Fuzzy time series forecasting for supply chain disruptions

The purpose of this paper is to test the effectiveness of fuzzy time series (FTS) forecasting system in a supply chain experiencing disruptions and also to examine the changes in performance as the authors move across different tiers.

A discrete event simulation based on the popular beer game model is used for these tests. A popular ordering management system is used to emulate the behavior of the system when the game is played with human players.

FTS is tested against some other well-known forecasting systems and it proves to be the best of the lot. It is also shown that it is better to go for higher order FTS for higher tiers, to match auto regressive integrated moving average.

This study fills an important research gap by proving that FTS forecasting system is the best for a supply chain during disruption scenarios. This is important because the forecasting performance deteriorates significantly and the effect is more pronounced in the upstream tiers because of bullwhip effect.

Having a system which works best in all scenarios and also across the tiers in a chain simplifies things for the practitioners. The costs related to acquiring and training comes down significantly.

This study contributes by suggesting a forecasting system which works best for all the tiers and also for every scenario tested and simultaneously significantly improves on the previous studies available in this area.