Disentangling Production Smoothing from the Bullwhip Effect

2013 ◽  
Author(s):  
Robert Louis Bray ◽  
Haim Mendelson
Keyword(s):  
Bullwhip Effect ◽  
Production Smoothing

Production Smoothing and the Bullwhip Effect

2015 ◽  
Vol 17 (2) ◽  
pp. 208-220 ◽  
Author(s):  
Robert L. Bray ◽  
Haim Mendelson
Keyword(s):  
Bullwhip Effect ◽  
Production Smoothing

The production smoothing problem

1972 ◽  
Vol 51 (4) ◽  
pp. 123 ◽  
Author(s):  
S. Eilon
Keyword(s):  
Production Smoothing

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

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.

Un-Supervised approach to backorder prediction using deep autoencoder

2019 ◽  
Vol 12 ◽  
Author(s):  
Gunjan Saraogi ◽  
Deepa Gupta ◽  
Lavanya Sharma ◽  
Ajay Rana
Keyword(s):  
Anomaly Detection ◽  
Predictive Model ◽  
Inventory Management ◽  
Supervised Classification ◽  
Operating Characteristic ◽  
Imbalanced Data ◽  
Bullwhip Effect ◽  
Detection Methods ◽  
Good Representation ◽  
Operational Management

Background: Backorders are an accepted abnormality affecting accumulation alternation and logistics, sales, chump service, and manufacturing, which generally leads to low sales and low chump satisfaction. A predictive archetypal can analyse which articles are best acceptable to acquaintance backorders giving the alignment advice and time to adjust, thereby demography accomplishes to aerate their profit. Objective: To address the issue of predicting backorders, this paper has proposed an un-supervised approach to backorder prediction using Deep Autoencoder. Method: In this paper, artificial intelligence paradigms are researched in order to introduce a predictive model for the present unbalanced data issues, where the number of products going on backorder is rare. Result: Un-supervised anomaly detection using deep auto encoders has shown better Area under the Receiver Operating Characteristic and precision-recall curves than supervised classification techniques employed with resampling techniques for imbalanced data problems. Conclusion: We demonstrated that Un-supervised anomaly detection methods specifically deep auto-encoders can be used to learn a good representation of the data. The method can be used as predictive model for inventory management and help to reduce bullwhip effect, raise customer satisfaction as well as improve operational management in the organization. This technology is expected to create the sentient supply chain of the future – able to feel, perceive and react to situations at an extraordinarily granular level

Ration Gaming and the Bullwhip Effect

2019 ◽  
Author(s):  
Robert L. Bray ◽  
Yuliang Yao ◽  
Yongrui Duan ◽  
Jiazhen Huo
Keyword(s):  
Bullwhip Effect
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