An advanced supply chain network visualizer demonstrating Jebel Ali (JAFZA) central echelon buffering. Toggle network strategies to observe how multi-echelon math optimizes safety stocks across retail outlets in Riyadh and Muscat.
Under the traditional Decentralized model, each regional store operates as an isolated silo. Muscat and Riyadh calculate their safety buffers independently based on their local demand variance. Because they cannot rely on immediate delivery, they must hold massive stock buffers, leading to high capital lockup.
The Multi-Echelon (MEIO) strategy models the network as a single mathematical entity. We hold the primary safety buffer at the JAFZA Jebel Ali Hub (where transit variability is pooled and lower) and feed the store nodes dynamically using rapid regional cross-docking. Pooling cuts the long-leg buffer by about 28% (two stores' buffers combine as √(σ₁²+σ₂²), not σ₁+σ₂), but each store still needs a buffer for its hub leg. Whether the network saves depends on how short those legs are and how many stores share the hub. Move the transit sliders to find the break-even.
Based on JAFZA central pooling calculations and a standard 25% annual carrying cost rate.
SS_hub = z × √(L × (σ₁² + σ₂²)) replaces the two store buffers on the long China leg (for N identical stores this is the square-root law, ∑SS_local ÷ √N). The stores then still need z × σ × √(hub leg) each. Pooling pays only when the pooling gain is bigger than those hub-to-store buffers: with two stores at 95%, that means hub legs of about 2 days or less.