Modeling demand forecast variance in a distributed supply chain network using generalized stochastic petri nets

• Authors: Bidyut Biman Sarkar , Agostino Cortesi , Nabendu Chaki
• Languages of publication: EN

Abstracts

EN

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 safety stock without affecting customer demand. This paper studies supply chain demand variability with multiple suppliers, manufacturers, distributors, wholesalers, retailers, and customers as tiers, and each stage as an 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.

Keywords

EN

Supply Chain Management; Bullwhip Effect; Generalized Stochastic Petri Nets; Retail Network

• Publisher: Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu
• Journal: Informatyka Ekonomiczna
• Year: 2013
• Issue: 3(29)
• Pages: 128-151

Contributors

author

Bidyut Biman Sarkar

• Techno India University, Calcutta

author

Agostino Cortesi

• Ca’ Foscari University of Venice

author

Nabendu Chaki

• University of Calcutta

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