Artificial intelligence in production management: A review of the current state of affairs and research trends in academia

Machine Learning-Based Prediction of Missing Components for Assembly – a Case Study at an Engineer-to-Order Manufacturer

“…In production, lead times are determined by setting up a production schedule taking into account the available production capacities, the technical requirements, the demand dates and the system status [8,15,16]. The order sequence is defined according to certain rules in order to calculate start and end dates of the orders at the workstations [17] and is one of the main applications for machine learning (ML) [18]. In addition to the calculation of the lead time based on scheduling, it is also possible to predict lead time directly.…”

Trust in artificial intelligence within production management – an exploration of antecedents

“…In this sense, AI is the counterpart to robotics in manufacturing companies: while robots facilitate physical doing of blue-collar workers, AI will support cognitive deciding of white-collar workers (McAfee and Brynjolfsson 2017). The long-term estimation is that AI will transform the system of production management to a cyber production management system where human and artificial intelligence cooperate successfully (Burggr€ af, Wagner, and Koke 2018).…”

“…This requires interactive, cooperative decision-making mechanisms shared between artificial and human intelligence (McAfee and Brynjolfsson 2017). In line with Parasuraman, Sheridan, and Wickens (2000), Burggr€ af, Wagner, and Koke (2018) describes the forms of cooperation between AI and humans in production management in five stages: from sole human management via various degrees of assistance to fully autonomous decision-making by AI. Employing AI in production management has been subject of research since 1980 (Russell and Norvig 2016).…”

Predictive analytics in quality assurance for assembly processes: lessons learned from a case study at an industry 4.0 demonstration cell

“…With the evolution of machine learning (ML) applications [3,4], approaches combining QC and predictive models are becoming more relevant. The applications range from the defect analysis of the produced piece [6,20], to process-oriented QC [12,11,2,16], see Section 2.…”