A grey approach for forecasting in a supply chain during intermittentdisruptions

The supply chains today have become vulnerable to frequent disruptions, and with continuing emphasis on efficiency, lacks robustness to deal with them. A part of the solution lies in forecasting the disruption beforehand and the other part in knowing which policies will suit such disrupted conditions best. Accurate and immediate forecasts are a must in a supply chain and hence play a huge role in stabilizing. This study compares the performance of three established forecasting methods (moving average, weighted moving average and exponential smoothing) as well as grey prediction method, during disruptions and stable situations. The experiments are performed in the form of discrete event simulation, on a four stage beer game settings. The results show that moving average and weighted moving average methods become incompetent during disruptions, and are useful only during stable times, when the demand hovers around a predefined mean value. Exponential smoothing and grey method seems to give better results during disruptions and also during stable times in upstream tiers. Grey prediction method in particular is the best method when the disruption frequency is high and also when the disruption impact is gradual rather than sudden.