This talk explores the complexities and optimizations within generalized multi-echelon cross-docking systems, focusing on the balance between holding costs and backorder costs under variable demand scenarios. By incorporating probability theory and supply chain modeling, we present a detailed analysis of two-echelon, three-echelon cross-docking systems, and possible generic systems, emphasizing the optimization of inventory levels and the strategic distribution of goods from central warehouses to retailers. The investigation reveals the critical role of lead time and demand distribution in determining optimal order-up-to levels and discusses the implications of imbalance assumptions in multi-echelon structures. Through theoretical models and calculations, we propose strategies for achieving balanced stock-out probabilities across the supply chain, aiming to minimize the total expected cost under varying demand. The paper further discusses potential strategies in scenarios where the imbalance assumption is violated, offering insights into the resilience and efficiency of supply chain designs.