Workforce Analysis Platform Using ML for Capability Alignment
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Solution Overview
Problem
Organizations face challenges in identifying and implementing effective changes to their workforce to align with industry trends, as existing methods are inefficient and often rely on manual processes that consume resources and time.
Innovation Solution
A cloud-based organization analysis platform that receives and processes organization data, industry trend data, and workforce psychology data to provide recommendations for workforce changes, using machine learning models to determine necessary capabilities and actions, such as hiring, re-training, or outsourcing, to align the workforce with industry trends.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual processes are used to identify and implement workforce changes, then organizations can make workforce adjustments, but the process consumes excessive resources and time
Solution Approach 1:
The patent replaces manual workforce analysis processes with an automated machine learning-based system. The capability model automatically processes organization data, industry trend data, and workforce psychology data to generate workforce recommendations, eliminating the need for manual analysis and significantly reducing both time and resource consumption while maintaining high accuracy in workforce transformation planning.
2Measurement precision
If comprehensive data analysis is performed to align workforce with industry trends, then workforce recommendations become more accurate, but computing resources and processing time increase
Solution Approach 1:
The system performs preliminary data processing and feature extraction before the main analysis. Organization data, industry trend data, and workforce psychology data are pre-processed and structured in advance, allowing the capability model to work with optimized inputs that reduce computational complexity while maintaining analysis accuracy, thus lowering overall computing resource consumption.
Solution Approach 2:
The patent divides the comprehensive data analysis into multiple independent processing stages: data collection, data processing, capability modeling, and recommendation generation. Each stage handles specific data types and tasks independently, allowing for optimized resource allocation and parallel processing, which reduces total computing resource consumption while maintaining high recommendation accuracy.
3Adaptability or versatility
If detailed workforce psychology data is collected and analyzed, then worker-specific recommendations improve, but data processing complexity and time increase
Solution Approach 1:
The system applies different processing methods to different types of data based on their specific characteristics. Organization data, industry trend data, and workforce psychology data each receive tailored processing approaches within the capability model, allowing for optimized analysis of each data type's unique properties while maintaining overall system efficiency and reducing processing complexity.
Data Source
AI summary
A device may receive organization data defining first capabilities of an organization and industry trend data that is relevant to the organization. The industry trend data may define second capabilities that are relevant to the organization. The device may provide, as input to a capability model, the organization data and the industry trend data. The capability model may have been trained to produce, as output, data specifying recommended changes for the organization. The device may determine, based on the output of the capability model and the industry trend data, a recommendation. The device may perform an action based on the recommendation.