The complexity of supply chains, consisting of retailers, wholesalers, distributors, manufacturers, and suppliers, poses a significant challenge to effective coordination and management. One of the most widely recognized issues in these systems is the bullwhip effect, where small fluctuations in consumer demand can cause amplified variations in orders as they propagate upstream through the supply chain. This phenomenon results in distorted demand information, excessive inventory, increased production costs, and inefficient resource allocation, all of which compromise the overall performance of supply chains.

The bullwhip effect is primarily influenced by factors such as order batching, long lead times, price variations, and demand signal processing, with the latter being one of the most critical contributors. In demand signal processing, forecasts at one stage of the supply chain are based on the orders received from the immediate downstream partner rather than the actual customer demand. Previous research on the bullwhip effect has largely focused on multi-echelon supply chains with independent and identically distributed (i.i.d.) demand patterns, but the effects of correlated demand, where demand in one period is dependent on the demand in previous periods, remain underexplored—especially in multi-echelon systems. While some studies have addressed correlated demand in single-echelon systems, the gap in understanding its impact on multi-echelon supply chains under various operational conditions remains significant.

This research aims to fill this gap by investigating the impact of correlated demand on the dynamic performance of multi-echelon supply chains. Specifically, the research examines how correlated demand—modeled using a first-order autoregressive AR(1) demand process—affects the bullwhip effect in a multi-echelon system. The study also explores how collaboration and information sharing between supply chain partners can mitigate the adverse effects of demand distortion, providing insights into strategies for improving supply chain management practices.

The research employs a simulation modeling approach to analyze these dynamics. The simulation model represents a multi-echelon supply chain operating under a generalized order-up-to (OUT) policy and integrates a moving average forecasting method at each echelon. This allows for a detailed examination of how correlated demand and varying levels of information sharing between supply chain partners influence the bullwhip effect and the overall performance of the supply chain. The model incorporates several key variables, including the nature of demand, forecasting techniques, and ordering policies, which are analyzed under different operational and collaborative scenarios.

This research is expected to contribute to the body of knowledge on the bullwhip effect by providing a more comprehensive understanding of how correlated demand impacts multi-echelon supply chains. The study also highlights the potential benefits of collaborative practices such as information sharing of customer demand, forecasting methods, and ordering policies, which may help to reduce the negative consequences of demand amplification. By investigating these factors, the research aims to provide practical insights for supply chain managers seeking to improve the dynamic performance of multi-echelon supply chains and mitigate the risks associated with demand volatility.