Financial Services

As the number of customers grows , wait times increase , pressure builds , and efficiency drops . Traditional Global Order Promising (GOP) functions similarly—it processes order requests sequentially, making it difficult to scale efficiently under high demand. Now, imagine multiple self-checkout stations where customers are served simultaneously, significantly reducing wait times and improving overall efficiency. This is how High-Volume GOP (HVGOP) revolutionizes order fulfillment, processing multiple orders in parallel and dynamically adjusting commitments based on real-time supply constraints. Figure 1. GOP Information Flow (Referenced from Oracle) The Power of Production Scheduling in Oracle Supply Chain Planning Cloud A similar transformation can be seen in the airline industry. Traditional flight booking systems often faced delays in confirming seat availability due to sequential processing of requests, leading to overbooking or inaccurate availability information. With modern real-time booking engines powered by high-performance computing, airlines now offer instant seat confirmation while dynamically adjusting for cancellations and demand fluctuations. Just as this has improved efficiency in air travel, HVGOP brings similar advancements to supply chain order promising. The Evolution of Global Order Promising in SCM Cloud Global Order Promising (GOP) plays a pivotal role in order management, ensuring customers receive accurate delivery commitments based on inventory availability and fulfillment capabilities. However, traditional GOP faced significant challenges when dealing with high order volumes, often limiting its effectiveness for businesses with large-scale operations. Figure 2. Order promising effectiveness Analytics Challenges in Traditional GOP Approaches Traditional GOP encountered several limitations when processing high transaction volumes: Performance Bottlenecks: Sequential order processing resulted in delays and inefficiencies. Limited Parallel Processing: Inability to distribute processing loads effectively led to slower response times. Delayed Order Commitments: Lack of real-time data integration caused outdated or inaccurate promises. Scalability Constraints: As order volumes increased, system performance degraded, affecting fulfillment accuracy. The Next Generation of Global Order Promising in the SCM Cloud Oracle’s High-Volume GOP is a game-changer, delivering enhanced scalability, resilience, and speed as part of the Oracle Fusion Cloud Order Management suite. This new approach eliminates traditional bottlenecks and ensures seamless performance even during peak demand. Key Features of High-Volume GOP Scalability: Processes thousands of orders simultaneously while maintaining accuracy and speed. Multi-Sourcing Capability: Identifies the best fulfillment locations based on inventory availability and logistics. Intelligent Order Prioritization: Optimizes commitments based on lead times, urgency, and inventory levels. Enhanced Supply Chain Visibility: Provides real-time insights into order commitments and fulfillment status. Real-Time Inventory Access: Ensures up-to-the-minute inventory tracking and more accurate sourcing decisions. Streamlined Capable-to-Promise: Uses a Bill of Resources for smarter allocation and commitment. Figure 3. Suggested Multiple fulfilment alternatives Business Benefits Improved Customer Satisfaction: Faster, more accurate order commitments enhance customer trust and retention. Operational Efficiency: Reduces processing time, enabling businesses to manage high demand seamlessly. Cost Reduction: Optimized fulfillment minimizes unnecessary logistics and operational costs. Greater Flexibility: Adapts dynamically to fluctuations in demand and supply chain constraints. Automated Failover: Ensures resilience with horizontal scaling across servers, eliminating system downtime. Figure 4. Fulfilment Analytics and real time availability in order promising Conclusion High-Volume GOP is a game-changer for businesses handling large-scale order volumes. By combining parallel processing, real-time data integration, and intelligent prioritization, it eliminates bottlenecks, enhances responsiveness, and optimizes fulfillment decisions. As organizations advance in their digital transformation journeys, HVGOP will be a critical enabler of efficiency, cost reduction, and superior customer service. Just as self-checkout systems revolutionized retail by offering speed and convenience, HVGOP is redefining order promising—making it faster, smarter, and infinitely scalable for the modern supply chain. Businesses that embrace this cutting-edge capability will gain a competitive edge, ensuring greater agility, operational resilience, and a seamless fulfillment experience in today’s dynamic market.

How Rapidflow Reinvented ATP Continuity for Mission-Critical Supply Chains When a supply chain stops, business doesn’t just slow—it suffers. In today’s global, always-on economy, even a short interruption in system responsiveness can ripple into missed shipments, unhappy customers, and costly delays. And yet, for many Oracle EBS users, this is still a routine risk. One such known example: The temporary unavailability of Available to Promise (ATP) functionality during the Planning Data Collections process. It’s a standard product behaviour. But in the real world—where customer expectations don’t pause—this leads to halted order scheduling, delayed bookings, and eroded trust. What if your business could operate without that interruption? What if ATP didn’t go offline, even when collections ran in the background? There are customers who exactly expect the above behaviour. The Real Challenge: System Design vs. Business Reality In traditional Oracle EBS environments, during specific phases of collections (particularly ODS load), ATP becomes unavailable. This is expected behaviour from a system perspective—but from a business standpoint, it’s an obstacle. Orders can’t be scheduled. Availability dates may rely on cached data—leading to false promises. Sales reps are forced to wait, customers left guessing. Customers want more. They want: No gaps in order booking or scheduling. No cache-driven promises. No compromise on data accuracy. And above all—they want real-time continuity. Rapidflow’s Vision: Always-On ATP A leading global appliances manufacturer brought this concern to Rapidflow. The challenge was clear: Can we maintain real-time ATP availability—even while collections run? The answer wasn’t easy. But we knew it could be done. The Breakthrough Rapidflow’s Oracle Supply Chain experts reimagined the way system processes, order management, and data integrity work together developing an innovative orchestration layer that keeps ATP functionality available even when collections are in progress. The Results Collections run as scheduled. ATP goes offline briefly at the backend but remains accessible for order scheduling. Orders continue to flow. No disruption. No false promises. No missed commitments. This is more than a patch. It’s a business-resilient architecture designed for 24×7 operations. Key Business Benefits Continuous Order Processing Order promising and scheduling remain fully available—even during backend loads. Enhanced Customer Trust No more inaccurate availability dates. Customers get the truth, in real time. Operational Resilience Avoids ATP failures caused by collections delays or system downtimes. Hear the Full Story at ASCEND 2025 Join us in Orlando, where Rapidflow will present this powerful innovation alongside our partner client. You’ll get a deep dive into how we solved one of the most common—and critical—challenges in Oracle EBS supply chains. Session Title: No Stops, Just Orders: 24/7 Supply Chains Location: Rosen Shingle Creek, Orlando, Florida Date: June 8–11, 2025 About Rapidflow Inc. Rapidflow is your end-to-end Oracle solution partner—on-premise, Cloud, and everything in between. With over a decade of experience and 100+ global clients, we blend deep Oracle expertise with modern innovation to deliver transformative results. Our approach combines industry-leading practices like Oracle Unified Method (OUM) and our own Rapid Discovery & Design (RD²) framework—ensuring speed, quality, and agility in every project. Have a similar challenge in your business? Let’s Connect!

Imagine an orchestra where every musician must play in perfect harmony to create a flawless symphony. If even one instrument falls out of sync, the entire performance suffers. Manufacturing operations work in much the same way—production activities must be precisely scheduled and synchronized to ensure efficiency. However, unforeseen challenges like equipment breakdowns, material shortages, or changing demand patterns can disrupt workflows, causing inefficiencies and delays. Now, consider a modern adaptive music system that dynamically adjusts tempo based on real-time input. Oracle’s introduction of Production Scheduling in Oracle Supply Chain Planning Cloud functions in a similar way, enabling manufacturers to review, adjust, and optimize production schedules in real-time while seamlessly integrating with Oracle Manufacturing. This powerful enhancement ensures that production plans remain aligned with The Power of Production Scheduling in Oracle Supply Chain Planning Cloud Oracle’s Production Scheduling transforms manufacturing execution by offering: Real-time Schedule Adjustments: Dynamically revises schedules based on production constraints, equipment availability, and material supply. Seamless Integration with Oracle Manufacturing: Automatically releases production signals to shop floor execution, ensuring synchronized operations. Intelligent Prioritization: Optimizes job sequencing based on due dates, demand fluctuations, and resource efficiency. Enhanced Visibility: Provides a holistic view of production loads, work center capacity, and dependencies for proactive decision-making. What we understand from the above? Production scheduling in Oracle Supply Chain Planning Cloud enables planners to continuously review and fine-tune schedules through an intuitive interface. Key capabilities include: Automated Schedule Generation: System-generated production schedules based on demand forecasts, material availability, and constraints. Capacity-aware scheduling ensures optimal utilization of work centres and machines. Interactive Adjustments: Drag-and-drop functionality to reprioritize jobs and reallocate resources as needed. Scenario modelling to evaluate the impact of changes before finalizing updates. Real-Time Constraint Resolution: Identification of bottlenecks and proactive recommendations to mitigate delays. Adaptive scheduling that considers lead times, machine downtime, and labour availability. Automated Signal Release to Oracle Manufacturing: Once the schedule is finalized, production signals are seamlessly transmitted to Oracle Manufacturing Cloud. Ensures execution teams receive up-to-date job priorities and task assignments. Conclusion With the introduction of Production Scheduling in Oracle Supply Chain Planning Cloud, Oracle has reinforced the synergy between supply planning and manufacturing execution, creating a unified, intelligent scheduling solution. This marks a major milestone in cloud-based supply chain management, allowing manufacturers to dynamically optimize schedules, enhance resource efficiency, and seamlessly release production signals to Oracle Manufacturing—all in real-time. By bridging the gap between planning and execution, this integrated approach eliminates traditional scheduling inefficiencies, enhances shop floor responsiveness, and empowers businesses to meet demand with greater precision and agility. As manufacturers continue to embrace digital transformation, this powerful combination of supply planning and production scheduling will be a game-changer—driving efficiency, improving on-time delivery, and strengthening supply chain resilience in an increasingly complex and competitive landscape.

Accurate demand forecasting is the cornerstone of an efficient supply chain, allowing organizations to optimize inventory, reduce stockouts, and enhance customer satisfaction. Oracle Cloud Demand Management leverages advanced machine-learning algorithms to predict future demand based on historical data, employing multiple forecasting methods to generate a statistically reliable forecast. The system further refines predictions using a Bayesian approach to blend results from different methods and configurable forecasting parameters to fine-tune the outputs. One of the key objectives in demand forecasting is achieving the best-fit forecast, which involves fine-tuning forecasting methods, causal factors, and parameter settings to enhance accuracy. The Evolution of Forecast Tuning Forecast tuning plays a crucial role in ensuring demand predictions align closely with actual market behaviour. The traditional approach can be described as below: Manually adjusting forecasting parameters through trial and error. Selecting appropriate forecasting methods (e.g., ARIMA, Exponential Smoothing, Machine Learning models). Configuring seasonality settings (e.g., monthly, quarterly, annual trends). Testing different lookback periods (e.g., using 6 months, 12 months, or 24 months of historical data). Evaluating forecast accuracy using metrics like MAPE, RMSE, and bias. While effective, this manual approach can be labour-intensive, time-consuming, and possibly inconsistent across forecasting nodes. A Simple Analogy: Baking a Cake Imagine you are baking a cake, and you need to decide: The oven temperature (e.g., 160°C, 180°C, 200°C) The baking time (e.g., 25 min, 30 min, 35 min) The amount of sugar (e.g., 100g, 150g, 200g) Any combination could result into a cake being baked, but to get the best one you might bake multiple cakes, adjusting the temperature and time to get the best texture and taste. If you manually test all combinations, it would take a long time. This trial-and-error method is similar to traditional forecast tuning. Introducing Automated Hyperparameter Tuning Automated hyperparameter tuning eliminates this trial-and-error process by leveraging machine learning to optimize parameter selection. Instead of manually testing various combinations, the system: Runs multiple models with different parameter values. Evaluates performance using accuracy metrics like MAPE. Adjusts parameters intelligently to find the best combination. Similar to how a smart baking assistant would test and adjust baking conditions for the perfect cake, automated tuning refines forecast parameters for optimal accuracy. A Game-Changer in Oracle Cloud Demand Management Oracle Cloud Demand Management now integrates Automated Hyperparameter Tuning to streamline forecast optimization. This feature enhances forecasting accuracy with minimal manual intervention. With Automated Hyperparameter Tuning, Oracle Cloud Demand Management intelligently selects the best parameter settings to achieve the most accurate forecast with the lowest possible Mean Absolute Percentage Error (MAPE). Planning Advisor – Summary of Hyperparameter Tuning Results Oracle Cloud Demand Management’s Planning Advisor plays a crucial role in streamlining forecasting accuracy by providing real-time insights into hyperparameter tuning results. This feature helps analysts and demand planners make informed decisions based on data-driven recommendations. The Planning Advisor Summary includes: Optimal Hyperparameter Settings: Displays the best parameter configurations identified through automated tuning. Forecast Accuracy Improvements: Highlights changes in key metrics like MAPE (Mean Absolute Percentage Error) before and after tuning. Model Performance Insights: Provides a comparison of different forecasting models and their effectiveness in improving demand predictions. Recommendation Score: Suggests the most suitable forecasting approach based on historical performance and real-time data. By leveraging these insights, organizations can continuously refine their forecasting processes, ensuring demand predictions align more closely with real-world trends and operational needs. The Role of Forecasting Profiles and Graphical Analysis Oracle Cloud Demand Management provides powerful forecasting profiles that allow users to analyse: The best-fit forecast model for each product. A visual comparison of different forecasting methods. The impact of hyperparameter tuning on forecast accuracy. Graphical representations, such as historical demand vs. predicted demand graphs, enable users to: Identify trends and seasonality. Compare pre-tuned vs. post-tuned forecast accuracy. Ensure forecasts align with real-world business expectations. Conclusion Oracle Cloud Demand Management’s Automated Hyperparameter Tuning marks a significant advancement in statistical forecasting. Oracle Cloud Demand Management’s Automated Hyperparameter Tuning is more than just an enhancement—it’s a paradigm shift in statistical forecasting. By seamlessly integrating machine learning-driven optimization, businesses can now unlock a new level of precision, agility, and efficiency in demand planning. This innovation is particularly transformative for industries like discrete manufacturing, retail, and consumer goods or any industry, where fluctuating demand patterns require continuous adaptability. By eliminating manual guesswork and enabling forecasts to self-optimize, organizations can confidently navigate market volatility, optimize inventory, and enhance service levels. As businesses embrace data-driven supply chain strategies, automated tuning becomes a powerful enabler of growth, resilience, and competitive advantage. The future of forecasting is not just about predicting demand—it’s about staying ahead of it.

Oracle APEX has long been at the forefront of simplifying web application development, evolving into a powerful low-code platform that enables rapid application deployment. With the release of APEX 24.2, Oracle has taken a significant leap forward by integrating AI-driven capabilities, empowering businesses to build smarter, faster, and more scalable applications in the Oracle Cloud ecosystem. A Brief History of Oracle APEX Oracle APEX (Application Express) was first introduced in the early 2000s as an evolution of Oracle HTML DB. Over the years, it has transformed into a robust low-code platform, streamlining application development and enabling organizations to modernize their business processes. Key milestones in its development include: 2004: Initial launch as Oracle HTML DB. 2006: Rebranded as Oracle APEX, introducing significant UI and database-driven enhancements. 2010: Expanded support for REST web services, improved UI components, and greater security features. 2020: Cloud-native capabilities introduced, enhancing integration with Oracle Cloud. 2024 (APEX 24.2): AI-powered enhancements take low-code development to the next level, with automation and intelligent application capabilities. Why APEX 24.2 is the Future of Low-Code Development The APEX 24.2 release introduces groundbreaking features that redefine the low-code development landscape: Seamless Extension of Oracle Fusion Applications Organizations using Oracle Fusion Cloud (ERP, HCM, SCM) often require custom extensions to tailor business functionality. APEX streamlines this process with pre-built integration tools, reducing development time and ensuring seamless compatibility with Oracle Fusion APIs. Flexible Data Source Integration APEX now supports JSON data sources alongside REST APIs, expanding integration possibilities. Developers can fetch data from diverse sources, including Oracle Autonomous Database, on-premises databases, and external cloud services. The new unified data model feature ensures seamless connectivity across enterprise data landscapes. AI-Driven Development Assistance The new APEX Assistant simplifies coding by: Generating SQL queries based on natural language input. Debugging and optimizing PL/SQL code in real-time. Suggesting performance improvements and best practices. AI-driven automation accelerates application development and enhances developer productivity. AI-Powered Chatbots and Automation APEX 24.2 introduces seamless integration with Oracle Generative AI Services. Developers can:

In today’s hyperconnected world, security in the cloud is not just an add-on—it’s a necessity. As businesses shift critical operations to the cloud, the demand for a trusted, resilient, and intelligent security model is more urgent than ever. Oracle Cloud Infrastructure (OCI) meets this demand with a comprehensive suite of security-first services that are designed to safeguard workloads from the ground up. Why OCI Security Stands Out OCI takes a layered approach to security, embedding protection across every level of the stack. From compute and storage to identity and access management, OCI prioritizes proactive defense, policy-driven governance, and continuous visibility. Its architecture supports Zero Trust principles, ensuring that no access is granted without context and verification. A Defense-in-Depth Strategy OCI enables organizations to design secure architectures with defense-in-depth at its core. Whether you’re building from scratch or migrating legacy applications, OCI provides: Isolated networking through customizable virtual cloud networks Granular access control using policies, compartments, and dynamic groups Encryption by default, with customer-managed keys for additional control Intelligent threat detection and automated remediation via built-in tools This layered approach helps reduce the risk of compromise even in complex, multi-tiered environments. Automation for Agility and Compliance Manual security operations can be error-prone and slow. OCI addresses this challenge through automation—automated patching, compliance checks, anomaly detection, and policy enforcement streamline operations and reduce human error. Services like Cloud Guard and Security Zones ensure that environments stay compliant with best practices and regulatory standards. Visibility and Monitoring at Scale Visibility is key to any strong security posture. OCI provides detailed telemetry across services, including audit logs, metrics, and flow logs. Integrated monitoring and alerting allow for real-time insights, while connections to external SIEM tools support enterprise-grade security operations centers (SOCs). Supporting Zero Trust with Native Services OCI is purpose-built for organizations embracing a Zero Trust model. By enforcing strict identity verification, minimizing network trust, and evaluating risk continuously, OCI enables consistent, policy-driven control across distributed environments. Services like Bastion, Vault, and Data Safe further enforce secure access and data governance. Adapting to the Evolving Threat Landscape The security landscape is dynamic—OCI is built to evolve with it. With ongoing updates, Oracle’s global threat intelligence integration, and support for hybrid cloud strategies, OCI is well-positioned to meet the challenges of modern security threats while empowering innovation and scale. Final Thoughts Security in the cloud doesn’t have to be complex or reactive. With Oracle Cloud Infrastructure, organizations can build confidently knowing their data, users, and workloads are protected by a robust security architecture. By adopting a proactive and policy-driven approach, OCI customers can focus on accelerating growth—without compromising on trust.