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

Burggräf, Peter; Wagner, Johannes; Koke, Benjamin

doi:10.1109/icimp1.2018.8325846

Cited by 32 publications

(22 citation statements)

References 23 publications

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“…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.…”

Section: State Of the Artmentioning

confidence: 99%

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

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Section: State Of the Artmentioning

confidence: 99%

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

et al. 2021

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“…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).…”

Section: Introductionmentioning

confidence: 99%

“…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).…”

Section: Introductionmentioning

confidence: 99%

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

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Industry 4.0, big data, predictive analytics, and robotics are leading to a paradigm shift on the shop floor of industrial production. However, complex, cognitive tasks are also subject of change, due to the development of artificial intelligence (AI). Smart assistants are finding their way into the world of knowledge work and require cooperation with humans. Here, trust is an essential factor that determines the success of human-AI cooperation. Within this article, an analysis within production management identifies possible antecedent variables on trust in AI and evaluates these due to interaction scenarios with AI. The results of this research are five antecedents for human trust in AI within production management. From these results, preliminary design guidelines are derived for a socially sustainable human-AI interaction in future production management systems.

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“…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.…”

Section: Introductionmentioning

confidence: 99%

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

Burggräf¹,

Wagner²,

Heinbach³

et al. 2021

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Quality assurance (QA) is an important task in manufacturing to assess whether products meet their specifications. However, QA might be expensive, time-consuming, or incomplete. This paper presents a solution for predictive analytics in QA based on machine sensor values during production while employing specialized machine-learning models for classification in a controlled environment. Furthermore, we present lessons learned while implementing this model, which helps to reduce complexity in further industrial applications. The paper’s outcome proves that the developed model was able to predict product quality, as well as to identify the correlation between machine-status and faulty product occurrence.

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

Cited by 32 publications

References 23 publications

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

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

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

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

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