HUMAN-CENTERED MODELING APPLICATIONS IN INTELLIGENT MANUFACTURING SYSTEMS

Abstract

Industrial Internet of Things technologies, software algorithms, and advanced machine-learning systems are capable of extracting up-to-the-minute data from internal manufacturing industry processes which contain a wealth of useful and actionable information for various decision-making stages, including leadership-level strategic planning. While manufacturing data are often used to control quality, improve process efficiency, or to ensure that all manufacturing stations have the necessary tools and parts, this information also contains a wealth of data related to human factors that is not easily identified. By integrating human factors data into a causal modeling approach, this paper presents ways in which latent data can be used to aid in human-centered management of industrial systems. Information processing theory and causal modeling are presented as potential methods of incorporating human factors into modern data analytics. This research highlights ways to integrate human factors IIoT data into the overall company human factors’ planning and decisions.