Dynamic simulation and optimal control strategy of a decentralized supply chain system

Efficient management of inventory in supply chains is critical to the profitable operation of modern enterprises. The supply/demand networks characteristic of discrete-parts industries represent highly stochastic, nonlinear, and constrained dynamical systems whose study merits a control-oriented approach. Minimum variance control (MVC) strategy is applied to solve the dynamic optimization problems of the inventory for a decentralized supply chain system. Transfer functions for each unit in the supply chain are obtained by z-transform. The entire chain can be modeled by combining these transfer functions into a close loop transfer function for the network. The model proves to be very useful in maintaining an inventory level that is just enough to satisfy customer demand. Customer demand trends are described by a general Auto Regressive Integrated Moving Average Model(ARIMA) model. The order policy is obtained by minimizing the errors between predicted inventory levels and set points and using a function that penalizes large changes in orders. Simulation results show that this approach can track customer demand and maintain a proper inventory level without causing a bullwhip effect.