Data-Driven Fuzzy Demand Forecasting Models for Resilient Supply Chains

Abstract

Uncertainty in supply chain leads to what is known as bullwhip effect (BE), which causes multiple inefficiencies such as higher costs of production (of more than what is needed), wastage and logistics. Though there are many studies reported in the literature, the impact of the quality of dynamic forecasts on the BE has not received sufficient coverage. In this paper, a fuzzy data-driven weighted moving average (DDWMA) forecasts of the future demand strategy is proposed for supply chain. Also, data-driven random weighted volatility forecasting model is used to study the fuzzy extended Bollinger bands forecasts of the demand. The main reason of using the fuzzy approach is to provide α-cuts for DDWMA demand forecasts as well as extended Bollinger bands forecasts. The proposed fuzzy extended Bollinger bands forecast is a two steps procedure as it uses optimal weights for both the demand forecasts as well as the volatility forecasts of the demand process. In particular, a novel dynamic fuzzy forecasting algorithm of the demand is proposed which bypasses complexities associated with traditional forecasting steps of fitting any time series model. The proposed data-driven fuzzy forecasting approach focuses on defining a dynamic fuzzy forecasting intervals of the demand as well as the volatility of the demand in supply chain. The performance of proposed approaches is evaluated through numerical experiments using simulated data and weekly demand data. The results show that the proposed methods perform well in terms of narrower fuzzy forecasting bands for demand as well as the volatility of the demand.