Evaluating of the bullwhip effect using intelligent forecasting methods

Macro and micro changes in the markets are reflected as changes in demand in the chain from the end consumer to the supplier of the supplier, and the effect of these changes is shown. There are many different techniques to predict and mitigate this effect. In this thesis, while looking at the effects of the Whip Effect, an application has been made by using intelligent prediction methods such as artificial neural networks for a better estimation model in order to decrease effect of bullwhip and gain values to companies. Thanks to these proposed models, while eliminating existing inefficiencies by making better forecasting, improvements have been observed in deadlines and inventory management.