An Application-centric Perspective on Industrial Artificial Intelligence

2022 | Americas Conference on Information Systems | Citations: 0

Authors: Schmitz, Markus; Yesilbas, Dilara

Abstract: Advances in Artificial Intelligence have made its application increasingly rele ... Expand

Abstract: Advances in Artificial Intelligence have made its application increasingly relevant to all types of Information Systems. One area where researchers and practitioners see massive potential is the interface between Artificial Intelligence-empowered Information Systems and industrial processes. This explicit area of Industrial Artificial Intelligence and Industry 4.0 has been a popular topic of recent work and has opened up new research streams and applications. However, given the increasing number of publications, it is difficult to discern where the research field is heading. In our work, we conduct a systematic literature review of 296 scientific articles to provide a comprehensive overview of the current state of Industrial Artificial Intelligence in terms of research streams and application areas. We present both a metadata analysis as well as an application-specific analysis. Our results reveal insights into 14 major application areas as well as several findings on applied algorithms and approaches of Artificial Intelligence. Collapse

Topics: artificial intelligence industry 4.0 production management mobile system smart wearable device

Methods: literature sample experiment literature study computational algorithm anomaly detection

Theories: socio technical theory

LOCAL POST-HOC EXPLANATIONS FOR PREDICTIVE PROCESS MONITORING IN MANUFACTURING

2021 | European Conference On Information Systems | Citations: 0

Authors: Mehdiyev, Nijat; Fettke, Peter

Abstract: This study proposes an innovative explainable predictive quality analytics solu ... Expand

Abstract: This study proposes an innovative explainable predictive quality analytics solution to facilitate the data-driven decision-making for process planning in manufacturing by combining process mining, machine learning, and explainable artificial intelligence (XAI) methods. For this purpose, after integrating the top-floor and shop-floor data obtained from various enterprise information systems, a deep learning model was applied to predict the process outcomes. Since this study aims to operationalize the delivered predictive insights by embedding them into decision-making processes, it is essential to generate the relevant explanations for domain experts. To this end, two complementary local post-hoc explanation approaches, Shapley values and Individual Conditional Expectation (ICE) plots are adopted, which are expected to enhance the decision-making capabilities by enabling experts to examine explanations from different perspectives. After assessing the predictive strength of the applied deep neural network with relevant binary classification evaluation measures, a discussion of the generated explanations is provided. Collapse

Topics: shapley additive explanation explainable artificial intelligence decision making artificial intelligence process mining

Methods: deep learning machine learning binary classification artificial neural network descriptive statistic

A Holistic Framework for AI Systems in Industrial Applications

2021 | International Conference on Business Informatics | Citations: 0

Authors: Kaymakci, Can; Wenninger, Simon; Sauer, Alexander

Abstract: Although several promising use cases for artificial intelligence (AI) for manufa ... Expand

Abstract: Although several promising use cases for artificial intelligence (AI) for manufacturing companies have been identified, these are not yet widely used. Existing literature covers a variety of frameworks, methods and processes related to AI systems. However, the application of AI systems in manufacturing companies lacks a uniform understanding of components and functionalities as well as a structured process that supports developers and project managers in planning, implementing, and optimizing AI systems. To close this gap, we develop a generic conceptual model of an AI system for the application in manufacturing systems and a four-phase model to guide developers and project managers through the realization of AI systems. Collapse

Topics: artificial intelligence database system manufacturing information system systems development industrial artificial intelligence

Methods: conceptual modelling qualitative interview design science design artifact experiment