Automability Engine for Process Automation Selection
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Solution Overview
Problem
Selecting an appropriate automation service for business processes is complex, time-consuming, and prone to errors due to the complexity of process stages and the large number of available automation services, with varying execution constraints and cost considerations.
Innovation Solution
A platform, referred to as an 'automability' engine, maps a business process model to a reference model, identifies inefficiencies, and recommends relevant automation services by calculating scores based on project factors and historical data, using machine learning techniques for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual evaluation of automation services is performed, then selection accuracy may be maintained, but time consumption and complexity increase significantly
Solution Approach 1:
The system performs self-evaluation by automatically comparing process models against reference models and computing automation suitability scores without requiring manual expert intervention. The automated evaluation engine independently identifies deviations, maps process stages, and generates recommendations, enabling the system to serve itself in the evaluation process.
Solution Approach 2:
Manual expert evaluation (mechanical human analysis) is replaced with an automated computational system that uses machine learning models and algorithms to perform the same evaluation function. The system substitutes human cognitive processes with computational processes, achieving both speed and consistency.
2Measurement precision
If comprehensive process model comparison is performed, then identification accuracy improves, but computing resource consumption increases
Solution Approach 1:
The process model comparison is divided into discrete, manageable segments including stage mapping, deviation identification, and metric comparison. Each segment is processed independently and contributes to the overall evaluation, allowing the system to handle complex comparisons through modular processing that optimizes resource usage.
Solution Approach 2:
The system performs preliminary actions by pre-processing process models, pre-identifying potential deviations, and pre-computing similarity metrics before the full evaluation. This preliminary processing reduces the computational burden of the main comparison operation while maintaining identification accuracy.
3Reliability
If detailed deviation analysis is conducted, then recommendation quality improves, but system complexity increases
Solution Approach 1:
The system introduces an intermediate evaluation layer that translates complex process deviations into standardized metrics and scores. This intermediary layer includes the automated evaluation engine that converts detailed technical deviations into comprehensible recommendations, simplifying the interface between complex analysis and user-friendly output.
Solution Approach 2:
The system changes parameters by transforming detailed qualitative deviations into quantitative metrics and scores. By converting complex process differences into standardized numerical evaluations, the system maintains high recommendation quality while reducing the apparent complexity for users through parameter transformation.
Data Source
AI summary
A device may receive a process model and a reference process model. The process model may include a first plurality of nodes corresponding to a first plurality of stages of a particular process. The reference process model may include a second plurality of nodes corresponding to a second plurality of stages of a reference process. The device may map the process model to the reference process model, identify that a first stage corresponds to a second stage, determine a difference between a value, of a metric, associated with the first stage and a value, of the metric, associated with the second stage, cause an interactive user interface to display a visual representation of the difference, identify automation service(s) that compensate for the difference, determine a score for the process model, and cause the interactive user interface to display the score to enable selection of optimal automation service(s) to deploy.