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.
Consumer preference for fish safety inspection in Bangladesh
