An Automated Compatibility Prediction Engine using DISC Theory Based Classification and Neural Networks

TL;DR

This paper presents an automated system that uses DISC personality profiling and neural networks to predict compatibility between workers and managers, enhancing organizational fit assessments.

Contribution

It introduces a novel neural network-based approach for predicting interpersonal compatibility using DISC profiles, extending traditional psychometric methods.

Findings

Effective prediction of worker-manager compatibility demonstrated

Neural network model accurately classifies compatibility levels

Prototype establishes a scalable data pipeline for future deployment

Abstract

Traditionally psychometric tests were used for profiling incoming workers. These methods use DISC profiling method to classify people into distinct personality types, which are further used to predict if a person may be a possible fit to the organizational culture. This concept is taken further by introducing a novel technique to predict if a particular pair of an incoming worker and the manager being assigned are compatible at a psychological scale. This is done using multilayer perceptron neural network which can be adaptively trained to showcase the true nature of the compatibility index. The proposed prototype model is used to quantify the relevant attributes, use them to train the prediction engine, and to define the data pipeline required for it.