[System Design] Part 4 — Amazon CONDOR & Anticipatory Shipping

Amazon processes billions of orders annually through over 175 fulfillment centers using a 3-tier optimization architecture. The system includes anticipatory shipping, which uses machine learning to predict demand and move inventory closer to customers before orders are placed, and CONDOR, which continuously re-optimizes fulfillment plans within a 5-6 hour window. Amazon holds a patent (US Patent 8,615,473) for a system that begins shipping items before a customer places an order.

Amazon processes billions of orders annually through a network of over 175 fulfillment centers globally. To maintain their 1-2 day or same-day delivery guarantees, they built a 3-tier optimization architecture: ┌─────────────────────────────────────────────────────────────┐ │ TIER 1: ANTICIPATORY SHIPPING Long-term — weeks/months │ │ → ML predicts demand → Moves inventory close to customers │ │ BEFORE they place an order │ ├─────────────────────────────────────────────────────────────┤ │ TIER 2: REGIONALIZATION Medium-term — days/weeks │ │ → Partitions the fulfillment network into autonomous zones│ │ → Ensures 70-80% of orders are fulfilled intra-region │ ├─────────────────────────────────────────────────────────────┤ │ TIER 3: CONDOR Short-term — hours │ │ → Continuously re-optimizes the fulfillment plan within │ │ a 5-6 hour window before pick-and-pack begins. │ └─────────────────────────────────────────────────────────────┘ Amazon holds a patent US Patent 8,615,473 describing a system that begins shipping items BEFORE a customer places an order . It sounds like science fiction, but it's a reality. Traditional Model: Customer orders → Warehouse processes → Ships → Delivered 2-5 days Anticipatory Shipping: ML predicts: "Customers in Region X will buy 200 iPhone 16s in the next 3 days" → Amazon ships 200 iPhones from a central hub to local delivery hubs in Region X → Customer places order → The item is already locally staged → Delivered same-day | Input Feature | Significance | |---|---| | Purchase history | What do they buy, and how often? | | Browsing behavior | What are they looking at? Cart abandonment? | | Wishlists | Explicitly desired items | | Seasonal patterns | Winter coats in November, sunscreen in June | | Regional demographics | High-income areas? Young families? College towns? | | Trending products | Items going viral on social media | | Weather forecast | Rain forecasted → move umbrellas to local hubs | | Events calendar | Black Friday, Prime Day, major sports events | The Anticipatory Flow: 1. ML Model: "Zip code 10001 NY has an 87% probability of ordering 200 cases of water in the next 3 days." 2. System: Ships 200 cases from the Midwest Central Hub → NY Local Hub. These packages DO NOT HAVE A SPECIFIC CUSTOMER ADDRESS YET → They are just labeled "Destination: NY Hub". 3. Customer A in NY orders 2 cases of water: → The system assigns an address to 2 of the pre-staged cases at the NY Hub. → Delivered in 2 hours 4. If predictions are slightly off e.g., 50 cases remain unsold : → Amazon might run a targeted flash sale for that zip code. → Or re-route them back to the central hub. When you place an order, Amazon doesn't process it immediately. There is a 5-6 hour window between the order being placed and the warehouse actually starting the pick-and-pack process. CONDOR exploits this window to optimize delivery routes. 17:00 — Order A is placed in Zone 1 → CONDOR Plan v1: Ship from WH Alpha, individual truck. 17:15 — Order B is placed in Zone 1 near Order A → CONDOR Plan v2: Consolidate A+B onto the same route → saves 1 truck trip. 17:30 — Order C is placed in Zone 2 along the same route → CONDOR Plan v3: Consolidate A+B+C → highly dense, efficient route. 17:45 — Order D is placed in Zone 9 opposite direction → CONDOR Plan v4: Route 1 A+B+C + Route 2 D only . → Every 15 minutes, CONDOR re-evaluates the entire network to find better plans. → Deadline: When the window closes e.g., 23:00 , the warehouse executes the final optimized plan. CONDOR solves a variation of the Prize Collecting Vehicle Routing Problem PCVRP , which is vastly more complex than standard VRP: PCVRP: Maximize: Total "prize" value of orders delivered on time Minimize: Total transportation cost Subject to: - Capacity constraints vehicle limits - Time windows delivery SLAs - Fleet size limits - Network density bonuses: bundling orders in the same neighborhood significantly reduces cost-per-package. Solving Techniques: 1. Mathematical optimization LP/MIP relaxation 2. Local search heuristics 2-opt, 3-opt swaps between routes 3. Iterative re-optimization running the solver continuously as new data arrives Amazon has stated that CONDOR reduces the number of feasible routing decisions for a given area from millions to under 10 viable options , transforming an NP-hard problem into something solvable in near real-time. Prior to 2022, if a customer in New York ordered an item, it might have shipped from a warehouse in California 3,000 miles away if the local warehouse was out of stock. This was incredibly inefficient. Amazon restructured its US network into 8 autonomous regions : Pre-Regionalization: Customer in NY → Order fulfilled from CA 3,000 miles → 3-5 day delivery. Post-Regionalization: Customer in NY → Order fulfilled from NJ or PA 100 miles → Same/Next day delivery. Results: - Average travel distance per package dropped by ~60% - Significant reduction in shipping costs - Delivery times dropped by 1-2 days - Massive reduction in carbon footprint SKU "IPHONE-16-256GB": National Demand: 100,000/month Northeast Region NY, NJ, PA : 25,000/mo → Stock 30,000 West Region CA, WA, OR : 20,000/mo → Stock 25,000 South Region TX, FL, GA : 18,000/mo → Stock 22,000 ... Buffer: 22,000 kept at a central cross-dock facility for overflow/rebalancing. | Aspect | Amazon | eBay | Regional Marketplaces | |---|---|---|---| Model | 1P + FBA Owns warehouses | Marketplace Sellers ship | Marketplace + Fulfillment e.g., Shopee | Facilities | 175+ Global FCs | Seller warehouses + 3PLs | Regional fulfillment hubs | Allocation | CONDOR Global/Continuous optimization | Rule-based Seller-defined | Regional matching engines | Anticipatory | Yes Late-Select Addressing | No | No | Structure | 8 Autonomous Regions US | Decentralized | Geographic partitioning | You don't need to build CONDOR to apply its principles: Next, we explore split shipments, consolidation, and the last-mile delivery problem—which accounts for 53% of all logistics costs. Read Part 5 — Split Shipment, Consolidation & Last-Mile Delivery . This post was originally published on my blog at Part 4 — Amazon CONDOR & Anticipatory Shipping. Hi, I'm Lê Tuấn Anh vesviet 👋 I am a Senior Go Backend Architect & Distributed Systems Engineer with 17+ years of experience building high-traffic platforms 25M+ requests/month . If you enjoyed this deep-dive, let's connect on LinkedIn or explore my consulting services at tanhdev.com/hire.