2025
Fekete, Tamas; Wicaksono, Hendro
Ontology-guided causal discovery and inference for reducing CO2 emissions in transportation Journal Article
In: International Journal of Sustainable Transportation, pp. 1–21, 2025.
Abstract | Links | BibTeX | Tags: artificial intelligence, causal AI, causal inference, machine learning, ontologies, semantic web, sustainability, transportation
@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}
}
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.
Chawalitanont, Akarawint; Bashyal, Atit; Wicaksono, Hendro
In: Journal of Manufacturing Systems, vol. 83, pp. 713–735, 2025.
Abstract | Links | BibTeX | Tags: artificial intelligence, deep learning, industry 4.0, industry 5.0, machine learning, manufacturing, sustainability
@article{chawalitanont2025uncertaintyb,
title = {Uncertainty-aware power consumption prediction in customized stainless-steel manufacturing: A comparative study of hierarchical Bayesian and deep neural models},
author = {Akarawint Chawalitanont and Atit Bashyal and Hendro Wicaksono},
doi = {https://doi.org/10.1016/j.jmsy.2025.10.010},
year = {2025},
date = {2025-12-01},
urldate = {2025-12-01},
journal = {Journal of Manufacturing Systems},
volume = {83},
pages = {713–735},
publisher = {Elsevier},
abstract = {Energy-efficient and data-driven decision-making has become a critical priority in modern manufacturing, particularly in customized or make-to-order (MTO) production where product variability causes large fluctuations in power consumption. Existing prediction models in this domain are often deterministic, lacking the ability to quantify uncertainty and capture hierarchical data dependencies, which limits their reliability for operational use. This study addresses this gap by developing a hierarchical Bayesian learning framework for power consumption prediction in customized stainless-steel manufacturing. The objective is to design models that not only achieve high predictive accuracy but also provide calibrated uncertainty estimates to support risk-aware production decisions. Four models, i.e., Hierarchical Bayesian Linear Regression (HBLR), Hierarchical Bayesian Neural Network (HBNN), Fully Connected Neural Network (FCN), and One-Dimensional Convolutional Neural Network (1D-CNN), were implemented and benchmarked using three inference algorithms: No-U-Turn Sampler (NUTS), Automatic Differentiation Variational Inference (ADVI), and Stein Variational Gradient Descent (SVGD). The innovation lies in systematically quantifying uncertainty using coverage probability, sharpness, and calibration error, and in establishing a unified comparison between probabilistic and deterministic models. Results show that the HBLR–NUTS model achieves the best trade-off between accuracy (RMSE = 11.85) and calibration quality (coverage 0.98), while ADVI offers near-equivalent performance with significantly lower computation time. These uncertainty-aware predictions can be directly integrated into Manufacturing Execution System (MES) and Enterprise Resource Planning (ERP) environments for energy-optimized scheduling and cost-aware planning. The proposed framework provides a scalable, interpretable, and statistically reliable foundation for advancing sustainable, data-driven manufacturing analytics.},
keywords = {artificial intelligence, deep learning, industry 4.0, industry 5.0, machine learning, manufacturing, sustainability},
pubstate = {published},
tppubtype = {article}
}
Energy-efficient and data-driven decision-making has become a critical priority in modern manufacturing, particularly in customized or make-to-order (MTO) production where product variability causes large fluctuations in power consumption. Existing prediction models in this domain are often deterministic, lacking the ability to quantify uncertainty and capture hierarchical data dependencies, which limits their reliability for operational use. This study addresses this gap by developing a hierarchical Bayesian learning framework for power consumption prediction in customized stainless-steel manufacturing. The objective is to design models that not only achieve high predictive accuracy but also provide calibrated uncertainty estimates to support risk-aware production decisions. Four models, i.e., Hierarchical Bayesian Linear Regression (HBLR), Hierarchical Bayesian Neural Network (HBNN), Fully Connected Neural Network (FCN), and One-Dimensional Convolutional Neural Network (1D-CNN), were implemented and benchmarked using three inference algorithms: No-U-Turn Sampler (NUTS), Automatic Differentiation Variational Inference (ADVI), and Stein Variational Gradient Descent (SVGD). The innovation lies in systematically quantifying uncertainty using coverage probability, sharpness, and calibration error, and in establishing a unified comparison between probabilistic and deterministic models. Results show that the HBLR–NUTS model achieves the best trade-off between accuracy (RMSE = 11.85) and calibration quality (coverage 0.98), while ADVI offers near-equivalent performance with significantly lower computation time. These uncertainty-aware predictions can be directly integrated into Manufacturing Execution System (MES) and Enterprise Resource Planning (ERP) environments for energy-optimized scheduling and cost-aware planning. The proposed framework provides a scalable, interpretable, and statistically reliable foundation for advancing sustainable, data-driven manufacturing analytics.
Bashyal, Atit; Veerachanchi, Pakin; Boroukhian, Tina; Wicaksono, Hendro
Open innovation in industrial demand response: A computing continuum approach to overcoming technological barriers Journal Article
In: Journal of Open Innovation: Technology, Market, and Complexity, vol. 11, iss. 4, pp. 100678, 2025.
Abstract | Links | BibTeX | Tags: artificial intelligence, data management, data science, demand response system, energy management, green energy, industry 4.0, ontologies, sustainability
@article{bashyal2025open,
title = {Open innovation in industrial demand response: A computing continuum approach to overcoming technological barriers},
author = {Atit Bashyal and Pakin Veerachanchi and Tina Boroukhian and Hendro Wicaksono},
doi = {https://doi.org/10.1016/j.joitmc.2025.100678},
year = {2025},
date = {2025-11-06},
urldate = {2025-11-06},
journal = {Journal of Open Innovation: Technology, Market, and Complexity},
volume = {11},
issue = {4},
pages = {100678},
publisher = {Elsevier},
abstract = {The rise of Industrial IoT (IIoT) alongside cloud, edge, and fog computing is transforming industrial operations and enabling new Demand Response (DR) opportunities in the Smart Grid. DR allows end-users to adjust energy consumption in response to external signals. Still, its adoption in industry is limited by challenges such as communication, security, interoperability, and computing constraints, especially in environments requiring real-time decision-making. This paper explores how the Computing Continuum can help overcome these barriers and support scalable, flexible, and responsive Industrial Demand Response (IDR) systems. We propose a reference architecture that integrates key IIoT and energy management trends to support real-time processing and system interoperability. A central focus is the role of aggregators and the importance of open innovation and cloud service providers in enabling adaptive and collaborative IDR solutions. Our findings offer a roadmap for aligning technological advancements with IDR needs, contributing to more effective and sustainable energy management in Industry 4.0 settings.},
keywords = {artificial intelligence, data management, data science, demand response system, energy management, green energy, industry 4.0, ontologies, sustainability},
pubstate = {published},
tppubtype = {article}
}
The rise of Industrial IoT (IIoT) alongside cloud, edge, and fog computing is transforming industrial operations and enabling new Demand Response (DR) opportunities in the Smart Grid. DR allows end-users to adjust energy consumption in response to external signals. Still, its adoption in industry is limited by challenges such as communication, security, interoperability, and computing constraints, especially in environments requiring real-time decision-making. This paper explores how the Computing Continuum can help overcome these barriers and support scalable, flexible, and responsive Industrial Demand Response (IDR) systems. We propose a reference architecture that integrates key IIoT and energy management trends to support real-time processing and system interoperability. A central focus is the role of aggregators and the importance of open innovation and cloud service providers in enabling adaptive and collaborative IDR solutions. Our findings offer a roadmap for aligning technological advancements with IDR needs, contributing to more effective and sustainable energy management in Industry 4.0 settings.
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.
Bashyal, Atit; Boroukhian, Tina; Veerachanchai, Pakin; Naransukh, Myanganbayar; Wicaksono, Hendro
Multi-agent deep reinforcement learning based demand response and energy management for heavy industries with discrete manufacturing systems Journal Article
In: Applied Energy, vol. 392, pp. 125990, 2025.
Abstract | Links | BibTeX | Tags: artificial intelligence, data science, deep learning, demand response system, energy management, green energy, machine learning, manufacturing, operation research, reinforcement learning, sustainability
@article{bashyal2025multi,
title = {Multi-agent deep reinforcement learning based demand response and energy management for heavy industries with discrete manufacturing systems},
author = {Atit Bashyal and Tina Boroukhian and Pakin Veerachanchai and Myanganbayar Naransukh and Hendro Wicaksono},
doi = {https://doi.org/10.1016/j.apenergy.2025.125990},
year = {2025},
date = {2025-08-15},
urldate = {2025-01-01},
journal = {Applied Energy},
volume = {392},
pages = {125990},
publisher = {Elsevier},
abstract = {Energy-centric decarbonization of heavy industries, such as steel and cement, necessitates their participation in integrating Renewable Energy Sources (RES) and effective Demand Response (DR) programs. This situation has created the opportunities to research control algorithms in diverse DR scenarios. Further, the industrial sector’s unique challenges, including the diversity of operations and the need for uninterrupted production, bring unique challenges in designing and implementing control algorithms. Reinforcement learning (RL) methods are practical solutions to the unique challenges faced by the industrial sector. Nevertheless, research in RL for industrial demand response has not yet achieved the level of standardization seen in other areas of RL research, hindering broader progress. To propel the research progress, we propose a multi-agent reinforcement learning (MARL)-based energy management system designed to optimize energy consumption in energy-intensive industrial settings by leveraging dynamic pricing DR schemes. The study highlights the creation of a MARL environment and addresses these challenges by designing a general framework that allows researchers to replicate and implement MARL environments for industrial sectors. The proposed framework incorporates a Partially Observable Markov Decision Process (POMDP) to model energy consumption and production processes while introducing buffer storage constraints and a flexible reward function that balances production efficiency and cost reduction. The paper evaluates the framework through experimental validation within a steel powder manufacturing facility. The experimental results validate our framework and also demonstrate the effectiveness of the MARL-based energy management system.},
keywords = {artificial intelligence, data science, deep learning, demand response system, energy management, green energy, machine learning, manufacturing, operation research, reinforcement learning, sustainability},
pubstate = {published},
tppubtype = {article}
}
Energy-centric decarbonization of heavy industries, such as steel and cement, necessitates their participation in integrating Renewable Energy Sources (RES) and effective Demand Response (DR) programs. This situation has created the opportunities to research control algorithms in diverse DR scenarios. Further, the industrial sector’s unique challenges, including the diversity of operations and the need for uninterrupted production, bring unique challenges in designing and implementing control algorithms. Reinforcement learning (RL) methods are practical solutions to the unique challenges faced by the industrial sector. Nevertheless, research in RL for industrial demand response has not yet achieved the level of standardization seen in other areas of RL research, hindering broader progress. To propel the research progress, we propose a multi-agent reinforcement learning (MARL)-based energy management system designed to optimize energy consumption in energy-intensive industrial settings by leveraging dynamic pricing DR schemes. The study highlights the creation of a MARL environment and addresses these challenges by designing a general framework that allows researchers to replicate and implement MARL environments for industrial sectors. The proposed framework incorporates a Partially Observable Markov Decision Process (POMDP) to model energy consumption and production processes while introducing buffer storage constraints and a flexible reward function that balances production efficiency and cost reduction. The paper evaluates the framework through experimental validation within a steel powder manufacturing facility. The experimental results validate our framework and also demonstrate the effectiveness of the MARL-based energy management system.
Gupta, Ishansh; Martinez, Adriana; Correa, Sergio; Wicaksono, Hendro
In: Supply Chain Analytics, vol. 10, pp. 100116, 2025.
Abstract | Links | BibTeX | Tags: 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
@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}
}
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.
Wicaksono, Hendro; Trat, Martin; Bashyal, Atit; Boroukhian, Tina; Felder, Mine; Ahrens, Mischa; Bender, Janek; Groß, Sebastian; Steiner, Daniel; July, Christoph; others,
Artificial-intelligence-enabled dynamic demand response system for maximizing the use of renewable electricity in production processes Journal Article
In: The International Journal of Advanced Manufacturing Technology, vol. 138, pp. 247–271, 2025.
Abstract | Links | BibTeX | Tags: artificial intelligence, data management, data science, demand response system, energy management, green energy, industry 4.0, interoperability, machine learning, manufacturing, ontologies, reinforcement learning, semantic web, sustainability
@article{wicaksono2024artificial,
title = {Artificial-intelligence-enabled dynamic demand response system for maximizing the use of renewable electricity in production processes},
author = {Hendro Wicaksono and Martin Trat and Atit Bashyal and Tina Boroukhian and Mine Felder and Mischa Ahrens and Janek Bender and Sebastian Groß and Daniel Steiner and Christoph July and others},
url = {https://link.springer.com/article/10.1007/s00170-024-13372-7},
doi = {https://doi.org/10.1007/s00170-024-13372-7},
year = {2025},
date = {2025-05-01},
urldate = {2025-05-01},
journal = {The International Journal of Advanced Manufacturing Technology},
volume = {138},
pages = { 247–271},
publisher = {Springer London},
abstract = {The transition towards renewable electricity provides opportunities for manufacturing companies to save electricity costs through participating in demand response programs. End-to-end implementation of demand response systems focusing on manufacturing power consumers is still challenging due to multiple stakeholders and subsystems that generate a heterogeneous and large amount of data. This work develops an approach utilizing artificial intelligence for a demand response system that optimizes industrial consumers’ and prosumers’ production-related electricity costs according to time-variable electricity tariffs. It also proposes a semantic middleware architecture that utilizes an ontology as the semantic integration model for handling heterogeneous data models between the system’s modules. This paper reports on developing and evaluating multiple machine learning models for power generation forecasting and load prediction, and also mixed-integer linear programming as well as reinforcement learning for production optimization considering dynamic electricity pricing represented as Green Electricity Index (GEI). The experiments show that the hybrid auto-regressive long-short-term-memory model performs best for solar and convolutional neural networks for wind power generation forecasting. Random forest, k-nearest neighbors, ridge, and gradient-boosting regression models perform best in load prediction in the considered use cases. Furthermore, this research found that the reinforcement-learning-based approach can provide generic and scalable solutions for complex and dynamic production environments. Additionally, this paper presents the validation of the developed system in the German industrial environment, involving a utility company and two small to medium-sized manufacturing companies. It shows that the developed system benefits the manufacturing company that implements fine-grained process scheduling most due to its flexible rescheduling capacities.
},
keywords = {artificial intelligence, data management, data science, demand response system, energy management, green energy, industry 4.0, interoperability, machine learning, manufacturing, ontologies, reinforcement learning, semantic web, sustainability},
pubstate = {published},
tppubtype = {article}
}
The transition towards renewable electricity provides opportunities for manufacturing companies to save electricity costs through participating in demand response programs. End-to-end implementation of demand response systems focusing on manufacturing power consumers is still challenging due to multiple stakeholders and subsystems that generate a heterogeneous and large amount of data. This work develops an approach utilizing artificial intelligence for a demand response system that optimizes industrial consumers’ and prosumers’ production-related electricity costs according to time-variable electricity tariffs. It also proposes a semantic middleware architecture that utilizes an ontology as the semantic integration model for handling heterogeneous data models between the system’s modules. This paper reports on developing and evaluating multiple machine learning models for power generation forecasting and load prediction, and also mixed-integer linear programming as well as reinforcement learning for production optimization considering dynamic electricity pricing represented as Green Electricity Index (GEI). The experiments show that the hybrid auto-regressive long-short-term-memory model performs best for solar and convolutional neural networks for wind power generation forecasting. Random forest, k-nearest neighbors, ridge, and gradient-boosting regression models perform best in load prediction in the considered use cases. Furthermore, this research found that the reinforcement-learning-based approach can provide generic and scalable solutions for complex and dynamic production environments. Additionally, this paper presents the validation of the developed system in the German industrial environment, involving a utility company and two small to medium-sized manufacturing companies. It shows that the developed system benefits the manufacturing company that implements fine-grained process scheduling most due to its flexible rescheduling capacities.
Fekete, Tamas; Mengistu, Girum; Wicaksono, Hendro
Leveraging causal AI to uncover the dynamics in sustainable urban transport: A bike sharing time-series study Journal Article
In: Sustainable Cities and Society, vol. 122, pp. 106240, 2025.
Abstract | Links | BibTeX | Tags: artificial intelligence, causal AI, causal inference, industry 5.0, machine learning, sustainability, transportation
@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}
}
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.
Prasetyo, Moonita Limiany; Peranginangin, Randall Aginta; Martinovic, Nada; Ichsan, Mohammad; Wicaksono, Hendro
In: Journal of Open Innovation: Technology, Market, and Complexity, vol. 11, iss. 1, no. 100445, 2025.
Abstract | Links | BibTeX | Tags: artificial intelligence, industry 4.0, innovation management, project management
@article{nokey,
title = {Artificial Intelligence in Open Innovation Project Management: A Systematic Literature Review on Technologies, Applications, and Integration Requirements},
author = {Moonita Limiany Prasetyo and Randall Aginta Peranginangin and Nada Martinovic and Mohammad Ichsan and Hendro Wicaksono},
url = {https://www.sciencedirect.com/science/article/pii/S2199853124002397},
doi = {https://doi.org/10.1016/j.joitmc.2024.100445},
year = {2025},
date = {2025-03-01},
urldate = {2025-03-01},
journal = {Journal of Open Innovation: Technology, Market, and Complexity},
volume = {11},
number = {100445},
issue = {1},
abstract = {This study aims to provide insights to support organizations in building effective strategies for adopting Artificial Intelligence (AI) and improving project management processes. It focuses on open innovation projects. It employs a comprehensive and systematic literature review (SLR). A total of 365 publications have been chosen from a pool of 1265 papers in the IEEE and Scopus databases. The study develops a framework for literature synthesis guided by five research questions. Those questions address AI technologies, project management tasks, industries adopting AI, and requirements for successful adoption. The analysis reveals that Machine Learning is widely employed in project management, especially for predicting analytics, optimizing resources, and managing risks. AI improves open innovation project management by integrating diverse knowledge sources, enhancing collaboration, and providing strategic insights for decision-making. This study also found that AI adoption depends not only on technical infrastructure, integration with existing systems, and data readiness but also on leadership support, strategic alignment, financial resources, skills development, and organizational culture. The findings also highlight the importance of aligning AI initiatives with open innovation requirements, where collaboration, agility, and external knowledge integrations are crucial. The construction sector is at the forefront of adopting AI. This study fills a significant gap in previous research by identifying the technical and non-technical prerequisites for effectively incorporating AI into open innovation project management methodologies.},
keywords = {artificial intelligence, industry 4.0, innovation management, project management},
pubstate = {published},
tppubtype = {article}
}
This study aims to provide insights to support organizations in building effective strategies for adopting Artificial Intelligence (AI) and improving project management processes. It focuses on open innovation projects. It employs a comprehensive and systematic literature review (SLR). A total of 365 publications have been chosen from a pool of 1265 papers in the IEEE and Scopus databases. The study develops a framework for literature synthesis guided by five research questions. Those questions address AI technologies, project management tasks, industries adopting AI, and requirements for successful adoption. The analysis reveals that Machine Learning is widely employed in project management, especially for predicting analytics, optimizing resources, and managing risks. AI improves open innovation project management by integrating diverse knowledge sources, enhancing collaboration, and providing strategic insights for decision-making. This study also found that AI adoption depends not only on technical infrastructure, integration with existing systems, and data readiness but also on leadership support, strategic alignment, financial resources, skills development, and organizational culture. The findings also highlight the importance of aligning AI initiatives with open innovation requirements, where collaboration, agility, and external knowledge integrations are crucial. The construction sector is at the forefront of adopting AI. This study fills a significant gap in previous research by identifying the technical and non-technical prerequisites for effectively incorporating AI into open innovation project management methodologies.
Wicaksono, Hendro; Mengistu, Abel; Bashyal, Atit; Fekete, Tamas
Digital Product Passport (DPP) technological advancement and adoption framework: A systematic literature review Journal Article
In: Procedia Computer Science, vol. 253, pp. 2980-2989, 2025.
Abstract | Links | BibTeX | Tags: artificial intelligence, digital product passport, technology adoption
@article{nokey,
title = {Digital Product Passport (DPP) technological advancement and adoption framework: A systematic literature review},
author = {Hendro Wicaksono and Abel Mengistu and Atit Bashyal and Tamas Fekete},
url = {https://www.sciencedirect.com/science/article/pii/S1877050925003655},
doi = {https://doi.org/10.1016/j.procs.2025.02.022},
year = {2025},
date = {2025-02-25},
journal = {Procedia Computer Science},
volume = {253},
pages = {2980-2989},
abstract = {This research investigates the integration of Digital Product Passports (DPPs) into the Circular Economy (CE) paradigm. DPPs are digital papers that accompany products and contain detailed lifecycle data on materials, manufacturing processes, distribution networks, environmental effects, and end-of-life treatment. They improve industry openness, traceability, and sustainability by closing information gaps and encouraging sustainable product management. Despite the growing interest in DPPs, there is a significant gap in understanding the practical challenges and scalability of DPP adoption across the industry. This paper digs into technology developments and their adoptions for efficient DPP implementation within the CE framework through a systematic literature review (SLR). It investigates how DPPs promote resource efficiency, improve lifecycle assessments, and strengthen end-of-life management techniques. It also looks at the economic and legal consequences of integrating DPP into existing supply chains, stressing potential cost issues and the need for regulatory frameworks. The findings highlight DPPs’ significance in facilitating long-term product management decisions by providing openness and accountability across the product lifecycle. This paper also underlines the importance of stakeholder collaboration in realizing DPPs’ revolutionary potential for advancing the CE agenda. It proposes a conceptual model illustrating the technical architecture of DPPs, adoption framework, and DPP adoption ecosystem. Finally, this paper discusses the future research directions around DPPs based on the research gap identified through the SLR.
},
keywords = {artificial intelligence, digital product passport, technology adoption},
pubstate = {published},
tppubtype = {article}
}
This research investigates the integration of Digital Product Passports (DPPs) into the Circular Economy (CE) paradigm. DPPs are digital papers that accompany products and contain detailed lifecycle data on materials, manufacturing processes, distribution networks, environmental effects, and end-of-life treatment. They improve industry openness, traceability, and sustainability by closing information gaps and encouraging sustainable product management. Despite the growing interest in DPPs, there is a significant gap in understanding the practical challenges and scalability of DPP adoption across the industry. This paper digs into technology developments and their adoptions for efficient DPP implementation within the CE framework through a systematic literature review (SLR). It investigates how DPPs promote resource efficiency, improve lifecycle assessments, and strengthen end-of-life management techniques. It also looks at the economic and legal consequences of integrating DPP into existing supply chains, stressing potential cost issues and the need for regulatory frameworks. The findings highlight DPPs’ significance in facilitating long-term product management decisions by providing openness and accountability across the product lifecycle. This paper also underlines the importance of stakeholder collaboration in realizing DPPs’ revolutionary potential for advancing the CE agenda. It proposes a conceptual model illustrating the technical architecture of DPPs, adoption framework, and DPP adoption ecosystem. Finally, this paper discusses the future research directions around DPPs based on the research gap identified through the SLR.
Jeong, Heonyoung; Fekete, Tamas; Bashyal, Atit; Wicaksono, Hendro
From Theory to Practice: Implementing Causal AI in Manufacturing for Sustainability Journal Article
In: Procedia Computer Science, vol. 253, pp. 1495-1504, 2025.
Abstract | Links | BibTeX | Tags: artificial intelligence, causal AI, causal inference, energy management, manufacturing, sustainability
@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}
}
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.
Bashyal, Atit; Alnahas, Hani; Boroukhian, Tina; Wicaksono, Hendro
Demand response based industrial energy management with focus on consumption of renewable energy: a deep reinforcement learning approach Journal Article
In: Procedia Computer Science, vol. 253, pp. 1442-1451, 2025.
Abstract | Links | BibTeX | Tags: artificial intelligence, demand response system, energy management, manufacturing, reinforcement learning
@article{nokey,
title = {Demand response based industrial energy management with focus on consumption of renewable energy: a deep reinforcement learning approach},
author = {Atit Bashyal and Hani Alnahas and Tina Boroukhian and Hendro Wicaksono},
url = {https://www.sciencedirect.com/science/article/pii/S1877050925002145},
doi = {https://doi.org/10.1016/j.procs.2025.01.206},
year = {2025},
date = {2025-02-25},
journal = {Procedia Computer Science},
volume = {253},
pages = {1442-1451},
abstract = {Integrating Renewable Energy Resources (RESs) into power grids requires effective Demand Response (DR) programs. Despite high DR potential in industrial sectors, adoption lags behind that of residential and commercial sectors due to diverse operations and production continuity requirements. This paper explores a reinforcement learning (RL)-based DR scheme for energy-intensive industries, promoting the consumption of distributed Renewable Energy (RE) generation. Our approach introduces modifications to the existing Markov Decision Process (MDP) framework. It proposes a flexible reward structure that provides flexibility in balancing production requirements and promotes the consumption of RE. This study addresses the gap in industrial DR literature, emphasizing tailored DR solutions for industrial settings. The key highlight of our RL-based DR solution is its ability to facilitate a price-based DR scheme while promoting the integration of RE into the smart grid.
},
keywords = {artificial intelligence, demand response system, energy management, manufacturing, reinforcement learning},
pubstate = {published},
tppubtype = {article}
}
Integrating Renewable Energy Resources (RESs) into power grids requires effective Demand Response (DR) programs. Despite high DR potential in industrial sectors, adoption lags behind that of residential and commercial sectors due to diverse operations and production continuity requirements. This paper explores a reinforcement learning (RL)-based DR scheme for energy-intensive industries, promoting the consumption of distributed Renewable Energy (RE) generation. Our approach introduces modifications to the existing Markov Decision Process (MDP) framework. It proposes a flexible reward structure that provides flexibility in balancing production requirements and promotes the consumption of RE. This study addresses the gap in industrial DR literature, emphasizing tailored DR solutions for industrial settings. The key highlight of our RL-based DR solution is its ability to facilitate a price-based DR scheme while promoting the integration of RE into the smart grid.
Ghribi, Youssef; Graha, Ega Rudy; Wicaksono, Hendro
Comparative Analysis of Statistical and Machine Learning Models for Enhancing Demand Forecasting Accuracy in the Medical Device Industry Journal Article
In: Procedia CIRP, vol. 134, pp. 849–854, 2025.
Abstract | Links | BibTeX | Tags: artificial intelligence, data science, deep learning, demand forecasting, healthcare, machine learning, manufacturing, supply chain management
@article{ghribi2025comparative,
title = {Comparative Analysis of Statistical and Machine Learning Models for Enhancing Demand Forecasting Accuracy in the Medical Device Industry},
author = {Youssef Ghribi and Ega Rudy Graha and Hendro Wicaksono},
doi = {https://doi.org/10.1016/j.procir.2025.02.209},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
journal = {Procedia CIRP},
volume = {134},
pages = {849–854},
publisher = {Elsevier},
abstract = {Demand forecasting is a crucial instrument in the business strategy. The medical devices in the healthcare system are further significant as critical roles. Multiple businesses rely on traditional forecasting techniques due to their simplicity and understandable algorithm’s easy-to-use nature characteristics. The research conducted for each model analyzes how traditional statistical, Machine Learning (ML), and Deep Learning (DL) models can be used to make demand forecasting more accurate and valuable in the medical device industry. The work expands beyond prior research to demonstrate the enhanced effectiveness of DL models compared to statistical and ML models across multiple areas. However, research still needs to identify studies that adopt a business-centric perspective on the practical applicability of these models. Research utilizing SARIMAX, Exponential Smoothing, Linear Regression, Average, Support Vector Regression (SVR), Random Forest (RF), K-Nearest Neighbour Regression (KNR), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Convolution 1D (CONV1D) models to forecast what people demand to order. The data comes from a German medical device manufacturer’s past sales record. We evaluated the model’s performance using the weighted Mean Absolute Percentage Error (wMAPE) method. These showed that DL models needed a lot of knowledge and preprocessing, but they were the most accurate at predicting what would happen. The LSTM model exhibited outstanding performance, achieving an average wMAPE of 0.3102, surpassing all other models. The research results for more sophisticated models surpass traditional statistical models despite limited datasets, recommending that medical device businesses consider investing in advanced demand forecasting models.
publisher={Elsevier}
}},
keywords = {artificial intelligence, data science, deep learning, demand forecasting, healthcare, machine learning, manufacturing, supply chain management},
pubstate = {published},
tppubtype = {article}
}
Demand forecasting is a crucial instrument in the business strategy. The medical devices in the healthcare system are further significant as critical roles. Multiple businesses rely on traditional forecasting techniques due to their simplicity and understandable algorithm’s easy-to-use nature characteristics. The research conducted for each model analyzes how traditional statistical, Machine Learning (ML), and Deep Learning (DL) models can be used to make demand forecasting more accurate and valuable in the medical device industry. The work expands beyond prior research to demonstrate the enhanced effectiveness of DL models compared to statistical and ML models across multiple areas. However, research still needs to identify studies that adopt a business-centric perspective on the practical applicability of these models. Research utilizing SARIMAX, Exponential Smoothing, Linear Regression, Average, Support Vector Regression (SVR), Random Forest (RF), K-Nearest Neighbour Regression (KNR), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Convolution 1D (CONV1D) models to forecast what people demand to order. The data comes from a German medical device manufacturer’s past sales record. We evaluated the model’s performance using the weighted Mean Absolute Percentage Error (wMAPE) method. These showed that DL models needed a lot of knowledge and preprocessing, but they were the most accurate at predicting what would happen. The LSTM model exhibited outstanding performance, achieving an average wMAPE of 0.3102, surpassing all other models. The research results for more sophisticated models surpass traditional statistical models despite limited datasets, recommending that medical device businesses consider investing in advanced demand forecasting models.
publisher={Elsevier}
}
publisher={Elsevier}
}
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.
Almais, Agung Teguh Wibowo; Susilo, Adi; Naba, Agus; Sarosa, Moechammad; Juwono, Alamsyah Muhammad; Crysdian, Cahyo; Muslim, Muhammad Aziz; Wicaksono, Hendro
Characterization of Structural Building Damage in Post-Disaster Using GLCM-PCA Analysis Integration Journal Article
In: IEEE Access, vol. 12, 2024.
Abstract | Links | BibTeX | Tags: artificial intelligence, data science, machine learning
@article{nokey,
title = {Characterization of Structural Building Damage in Post-Disaster Using GLCM-PCA Analysis Integration},
author = {Agung Teguh Wibowo Almais and Adi Susilo and Agus Naba and Moechammad Sarosa and Alamsyah Muhammad Juwono and Cahyo Crysdian and Muhammad Aziz Muslim and Hendro Wicaksono},
url = {https://ieeexplore.ieee.org/abstract/document/10697160},
doi = {https://doi.org/10.1109/ACCESS.2024.3469637},
year = {2024},
date = {2024-09-27},
urldate = {2024-09-27},
journal = {IEEE Access},
volume = {12},
abstract = {Objective: To determine the characteristics of a building after a natural disaster using image input through the integration of image analysis techniques. Methods: Several image analysis techniques, including GLCM and PCA, were employed. The GLCM process converts image input into numerical values using 8 different angles and pixel distances of 1 and 0.5 pixels. The numerical values from GLCM are then processed by PCA to extract information stored in the images of buildings post-disaster. Results: The PCA process revealed different information between images processed with GLCM at 1 pixel distance and those at 0.5 pixel distance. Validation by surveyors confirmed that the accurate information corresponding to real images was obtained from GLCM with a 0.5 pixel distance, indicating severe damage. The PCA results using GLCM at 0.5 pixel distance produced 2D and 3D visualizations with dominant coordinates in the severely damaged cluster, with a value range (n) of n ≥ 2. Conclusion: Based on these findings, the integration of image analysis techniques, specifically GLCM and PCA, can be used to determine the level of damage to buildings after a natural disaster.},
keywords = {artificial intelligence, data science, machine learning},
pubstate = {published},
tppubtype = {article}
}
Objective: To determine the characteristics of a building after a natural disaster using image input through the integration of image analysis techniques. Methods: Several image analysis techniques, including GLCM and PCA, were employed. The GLCM process converts image input into numerical values using 8 different angles and pixel distances of 1 and 0.5 pixels. The numerical values from GLCM are then processed by PCA to extract information stored in the images of buildings post-disaster. Results: The PCA process revealed different information between images processed with GLCM at 1 pixel distance and those at 0.5 pixel distance. Validation by surveyors confirmed that the accurate information corresponding to real images was obtained from GLCM with a 0.5 pixel distance, indicating severe damage. The PCA results using GLCM at 0.5 pixel distance produced 2D and 3D visualizations with dominant coordinates in the severely damaged cluster, with a value range (n) of n ≥ 2. Conclusion: Based on these findings, the integration of image analysis techniques, specifically GLCM and PCA, can be used to determine the level of damage to buildings after a natural disaster.
2023
Reinhold, Ylva; Valilai, Omid Fatahi; Wicaksono, Hendro
Will Industry 4.0 Applications Help in Designing Sustainable Forest Management? A Conceptual Framework of Connected Networks in Novel Sectors Proceedings Article
In: 2023 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), pp. 0918–0922, IEEE 2023.
Abstract | Links | BibTeX | Tags: artificial intelligence, design science research, digital twins, industry 4.0, sustainability
@inproceedings{reinhold2023will,
title = {Will Industry 4.0 Applications Help in Designing Sustainable Forest Management? A Conceptual Framework of Connected Networks in Novel Sectors},
author = {Ylva Reinhold and Omid Fatahi Valilai and Hendro Wicaksono},
doi = {https://doi.org/10.1109/IEEM58616.2023.10406763},
year = {2023},
date = {2023-12-18},
urldate = {2023-01-01},
booktitle = {2023 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)},
pages = {0918–0922},
organization = {IEEE},
abstract = {Using Industry 4.0 technologies creates new opportunities in many fields. This paper examines the potential of such technologies for the forest sector. Existing research mainly proposes solutions to collect and analyze data on specific topics. This research aims to create a model combining different data inputs to draw a comprehensive picture of forest conditions by closing the gap between science and policymaking. With the help of a pre-defined set of indicators, the output is communicable across sectors and countries while maintaining practicability on a local level. The model evaluation has been completed according to the Design Science Research (DSR) Guidelines proposed by Hevner, et al., which prospected good chances of adoptability. With the successful implementation of the model, ways of decision-making for sustainable forest management could be revolutionized.
},
keywords = {artificial intelligence, design science research, digital twins, industry 4.0, sustainability},
pubstate = {published},
tppubtype = {inproceedings}
}
Using Industry 4.0 technologies creates new opportunities in many fields. This paper examines the potential of such technologies for the forest sector. Existing research mainly proposes solutions to collect and analyze data on specific topics. This research aims to create a model combining different data inputs to draw a comprehensive picture of forest conditions by closing the gap between science and policymaking. With the help of a pre-defined set of indicators, the output is communicable across sectors and countries while maintaining practicability on a local level. The model evaluation has been completed according to the Design Science Research (DSR) Guidelines proposed by Hevner, et al., which prospected good chances of adoptability. With the successful implementation of the model, ways of decision-making for sustainable forest management could be revolutionized.
Sarafanov, Egor; Valilai, Omid Fatahi; Wicaksono, Hendro
Causal Analysis of Artificial Intelligence Adoption in Project Management Proceedings Article
In: Intelligent Systems Conference, pp. 245–264, Springer Nature Switzerland Cham 2023.
BibTeX | Tags: artificial intelligence, causal inference, data science, project management, technology adoption
@inproceedings{sarafanov2023causal,
title = {Causal Analysis of Artificial Intelligence Adoption in Project Management},
author = {Egor Sarafanov and Omid Fatahi Valilai and Hendro Wicaksono},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
booktitle = {Intelligent Systems Conference},
pages = {245–264},
organization = {Springer Nature Switzerland Cham},
keywords = {artificial intelligence, causal inference, data science, project management, technology adoption},
pubstate = {published},
tppubtype = {inproceedings}
}
2021
Wicaksono, Hendro
Accelerating Energy Transition to Green Electricity through Artificial Intelligence Journal Article
In: 2021.
BibTeX | Tags: artificial intelligence, energy management, machine learning, ontologies, semantic web, sustainability
@article{wicaksono2021accelerating,
title = {Accelerating Energy Transition to Green Electricity through Artificial Intelligence},
author = {Hendro Wicaksono},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
publisher = {OSF Preprints},
keywords = {artificial intelligence, energy management, machine learning, ontologies, semantic web, sustainability},
pubstate = {published},
tppubtype = {article}
}