Automation Decision System for Metric Validation
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Solution Overview
Problem
Entities face challenges in deciding whether to implement automation tools, including determining their economic value, quantifying benefits, and validating return on investment, due to significant upfront costs and complexity in collecting usage metrics.
Innovation Solution
A method and system that receive quantification and qualification metrics from entities, transform them into standardized formats, and use a processor to provide automation decisions, including the implementation of bots, while allowing for continuous data analysis and validation metrics to assess the efficacy of automated processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automation tools are implemented to improve productivity and reduce operational costs, then productivity increases and operational costs decrease, but the complexity of collecting and analyzing usage metrics increases
Solution Approach 1:
The patent introduces an intermediary system that automatically collects, standardizes, and analyzes usage metrics from multiple automation tools. This intermediary layer simplifies the complexity by centralizing data management and providing pre-processed insights, eliminating the need for entities to manually collect and analyze metrics from diverse sources.
Solution Approach 2:
The system implements continuous feedback loops where usage metrics are collected, analyzed, and used to provide real-time insights and recommendations. This feedback mechanism automatically adjusts automation strategies based on actual performance data, reducing the manual analysis required while maintaining high productivity improvements.
2Reliability
If automation tools are implemented to eliminate or reduce errors, then error reduction increases, but the difficulty of demonstrating efficacy and validating return on investment increases
Solution Approach 1:
The patent replaces manual measurement and verification processes with automated systems that continuously monitor error rates and validate automation efficacy. This substitution eliminates manual auditing and provides objective, real-time metrics that clearly demonstrate return on investment and effectiveness.
Solution Approach 2:
The system provides continuous feedback on automation performance by collecting usage metrics, comparing them against predefined success criteria, and generating validation reports. This feedback mechanism makes it straightforward to demonstrate efficacy by automatically highlighting areas where automation has reduced errors and improved reliability.
3Measurement precision
If multiple metrics are collected to quantify benefits and validate automation decisions, then the accuracy of automation decision-making improves, but the time required for data collection and analysis increases
Solution Approach 1:
The patent performs preliminary actions by pre-defining success criteria, key performance indicators, and data collection templates before automation implementation. This preparation enables rapid, accurate measurement of benefits without time-consuming on-site analysis, as the framework is already in place to capture precise metrics.
Solution Approach 2:
The system implements continuous data collection and real-time analysis, eliminating interruptions for manual auditing or periodic reviews. This continuous operation provides ongoing accurate metrics while minimizing time loss, as the system operates autonomously to maintain constant surveillance and evaluation of automation performance.
Data Source
AI summary
Systems and methods for implementing an automated process. The systems include an interface for receiving at least one quantification metric associated with a first entity and at least one qualification metric associated with the first entity; and a processor executing instructions stored on memory to provide an automation decision for the first entity to implement an automated process based on the quantification metric associated with the first entity and the qualification metric associated with the first entity, wherein the interface is further configured to receive a validation metric associated with the implemented automated process.