Cycle ◽  
Consumer Preference ◽  
Bullwhip Effect ◽  
Machine Learning Techniques ◽  
Chain Management ◽  
Financial Variables

Background: The increased variability in production or procurement with respect to less increase of variability in demand or sales is considered as bullwhip effect. Bullwhip effect is considered as an encumbrance in optimization of supply chain as it causes inadequacy in the supply chain. Various operations and supply chain management consultants, managers and researchers are doing a rigorous study to find the causes behind the dynamic nature of the supply chain management and have listed shorter product life cycle, change in technology, change in consumer preference and era of globalization, to name a few. Most of the literature that explored bullwhip effect is found to be based on simulations and mathematical models. Exploring bullwhip effect using machine learning is the novel approach of the present study. Methods: Present study explores the operational and financial variables affecting the bullwhip effect on the basis of secondary data. Data mining and machine learning techniques are used to explore the variables affecting bullwhip effect in Indian sectors. Rapid Miner tool has been used for data mining and 10-fold cross validation has been performed. Weka Alternating Decision Tree (w-ADT) has been built for decision makers to mitigate bullwhip effect after the classification. Results: Out of the 19 selected variables affecting bullwhip effect 7 variables have been selected which have highest accuracy level with minimum deviation. Conclusion: Classification technique using machine learning provides an effective tool and techniques to explore bullwhip effect in supply chain management.

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

2008 ◽  
Vol 34 (3) ◽  
pp. 1680-1691 ◽  
Author(s):  
M.H. Fazel Zarandi ◽  
M. Pourakbar ◽  
I.B. Turksen
Keyword(s):  
Supply Chain ◽  
Bullwhip Effect ◽  
Agent Based Model ◽  
Agent Based

Buffet Effect Simulation Model on Demand Chain Management

2010 ◽  
Vol 44-47 ◽  
pp. 688-692
Author(s):  
Xiao Yan Wang ◽  
Jian Sun
Keyword(s):  
Supply Chain ◽  
Side Information ◽  
Bullwhip Effect ◽  
Chain Management ◽  
Demand Chain ◽  
Supply Chain Efficiency ◽  
Demand Fluctuations ◽  
Partner Collaboration ◽  
Transmission Ability ◽  
Demand Chain Management

Bullwhip effect means the magnification of demand fluctuations, which is evident in a supply chain when demand increases and decreases, while the concept of Demand Chain Management means to make the planning on the basis of the demand side information so as to solve the problem of inconsistent upstream and downstream information by means of partner collaboration in the supply chain. Demand chain emphasizes the customer demand as its core value so as to achieve the best balance between the supply chain efficiency and customer satisfaction. Compared with the supply chain, the demand chain advises the enterprise to strengthen the information transmission ability to promote the performance. Under the demand chain management, the extent of bullwhip effect are weakened, and the fluctuation range against demand chain management is lower than against traditional supply chain.

Vertical Contracts in a Supply Chain and the Bullwhip Effect

2020 ◽  
Author(s):  
Zhan Qu ◽  
Horst Raff
Keyword(s):  
Supply Chain ◽  
Supply Chains ◽  
Operations Management ◽  
Bullwhip Effect ◽  
Price Effect ◽  
Demand Shocks

This paper shows that decentralized supply chains, in which upstream firms use linear wholesale prices, may experience lower upstream production and downstream sales volatility than vertically integrated supply chains and may be less susceptible to the bullwhip effect by which the variance of upstream production exceeds the variance of downstream sales. The reason is that decentralized supply chains exhibit a price effect, whereby upstream producers raise wholesale prices in the case of positive demand shocks and lower wholesale prices in the case of negative demand shocks. Whereas upstream producers benefit from the price effect and, thus, from a dampening of the bullwhip effect, downstream firms may lose, and overall supply chain profit may decrease. This paper was accepted by Vishal Gaur, operations management.

Optimization Control of the Bullwhip Effect in Supply Chain Inventory

2014 ◽  
Vol 945-949 ◽  
pp. 3187-3190
Author(s):  
Hai Dong ◽  
Jin Hua Liu ◽  
Liang Yu Liu
Keyword(s):  
Mathematical Model ◽  
Supply Chain ◽  
Control Theory ◽  
Inventory Control ◽  
Pid Controller ◽  
Bullwhip Effect ◽  
Control Gain ◽  
Optimization Control ◽  
Suitable Combination ◽  
The Mathematical Model

The bullwhip effect was caused by fuzzy demand among the enterprises. In order to reduce this effect, control theory was applied to solve the inventory in supply chain. Firstly, inventory control in supply chain and the bullwhip effect was researched. Secondly, a kind of proportional integral differential (PID) controller was developed for inventory control in a three-level supply chain, and the mathematical model of the PID controller for inventory control was presented. Finally, the results show that the PID controller can evidently alleviate the bullwhip effect and inventory fluctuations under the suitable combination of control gain.

Sign in / Sign up

Export Citation Format

Share Document