Publication

@article{fekete2025ontology,

title = {Ontology-guided causal discovery and inference for reducing CO2 emissions in transportation},

author = {Tamas Fekete and Hendro Wicaksono},

url = {https://www.tandfonline.com/eprint/4CUZX6ZZ5J8R8UCGIUEH/full?target=10.1080/15568318.2025.2588608},

doi = {https://doi.org/10.1080/15568318.2025.2588608},

year  = {2025},

date = {2025-12-07},

urldate = {2025-12-07},

journal = {International Journal of Sustainable Transportation},

pages = {1–21},

publisher = {Taylor & Francis},

abstract = {This study investigates how ontology-guided causal discovery can be applied to reduce CO2 emissions in transportation. The analysis uses a cross-sectional dataset of 463,568 passenger vehicles inspected in Hungary between January and March 2023, which includes key technical attributes such as engine performance, cylinder capacity, and drive-by noise levels. Using causal discovery algorithms (PC, FCI, and GES) with and without ontology-based constraints, directed acyclic graphs are constructed to identify structural relationships among these variables and CO2 emissions. For causal inference, effect sizes of engine performance and other technical characteristics on emissions are estimated while considering potential confounding factors. The findings show that ontology-informed models improve both the plausibility and interpretability of the discovered causal structures, though limitations remain regarding unobserved variables and nonlinear relationships; accordingly, this validation-focused case study provides a foundation for extensions to behavior-driven contexts (e.g. usage patterns, compliance, market responses) where causal structure and effect magnitudes are more uncertain in advance. The results indicate that cylinder capacity and specific power (engine performance at fixed displacement) are among the strongest contributors to CO2 emissions, with ontology constraints reducing spurious links and increasing robustness across algorithms. Policy implications include the need for regulatory measures that integrate domain knowledge into emissions assessments, as well as the importance of updating technical standards and testing frameworks to reflect causal interactions rather than simple correlations. These insights can support more reliable interventions to lower vehicle-related emissions and contribute to sustainable transportation strategies.},

keywords = {artificial intelligence, causal AI, causal inference, machine learning, ontologies, semantic web, sustainability, transportation},

pubstate = {published},

tppubtype = {article}

}

@article{fekete2025comprehensive,

title = {A comprehensive causal AI framework for analysing factors affecting energy consumption and costs in customised manufacturing},

author = {Tamas Fekete and Isabella Marquez Petrone and Hendro Wicaksono},

url = {https://hendro-wicaksono.de/a-comprehensive-causal-ai-framework-for-analysing-factors-affecting-energy-consumption-and-costs-in-customised-manufacturing-2/},

doi = {https://doi.org/10.1080/00207543.2025.2580541},

year  = {2025},

date = {2025-10-29},

urldate = {2025-10-29},

journal = {International Journal of Production Research},

pages = {1–38},

publisher = {Taylor & Francis},

abstract = {The manufacturing sector is a major energy consumer, resulting in high operational costs and environmental impacts. In customised manufacturing, optimising energy use is especially challenging due to high variability and complex interdependencies between process factors. Meanwhile, the increasing availability of operational data presents opportunities for advanced analytics. Unlike traditional machine learning, which identifies correlations, causal AI uncovers cause-and-effect relationships – enabling more explainable and actionable decision-making. This paper presents a causal AI framework that combines causal discovery and inference methods to analyse drivers of energy consumption and process duration in customised manufacturing. We integrate three core components: DirectLiNGAM and RESIT for causal discovery, and DoWhy for causal inference. Applied to a real-world case study in a German energy-intensive manufacturing Small and Medium-sized Enterprise (SME), the framework demonstrates its ability to identify key causal drivers of inefficiency and energy use. Results show improved interpretability, revealing, for example, that increasing product weight can reduce energy consumption by up to 4.70 kWh per unit, enabling targeted, data-driven interventions for optimisation. Compared to correlation-based models, the framework reveals underlying causes, helping decision-makers focus on critical levers for sustainability and cost reduction. The findings lay a foundation for applying causal AI in industrial settings through a structured, data-driven approach.},

keywords = {artificial intelligence, causal AI, causal inference, energy management, explainable AI, industry 4.0, industry 5.0, machine learning, manufacturing, sustainability},

pubstate = {published},

tppubtype = {article}

}

@article{gupta2025comparative,

title = {A comparative assessment of causal machine learning and traditional methods for enhancing supply chain resiliency and efficiency in the automotive industry},

author = {Ishansh Gupta and Adriana Martinez and Sergio Correa and Hendro Wicaksono},

doi = {https://doi.org/10.1016/j.sca.2025.100116},

year  = {2025},

date = {2025-06-01},

urldate = {2025-06-01},

journal = {Supply Chain Analytics},

volume = {10},

pages = {100116},

publisher = {Elsevier},

abstract = {Efficient supplier escalation is crucial for maintaining smooth operational supply chains in the automotive industry, as disruptions can lead to significant production delays and financial losses. Many companies still rely on traditional escalation methods, which may lack the precision and adaptability offered by modern technologies. This study presents a comparative analysis of decision-making strategies for supplier escalation, evaluating causal machine learning (CML), traditional machine learning (ML), and current escalation practices in a leading German automotive company. The study employs an explanatory sequential mixed method, integrating the Analytical Hierarchy Process (AHP) with in-depth interviews with 25 industry experts. These methods are assessed based on several performance metrics: accuracy, business impact, explanation capability, human bias, stress test, and time-to-recover. Findings reveal that CML outperforms traditional ML and existing approaches, offering superior risk prediction, interpretability, and decision-making support Additionally, the research explores the internal acceptance of these technologies through the Technology Acceptance Model (TAM). The results highlight the transformative potential of CML in enhancing supply chain resilience and efficiency. By bridging the gap between predictive analytics and explainable AI, this research offers valuable guidance for firms seeking to optimize supplier management using advanced analytics.},

keywords = {artificial intelligence, causal AI, causal inference, data science, decision support systems, industry 4.0, industry 5.0, machine learning, multi criteria decision making, resillience, supply chain management, technology adoption},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Leveraging causal AI to uncover the dynamics in sustainable urban transport: A bike sharing time-series study},

author = {Tamas Fekete and Girum Mengistu and Hendro Wicaksono },

doi = {https://doi.org/10.1016/j.scs.2025.106240},

year  = {2025},

date = {2025-03-15},

urldate = {2025-03-15},

journal = {Sustainable Cities and Society},

volume = {122},

pages = {106240},

abstract = {The importance of developing sustainable urban transportation systems to protect the environment is increasingly recognized worldwide, particularly within the European Union. In the era of digitalization, data-driven approaches are crucial for informed decision-making. This study introduces a methodology leveraging causal artificial intelligence (causal AI) to uncover cause-and-effect relationships in urban transport data. Unlike traditional methods relying on correlations, causal AI identifies the true drivers of transport dynamics. A case study using MOL Bubi bike-sharing data from Budapest demonstrates how the PCMCI (Peter and Clark Momentary Conditional Independence) algorithm revealed complex temporal dependencies within the data, with temperature emerging as the strongest causal factor positively influencing bike usage. Additionally, the reopening of the Chain Bridge led to a 10.7% increase in bike trips, as quantified by Causal Impact analysis. This case study can be extended to more complex scenarios with unpredictable outcomes. The insights gained provide policymakers with a deeper understanding, enabling them to design policies fostering sustainable urban mobility. These results showcase the potential of causal AI to guide policies that enhance sustainable urban mobility.},

keywords = {artificial intelligence, causal AI, causal inference, industry 5.0, machine learning, sustainability, transportation},

pubstate = {published},

tppubtype = {article}

}

@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}

}