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Sterling OMS

By Industry, By Tech/Product, By Topic, Distribution, Manufacturing, Order Management, Retail, Sterling OMS

OpenSearch in IBM OMS SaaS & Beyond

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OpenSearch in IBM OMS SaaS & Beyond: A Modern, Open, and Scalable Foundation for Commerce Observability As digital commerce platforms continue moving toward SaaS architectures, real-time visibility into order and inventory transactions, integrations, and the associated events has become a core operational requirement. For IBM OMoC, i.e., Order Management on Cloud (OMS SaaS)customers, IBM’s transition from Graylog to OpenSearch brings next-generation observability and log analytics capabilities to their Digital Commerce operations. OpenSearch is not just an open-source search engine; it has become a foundational component of the OMS SaaS observability model, enabling faster troubleshooting, deeper analytics, and richer insights across all OMS workloads. Why OpenSearch Matters in the IBM OMS World IBM has adopted OpenSearch as its strategic platform for logging, search, and analytics in the Next Gen OMS Platform (OMoC 2.0). This shift brings several important benefits: Unified Observability Across OMS Components Instead of relying on distributed log viewers or siloed monitoring tools, customers gain: A centralized view of OMS logs High-speed search for order and integration flows Standardized event formats across applications and system components This drives clearer visibility for operational, support, and integration teams. The dashboards below represent the Server and OMS health errors – Handling Large Numbers of Regions OpenSearch improves the ability to quickly spot and diagnose issues such as: Slow or failing APIs Delayed message queues Payment or tax service failures Integration delays across commerce systems Interactive dashboards make problem detection significantly faster. Built for High-Volume Retail and Peak Seasons Designed for distributed, high-ingest workloads, OpenSearch scales seamlessly to match the log volume generated by large retailers — particularly during major seasonal peaks. Comparing Distribution Group Model vs. Dynamic Sourcing Model Capability Graylog OpenSearch Examples Full-Text Search Basic Lucene-powered, enterprise-grade Quickly search thousands of OMS logs for a specific order number, API error, or correlation ID using fast, Lucene-based indexing. Scalability Moderate Distributed, high-ingest Handle massive log spikes during holiday peaks or flash sales without performance degradation, due to distributed high-ingest clustering. Machine Learning No Yes, with anomaly detection OpenSearch’s anomaly detection can automatically identify unusual patterns without predefined rules – • Unexpected drops in API throughput for core flows like createOrder or releaseOrder. • Abnormal increases in message queue delays for integrations with WMS, ERP, or tax systems. Extensibility Restricted Plug-ins, ML models, open ecosystem OpenSearch allows additional plug-ins or ML-based features that extend the platform: • Supports ML plug-ins for predicting order volume or latency trends. • Allows visualization plug-ins for richer OMS dashboards. These advancements make OpenSearch a more flexible and future-ready fit for OMS observability. How OpenSearch Enhances Digital Commerce Operations OpenSearch plays a critical role in strengthening observability across digital commerce and OMS implementations, providing deeper operational insight and faster issue resolution. 1. Faster Diagnostics Across the Order Lifecycle OpenSearch dashboards enable teams to: Trace order journeys from capture to fulfillment Detect integration failures in real time Identify API issues, routing problems, or custom logic errors quickly Perform detailed log analysis to accelerate root-cause identification These capabilities help reduce Mean Time to Resolve (MTTR) for OMS-related incidents and improve overall system reliability. 2. Improved Monitoring for Peak Readiness During high-demand periods such as holidays, flash sales, and promotional events, OpenSearch provides visibility into: Log volume spikes and traffic patterns API latency and throughput Fulfillment delays and routing bottlenecks JVM and infrastructure behavior across OMS components This insight supports proactive capacity planning and smoother peak-season operations. 3. Greater Visibility Into Custom Extensions Many OMS implementations incorporate custom elements—such as payment adaptors, inventory or allocation services, specialized order-routing logic, or external commerce integrations.  Here are a few relevant examples: Example 1 – Payment Adaptor MonitoringAn organization using a custom payment adaptor (e.g., for gift cards, or third-party payment gateways) can create an OpenSearch dashboard to track authorization failures, timeout rates, and retry patterns in real time—helping teams detect issues before they impact checkout. Example 2 – Allocation or Inventory Service TrackingIf an implementation uses a custom ATP service to determine inventory availability, OpenSearch can visualize trends such as response latency, allocation decision outcomes, exceptions, or API degradation—ensuring smoother order promising. Example 3 – Custom Order Routing LogicOrganizations with bespoke routing rules (store-first, region-first, cost-based routing, etc) can use OpenSearch to monitor routing decisions, identify bottlenecks, and detect mis-routed orders through custom logs. Example 4 – External Commerce or ERP IntegrationsFor integrations with SAP, Salesforce Commerce, Shopify, or warehouse systems, OpenSearch dashboards can highlight message failures, queue delays, or payload anomalies, enabling faster triage when an external dependency slows down the OMS. OpenSearch enables the creation of targeted dashboards to monitor these custom components alongside core OMS flows, ensuring unified observability across the entire digital commerce ecosystem. OpenSearch dashboards enable teams to: Trace order journeys from capture to fulfillment Detect integration failures in real time Identify API issues, routing problems, or custom logic errors quickly Perform detailed log analysis to accelerate root-cause identification These capabilities help reduce Mean Time to Resolve (MTTR) for OMS-related incidents and improve overall system reliability.During high-demand periods such as holidays, flash sales, and promotional events, OpenSearch provides visibility into: Log volume spikes and traffic patterns API latency and throughput Fulfillment delays and routing bottlenecks JVM and infrastructure behavior across OMS components This insight supports proactive capacity planning and smoother peak-season operations. Many OMS implementations incorporate custom elements—such as payment adaptors, inventory or allocation services, specialized order-routing logic, or external commerce integrations.  Here are a few relevant examples: Example 1 – Payment Adaptor MonitoringAn organization using a custom payment adaptor (e.g., for gift cards, or third-party payment gateways) can create an OpenSearch dashboard to track authorization failures, timeout rates, and retry patterns in real time—helping teams detect issues before they impact checkout. Example 2 – Allocation or Inventory Service TrackingIf an implementation uses a custom ATP service to determine inventory availability, OpenSearch can visualize trends such as response latency, allocation decision outcomes, exceptions, or API degradation—ensuring smoother order promising. Example 3 – Custom Order Routing LogicOrganizations with bespoke routing rules (store-first, region-first, cost-based routing, etc) can use

By Tech/Product, Customer Service, IBM, IBM Call Center, Sterling OMS

Elevating Customer Service: How Perfaware is rewriting the CSR playbook with IBM Call Center

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Putting the Customer First with IBM Sterling Call Center Application In today’s competitive retail landscape, the customer journey doesn’t end at checkout — in fact, that’s where the most critical phase begins. A smooth and frictionless post-purchase experience is essential for retailers aiming to build long-term customer loyalty and drive repeat business. Part of delivering that seamless experience involves meeting customers where they are — whether they need help placing an order or canceling/tracking orders. In these moments, a customer service representative (CSR) may need to step in and assist with the purchase. This is where assisted order placement, powered by IBM Sterling Next Gen Call Center, becomes a game-changer. Pre-purchase challenges: what ails cart-to-order conversion Despite the rise of digital shopping, a staggering 75% of all pre-purchase interactions with CSRs involve a cart that the customer has already created online. Yet, CSRs often lack visibility into these carts—forcing customers to repeat their intent and preferences during support calls. This disconnect leads to lower conversion rates, reduced average order value (AOV), and longer interaction times, ultimately increasing the cost to serve. Some of the major challenges faced by businesses are: 01 Visibility into customer’s open carts from web 02 Customer’s shopping patterns 03 Inaccurate inventory availability 04 Out-of-sync promotions and discounts compared to web 05 Lack of flexibility in payment methods Bridging the Gap Perfaware partnered with a leading Canadian multinational athletic apparel brand to address these issues head-on. The solution? Integrating the customer’s shopping history and bringing web equivalent capabilities directly into the IBM Sterling Call Center. This integration empowers CSRs with real-time visibility into customer buying patterns, enabling them to: Understand customer intent without repetitive questioning Proactively guide customers toward completing their purchases Recommend relevant products or promotions based on cart contents The result is a smoother, more personalized shopping experience that drives higher conversions and customer satisfaction. While the out-of-the-box Call Center Customer page provides basic visibility into customer address, payments, orders, and draft orders, it lacks insight into the customer’s browsing behavior and preferences on the website — such as wishlist items or shopping interests. To bridge this gap and further streamline the buying journey, we have enhanced the Customer Details view with custom features that empower CSRs to offer more personalized and proactive support. These enhancements include: Shopping patterns Visibility into the customer’s closet, interests, and wishlist items Product availability and discounts Enabling CSRs to make timely, relevant recommendations Wishlist-to-order conversion tools Helping turn browsing intent into actual purchases. Display all Customer Gift cards provided for earlier Returns/ Appeasements, which can be utilized for future purchases. The image displays the IBM Sterling Call Center interface with a comprehensive view of a customer’s profile details.   Order Preview – for fast order summary Today’s CSRs expect fast, intuitive interfaces that deliver concise, clear information with minimal clicks. While the OOTB IBM Sterling Call Center allows CSRs to search and view orders in detail, it often requires opening the Order/Return summary page to access item specific information. To streamline this, we introduced a custom Order Preview feature. This enhancement reduces response time and improves the overall support experience, especially during live customer interactions. Below is a snapshot of the IBM Sterling Call Center Advanced Order Search Screen with Preview. The CSR’s Digital Colleague The next frontier in customer service is AI assistance. Perfaware has developed an AI-powered retail chatbot designed as a call center plug-in to enhance customer service by efficiently handling inquiries like order status, store location, and order cancellation. When a customer wants to return an item, the chatbot can instantly scan return policies and provide a clear answer—no waiting, no transfers. This not only improves customer satisfaction but also frees up CSRs to focus on high-value interactions. Ready to transform your customer service experience? Let’s connect and explore so we can develop Next Generation Call Center solutions together.  LinkedIn X Email Author Details Pooja V IBM Sterling consultant An IBM Sterling consultant with hands-on experience across call center legacy and next-gen platforms. I’ve designed and implemented pre-purchase and post-purchase flows in IBM Sterling Call Center using the Dojo framework, and lead teams through complex Next-Gen integrations and customizations

B2C, By Topic, Order Management, Retail, Retail, Sterling OMS

Region-Based Sourcing in Retail: Models, Challenges, and Best Practices

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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

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