Closed-Loop Nodal Analysis for Enterprise Process Automation
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Solution Overview
Problem
Many enterprises rely on manual processes due to the lack of technology to automate and improve efficiency, leading to suboptimal productivity and inefficiencies in their operations.
Innovation Solution
An automation optimization engine (AOE) is developed to analyze enterprise processes, identify automatable tasks, and provide tailored recommendations to convert non-automatable processes into automatable ones by consolidating user activities, objective decision-making, repeatable actions, and digital data handling, using keystroke analysis and machine learning to create a roadmap for automation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual processes are used to operate enterprise tasks, then flexibility and adaptability are maintained, but productivity and efficiency deteriorate
Solution Approach 1:
The system performs preliminary analysis of enterprise processes to identify automatable tasks before automation is implemented. The entitlement study and process analysis are conducted in advance to prepare a roadmap for automation, allowing the system to transition from manual to automated operations systematically without disrupting current productivity
Solution Approach 2:
The system continuously monitors enterprise processes and provides feedback on automation opportunities. By analyzing process data and identifying tasks that can be automated, the system creates a feedback loop that progressively improves automation levels while maintaining productivity through iterative optimization
2Productivity
If automation technology is implemented to improve efficiency, then productivity increases, but system complexity increases
Solution Approach 1:
The system segments enterprise processes into discrete tasks and identifies which specific tasks are automatable. By breaking down complex processes into smaller units, the system can implement automation incrementally on individual tasks rather than overhauling entire process flows, thereby reducing overall system complexity
Solution Approach 2:
The system employs a universal automation framework that can handle multiple types of tasks through a single platform. The entitlement study and analysis engine serve multiple functions including process mapping, task identification, and roadmap generation, reducing the need for separate specialized systems and lowering overall complexity
3Measurement precision
If comprehensive process analysis is performed to identify automatable tasks, then automation accuracy improves, but analysis time and resources increase
Solution Approach 1:
The system performs partial analysis by focusing on specific process areas or task types that offer the highest automation potential. Rather than analyzing every single task in an enterprise process comprehensively, the system identifies and prioritizes key automatable tasks, achieving sufficient accuracy for effective automation planning without excessive time investment
Solution Approach 2:
The system conducts preliminary assessments to quickly identify obvious automation opportunities before performing detailed analysis. By screening processes at multiple levels of depth, the system achieves accurate task identification efficiently by allocating analysis resources strategically rather than uniformly
Data Source
AI summary
An automation optimization engine is disclosed that provides a closed loop nodal analysis on a process performance tree. By analyzing an observed process and identifying automatable tasks from non-automatable tasks from the observed process, the automation optimization engine then applies machine learning techniques to generate a recommendation report identifying steps for implementing in the observed process to convert the non-automatable tasks into automatable tasks. Optimization is thus achieved to close the loop to further automate the observed process.