Process Automation Fit Assessment Using Calibrated Scoring
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Solution Overview
Problem
Existing automation systems lack a comprehensive method to determine if they are fit-for-purpose (FFP) relative to an entity's objectives, leading to inefficient use of computing and networking resources in generating, correcting, and configuring erroneous automations.
Innovation Solution
A recommendation system uses a machine learning model to calculate scores and generate recommendations for automating processes by analyzing input and historical data, determining a FFP rating through a series of scores and adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If automation systems are implemented without comprehensive fit-for-purpose assessment, then automation deployment speed increases, but resource efficiency and automation reliability deteriorate due to erroneous automations requiring correction and reconfiguration
Solution Approach 1:
The system performs preliminary assessment of automation fit-for-purpose before full deployment by calculating scores based on process characteristics, historical data, and organizational goals. This preliminary evaluation identifies potential issues early, allowing corrections before resource-intensive implementation, thus maintaining deployment speed while improving reliability.
Solution Approach 2:
The system incorporates feedback loops where automation performance data and correction outcomes are fed back into the scoring model. Historical data from prior automations continuously refines the fit-for-purpose assessment, enabling the system to learn from past errors and improve future automation reliability without slowing deployment.
2Reliability
If comprehensive fit-for-purpose assessment with multiple scores and historical data analysis is performed, then automation reliability improves, but computational complexity and processing time increase
Solution Approach 1:
The comprehensive assessment is segmented into distinct scoring components: process score, integration score, historical performance score, and alignment score. Each component is calculated independently using specific algorithms and data sources, then aggregated to form the overall fit-for-purpose rating. This segmentation makes the complex computation more manageable and efficient.
Solution Approach 2:
The system dynamically adjusts assessment parameters and data weights based on the specific automation context, organizational priorities, and available historical data quality. By changing parameters adaptively rather than using fixed complex models, the system achieves high reliability assessments with optimized computational requirements.
3Measurement precision
If detailed scoring and calibration processes are used to assess automation fit-for-purpose, then measurement precision improves, but processing time and computational resources increase
Solution Approach 1:
The system implements a tiered assessment approach where essential scoring components are calculated first to provide a baseline fit-for-purpose rating quickly. Additional detailed scoring and calibration are performed only when needed for high-stakes automations or when initial scores indicate borderline cases requiring further analysis, thus balancing precision with processing time.
Data Source
AI summary
A device may receive input data identifying variables and features associated with a target process automation, and may receive historical data associated with the target process. The device may process the input data, with a model, to calculate a future process score, a future integration score, a current process score, and a current integration score, and may calculate a combined score based on the scores. The device may calculate a calibrated combined score based on the combined score and a calibration factor, and may calculate an initial fit-for-purpose score for the target process automation based on the calibrated combined score and a probability metric. The device may generate a refined fit-for-purpose score based on the initial fit-for-purpose score and the historical data, and may determine recommendations for the target process automation based on the refined fit-for-purpose score. The device may perform one or more of the recommendations.