Mitigation of Bullwhip Effect in Supply Chain using Data Analytic Methods


Ishan Aggarwal†, Anurag Pandey†, Kaja Bantha Navas Raja Mohamed‡, Jose S†, John Rajan A†*


†School of Mechanical Engineering, Vellore Institute of Technology Vellore, India.
‡School of Mechanical Engineering, Sathyabama Institute of Science and Technology, Chennai, India.

Corresponding Author Email: ajohnrajan@gmail.com

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

One of the most significant problems faced by supply chain managers all around the world in a smooth supply chain functioning is the “Bullwhip effect”. An accurate demand forecast can help in reducing the BWE. In this paper, a comparative study is performed on two time-series forecasting models – ARIMA and TBATS – to analyse which one of them is more accurate method for two time-series patterns – one with seasonality variations and one without seasonality variations. The results obtained, proved that ARIMA was the better option for seasonality variation data while TBATS was superior for data with complex seasonality. The choice of the forecasting model still poses a problem for marketing companies and supply chain managers. The paper explorers how the less advanced methods may be better than newer methods in specific cases. To the author’s knowledge, none of the previous research papers have explored the applicability of these methods for different time series pattern data sets.