RPA Workflow Micro-Optimization Through Analytics Feedback Loops
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Solution Overview
Problem
Current robotic process automation (RPA) implementations are often done piecemeal, leading to suboptimal processes and inefficiencies, as they lack a comprehensive approach to align operations with strategic business outcomes.
Innovation Solution
A computer-implemented method that involves receiving a plan for RPA implementation, performing analytics on business data to measure and align RPA operations with strategic outcomes, generating and deploying RPA workflows through RPA robots, and iteratively improving these workflows based on performance criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If RPA implementations are done piecemeal with single isolated implementations, then implementation simplicity is maintained, but overall process optimization and alignment with strategic business outcomes deteriorates
Solution Approach 1:
The patent combines multiple isolated RPA implementations into a unified, comprehensive automation framework that integrates various business processes. This merging approach allows the system to achieve strategic alignment and holistic optimization while maintaining manageable implementation through standardized templates and centralized governance.
Solution Approach 2:
The invention creates universal RPA templates and frameworks that can be applied across multiple business processes and departments. This multi-functional approach enables consistent implementation simplicity while achieving broad process optimization and strategic alignment across the organization.
2Productivity
If comprehensive analytics and iterative optimization are implemented, then RPA workflow performance improves, but system complexity and implementation effort increases
Solution Approach 1:
The patent implements pre-defined analytics frameworks, performance metrics, and optimization templates before RPA deployment. This preliminary preparation establishes structured measurement and improvement mechanisms that enhance workflow performance while managing complexity through standardized approaches rather than ad-hoc solutions.
Solution Approach 2:
The invention incorporates continuous feedback loops through analytics that monitor RPA workflow performance and automatically trigger optimizations. This feedback mechanism improves productivity systematically while containing complexity through automated decision-making and standardized optimization protocols.
Data Source
AI summary
Process evolution for robotic process automation (RPA) and RPA workflow micro-optimization are disclosed. Initially, an RPA implementation may be scientifically planned, potentially using artificial intelligence (AI). Embedded analytics may be used to measure, report, and align RPA operations with strategic business outcomes. RPA may then be implemented by deploying AI skills (e.g., in the form of machine learning (ML) models) through an AI fabric that seamlessly applies, scales, manages AI for RPA workflows of robots. This cycle of planning, measuring, and reporting may be repeated, potentially guided by more and more AI, to iteratively improve the effectiveness of RPA for a business. RPA implementations may also be identified and implemented based on their estimated return on investment (ROI).