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

Amazon Fulfillment: The Three Tiers of Optimization 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. │ └─────────────────────────────────────────────────────────────┘ Anticipatory Shipping — Shipping Before You Buy A Crazy but Effective Idea 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! ML Model Input Features | 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 | Late-Select Addressing — The Key Technique 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. CONDOR — Customer Order and Network Density OptimizeR The Problem CONDOR Solves 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. The CONDOR Algorithm 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) The Major Breakthrough 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. Regionalization — Partitioning the Network 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 Regional Inventory Strategy 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. Comparing Amazon vs. eBay vs. Regional Marketplaces | 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 | Lessons Learned for System Design You don't need to build CONDOR to apply its principles: - Batch processing over real-time: Don't dispatch an order the second it arrives. Hold it in a 15-30 minute batch window and optimize the entire batch together. It is always mathematically superior. - Re-optimization: The best route at 17:00 may be terrible by 17:30 as new orders arrive. Run iterative optimizations. - Predictive placement: If data shows consistent regional demand, stage the inventory there beforehand. - Partitioning: Break massive NP-hard routing problems into smaller regional chunks to make them solvable. 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. References & Further Reading - US Patent 8,615,473: Method and system for anticipatory package shipping - Amazon CONDOR routing optimization - Amazon CEO Andy Jassy's 2022 Letter to Shareholders: 8-region network 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. Top comments (0)