Abstract
The bullwhip effect can be described as increased demand variability in supply chains. Bullwhip effect is one of the most popular research problems in the field of supply chain management because in the case of bullwhip effect the small changes in demands of customers may lead to a large variations in the orders consequently, the organization faces the overestimated in their orders. There are different methods of forecasting which are used in supply chain in order to decrease the bullwhip effect. In recent years many researches are using neural networks as one of the useful tools for forecasting. The aim of this literature review is to describe the concept of bullwhip effect and its disadvantages and causes of it. Moreover, to provide a list of applications of neural networks in decreasing bullwhip effect in supply chain and suggest future studies based on the literature.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Review of Mechanical Engineering (IREME)
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
