Modeling Demand Forecast Variance in a Distributed Supply Chain Network using Generalized Stochastic Petri Nets

There is a trade-off in Supply Chain Management Systems between efficiency and demand variability. When no variation occurs in consumer need, order cycle, product portfolios, and in distribution lead time, then the supply chain would be just a routine business process. Unfortunately, in practice this is not often the case. Thus, ranking demand variability is one of the prime challenges to reduce the safety stock without affecting the customer demand. This paper studies supply chain demand variability with multiple suppliers, manufacturers, distributors, wholesalers, retailers, and customers as tiers, and each stage as echelon that faces stochastic demand volatility. A Generalized Stochastic Petri-Net (GSPN) model is proposed in a distributed scenario to synchronize the response capabilities among the players in the chain, and to lower down the supplier demand variance with scheduled ordering policies. Maintaining a uniform inventory stock throughout the chain has two main effects: the bullwhip effect (BWE) will be negligible, and uncertainty in decision making at each echelon will be reduced substantially.