A machine learning approach for improved shop-floor operator support using a two-level collaborative filtering and gamification features
Nikolaos Nikolakis, George Siaterlis, Kosmas Alexopoulos · Procedia CIRP · 2020
The increasing gap in shopfloor operators’ skillset regarding advanced information and communication technologies along with workforce’s diversity require a cognitive system bridging such technical gaps in order to address evolving production demands and satisfy the human need for self-fulfillment and self-actualization at work. This study discusses on a two-level collaborative filtering approach to improve the distribution of information content provided to an operator for completing a manufacturing activity while considering his or her feedback. A prototype implementation is evaluated in a case study related to the operator’s job rotation on a shopfloor that involves multiple workstations and tasks.