Study of Electroencephalography for Enhanced Understanding of Consumer Preference

2021 ◽  
pp. 161-169
Author(s):  
B. Neeharika ◽  
W. Jessie Suneetha ◽  
B. Anila Kumari ◽  
M. Tejashree
Keyword(s):  
Consumer Preference

THREE KINDS OF ANTHROPOMORPHIZED BRANDS AND CONSUMER PREFERENCE: MEDIATING EFFECTS OF INSTRUMENTAL AND TERMINAL MATERIALISM

2018 ◽  
Vol 2018 ◽  
pp. 501-501
Author(s):  
Ono Akinori ◽  
◽  
Aiba Masaki ◽  
Takeda Koshi ◽  
Hokari Hanako ◽  
...  
Keyword(s):  
Consumer Preference ◽  
Mediating Effects

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.

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