Analysis of Supply Chain Dynamics using Distributed Simulation

Dynamics in a supply chain are attributed to the delays and amplifications in the flow of information across the supply chain system. Demand is an important form of information that passes from one firm to another. During these transfers, the demand information gets distorted leading to demand amplifications. This demand amplification effect (popularly known as the bullwhip effect) has been widely discussed in the literature over many years. However, the bullwhip effect is still prevalent in many supply chains, generally leading to excessive inventory throughout the system, poor product forecasts, insufficient or excessive capacities, product unavailability and higher costs. Causes for demand amplifications include (i) demand signal processing, (ii) order batching, (iii) rationing game, and (iv) price variations [1]. Several alternatives have been identified to minimize the supply chain dynamics. They are (i) single control of replenishment (e.g., vendor managed inventory); (ii) lead time reduction; (iii) regular delivery schedule; (iv) changing individual echelon decision rules; (v) identifying inventory levels; (vi) improved information exchange (e.g., access to point of sale data); and (vii) type of information sharing (centralized, decentralized, and coordinated).