An Agent-Based Simulation Model to Mitigate the Bullwhip Effect via Information Sharing and Risk Pooling

The bullwhip effect, a phenomenon of progressively larger distortion of demands across a supply chain, can cause chaos and disorder with amplified supply and demand misalignment. In this research, the researchers investigated ways to decrease the bullwhip effect via risk pooling and information sharing through a simulation study.

Traditionally, two approaches are used in studies related to the bullwhip effect: simulation-based and analytical methods. Analytical or numerical studies produce exact and controlled solutions and assess pertinent factors that affect the system. However, it is challenging to comprehend the effect of each factor and integrate multiple factors on the entire supply chain due to its complexities.

With the increase in computational power and the availability of such resources, simulation-based models have gained popularity and enable more complex and thorough studies in supply chain management. Simulation-based studies also enable real-time adaptability and stochasticity tolerance, which is not possible with analytical models.

So, an agent-based simulation model was developed to evaluate how risk pooling and information sharing between distinct entities in a supply chain can reduce the bullwhip effects. In the agent-based paradigm, different components of a system were described as agents, which interact with each other in an environment.

Specifically, the researchers were interested in the effectiveness of these two strategies through their interplay when they were applied simultaneously and separately. The developers simulated a three-echelon supply chain by considering one manufacturer, one wholesaler, and two retailers. Four scenarios were evaluated by varying the information sharing strategy (centralized and decentralized) and with and without a risk pooling policy. The results showed that when both strategies were adopted, the supply chain faced less order amplification throughout the supply chain.

The presented agent-based simulation model could be extended to study complex model configurations, for example, under situations where customer demands could be directly fulfilled from the wholesaler and manufacturer. Future research could also consider disruptions in the supply chain, order split in wholesaler and manufacture, and so on.

Supply chain with information sharing and with risk pooling
Supply chain with information sharing and with risk pooling

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