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Introduction to Region-based sourcing In today’s retail ecosystem, customer expectations are higher than ever. Shoppers don’t just want fast delivery; they expect accurate fulfillment from stores or warehouses closest to their location. Traditional city/state/zip-based sourcing methods often lack flexibility and can lead to higher delivery costs and longer lead times. That’s where Region-based sourcing comes into play. Region-based sourcing is an effective method of allocating orders to stores or distribution nodes based on geographical Regions. These Regions are defined by latitude and longitude rather than fixed state, city, or postal code boundaries. This system helps retailers match each order with the most suitable store in real-time. Curious to know more about this system? Let’s discuss how Region-based data sourcing works, two different implementation models retailers can adopt, and which models will suit you best. What is Region-Based Sourcing? Region-based sourcing in retail chains uses digitized geographic boundaries that accurately map store locations or service areas. Unlike traditional sourcing models that rely on rigid city, state, or zip code boundaries, Region-based sourcing defines dynamic, custom-shaped regions on a map. When a customer places an order, the system checks which Region the address falls into, and fulfillment is restricted to the stores or warehouses assigned to that region. Based on business requirements, each Region can have defined store priorities, transfer relationships, and BOPIS/STH eligibility. This approach: Ensures faster delivery times Reduces last-mile shipping costs Allows dynamic adjustments (Regions can be redrawn as business needs change) Supports compliance with local inventory regulations Are There Any Challenges with Region-Based Sourcing? Despite its clear advantages, retail chains face several technical hurdles when they implement Region-based sourcing. These challenges arise from the ever-changing nature of retail environments and the complex geographic calculations that occur in real-time. 01 Dynamic Region Boundaries Region boundaries grow as organizations expand. Systems must flexibly adapt to shifting Regions to keep fulfillment rules accurate. 02 Handling Large Numbers of Regions Some businesses define up to 192 regions in the current Region-based retail setups, demanding thousands of geographic calculations without slowing inventory checks or order creation. 03 External Services for Accurate Region Identification External apps define Regions and require live calls (latitude, longitude, zip) to locate customers. Integrations must stay stable to avoid service disruption. How to Implement Region-Based Sourcing? Implementing Region-based sourcing isn’t a one-size-fits-all process. Retailers can choose between two main approaches depending on their store network size, scalability needs, and technical capacity. Approach 1: The Distribution Group Model One way to implement Region-based sourcing is through a distribution group model. Under this model: Each Region is treated as a distribution group. The group contains all associated stores and distribution centers(DCs). Priority rules are set for each node within the group. Advantages of the Distribution Group Model Easy to implement, uses OOTB configurations. No code/customization required. Limitations of This Approach Every model has its set of limitations, and this Region-based sourcing implementation approach may not be ideal for every business because of the following factors: Requires upfront knowledge of which stores belong to which Regions. Poor scalability as Regions grow dynamically. During inventory inquiries or order creation, large numbers of static groups can degrade performance. Approach 2: The Dynamic Sourcing Model The Dynamic Sourcing Model is an alternative to implementing Region-Based Sourcing. Under this model: A common distribution group is created for all fulfilling stores Transfer relationships defined between stores remain unchanged A sourcing correction user exit (or plug-in) calls an external service in real time based on the ship-to address (latitude and longitude data fetched) The external service identifies the correct Region and returns the list of eligible stores Advantages of the Dynamic Sourcing Model No need for static mapping of stores to Regions. Supports dynamic region expansion with minimal reconfiguration. Better scalability across large or changing regions. Some Drawbacks of This Approach The Dynamic Sourcing Model requires a fallback plan in case external service calls fail It can become complex if multiple sequencing rules or capacity constraints are later introduced Note –  Many organizations implement a temporary cache or table to store frequently accessed region data, with a toggle to enable or disable it. This reduces data latency without sacrificing accuracy Comparing Distribution Group Model vs. Dynamic Sourcing Model Feature Distribution Group Model Dynamic Sourcing Model Code Customization None; configuration only Required for external integration calls Scalability Poor with many Regions Handles growth better Mapping Static, must be maintained Loaded dynamically in real time Risk Lower (out-of-box support) Higher; must handle integration failures Performance Degrades with large region set Relies on efficient external calls and caching Choosing the Right Approach to Implement Region-Based Sourcing The choice between the Distribution Group Model and the Dynamic Model depends on business maturity and growth stage: Smaller, stable retailers may benefit from the simplicity of the Distribution Group Model. Scaling, multi-region enterprises should invest in the Dynamic Model for long-term agility and accuracy. Both models highlight one truth: retailers must align sourcing strategies with evolving geographic and operational realities. Technical Considerations for Retail Chains to Use Region Data To make Region-based sourcing work at scale, retail chains need more than just accurate maps; they need strong technical foundations. Here are some basic considerations every retailer must keep in mind: REST API Integration Since Region data is often managed in an external application, the order management system must integrate through Rest APIs. Caching and Fallback Strategies A local cache of frequently used Region data minimizes the impact of external call failures. Performance Testing Simulate large region counts (e.g., 192+) and heavy order volumes to ensure acceptable response times. Inventory Transfer Logic Clearly define transfer relationships between stores  when inventory dips, to avoid back-order loops. Business Impact and Outcomes of Region-Based Sourcing The shift to Region-based sourcing delivered clear benefits: Higher fulfillment accuracy Orders were sourced only from appropriate stores within the correct Region. Reduced delays and mis-shipments Improved customer satisfaction by ensuring local relevance. Scalability Able to handle the client’s 192 regions without a performance bottleneck. Flexibility New Regions and stores could be added without reconfiguring large