AI KPI Qualifying System for Automation Impact Assessment

Solution Overview

Problem

Existing process monitoring systems lack an efficient method to select and evaluate Key Performance Indicators (KPIs) for automated processes, which hinders the accurate assessment of automation impact and resource allocation, as they rely on manual data filtering and selection, leading to suboptimal performance and inefficiencies.

Innovation Solution

An AI-based process KPI qualifying and monitoring system that employs machine learning and natural language processing to extract, classify, rank, and analyze KPIs from multiple data sources, providing dynamic and user-centric fitment scores to automatically select relevant KPIs for automation impact assessment.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Device complexity

If manual data filtering and selection is used for KPI identification, then system complexity is reduced, but measurement precision and productivity deteriorate

Engineering Contradiction:

Improvesystem complexityVSAvoidKPI selection accuracy

Core Design Contradiction:

Device complexityVSMeasurement precision

Solution Approach 1:

The patent replaces manual mechanical filtering and selection processes with an AI-based natural language processing system that automatically analyzes unstructured data sources, extracts KPIs, and ranks them by relevance. This substitution enables precise automated KPI identification without requiring manual intervention, thereby improving measurement precision while maintaining system complexity at acceptable levels through algorithmic processing.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables automated self-service KPI identification by having the AI model independently analyze data sources, extract relevant indicators, and rank them without human assistance. The system autonomously performs data filtering, extraction, and selection tasks that would traditionally require manual effort, thereby improving productivity and measurement precision simultaneously.

Inventive Principle:

Principle #25Self-service

2Device complexity

If manual data filtering and selection is used for KPI identification, then device complexity is reduced, but productivity deteriorates

Engineering Contradiction:

Improvesystem complexityVSAvoidKPI identification efficiency

Core Design Contradiction:

Device complexityVSProductivity

Solution Approach 1:

The patent replaces manual mechanical filtering and selection processes with an AI-based natural language processing system that automatically analyzes unstructured data sources, extracts KPIs, and ranks them by relevance. This substitution enables precise automated KPI identification without requiring manual intervention, thereby improving measurement precision while maintaining system complexity at acceptable levels through algorithmic processing.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables automated self-service KPI identification by having the AI model independently analyze data sources, extract relevant indicators, and rank them by relevance without human assistance. The system autonomously performs data filtering, extraction, and selection tasks that would traditionally require manual effort, thereby improving productivity and measurement precision simultaneously.

Inventive Principle:

Principle #25Self-service

3Measurement precision

If automated KPI selection using AI is implemented, then measurement precision and productivity improve, but device complexity increases

Engineering Contradiction:

ImproveKPI selection accuracyVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent replaces manual mechanical filtering and selection processes with an AI-based natural language processing system that automatically analyzes unstructured data sources, extracts KPIs, and ranks them by relevance. This substitution enables precise automated KPI identification without requiring manual intervention, thereby improving measurement precision while maintaining system complexity at acceptable levels through algorithmic processing.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS11526695B2Evaluating impact of process automation on KPIs

Publication Date: 2022.12.13 ACCENTURE GLOBAL SOLUTIONS LTD

AI summary

An AI-based process monitoring system access a plurality of data sources having different data formats to collect and analyze KPI data and shortlist KPIs that are to be used for determining the impact of automation of an automated process or sub-process. Information regarding an automated process is received and KPIs associated with the process and sub-processes of the process are identified. The identified KPIs are put through an approval process and the approved KPIs are presented to a user for selection. The user-selected KPIs are evaluated based on classification, ranking and sentiments associated therewith. The evaluations are again presented to the user along with a set of questionnaires wherein each of the questions has a dynamically controlled weight associated therewith. Based at least on the weights and user responses, a subset of the evaluated KPIs are shortlisted for use in evaluating the impact of process automation.