Resource Parity Scaling Through Real-Time KPI Allocation
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Solution Overview
Problem
Existing KPI metrics do not provide insights into how to improve process performance or address process-related problems, leading to inefficiencies and potential failures in multi-step processes involving human and machine resources.
Innovation Solution
A method that utilizes machine-generated input signals to determine performance levels, compares them with predetermined KPI metrics, and reallocates human and compute resources based on incremental conversion factors to optimize process performance and meet SLA requirements, using real-time monitoring and machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional KPI metrics are used to assess process performance, then process performance can be measured, but insights into how to improve performance are not provided
Solution Approach 1:
The system implements a feedback mechanism where KPI metrics are continuously monitored and fed into machine learning models that analyze performance patterns and generate actionable insights. The models process historical and real-time KPI data to provide recommendations for process improvement, creating a closed-loop system that transforms static measurements into dynamic improvement guidance.
Solution Approach 2:
Machine learning models serve as intermediaries between raw KPI metrics and human decision-makers. These models analyze complex KPI patterns, identify root causes of performance issues, and translate data into interpretable insights and recommendations, bridging the gap between measurement and actionable improvement strategies.
2Ease of operation
If resources are allocated based on static methods, then resource distribution is simple to manage, but process efficiency and KPI compliance are not optimized
Solution Approach 1:
The system transitions from static resource allocation to dynamic allocation that automatically adjusts resource distribution based on real-time KPI monitoring and machine learning predictions. Resource allocation parameters are continuously optimized based on changing process conditions, ensuring peak efficiency while maintaining manageable complexity through automation.
Solution Approach 2:
The system dynamically changes resource allocation parameters based on analyzed KPI data and machine learning model predictions. By adjusting allocation parameters in response to performance trends and predictions, the system optimizes productivity while maintaining ease of operation through automated parameter management rather than manual intervention.
3Productivity
If real-time resource reallocation is implemented to meet KPIs, then process efficiency is improved, but system complexity increases
Solution Approach 1:
The resource allocation system operates autonomously using self-service mechanisms where machine learning models automatically analyze KPI data, predict performance outcomes, and execute resource reallocation decisions without human intervention. This automation handles the complexity internally while presenting a simplified interface to users, maintaining productivity improvements without proportionally increasing operational complexity.
Solution Approach 2:
Manual resource allocation mechanisms are replaced with automated machine learning-based systems that process KPI data and execute reallocation decisions algorithmically. This substitution handles the computational complexity of real-time optimization while reducing the operational complexity for human users, achieving efficiency gains without proportional increases in manageable system complexity.
4Reliability
If machine learning models are used to predict process failure, then process reliability is improved, but computational resource requirements increase
Solution Approach 1:
The system applies machine learning models selectively to processes or time periods where they provide the most value, rather than continuously applying them to all processes. By focusing computational resources on critical predictions or high-impact scenarios, the system achieves improved reliability where needed while minimizing unnecessary computational resource consumption in lower-priority areas.
Data Source
AI summary
Resource parity scaling includes receiving in real time a plurality of machine-generated input signals from a computer network, the machine-generated signals generated during simultaneous real-time executions of multiple processes using human and compute resources. Based on analyzing the plurality of machine-generated signals, a performance level of each process is determined. The performance level of each process is compared with one or more predetermined key performance indicator (KPI) metrics corresponding to each process. Responsive to the comparing different combinations of the human and compute resources are allocated for performing different ones of the multiple processes. The allocating is based on resource-specific incremental conversion factors corresponding to the compute and human resources. One or more output signals are conveyed to the computer network indicating the different combinations.