Hybrid Cloud Integration Framework for Order Optimization
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Solution Overview
Problem
Large omni-channel retailers face challenges in efficiently managing order fulfillment across various channels due to the complexity of infrastructure and data science skills required for predictive analytics and optimization models, often necessitating the use of SaaS hosted Big Data Analytics Platforms, which can be costly and difficult to implement.
Innovation Solution
A hybrid cloud integration framework that enables real-time access to Big Data analytic and optimization technologies, allowing on-premise order management systems to 'punch-out' and utilize cloud-based optimization services, ensuring scalability, fault tolerance, and compliance with service level agreements (SLAs) through a punch-out approach and SaaS framework connector.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If retailers use on-premise order management systems with Big Data analytics and optimization models, then order fulfillment optimization is improved, but infrastructure complexity and implementation difficulty increase
Solution Approach 1:
The patent introduces a hybrid cloud integration framework that acts as an intermediary between on-premise order management systems and cloud-based Big Data analytics platforms. This framework includes API gateways, data virtualization layers, and integration middleware that enable retailers to access optimization capabilities without directly managing complex cloud infrastructure, thus resolving the contradiction between improved optimization and reduced infrastructure complexity
Solution Approach 2:
The system segments the order management architecture into distinct modular components: on-premise transactional systems, hybrid cloud integration layer, and cloud-based analytics platforms. Each segment operates independently with well-defined interfaces, allowing retailers to adopt optimization capabilities incrementally without overhauling entire infrastructure, thereby reducing implementation difficulty while maintaining productivity benefits
2Adaptability or versatility
If retailers deploy SaaS hosted Big Data Analytics Platforms, then predictive analytics capability is improved, but cost and implementation difficulty increase
Solution Approach 1:
The hybrid cloud integration framework provides universal access mechanisms that work across multiple SaaS analytics platforms and different on-premise systems. Standardized APIs, common data models, and platform-agnostic integration patterns enable retailers to leverage predictive analytics capabilities from various SaaS providers without custom implementation for each platform, reducing implementation difficulty while maintaining versatile analytics capability
Solution Approach 2:
The system employs lightweight, stateless API connectors and transient integration components that can be rapidly deployed and discarded. These disposable integration artifacts require minimal configuration and can be replaced or updated without affecting core systems, making it easier to experiment with different SaaS analytics platforms and reducing the barrier to implementation
3Speed
If cloud-based optimization services are called in real-time, then order processing speed is improved, but service reliability and fault tolerance must be ensured
Solution Approach 1:
The framework implements prior cushioning through multiple mechanisms: pre-established connection pools to cloud services, cached optimization results for common scenarios, and predefined fallback rules that kick in when cloud services are unavailable. These preparatory measures ensure that real-time optimization calls can proceed at high speed while having built-in protection against service failures, maintaining both speed and reliability
Solution Approach 2:
The system incorporates continuous feedback loops that monitor cloud service health, response times, and success rates. When degradation or failure is detected, the framework automatically adjusts by switching to fallback mechanisms, reducing call frequency, or routing to alternative services. This feedback-driven adaptation maintains service reliability while preserving real-time processing capability under normal conditions
Data Source
AI summary
A method for enhancing on-premise order management systems (OMS) designed for fulfillment transactions with analytic and optimization technologies and services hosted in a shared multi-tenant software-as-a-service (SaaS) environment, such as a hybrid cloud. The computer-implemented method improves an order management system by leveraging a “punch-out” approach based on user exits to integrate with and augment currently implemented order management processing and transaction flows. Using the hybrid cloud, an entity may retain data such as its accumulated business, sales, test and other data, and then run analytical queries, which can scale to support distributed computing tasks. A framework adaptor/connector is leveraged by the OMS to provide a web client for communicating with and integrating to the SaaS analytics runtime environment, encapsulating all necessary connection pooling, security, and data marshaling complexity away from the order management system to meet strict service response time windows.