Disruption recovery modeling in supply chain risk management

It is well known that disruptions can significantly affect the performance of a company's supply chain especially in highly volatile markets. It is therefore imperative to have appropriate mechanisms/tools to mitigate the effects of disruptions. We developed the concept for a disruption recovery-modelling approach that provides more accurate supply forecasts during supply chain disruptions (i.e. smaller variance), which are of prime importance to supply chain risk management. Specifically, we show that a combination of model forecasts performs no worse than the individual component models applied in this paper. In addition, the projections of the models updated through a Bayesian framework generate supply forecasts with smaller variances.