A Forecasting Model-Based Discovery of Causal Links of Key Influencing Performance Quality Indicators for Sinter Production Improvement

Vuković, Matej; Dhanoa, Vaishali; Jäger, Markus; Walchshofer, Conny; Küng, Josef; Krahwinkler, Petra; Mutlu, Belgin; Thalmann, Stefan

doi:10.33313/380/218

Cited by 4 publications

(8 citation statements)

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“…The second aspect is focused on the utilization of causal discovery methods for the improvement of acceptance of traditional data-driven approaches (i.e., achieving FAT AI). Some examples of such applications are given in [4] and [81]. This aspect is also apparent in empirical causal discovery approaches that were applied to gain insights of the production process through ML [4,30,76].…”

Section: Causality As a Facilitatormentioning

confidence: 99%

“…Understanding the reasoning behind the predictions made from ML models is crucial for their acceptance, especially in industrial scenarios [75]. This issue was addressed by Vukovic et al (2020) [4] by focusing on improving the acceptance of ML by means of visual analytics and forecasting model analysis. In their approach, the authors developed a causal model through interviews with domain experts.…”

Section: Causality As a Facilitatormentioning

confidence: 99%

“…The application of Bayesian networks in the context of improving acceptance is expected due to it being a graphical model and a transparent representation of a probabilistic model. However, some authors applied empirical approaches and traditional ML approaches for detecting causality, such as ML model analysis [76], causal modeling based on domain knowledge [4], LASSO, and relative importance-based methods [81]. A previous study [77] developed a novel multivariate causality model, while Jin et al (2018) applied the average causal effect method combined with Least Square Support Vector Machine and Particle Swarm Optimization to optimize scheduling in a coke oven gas system [73].…”

Section: Causality As a Facilitatormentioning

confidence: 99%

“…These big datasets present an opportunity for advanced data analytics to obtain new insights about the production process, provide better decision support, and enhance the quality and sustainability of manufacturing activities while reducing costs [3]. Aside from the aforementioned opportunities from a business perspective, some main drivers of this digital transformation can also be environmental and regulatory [4]. To harness these benefits, the manufacturing industry has started to increasingly invest in research on artificial intelligence (AI) and data-driven approaches and their applications in manufacturing [5,6].…”

Section: Introductionmentioning

confidence: 99%

“…To harness these benefits, the manufacturing industry has started to increasingly invest in research on artificial intelligence (AI) and data-driven approaches and their applications in manufacturing [5,6]. Machine learning (ML), a subset of AI and a currently dominant method for implementing AI, can address the following three important aspects of (industrial) data [4]: (1) ML approaches can learn nonlinear and complex relationships; (2) these approaches address the problem of generalization (i.e., once the model is trained, it can capture possible hidden relationships, enabling better predictions on unseen data in the future); and (3) these approaches do not impose any restrictions on the input variables and their distributions.…”

Section: Introductionmentioning

confidence: 99%

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Causal Discovery in Manufacturing: A Structured Literature Review

Vuković

Thalmann

2022

JMMP

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Industry 4.0 radically alters manufacturing organization and management, fostering collection and analysis of increasing amounts of data. Advanced data analytics, such as machine learning (ML), are essential for implementing Industry 4.0 and obtaining insights regarding production, better decision support, and enhanced manufacturing quality and sustainability. ML outperforms traditional approaches in many cases, but its complexity leads to unclear bases for decisions. Thus, acceptance of ML and, concomitantly, Industry 4.0, is hindered due to increasing requirements of fairness, accountability, and transparency, especially in sensitive-use cases. ML does not augment organizational knowledge, which is highly desired by manufacturing experts. Causal discovery promises a solution by providing insights on causal relationships that go beyond traditional ML’s statistical dependency. Causal discovery has a theoretical background and been successfully applied in medicine, genetics, and ecology. However, in manufacturing, only experimental and scattered applications are known; no comprehensive overview about how causal discovery can be applied in manufacturing is available. This paper investigates the state and development of research on causal discovery in manufacturing by focusing on motivations for application, common application scenarios and approaches, impacts, and implementation challenges. Based on the structured literature review, four core areas are identified, and a research agenda is proposed.

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Section: Causality As a Facilitatormentioning

confidence: 99%

Section: Causality As a Facilitatormentioning

confidence: 99%

Section: Causality As a Facilitatormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Causal Discovery in Manufacturing: A Structured Literature Review

Vuković

Thalmann

2022

JMMP

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The Creation, Formalization, and Transfer of Expert Knowledge with Visual Analytics in Industrial Manufacturing Processes of Electrical Vehicles

Eirich¹

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The 21st century will be heavily impacted by the capability to create value from recorded data of all kinds. In this regard, industrial manufacturing intuitions increasingly rely on new data-driven technologies, such as the Internet of Things or Machine Learning. In terms of data collection, manufacturing processes increasingly include sophisticated sensor equipment, which results in interconnected networks of manufacturing all assembling parts and producing data. However, manufacturing institutions currently face two challenges. First, large amounts of parts and hence data are produced during fully automated manufacturing processes. Second, due to the overwhelming amount of recorded data, it is particularly challenging to efficiently analyze manufacturing data. Hence, it is important to efficiently store and share gained knowledge from performed data analyses. Information visualization and Visual Analytics are two prolific branches of data analysis exploiting sophisticated visualization techniques to support the execution of analytical tasks and to store gained knowledge. This thesis looks at how data visualization approaches can help industrial manufacturing organizations to create value from large amounts of manufacturing data, as well as how to efficiently store and share knowledge from manufacturing data analyses. The goal is to understand how Visual Analytics can improve manufacturing processes in the context of knowledge management. Five Visual Analytics systems were designed, developed, and evaluated to tackle different domain problems that emerged from manufacturing setups. Findings from each system were used to carry out additional studies to enhance established theories of knowledge management. As a result, five success stories are provided of how Visual Analytics can significantly improve manufacturing processes and how knowledge can be efficiently created, formalized, and shared in an organization with Visual Analytics.

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