2025
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, 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},
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, 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.
Vijaya, Annas; Meisterknecht, Johanne Paula Sophia; Angreani, Linda Salma; Wicaksono, Hendro
Advancing sustainability in the automotive sector: A critical analysis of environmental, social, and governance (ESG) performance indicators Journal Article
In: Cleaner Environmental Systems, vol. 16, 2025.
Abstract | Links | BibTeX | Tags: automotive industry, ESG, multi criteria decision making, sustainability
@article{nokey,
title = {Advancing sustainability in the automotive sector: A critical analysis of environmental, social, and governance (ESG) performance indicators},
author = {Annas Vijaya and Johanne Paula Sophia Meisterknecht and Linda Salma Angreani and Hendro Wicaksono},
url = {https://www.sciencedirect.com/science/article/pii/S2666789424000862},
doi = {https://doi.org/10.1016/j.cesys.2024.100248},
year = {2025},
date = {2025-03-01},
journal = {Cleaner Environmental Systems},
volume = {16},
abstract = {ESG (Environment, Social, Governance) is becoming increasingly important as sustainability concerns in the industry increase. The automotive industry is one that receives significant attention and pressure on sustainability, with the ever-growing regulations pushing it towards sustainability. However, ESG improvement could be more effective due to the many factors. Although previous studies have revealed the evaluation and prioritization of ESG key performance indicators (KPIs) in the automotive sector, there is still a need for other approaches to identify the priorities and interdependencies between critical factors that enhance organizational strategic improvement measures. The study aims to address the gaps by identifying critical indicators in ESG reporting standards and utilizing Fuzzy DEMATEL and Fuzzy TOPSIS methodologies to explore the priorities and causal relationships of ESG KPIs in the automotive industry. The findings indicate that the top three of 17 identified factors are the top priorities that influence others in improving ESG performance, including corporate governance, air emissions, and sustainable product development. The importance of addressing social sustainability issues in strengthening stakeholder relationships is also highlighted in the research findings, such as human rights and labor practices. Businesses in the automotive sector can use the study's insights to enhance their sustainability strategies, determine critical opportunities for improvement, and rank their priorities to achieve sustainability objectives. Policymakers can use it to promote industry-wide efforts for sustainable development and create regulatory frameworks.},
keywords = {automotive industry, ESG, multi criteria decision making, sustainability},
pubstate = {published},
tppubtype = {article}
}
ESG (Environment, Social, Governance) is becoming increasingly important as sustainability concerns in the industry increase. The automotive industry is one that receives significant attention and pressure on sustainability, with the ever-growing regulations pushing it towards sustainability. However, ESG improvement could be more effective due to the many factors. Although previous studies have revealed the evaluation and prioritization of ESG key performance indicators (KPIs) in the automotive sector, there is still a need for other approaches to identify the priorities and interdependencies between critical factors that enhance organizational strategic improvement measures. The study aims to address the gaps by identifying critical indicators in ESG reporting standards and utilizing Fuzzy DEMATEL and Fuzzy TOPSIS methodologies to explore the priorities and causal relationships of ESG KPIs in the automotive industry. The findings indicate that the top three of 17 identified factors are the top priorities that influence others in improving ESG performance, including corporate governance, air emissions, and sustainable product development. The importance of addressing social sustainability issues in strengthening stakeholder relationships is also highlighted in the research findings, such as human rights and labor practices. Businesses in the automotive sector can use the study's insights to enhance their sustainability strategies, determine critical opportunities for improvement, and rank their priorities to achieve sustainability objectives. Policymakers can use it to promote industry-wide efforts for sustainable development and create regulatory frameworks.
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.
2024
Fan, Xiaotong; Valilai, Omid Fatahi; Wicaksono, Hendro
Integrating Economic, Technological, and Consumer Factors for Enhanced Accuracy in Electric Vehicle Demand Forecasting: A Case Study in Germany Proceedings Article
In: Lecture Notes in Mechanical Engineering (LNME): Flexible Automation and Intelligent Manufacturing: Manufacturing Innovation and Preparedness for the Changing World Order , Springer, 2024.
Abstract | Links | BibTeX | Tags: automotive industry, demand forecasting, machine learning, supply chain management, sustainability, timeseries analysis
@inproceedings{nokey,
title = {Integrating Economic, Technological, and Consumer Factors for Enhanced Accuracy in Electric Vehicle Demand Forecasting: A Case Study in Germany},
author = {Xiaotong Fan and Omid Fatahi Valilai and Hendro Wicaksono
},
doi = {https://doi.org/10.1007/978-3-031-74485-3_50},
year = {2024},
date = {2024-12-13},
booktitle = {Lecture Notes in Mechanical Engineering (LNME): Flexible Automation and Intelligent Manufacturing: Manufacturing Innovation and Preparedness for the Changing World Order },
publisher = {Springer},
abstract = {The development of the electric vehicle industry has the potential to reduce CO
emissions significantly and overcome energy supply challenges. However, manufacturing electric cars is a complex process consisting of procurement, logistics, and assembly. Accurate demand forecasting plays an essential role in the industry’s long-term development because it effectively fulfills customer needs while mitigating the risks of overproduction. Forecasting electric vehicle demand presents a significant challenge due to limited data availability and multiple factors influencing it. Comprehensive research integrating economic, technological, and consumer dynamics into demand forecasts remains notably deficient. The main goal of this research is to develop demand forecasting of electric vehicles within Germany, considering those factors, including variables like gasoline prices, the count of installed charging stations, and the Google search index. The research involves experimentation with various models, including Prophet, SARIMA, and their variants, incorporating different combinations of exogenous variables. The results demonstrate that SARIMA and its variants outperform other models regarding predictive accuracy. This research equips electric vehicle manufacturing companies with invaluable insights into market trends and the potential impact of diverse influencing variables. With this knowledge, companies can adapt their production strategies to align with market dynamics, enhancing their competitiveness in the rapidly evolving electric vehicle landscape.},
keywords = {automotive industry, demand forecasting, machine learning, supply chain management, sustainability, timeseries analysis},
pubstate = {published},
tppubtype = {inproceedings}
}
The development of the electric vehicle industry has the potential to reduce CO
emissions significantly and overcome energy supply challenges. However, manufacturing electric cars is a complex process consisting of procurement, logistics, and assembly. Accurate demand forecasting plays an essential role in the industry’s long-term development because it effectively fulfills customer needs while mitigating the risks of overproduction. Forecasting electric vehicle demand presents a significant challenge due to limited data availability and multiple factors influencing it. Comprehensive research integrating economic, technological, and consumer dynamics into demand forecasts remains notably deficient. The main goal of this research is to develop demand forecasting of electric vehicles within Germany, considering those factors, including variables like gasoline prices, the count of installed charging stations, and the Google search index. The research involves experimentation with various models, including Prophet, SARIMA, and their variants, incorporating different combinations of exogenous variables. The results demonstrate that SARIMA and its variants outperform other models regarding predictive accuracy. This research equips electric vehicle manufacturing companies with invaluable insights into market trends and the potential impact of diverse influencing variables. With this knowledge, companies can adapt their production strategies to align with market dynamics, enhancing their competitiveness in the rapidly evolving electric vehicle landscape.
emissions significantly and overcome energy supply challenges. However, manufacturing electric cars is a complex process consisting of procurement, logistics, and assembly. Accurate demand forecasting plays an essential role in the industry’s long-term development because it effectively fulfills customer needs while mitigating the risks of overproduction. Forecasting electric vehicle demand presents a significant challenge due to limited data availability and multiple factors influencing it. Comprehensive research integrating economic, technological, and consumer dynamics into demand forecasts remains notably deficient. The main goal of this research is to develop demand forecasting of electric vehicles within Germany, considering those factors, including variables like gasoline prices, the count of installed charging stations, and the Google search index. The research involves experimentation with various models, including Prophet, SARIMA, and their variants, incorporating different combinations of exogenous variables. The results demonstrate that SARIMA and its variants outperform other models regarding predictive accuracy. This research equips electric vehicle manufacturing companies with invaluable insights into market trends and the potential impact of diverse influencing variables. With this knowledge, companies can adapt their production strategies to align with market dynamics, enhancing their competitiveness in the rapidly evolving electric vehicle landscape.
Yuniaristanto,; Sutopo, Wahyudi; Hisjam, Muhammad; Wicaksono, Hendro
Estimating the market share of electric motorcycles: A system dynamics approach with the policy mix and sustainable life cycle costs Journal Article
In: Energy Policy, vol. 195, pp. 114345, 2024.
Abstract | Links | BibTeX | Tags: e-mobility, sustainability, system dynamics, technology adoption, transportation
@article{yuniaristanto2024estimating,
title = {Estimating the market share of electric motorcycles: A system dynamics approach with the policy mix and sustainable life cycle costs},
author = {Yuniaristanto and Wahyudi Sutopo and Muhammad Hisjam and Hendro Wicaksono},
url = {https://www.sciencedirect.com/science/article/pii/S0301421524003653},
doi = {https://doi.org/10.1016/j.enpol.2024.114345},
year = {2024},
date = {2024-12-01},
urldate = {2024-01-01},
journal = {Energy Policy},
volume = {195},
pages = {114345},
publisher = {Elsevier},
abstract = {Introducing electric vehicles is critical to maintaining air quality and reducing carbon emissions. The Indonesian government has issued several regulations to stimulate the diffusion of electric vehicles. This research attempts to forecast the electric vehicle market share, especially electric motorcycles, by involving the policy mix and sustainable life cycle costs. We propose a system dynamics approach that takes into account a policy mix including 0% down payment without credit interest subsidies, tax abolition, expansion of charging station network, and sustainable life cycle costs, i.e., total cost of ownership, social, and environment. The system dynamics model has four modules: the electric motorcycle cost, the conventional motorcycle cost, the economy module, and the consumer market. The simulation results show that the electric motorcycle market share will increase positively in 2021–2030, reaching 5.7% in 2030. Based on the scenario simulation results, providing more charging stations and vehicle tax abolition can significantly boost the market share of electric motorcycles in Indonesia. The study provides valuable insights for policymakers in formulating more appropriate policy instruments to promote electric vehicle diffusion in Indonesia.
},
keywords = {e-mobility, sustainability, system dynamics, technology adoption, transportation},
pubstate = {published},
tppubtype = {article}
}
Introducing electric vehicles is critical to maintaining air quality and reducing carbon emissions. The Indonesian government has issued several regulations to stimulate the diffusion of electric vehicles. This research attempts to forecast the electric vehicle market share, especially electric motorcycles, by involving the policy mix and sustainable life cycle costs. We propose a system dynamics approach that takes into account a policy mix including 0% down payment without credit interest subsidies, tax abolition, expansion of charging station network, and sustainable life cycle costs, i.e., total cost of ownership, social, and environment. The system dynamics model has four modules: the electric motorcycle cost, the conventional motorcycle cost, the economy module, and the consumer market. The simulation results show that the electric motorcycle market share will increase positively in 2021–2030, reaching 5.7% in 2030. Based on the scenario simulation results, providing more charging stations and vehicle tax abolition can significantly boost the market share of electric motorcycles in Indonesia. The study provides valuable insights for policymakers in formulating more appropriate policy instruments to promote electric vehicle diffusion in Indonesia.
Shah, Muhammad Abdullah; Wicaksono, Hendro
Leveraging Machine Learning for Power Consumption Prediction of Multi-Step Production Processes in Dynamic Electricity Price Environment Journal Article
In: Procedia CIRP, vol. 130, pp. 226-231, 2024.
Abstract | Links | BibTeX | Tags: energy management, machine learning, manufacturing, sustainability
@article{Shah2024,
title = {Leveraging Machine Learning for Power Consumption Prediction of Multi-Step Production Processes in Dynamic Electricity Price Environment},
author = {Muhammad Abdullah Shah and Hendro Wicaksono},
url = {https://www.sciencedirect.com/science/article/pii/S2212827124012332},
doi = {https://doi.org/10.1016/j.procir.2024.10.080},
year = {2024},
date = {2024-11-27},
urldate = {2024-11-27},
journal = {Procedia CIRP},
volume = {130},
pages = {226-231},
abstract = {Rising energy costs drive a compelling demand for energy-efficient manufacturing across sectors, paralleled by increasing consumer preferences for eco-friendly products. To remain competitive, companies are actively enhancing their energy efficiency. Integrating dynamic pricing in manufacturing, aimed at optimizing renewable energy use, requires strategic adjustments in production planning for sustainability. This research highlights the importance of incorporating dynamic pricing into production planning, emphasizing the need to shift processes to time slots when the energy prices are low or optimal. This study focuses on predicting the power consumption of multi-step CNC machine operations within a production cycle. Utilizing advanced Machine Learning (ML), including neural networks, statistical, and additive models, this research found unique time series characteristics influencing model performance across production steps. A practical use case within a German manufacturing Small and Medium Enterprises (SME) demonstrates how prediction results can optimize production processes in a dynamic pricing environment, providing a blueprint for diverse machinery forecasting models. This research’s insights extend to any industry managing production schedules for multiple machines with various steps in a process cycle. Industries with high energy consumption will benefit significantly through aligning operational efficiency with environmental sustainability goals.},
keywords = {energy management, machine learning, manufacturing, sustainability},
pubstate = {published},
tppubtype = {article}
}
Rising energy costs drive a compelling demand for energy-efficient manufacturing across sectors, paralleled by increasing consumer preferences for eco-friendly products. To remain competitive, companies are actively enhancing their energy efficiency. Integrating dynamic pricing in manufacturing, aimed at optimizing renewable energy use, requires strategic adjustments in production planning for sustainability. This research highlights the importance of incorporating dynamic pricing into production planning, emphasizing the need to shift processes to time slots when the energy prices are low or optimal. This study focuses on predicting the power consumption of multi-step CNC machine operations within a production cycle. Utilizing advanced Machine Learning (ML), including neural networks, statistical, and additive models, this research found unique time series characteristics influencing model performance across production steps. A practical use case within a German manufacturing Small and Medium Enterprises (SME) demonstrates how prediction results can optimize production processes in a dynamic pricing environment, providing a blueprint for diverse machinery forecasting models. This research’s insights extend to any industry managing production schedules for multiple machines with various steps in a process cycle. Industries with high energy consumption will benefit significantly through aligning operational efficiency with environmental sustainability goals.
Nesro, Vahid Menu; Fekete, Tamas; Wicaksono, Hendro
Leveraging Causal Machine Learning for Sustainable Automotive Industry: Analyzing Factors Influencing CO2 Emissions Journal Article
In: Procedia CIRP, vol. 130, pp. 161-166, 2024.
Abstract | Links | BibTeX | Tags: causal AI, causal inference, sustainability
@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}
}
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.
Prayitno, Kutut Aji; Wicaksono, Hendro
Leveraging Causal Machine Learning for Sustainable Automotive Industry: Analyzing Factors Influencing CO2 Emissions Journal Article
In: Procedia CIRP, vol. 130, pp. 1070-1076, 2024.
Abstract | Links | BibTeX | Tags: causal AI, logistics, ontologies, reinforcement learning, sustainability
@article{nokey,
title = {Leveraging Causal Machine Learning for Sustainable Automotive Industry: Analyzing Factors Influencing CO2 Emissions},
author = {Kutut Aji Prayitno and Hendro Wicaksono},
url = {https://www.sciencedirect.com/science/article/pii/S2212827124013659},
doi = {https://doi.org/10.1016/j.procir.2024.10.208},
year = {2024},
date = {2024-11-27},
urldate = {2024-11-27},
journal = {Procedia CIRP},
volume = {130},
pages = {1070-1076},
abstract = {Efficiently managing logistics operations is crucial in elevating sustainability and tackling the challenges urbanization brings in today’s urban environment. Collaborations among the public and private sectors in urban logistics are essential to minimize environmental impacts. This study aims to create a novel conceptual framework for collaborative logistics designed explicitly for sustainable metropolitan areas. The framework aims to enable collaborative data-driven sustainability optimization in urban logistics. It comprises ontologies to facilitate interoperability among stakeholders by providing a shared understanding of the exchanged data. The framework utilizes causal artificial intelligence to enable traceability and transparency of data-driven decisions compared to conventional machine learning working based on correlations. Furthermore, the framework also employs causal reinforcement learning that enables agents to learn what actions lead to targeted outcomes and why those actions are effective. The developed framework optimizes vehicle routes and conveyance selection while considering several operational constraints such as time windows, split-load scenarios, and commodity-specific requirements. Moreover, the system integrates the distinctive features of public transport networks. The suggested strategy minimizes fuel use and overall delivery costs, promoting a more sustainable logistics environment in metropolitan areas measured using Environmental, Social, and Governance (ESG) indicators. This study contributes to the theoretical understanding of collaborative logistics. It underscores the importance of environmental stewardship and societal well-being in logistics planning and implementation by utilizing a data-driven approach.
},
keywords = {causal AI, logistics, ontologies, reinforcement learning, sustainability},
pubstate = {published},
tppubtype = {article}
}
Efficiently managing logistics operations is crucial in elevating sustainability and tackling the challenges urbanization brings in today’s urban environment. Collaborations among the public and private sectors in urban logistics are essential to minimize environmental impacts. This study aims to create a novel conceptual framework for collaborative logistics designed explicitly for sustainable metropolitan areas. The framework aims to enable collaborative data-driven sustainability optimization in urban logistics. It comprises ontologies to facilitate interoperability among stakeholders by providing a shared understanding of the exchanged data. The framework utilizes causal artificial intelligence to enable traceability and transparency of data-driven decisions compared to conventional machine learning working based on correlations. Furthermore, the framework also employs causal reinforcement learning that enables agents to learn what actions lead to targeted outcomes and why those actions are effective. The developed framework optimizes vehicle routes and conveyance selection while considering several operational constraints such as time windows, split-load scenarios, and commodity-specific requirements. Moreover, the system integrates the distinctive features of public transport networks. The suggested strategy minimizes fuel use and overall delivery costs, promoting a more sustainable logistics environment in metropolitan areas measured using Environmental, Social, and Governance (ESG) indicators. This study contributes to the theoretical understanding of collaborative logistics. It underscores the importance of environmental stewardship and societal well-being in logistics planning and implementation by utilizing a data-driven approach.
Yuniaristanto,; Sutopo, Wahyudi; Hisjam, Muhammad; Wicaksono, Hendro
Exploring the determinants of intention to purchase electric motorcycles: the role of national culture in the UTAUT Journal Article
In: Transportation research part F: traffic psychology and behaviour, vol. 100, pp. 475–492, 2024.
Links | BibTeX | Tags: data science, sustainability, technology adoption
@article{sutopo2024exploring,
title = {Exploring the determinants of intention to purchase electric motorcycles: the role of national culture in the UTAUT},
author = {Yuniaristanto and Wahyudi Sutopo and Muhammad Hisjam and Hendro Wicaksono},
url = {https://www.sciencedirect.com/science/article/pii/S1369847823002772},
doi = {https://doi.org/10.1016/j.trf.2023.12.012},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Transportation research part F: traffic psychology and behaviour},
volume = {100},
pages = {475–492},
publisher = {Pergamon},
keywords = {data science, sustainability, technology adoption},
pubstate = {published},
tppubtype = {article}
}
2023
Llanos, Alan Francisco Caraveo Gomez; Vijaya, Annas; Wicaksono, Hendro
Rating ESG key performance indicators in the airline industry Journal Article
In: Environment, Development and Sustainability, vol. 26, pp. 27629–27653, 2023.
Abstract | Links | BibTeX | Tags: ESG, multi criteria decision making, sustainability
@article{caraveo2023rating,
title = {Rating ESG key performance indicators in the airline industry},
author = {Alan Francisco Caraveo Gomez Llanos and Annas Vijaya and Hendro Wicaksono},
url = {https://link.springer.com/article/10.1007/s10668-023-03775-z},
doi = {https://doi.org/10.1007/s10668-023-03775-z},
year = {2023},
date = {2023-08-30},
urldate = {2023-01-01},
journal = {Environment, Development and Sustainability},
volume = {26},
pages = {27629–27653},
publisher = {Springer Netherlands},
abstract = {The environmental, social, and governance (ESG) integration finds itself in a transition with rapid developments worldwide, given that the pandemic incentivized companies and investors to focus on other social and governance measures such as ESG ratings. However, the divergence of ratings from the ESG and a lack of transparency lead the companies to report voluntary indicators without standardization. This study aimed to identify the ESG criteria and the most suitable set of key performance indicators (KPIs) in the airline industry after the impact of COVID-19. Furthermore, the second objective was to determine the appropriate weights and ranking of the identified criteria. The multi-criteria decision-making analytical hierarchical process was applied for this purpose. Additionally, the use of intuitionistic variables delivers a comprehensive model for rating the airlines according to their ESG performance. The most relevant criteria found in the study were critical risk management, greenhouse gas emissions, and systemic risk management. Regarding the KPIs, the top-3 weights were the number of flight accidents, jet fuel consumed and sustainable aviation used, and the number of digital transformation initiatives.
},
keywords = {ESG, multi criteria decision making, sustainability},
pubstate = {published},
tppubtype = {article}
}
The environmental, social, and governance (ESG) integration finds itself in a transition with rapid developments worldwide, given that the pandemic incentivized companies and investors to focus on other social and governance measures such as ESG ratings. However, the divergence of ratings from the ESG and a lack of transparency lead the companies to report voluntary indicators without standardization. This study aimed to identify the ESG criteria and the most suitable set of key performance indicators (KPIs) in the airline industry after the impact of COVID-19. Furthermore, the second objective was to determine the appropriate weights and ranking of the identified criteria. The multi-criteria decision-making analytical hierarchical process was applied for this purpose. Additionally, the use of intuitionistic variables delivers a comprehensive model for rating the airlines according to their ESG performance. The most relevant criteria found in the study were critical risk management, greenhouse gas emissions, and systemic risk management. Regarding the KPIs, the top-3 weights were the number of flight accidents, jet fuel consumed and sustainable aviation used, and the number of digital transformation initiatives.
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.
Pidikiti, Vamsi Sai; Vijaya, Annas; Valilai, Omid Fatahi; Wicaksono, Hendro
An Ontology Model to Facilitate the Semantic Interoperability in Assessing the Circular Economy Performance of the Automotive Industry Journal Article
In: Procedia CIRP, vol. 120, pp. 1351–1356, 2023.
BibTeX | Tags: circular economy, ontologies, semantic web, sustainability
@article{pidikiti2023ontology,
title = {An Ontology Model to Facilitate the Semantic Interoperability in Assessing the Circular Economy Performance of the Automotive Industry},
author = {Vamsi Sai Pidikiti and Annas Vijaya and Omid Fatahi Valilai and Hendro Wicaksono},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
journal = {Procedia CIRP},
volume = {120},
pages = {1351–1356},
publisher = {Elsevier},
keywords = {circular economy, ontologies, semantic web, sustainability},
pubstate = {published},
tppubtype = {article}
}
Yuniaristanto, Wahyudi Sutopo; Hisjam, Muhammad; iD, Hendro Wicaksono
Electric Motorcycle Adoption Research: A Bibliometric Analysis Check for updates Proceedings Article
In: Proceedings of the 6th Asia Pacific Conference on Manufacturing Systems and 4th International Manufacturing Engineering Conference: APCOMS-IMEC 2022, Surakarta, Indonesia, pp. 131, Springer Nature 2023.
BibTeX | Tags: sustainability, technology adoption
@inproceedings{yuniaristanto2023electric,
title = {Electric Motorcycle Adoption Research: A Bibliometric Analysis Check for updates},
author = {Wahyudi Sutopo Yuniaristanto and Muhammad Hisjam and Hendro Wicaksono iD},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
booktitle = {Proceedings of the 6th Asia Pacific Conference on Manufacturing Systems and 4th International Manufacturing Engineering Conference: APCOMS-IMEC 2022, Surakarta, Indonesia},
pages = {131},
organization = {Springer Nature},
keywords = {sustainability, technology adoption},
pubstate = {published},
tppubtype = {inproceedings}
}
Yuniaristanto, Yuniaristanto; Sutopo, Wahyudi; Hisjam, Muhammad; Wicaksono, Hendro
Factors Influencing Electric Motorcycle Adoption: A Logit Model Analysis Proceedings Article
In: E3S Web of Conferences, pp. 02035, EDP Sciences 2023.
BibTeX | Tags: data science, sustainability, technology adoption
@inproceedings{yuniaristanto2023factors,
title = {Factors Influencing Electric Motorcycle Adoption: A Logit Model Analysis},
author = {Yuniaristanto Yuniaristanto and Wahyudi Sutopo and Muhammad Hisjam and Hendro Wicaksono},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
booktitle = {E3S Web of Conferences},
volume = {465},
pages = {02035},
organization = {EDP Sciences},
keywords = {data science, sustainability, technology adoption},
pubstate = {published},
tppubtype = {inproceedings}
}
2022
Istiqomah, Silvi; Sutopo, Wahyudi; Hisjam, Muhammad; Wicaksono, Hendro
Optimizing Electric Motorcycle-Charging Station Locations for Easy Accessibility and Public Benefit: A Case Study in Surakarta Journal Article
In: World Electr. Veh. J., vol. 13, no. 12, pp. 232, 2022.
BibTeX | Tags: operation research, sustainability, transportation
@article{istiqomah2022optimizing,
title = {Optimizing Electric Motorcycle-Charging Station Locations for Easy Accessibility and Public Benefit: A Case Study in Surakarta},
author = {Silvi Istiqomah and Wahyudi Sutopo and Muhammad Hisjam and Hendro Wicaksono},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {World Electr. Veh. J.},
volume = {13},
number = {12},
pages = {232},
keywords = {operation research, sustainability, transportation},
pubstate = {published},
tppubtype = {article}
}
Wicaksono, Hendro; Yuce, Baris; McGlinn, Kris; Calli, Ozum
Smart cities and buildings Book Section
In: Buildings and Semantics, pp. 25, CRC Press, 2022.
BibTeX | Tags: machine learning, ontologies, semantic web, smart cities, sustainability
@incollection{wicaksono2022smart,
title = {Smart cities and buildings},
author = {Hendro Wicaksono and Baris Yuce and Kris McGlinn and Ozum Calli},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {Buildings and Semantics},
pages = {25},
publisher = {CRC Press},
keywords = {machine learning, ontologies, semantic web, smart cities, sustainability},
pubstate = {published},
tppubtype = {incollection}
}
Khaturia, Roshaali; Wicaksono, Hendro; Valilai, Omid Fatahi
Srp: a sustainable dynamic ridesharing platform utilizing blockchain technology Proceedings Article
In: International Conference on Dynamics in Logistics, pp. 301–313, Springer International Publishing Cham 2022.
BibTeX | Tags: blockchain, logistics, operation research, sustainability
@inproceedings{khaturia2022srp,
title = {Srp: a sustainable dynamic ridesharing platform utilizing blockchain technology},
author = {Roshaali Khaturia and Hendro Wicaksono and Omid Fatahi Valilai},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {International Conference on Dynamics in Logistics},
pages = {301–313},
organization = {Springer International Publishing Cham},
keywords = {blockchain, logistics, operation research, sustainability},
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}
}
Farooq, Yousuf; Wicaksono, Hendro
Advancing on the analysis of causes and consequences of green skepticism Journal Article
In: Journal of Cleaner Production, vol. 320, pp. 128927, 2021.
BibTeX | Tags: data science, sustainability
@article{farooq2021advancing,
title = {Advancing on the analysis of causes and consequences of green skepticism},
author = {Yousuf Farooq and Hendro Wicaksono},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {Journal of Cleaner Production},
volume = {320},
pages = {128927},
publisher = {Elsevier},
keywords = {data science, sustainability},
pubstate = {published},
tppubtype = {article}
}