2025
Fekete, Tamas; Petrone, Isabella Marquez; Wicaksono, Hendro
A comprehensive causal AI framework for analysing factors affecting energy consumption and costs in customised manufacturing Journal Article
In: International Journal of Production Research, pp. 1–38, 2025.
Abstract | Links | BibTeX | Tags: artificial intelligence, causal AI, causal inference, energy management, explainable AI, industry 4.0, industry 5.0, machine learning, manufacturing, sustainability
@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}
}
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.
2024
Hidayat, Rahmat; Ourairat, Apivut; Wicaksono, Hendro
Explainable Artificial Intelligence in Agrifood Supply Chain: State of the Art Review Proceedings Article
In: Lecture Notes in Mechanical Engineering : Flexible Automation and Intelligent Manufacturing: Manufacturing Innovation and Preparedness for the Changing World Order, Springer, 2024.
Abstract | Links | BibTeX | Tags: agrifood, artificial intelligence, explainable AI, supply chain management
@inproceedings{nokey,
title = {Explainable Artificial Intelligence in Agrifood Supply Chain: State of the Art Review},
author = {Rahmat Hidayat and Apivut Ourairat and Hendro Wicaksono
},
doi = {https://doi.org/10.1007/978-3-031-74485-3_33},
year = {2024},
date = {2024-12-13},
booktitle = {Lecture Notes in Mechanical Engineering : Flexible Automation and Intelligent Manufacturing: Manufacturing Innovation and Preparedness for the Changing World Order},
publisher = {Springer},
abstract = {The increasing pressure to feed a growing population of humans for food security, with constantly changing food consumption behavior, as well as in recent light of livestock treatment and awareness for food sustainability both economically and ecologically, also due to the challenges of climate change, lead to challenges for the industries operating in the Agrifood Supply Chain (ASC). Recent technological strides in Data Analysis, Internet of Things (IoT), Machine Learning (ML), and Artificial Intelligence (AI) have ushered in a digitized and intelligent era within the ASC, reshaping production quality, sustainability, and food longevity. However, the nascent stage of AI and ML methods within the ASC raises questions about their reliability, value, transparency, and understandability. The prevalent use of black box methods underscores the need for more explainable methodologies, as the opacity of current approaches restricts widespread applicability. This paper presents a State-of-the-Art Review of Explainable AI (XAI) and ML methods in the ASC, delving into operations spanning “Farm-to-Fork,” encompassing agriculture production, processes, quality assurance, tracking, warehousing, distribution, packaging, retailing, safety, and sustainability. The research identifies challenges and proposes research directions, offering researchers an overview of opportunities to implement XAI methods in the ASC. The exploration of coexisting problems and their solutions enhances our understanding of intelligent systems in the ASC, providing valuable insights for stakeholders’ decision-making processes.
},
keywords = {agrifood, artificial intelligence, explainable AI, supply chain management},
pubstate = {published},
tppubtype = {inproceedings}
}
The increasing pressure to feed a growing population of humans for food security, with constantly changing food consumption behavior, as well as in recent light of livestock treatment and awareness for food sustainability both economically and ecologically, also due to the challenges of climate change, lead to challenges for the industries operating in the Agrifood Supply Chain (ASC). Recent technological strides in Data Analysis, Internet of Things (IoT), Machine Learning (ML), and Artificial Intelligence (AI) have ushered in a digitized and intelligent era within the ASC, reshaping production quality, sustainability, and food longevity. However, the nascent stage of AI and ML methods within the ASC raises questions about their reliability, value, transparency, and understandability. The prevalent use of black box methods underscores the need for more explainable methodologies, as the opacity of current approaches restricts widespread applicability. This paper presents a State-of-the-Art Review of Explainable AI (XAI) and ML methods in the ASC, delving into operations spanning “Farm-to-Fork,” encompassing agriculture production, processes, quality assurance, tracking, warehousing, distribution, packaging, retailing, safety, and sustainability. The research identifies challenges and proposes research directions, offering researchers an overview of opportunities to implement XAI methods in the ASC. The exploration of coexisting problems and their solutions enhances our understanding of intelligent systems in the ASC, providing valuable insights for stakeholders’ decision-making processes.
Thapaliya, Suman; Valilai, Omid Fatahi; Wicaksono, Hendro
Power Consumption and Processing Time Estimation of CNC Machines Using Explainable Artificial Intelligence (XAI) Journal Article
In: Procedia Computer Science, vol. 232, pp. 861–870, 2024.
Abstract | Links | BibTeX | Tags: energy management, explainable AI, machine learning, manufacturing
@article{thapaliya2024power,
title = {Power Consumption and Processing Time Estimation of CNC Machines Using Explainable Artificial Intelligence (XAI)},
author = {Suman Thapaliya and Omid Fatahi Valilai and Hendro Wicaksono},
url = {https://www.sciencedirect.com/science/article/pii/S1877050924000863},
doi = {https://doi.org/10.1016/j.procs.2024.01.086},
year = {2024},
date = {2024-03-20},
urldate = {2024-03-20},
journal = {Procedia Computer Science},
volume = {232},
pages = {861–870},
publisher = {Elsevier},
abstract = {Due to environmental issues such as climate change, companies are required to optimize their resource and energy consumption in their production process. Predicting power consumption and processing time of all production facilities is essential for manufacturing to develop mechanisms to prevent energy and resource waste and optimize their use. Machine learning is a powerful tool for prediction tasks using data in digitalized environments. In this paper, we present power consumption and processing time prediction of CNC milling machines using five machine learning regression models, i.e., decision tree, random forest, support vector regression (SVR), extreme gradient boosting (XGBoost), and artificial neural network (ANN). Since most of those models are black-box, we applied two explainable artificial intelligence (XAI) approaches, SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME), to give post-hoc explanations of the predictions given by the machine learning models. Our experiments indicated that random forest regression performed the best in predicting power consumption and processing time. The explanation showed that the number of axis rotations and the number of travels to the machine's zero point in rapid traverse were the most important factors that affected the processing time and power consumption. The companies using CNC milling machines can use our prediction models to optimally plan and schedule the operation of the milling machines in a time and energy-efficient manner. They can also optimize the factors that affect power consumption and processing time the most.
},
keywords = {energy management, explainable AI, machine learning, manufacturing},
pubstate = {published},
tppubtype = {article}
}
Due to environmental issues such as climate change, companies are required to optimize their resource and energy consumption in their production process. Predicting power consumption and processing time of all production facilities is essential for manufacturing to develop mechanisms to prevent energy and resource waste and optimize their use. Machine learning is a powerful tool for prediction tasks using data in digitalized environments. In this paper, we present power consumption and processing time prediction of CNC milling machines using five machine learning regression models, i.e., decision tree, random forest, support vector regression (SVR), extreme gradient boosting (XGBoost), and artificial neural network (ANN). Since most of those models are black-box, we applied two explainable artificial intelligence (XAI) approaches, SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME), to give post-hoc explanations of the predictions given by the machine learning models. Our experiments indicated that random forest regression performed the best in predicting power consumption and processing time. The explanation showed that the number of axis rotations and the number of travels to the machine's zero point in rapid traverse were the most important factors that affected the processing time and power consumption. The companies using CNC milling machines can use our prediction models to optimally plan and schedule the operation of the milling machines in a time and energy-efficient manner. They can also optimize the factors that affect power consumption and processing time the most.
2023
Wicaksono, Hendro; Nisa, Mehr Un; Vijaya, Annas
In: 2023 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), pp. 0528–0532, IEEE 2023.
Abstract | Links | BibTeX | Tags: digital twins, explainable AI, interoperability, ontologies, semantic web, transportation
@inproceedings{wicaksono2023towards,
title = {Towards Intelligent and Trustable Digital Twin Asset Management Platform for Transportation Infrastructure Management Using Knowledge Graph and Explainable Artificial Intelligence (XAI)},
author = {Hendro Wicaksono and Mehr Un Nisa and Annas Vijaya},
doi = {https://doi.org/10.1109/IEEM58616.2023.10406401},
year = {2023},
date = {2023-12-18},
urldate = {2023-01-01},
booktitle = {2023 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)},
pages = {0528–0532},
organization = {IEEE},
abstract = {In the transportation sector, implementing digital twins is part of the digitization measure to improve resource efficiency in infrastructure management. However, the use of digital twins is still limited due to challenges such as a lack of shared understanding of digital twin models, complex model integration, security issues, lack of access to essential data, and high costs due to inefficient business models. This research develops an asset management platform suitable for Small and Medium Enterprises (SMEs) for the cross-company, secure, and intuitive collaborative management of digital twin assets. It can be achieved by developing an ontology-based semantic model of the assets, explainable machine learning (XAI), and a scenario-based intelligent search and discovery mechanism.},
keywords = {digital twins, explainable AI, interoperability, ontologies, semantic web, transportation},
pubstate = {published},
tppubtype = {inproceedings}
}
In the transportation sector, implementing digital twins is part of the digitization measure to improve resource efficiency in infrastructure management. However, the use of digital twins is still limited due to challenges such as a lack of shared understanding of digital twin models, complex model integration, security issues, lack of access to essential data, and high costs due to inefficient business models. This research develops an asset management platform suitable for Small and Medium Enterprises (SMEs) for the cross-company, secure, and intuitive collaborative management of digital twin assets. It can be achieved by developing an ontology-based semantic model of the assets, explainable machine learning (XAI), and a scenario-based intelligent search and discovery mechanism.
Aikenov, Temirlan; Hidayat, Rahmat; Wicaksono, Hendro
Power Consumption and Process Cost Prediction of Customized Products Using Explainable AI: A Case in the Steel Industry Proceedings Article
In: International Conference on Flexible Automation and Intelligent Manufacturing, pp. 1183–1193, Springer Nature Switzerland Cham 2023.
Abstract | Links | BibTeX | Tags: energy management, explainable AI, machine learning, manufacturing, sustainability
@inproceedings{aikenov2023power,
title = {Power Consumption and Process Cost Prediction of Customized Products Using Explainable AI: A Case in the Steel Industry},
author = {Temirlan Aikenov and Rahmat Hidayat and Hendro Wicaksono},
url = {https://link.springer.com/chapter/10.1007/978-3-031-38165-2_135},
doi = {https://doi.org/10.1007/978-3-031-38165-2_135},
year = {2023},
date = {2023-08-25},
urldate = {2023-01-01},
booktitle = {International Conference on Flexible Automation and Intelligent Manufacturing},
pages = {1183–1193},
organization = {Springer Nature Switzerland Cham},
abstract = {Production shifted from a product-centered perspective (mass production of one article) to a customer-centered perspective (mass customization of product variants). It also happens in energy-intensive industries, such as steel production. Mass customization companies face a challenge in accurately estimating the total costs of an individual product. Furthermore, 20% to 40% of the costs related to steel products come from energy. Increasing the product variety can cause an inevitable loss of sustainability. This paper presents machine-learning approaches to improve the sustainability of the steel production industry. It is done by finding the most accurate way to predict the power consumption and the costs of customized products. Moreover, this research also finds the most energy-efficient machine mix based on the predictions. The method is validated in a steel manufacturing Small Medium Enterprise (SME). In this research, experiments were conducted with different machine learning models, and it was found that the most accurate results were achieved using regularization-based and random forest regression models. Explainable AI (XAI) is also used to clarify how product properties influence process costs and power consumption. This paper also discusses scenarios on how the prediction of costs and power consumption can assist production planners in performing workstation selection. This research improves the production planning of customized products by providing a trustable decision support system for machine selection based on explainable machine learning models for process time and power consumption predictions.
},
keywords = {energy management, explainable AI, machine learning, manufacturing, sustainability},
pubstate = {published},
tppubtype = {inproceedings}
}
Production shifted from a product-centered perspective (mass production of one article) to a customer-centered perspective (mass customization of product variants). It also happens in energy-intensive industries, such as steel production. Mass customization companies face a challenge in accurately estimating the total costs of an individual product. Furthermore, 20% to 40% of the costs related to steel products come from energy. Increasing the product variety can cause an inevitable loss of sustainability. This paper presents machine-learning approaches to improve the sustainability of the steel production industry. It is done by finding the most accurate way to predict the power consumption and the costs of customized products. Moreover, this research also finds the most energy-efficient machine mix based on the predictions. The method is validated in a steel manufacturing Small Medium Enterprise (SME). In this research, experiments were conducted with different machine learning models, and it was found that the most accurate results were achieved using regularization-based and random forest regression models. Explainable AI (XAI) is also used to clarify how product properties influence process costs and power consumption. This paper also discusses scenarios on how the prediction of costs and power consumption can assist production planners in performing workstation selection. This research improves the production planning of customized products by providing a trustable decision support system for machine selection based on explainable machine learning models for process time and power consumption predictions.