Towards measuring cognitive load through personalised models in a medium-scale enterprise: a worker allocation approach

Abstract

The assessment of cognitive load and its impact on individual performance is essential. This study aims to propose a worker allocation approach that considers cognitive concepts. The proposed approach is unique for a multitude of reasons. First, this study takes various cognitive abilities along with the performance of workers in a real industrial environment for worker. Second, this study is based on a relatively intensive data collection (N = 30) in a controlled setting that simulates a real-time work environment and induces different levels of cognitive load. By focusing on hypotheses and subjective measurements, personalised models for worker allocation are formulated. These models provide insights into identifying significant skills among individuals, determining cross-trained workers, and identifying workers who require training. Based on the complexity of tasks, the proposed approach to work allocation can enhance productivity.