Publication

@article{nokey,

title = {From Theory to Practice: Implementing Causal AI in Manufacturing for Sustainability},

author = {Heonyoung Jeong and Tamas Fekete and Atit Bashyal and Hendro Wicaksono },

url = {https://www.sciencedirect.com/science/article/pii/S1877050925002194},

doi = {https://doi.org/10.1016/j.procs.2025.01.211},

year  = {2025},

date = {2025-02-25},

journal = {Procedia Computer Science},

volume = {253},

pages = {1495-1504},

abstract = {The use of AI in industry is increasingly popular, but its black-box nature poses decision-making challenges due to the lack of understanding of how variables influence each other. Causal AI addresses this by studying cause-and-effect relationships in the data. This paper explores applying causal AI in industry through a case study of CNC machines, which are significant in manufacturing and consume large amounts of energy. Industry 4.0 is transforming manufacturing, with CNC machines generating vast data analyzed by often opaque machine learning methods. Causal AI can uncover and quantify causal relationships between variables, aiding decision-making. Our case study uses CNC power consumption data to demonstrate causal AI in manufacturing, with existing models verifying our methodology. Future studies should extend our research to include variables without existing models, such as human habits. This case study serves as a starting point for other researchers, facilitating similar studies on complex data.},

keywords = {artificial intelligence, causal AI, causal inference, energy management, manufacturing, sustainability},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Leveraging Causal Machine Learning for Sustainable Automotive Industry: Analyzing Factors Influencing CO2 Emissions},

author = {Vahid Menu Nesro and Tamas Fekete and Hendro Wicaksono},

url = {https://www.sciencedirect.com/science/article/pii/S2212827124012241},

doi = {https://doi.org/10.1016/j.procir.2024.10.071},

year  = {2024},

date = {2024-11-27},

urldate = {2024-11-27},

journal = {Procedia CIRP},

volume = {130},

pages = {161-166},

abstract = {Integrating artificial intelligence (AI) and machine learning (ML) in the industry has become increasingly essential with the rise of Industry 4.0. These technologies can revolutionize product development, decision-making, and operational optimization in industrial processes. However, most modern ML tools focus on identifying associations between data points rather than causal relationships, a crucial limitation for industries. This paper explores the potential for causal machine learning to overcome this limitation. The investigation involves selecting a focus area within the automotive sector, analyzing a dataset, and modeling, identifying, and estimating causal associations. The chosen case study investigates the potential causes of CO2 emissions from vehicles, which is a critical area in terms of sustainability. Furthermore, the applied dataset is suitable for demonstrating the proposed approach as an alternative to engineering techniques that explain factors influencing CO2 emissions. In the future, a more detailed model based on the case study can help learn more about the potential causes of environmental harm caused by vehicles. This paper provides valuable insights into how this technology can be leveraged to optimize industrial processes and improve outcomes in reducing CO2 emissions in product creation processes to achieve environmental sustainability goals.

},

keywords = {causal AI, causal inference, sustainability},

pubstate = {published},

tppubtype = {article}

}

@article{mechai2024causal,

title = {Causal Inference in Supply Chain Management: How Does Ever Given Accident at the Suez Canal Affect the Prices of Shipping Containers?},

author = {Nadhir Mechai and Hendro Wicaksono},

url = {https://www.sciencedirect.com/science/article/pii/S1877050924003119},

doi = {https://doi.org/10.1016/j.procs.2024.02.133},

year  = {2024},

date = {2024-09-20},

urldate = {2024-01-01},

journal = {Procedia Computer Science},

volume = {232},

pages = {3173–3182},

publisher = {Elsevier},

abstract = {In March 2021, the Ever Given, a colossal 4000-meter-long container ship with a capacity of 20,000 TEUs (Twenty-foot equivalent units), became lodged in the Suez Canal for six days, causing a significant disruption. This accident blocked approximately 400 ships, impacting not only the vessel itself and the canal but also global trade and the already strained global supply chain post-pandemic. Our research leverages causal inference techniques to rigorously assess and quantify the causal effects of the Ever Given accident on the World Container Index (WCI). We conducted experiments using time series data from eight major global shipping routes, achieving statistically significant results with a confidence level of 99.89%. This research conclusively demonstrates that the Ever Given incident at the Suez Canal had a substantial impact on shipping container prices, quantifying the effect as a remarkable 40% price increase post-exposure. By offering companies the ability to apply causal inference in understanding cause-and-effect dynamics within their supply chain networks, this study equips them with the knowledge needed to make well-informed decisions, especially in times of disruption, thus facilitating the optimization of their supply chain configuration and operations.

},

keywords = {causal AI, causal inference, supply chain management},

pubstate = {published},

tppubtype = {article}

}