2026
Vijaya, Annas; Qadri, Faris Dzaudan; Angreani, Linda Salma; Wicaksono, Hendro
In: Journal of Innovation & Knowledge, vol. 13, pp. 100924, 2026, ISSN: 2444-569X.
Abstract | Links | BibTeX | Tags: ESG, ESG maturity model, ESG reporting, interoperability, MCDM, Ontology, SDG, Semantic model, sustainability
@article{VIJAYA2026100924,
title = {From fragmentation to interoperability: How semantic models transform environmental, social, governance (ESG) reporting, knowledge, and sustainability governance},
author = {Annas Vijaya and Faris Dzaudan Qadri and Linda Salma Angreani and Hendro Wicaksono},
url = {https://www.sciencedirect.com/science/article/pii/S2444569X25002690},
doi = {https://doi.org/10.1016/j.jik.2025.100924},
issn = {2444-569X},
year = {2026},
date = {2026-05-01},
urldate = {2026-01-01},
journal = {Journal of Innovation & Knowledge},
volume = {13},
pages = {100924},
abstract = {Environmental, social, and governance (ESG) reporting faces persistent challenges, including fragmented standards, inconsistent metrics, misalignment with global sustainability goals, and limited stakeholder usability. Numerous studies prove that ontology-based solutions can address several challenges that occur during ESG reporting activities. Although semantic technologies offer valuable benefits for ESG reporting, their utilization in this field remains constrained. Most ontology-based solutions remain in developmental stages, and they are not broadly utilized since organizations lack an understanding of how these tools would help address their reporting problems. This study performs a systematic literature review (SLR) that investigates 19 peer-reviewed studies obtained from Scopus and Web of Science under Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) standards. The SLR identifies critical gaps: (1) existing ontology-driven solutions can address key problems in current ESG reporting; (2) quantitative evaluation methods are rarely integrated with semantic tools, limiting actionable insights; and (3) alignment with evolving standards like the Sustainable Development Goals (SDGs) remains superficial. Based on the SLR insights, this research develops a novel framework through SLR findings by combining ontology-driven methods with quantitative assessment techniques. The framework achieves standardization of various reporting standards through an ESG ontology system that maps essential concepts to build an extensive taxonomy. SDG targets become mutually compatible through established SDG ontologies to allow businesses to measure their activities against international sustainability goals. Fuzzy Multi-Criteria Decision-Making (MCDM) techniques used in combination with an ESG maturity model create quantitative measures to assess ESG performance. The method produces measurable performance indicators that are supported by clear semantic links that allow valid benchmark assessments combined with better data unification and improved decision-making capabilities. The research creates operational frameworks that enable ESG information interoperability, which advance sustainability governance innovation and guide ESG ontology transformations.},
keywords = {ESG, ESG maturity model, ESG reporting, interoperability, MCDM, Ontology, SDG, Semantic model, sustainability},
pubstate = {published},
tppubtype = {article}
}
Environmental, social, and governance (ESG) reporting faces persistent challenges, including fragmented standards, inconsistent metrics, misalignment with global sustainability goals, and limited stakeholder usability. Numerous studies prove that ontology-based solutions can address several challenges that occur during ESG reporting activities. Although semantic technologies offer valuable benefits for ESG reporting, their utilization in this field remains constrained. Most ontology-based solutions remain in developmental stages, and they are not broadly utilized since organizations lack an understanding of how these tools would help address their reporting problems. This study performs a systematic literature review (SLR) that investigates 19 peer-reviewed studies obtained from Scopus and Web of Science under Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) standards. The SLR identifies critical gaps: (1) existing ontology-driven solutions can address key problems in current ESG reporting; (2) quantitative evaluation methods are rarely integrated with semantic tools, limiting actionable insights; and (3) alignment with evolving standards like the Sustainable Development Goals (SDGs) remains superficial. Based on the SLR insights, this research develops a novel framework through SLR findings by combining ontology-driven methods with quantitative assessment techniques. The framework achieves standardization of various reporting standards through an ESG ontology system that maps essential concepts to build an extensive taxonomy. SDG targets become mutually compatible through established SDG ontologies to allow businesses to measure their activities against international sustainability goals. Fuzzy Multi-Criteria Decision-Making (MCDM) techniques used in combination with an ESG maturity model create quantitative measures to assess ESG performance. The method produces measurable performance indicators that are supported by clear semantic links that allow valid benchmark assessments combined with better data unification and improved decision-making capabilities. The research creates operational frameworks that enable ESG information interoperability, which advance sustainability governance innovation and guide ESG ontology transformations.
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; Bangre, Chidambar Prabhakar; Boroukhian, Tina; Wicaksono, Hendro
Unsupervised domain adaptation framework for photovoltaic power forecasting using variational auto-encoders Journal Article
In: Applied Energy, vol. 400, pp. 126606, 2025.
Abstract | Links | BibTeX | Tags: energy management, green energy, machine learning, sustainability, transfer learning
@article{bashyal2025unsupervised,
title = {Unsupervised domain adaptation framework for photovoltaic power forecasting using variational auto-encoders},
author = {Atit Bashyal and Chidambar Prabhakar Bangre and Tina Boroukhian and Hendro Wicaksono},
doi = {https://doi.org/10.1016/j.apenergy.2025.126606},
year = {2025},
date = {2025-12-01},
urldate = {2025-01-01},
journal = {Applied Energy},
volume = {400},
pages = {126606},
publisher = {Elsevier},
abstract = {The global transition towards renewable energy sources necessitates accurate forecasts of such energy sources for efficient grid management. While deep learning models offer effective solutions for intermittent renewable energy forecasts, they face challenges due to their inherent data intensity. Transfer learning methods have emerged as valuable tools to address such challenges. However, existing transfer learning frameworks used in renewable energy forecasting, require a significant amount of labelled training data for fine-tuning and knowledge transfer, limiting their applicability to scenarios where abundant data are available. This paper introduces a domain adaptation framework that enables seamless knowledge transfer from forecasting models trained with abundant data to models that need to be trained without labelled data. The proposed domain adaptation framework, leverages variational inference techniques to align feature spaces between source and target domains, utilizing a generative variational auto-encoder architecture. Experimental validation across solar parks with varying configurations demonstrates the replicability and adaptability of the proposed method. This research underscores the enduring potential of domain adaptation in advancing photovoltaic power forecasting while providing valuable insights into overcoming challenges in transfer learning-based renewable energy forecasting.},
keywords = {energy management, green energy, machine learning, sustainability, transfer learning},
pubstate = {published},
tppubtype = {article}
}
The global transition towards renewable energy sources necessitates accurate forecasts of such energy sources for efficient grid management. While deep learning models offer effective solutions for intermittent renewable energy forecasts, they face challenges due to their inherent data intensity. Transfer learning methods have emerged as valuable tools to address such challenges. However, existing transfer learning frameworks used in renewable energy forecasting, require a significant amount of labelled training data for fine-tuning and knowledge transfer, limiting their applicability to scenarios where abundant data are available. This paper introduces a domain adaptation framework that enables seamless knowledge transfer from forecasting models trained with abundant data to models that need to be trained without labelled data. The proposed domain adaptation framework, leverages variational inference techniques to align feature spaces between source and target domains, utilizing a generative variational auto-encoder architecture. Experimental validation across solar parks with varying configurations demonstrates the replicability and adaptability of the proposed method. This research underscores the enduring potential of domain adaptation in advancing photovoltaic power forecasting while providing valuable insights into overcoming challenges in transfer learning-based renewable energy forecasting.
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.
Boroukhian, Tina; Supyen, Kritkorn; Mclaughlan, Christopher William; Bashyal, Atit; Pham, Tuan; Wicaksono, Hendro
Semantic middleware for demand response systems: Enhancing data interoperability in green electricity management for manufacturing Journal Article
In: Computers in Industry, vol. 172, pp. 104354, 2025.
Abstract | Links | BibTeX | Tags: data management, demand response system, energy management, green energy, industry 4.0, industry 5.0, knowledge management, manufacturing, ontologies, semantic web, sustainability
@article{boroukhian2025semantic,
title = {Semantic middleware for demand response systems: Enhancing data interoperability in green electricity management for manufacturing},
author = {Tina Boroukhian and Kritkorn Supyen and Christopher William Mclaughlan and Atit Bashyal and Tuan Pham and Hendro Wicaksono},
doi = {https://doi.org/10.1016/j.compind.2025.104354},
year = {2025},
date = {2025-11-01},
urldate = {2025-01-01},
journal = {Computers in Industry},
volume = {172},
pages = {104354},
publisher = {Elsevier},
abstract = {Optimizing the consumption of green electricity across sectors, including manufacturing, is a critical strategy for achieving net-zero emissions and advancing clean production in Europe by 2050. Demand Response (DR) represents a promising approach to engaging power consumers from all sectors in the transition toward increased utilization of renewable energy sources. A functional DR system for manufacturing power consumers requires seamless data integration and communication between information systems across multiple domains, including both power consumption and generation. This paper introduces a semantic middleware specifically designed for DR systems in the manufacturing sector, using an ontology as the central information model. To develop this ontology, we adopted a strategy that reuses and unifies existing ontologies from multiple domains, ensuring comprehensive coverage of the data requirements for DR applications in manufacturing. To operationalize this strategy, we designed novel methods for effective ontology unification and implemented them within a dedicated unification tool. This process was followed by data-to-ontology mapping to construct a knowledge graph, and was further extended through the development of a querying system equipped with a natural language interface. Additionally, this paper offers insights into the deployment environment of the semantic middleware, encompassing multiple data sources and the applications that utilize this data. The proposed approach is implemented in multiple German manufacturing small and medium-sized enterprises connected to a utility company, demonstrating consistent data interpretation and seamless information integration. Consequently, the method offers practical potential for optimizing green electricity usage in the manufacturing sector and supporting the transition toward a more sustainable and cleaner future.},
keywords = {data management, demand response system, energy management, green energy, industry 4.0, industry 5.0, knowledge management, manufacturing, ontologies, semantic web, sustainability},
pubstate = {published},
tppubtype = {article}
}
Optimizing the consumption of green electricity across sectors, including manufacturing, is a critical strategy for achieving net-zero emissions and advancing clean production in Europe by 2050. Demand Response (DR) represents a promising approach to engaging power consumers from all sectors in the transition toward increased utilization of renewable energy sources. A functional DR system for manufacturing power consumers requires seamless data integration and communication between information systems across multiple domains, including both power consumption and generation. This paper introduces a semantic middleware specifically designed for DR systems in the manufacturing sector, using an ontology as the central information model. To develop this ontology, we adopted a strategy that reuses and unifies existing ontologies from multiple domains, ensuring comprehensive coverage of the data requirements for DR applications in manufacturing. To operationalize this strategy, we designed novel methods for effective ontology unification and implemented them within a dedicated unification tool. This process was followed by data-to-ontology mapping to construct a knowledge graph, and was further extended through the development of a querying system equipped with a natural language interface. Additionally, this paper offers insights into the deployment environment of the semantic middleware, encompassing multiple data sources and the applications that utilize this data. The proposed approach is implemented in multiple German manufacturing small and medium-sized enterprises connected to a utility company, demonstrating consistent data interpretation and seamless information integration. Consequently, the method offers practical potential for optimizing green electricity usage in the manufacturing sector and supporting the transition toward a more sustainable and cleaner future.
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.
Priyandari, Yusuf; Sutopo, Wahyudi; Nizam, Muhammad; Wicaksono, Hendro
In: Scientific Reports, vol. 15, no. 1, pp. 36613, 2025.
Abstract | Links | BibTeX | Tags: automotive industry, product service system, resillience, supply chain management, sustainability, transportation
@article{priyandari2025vulnerability,
title = {Vulnerability assessment model integrating outcome and characteristic-based metrics for electric motorcycle battery swapping and charging stations},
author = {Yusuf Priyandari and Wahyudi Sutopo and Muhammad Nizam and Hendro Wicaksono},
doi = {https://doi.org/10.1038/s41598-025-20325-x},
year = {2025},
date = {2025-10-21},
urldate = {2025-10-21},
journal = {Scientific Reports},
volume = {15},
number = {1},
pages = {36613},
publisher = {Nature Publishing Group UK London},
abstract = {Battery swapping and charging stations are essential for increasing the adoption of electric motorcycles. The stations address the range anxiety issue and quickly obtain a fully recharged battery. However, operational issues with swapping and charging activities drive operational vulnerability. Therefore, this study proposes a vulnerability assessment model utilizing the IoT Platform data of electric motorcycle battery swapping and charging stations. The model computes a vulnerability score by integrating vulnerability indicator metrics of the system outcome and characteristic. The system outcome uses performance data representing vulnerability impact. The system characteristic uses data from the vulnerability driver and exposure factors. The driver factor represents mitigation ability, and the exposure factor represents conditions that may affect both the mitigation ability and performance. The model also classifies the vulnerability of stations in four categories: not vulnerable, potentially vulnerable, moderately vulnerable, and vulnerable. The model was implemented in a case in Jakarta. The result reveals significant differences in vulnerability among stations, although most stations fall into the not vulnerable to moderately vulnerable categories. The findings facilitate identifying station characteristics that potentially affect performance quantitatively.},
keywords = {automotive industry, product service system, resillience, supply chain management, sustainability, transportation},
pubstate = {published},
tppubtype = {article}
}
Battery swapping and charging stations are essential for increasing the adoption of electric motorcycles. The stations address the range anxiety issue and quickly obtain a fully recharged battery. However, operational issues with swapping and charging activities drive operational vulnerability. Therefore, this study proposes a vulnerability assessment model utilizing the IoT Platform data of electric motorcycle battery swapping and charging stations. The model computes a vulnerability score by integrating vulnerability indicator metrics of the system outcome and characteristic. The system outcome uses performance data representing vulnerability impact. The system characteristic uses data from the vulnerability driver and exposure factors. The driver factor represents mitigation ability, and the exposure factor represents conditions that may affect both the mitigation ability and performance. The model also classifies the vulnerability of stations in four categories: not vulnerable, potentially vulnerable, moderately vulnerable, and vulnerable. The model was implemented in a case in Jakarta. The result reveals significant differences in vulnerability among stations, although most stations fall into the not vulnerable to moderately vulnerable categories. The findings facilitate identifying station characteristics that potentially affect performance quantitatively.
Vijaya, Annas; Qadri, Faris Dzaudan; Angreani, Linda Salma; Wicaksono, Hendro
In: Resources, Environment and Sustainability, vol. 22, pp. 100262, 2025.
Abstract | Links | BibTeX | Tags: data management, data science, ESG, interoperability, ontologies, semantic web, sustainability
@article{vijaya2025esgont,
title = {ESGOnt: An ontology-based framework for Enhancing Environmental, Social, and Governance (ESG) assessments and aligning with Sustainable Development Goals (SDG)},
author = {Annas Vijaya and Faris Dzaudan Qadri and Linda Salma Angreani and Hendro Wicaksono},
doi = {https://doi.org/10.1016/j.resenv.2025.100262},
year = {2025},
date = {2025-08-25},
urldate = {2025-08-25},
journal = {Resources, Environment and Sustainability},
volume = {22},
pages = {100262},
publisher = {Elsevier},
abstract = {This study proposes ESGOnt, an ontology-based framework that aligns Environmental, Social, and Governance (ESG) management with Sustainable Development Goals (SDGs). ESGOnt addresses key challenges in sustainable resource governance systems and cross-sector interoperability by providing a unified structure for ESG and SDG integration. The framework was developed through a systematic methodology that combines a literature review, standardization of ESG and SDG relationships, development of an adaptable maturity model, and ontology implementation using established methods such as Methontology and NeOn. ESGOnt enables the integration of diverse ESG taxonomies and ESG reporting standards, including GRI and ESRS, and assists companies in their ESG performance evaluation. Empirical validation through real-world use cases demonstrates its capability to (1) direct assessment of ESG assessments with specific SDG targets, such as SDG13 (Climate Action) and SDG 12 (Responsible Consumption and Production), (2) assess organizational ESG progress through different metrics, (3) facilitation of standardized and interoperable reporting for small and large enterprises, and (4) automatically validate organization compliance with EU Non-Financial Reporting Directive regulations. The findings show that ESGOnt resolves data inconsistency and transparency issues by enabling integrated and auditable sustainability reporting. The ontology-driven approach of the framework enables scalable and policy-relevant tools for tracking environmental and social impacts, while its maturity model focuses on strategic improvements in resource efficiency. Future studies will analyze and extend ESGOnt’s functionality for sector-specific capabilities, such as bioeconomy control systems, and explore advanced AI-driven inspection methods for real-time ESG-SDG assessment.},
keywords = {data management, data science, ESG, interoperability, ontologies, semantic web, sustainability},
pubstate = {published},
tppubtype = {article}
}
This study proposes ESGOnt, an ontology-based framework that aligns Environmental, Social, and Governance (ESG) management with Sustainable Development Goals (SDGs). ESGOnt addresses key challenges in sustainable resource governance systems and cross-sector interoperability by providing a unified structure for ESG and SDG integration. The framework was developed through a systematic methodology that combines a literature review, standardization of ESG and SDG relationships, development of an adaptable maturity model, and ontology implementation using established methods such as Methontology and NeOn. ESGOnt enables the integration of diverse ESG taxonomies and ESG reporting standards, including GRI and ESRS, and assists companies in their ESG performance evaluation. Empirical validation through real-world use cases demonstrates its capability to (1) direct assessment of ESG assessments with specific SDG targets, such as SDG13 (Climate Action) and SDG 12 (Responsible Consumption and Production), (2) assess organizational ESG progress through different metrics, (3) facilitation of standardized and interoperable reporting for small and large enterprises, and (4) automatically validate organization compliance with EU Non-Financial Reporting Directive regulations. The findings show that ESGOnt resolves data inconsistency and transparency issues by enabling integrated and auditable sustainability reporting. The ontology-driven approach of the framework enables scalable and policy-relevant tools for tracking environmental and social impacts, while its maturity model focuses on strategic improvements in resource efficiency. Future studies will analyze and extend ESGOnt’s functionality for sector-specific capabilities, such as bioeconomy control systems, and explore advanced AI-driven inspection methods for real-time ESG-SDG assessment.
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.
Almashaleh, Omaymah; Wicaksono, Hendro; Valilai, Omid Fatahi
A framework for social media analytics in textile business circularity for effective digital marketing Journal Article
In: Journal of Open Innovation: Technology, Market, and Complexity, vol. 11, iss. 2, pp. 100544, 2025.
Abstract | Links | BibTeX | Tags: circular economy, data science, decision support systems, sustainability
@article{almashaleh2025framework,
title = {A framework for social media analytics in textile business circularity for effective digital marketing},
author = {Omaymah Almashaleh and Hendro Wicaksono and Omid Fatahi Valilai},
doi = {https://doi.org/10.1016/j.joitmc.2025.100544},
year = {2025},
date = {2025-05-01},
urldate = {2025-01-01},
journal = {Journal of Open Innovation: Technology, Market, and Complexity},
volume = {11},
issue = {2},
pages = {100544},
publisher = {Elsevier},
abstract = {In the contemporary era of digital transformation, organizations are increasingly aligning their operations with sustainability objectives, particularly within the framework of circular economy (CE) principles in production and consumption systems. While the concept of circularity has been extensively explored through theoretical research, a notable gap remains in empirical studies that analyze user-generated content related to circularity in the textile industry. This study aims to bridge the gap by proposing a novel Instagramⓒ analytics framework that seamlessly integrates content and network analyses. A mixed-method approach is adopted, merging qualitative insights derived from unstructured data with quantitative techniques. To analyze content, unsupervised machine learning methods, including topic modeling and sentiment analysis, are employed. In parallel, Social Network Analysis (SNA) and hashtag co-occurrence analysis are applied to investigate the dynamics within the network. The findings demonstrate a significant level of interest and engagement in discussions surrounding Textile Circularity (TC). Moreover, consumer responses to sustainability initiatives show considerable variation, underscoring the necessity of strategies that foster meaningful interactions. Notably, content emphasizing positive sentiments and tangible benefits, such as cost savings and environmental improvements, consistently achieves higher engagement levels. This paper contributes to the field by integrating social media data with advanced data analytics techniques. Together, these approaches offer an unparalleled opportunity to investigate customer drivers within the context of TC. Additionally, the study presents a comprehensive analytical model and delivers actionable insights. These findings hold the potential to refine digital marketing strategies and enhance customer engagement, particularly by deepening the understanding of factors that motivate consumers to TC.},
keywords = {circular economy, data science, decision support systems, sustainability},
pubstate = {published},
tppubtype = {article}
}
In the contemporary era of digital transformation, organizations are increasingly aligning their operations with sustainability objectives, particularly within the framework of circular economy (CE) principles in production and consumption systems. While the concept of circularity has been extensively explored through theoretical research, a notable gap remains in empirical studies that analyze user-generated content related to circularity in the textile industry. This study aims to bridge the gap by proposing a novel Instagramⓒ analytics framework that seamlessly integrates content and network analyses. A mixed-method approach is adopted, merging qualitative insights derived from unstructured data with quantitative techniques. To analyze content, unsupervised machine learning methods, including topic modeling and sentiment analysis, are employed. In parallel, Social Network Analysis (SNA) and hashtag co-occurrence analysis are applied to investigate the dynamics within the network. The findings demonstrate a significant level of interest and engagement in discussions surrounding Textile Circularity (TC). Moreover, consumer responses to sustainability initiatives show considerable variation, underscoring the necessity of strategies that foster meaningful interactions. Notably, content emphasizing positive sentiments and tangible benefits, such as cost savings and environmental improvements, consistently achieves higher engagement levels. This paper contributes to the field by integrating social media data with advanced data analytics techniques. Together, these approaches offer an unparalleled opportunity to investigate customer drivers within the context of TC. Additionally, the study presents a comprehensive analytical model and delivers actionable insights. These findings hold the potential to refine digital marketing strategies and enhance customer engagement, particularly by deepening the understanding of factors that motivate consumers to TC.
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.
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
Investigating the Potential of Causal Reinforcement Learning in Collaborative Urban Logistics: A Systematic Literature Review Journal Article
In: Procedia CIRP, vol. 130, pp. 1070-1076, 2024.
Abstract | Links | BibTeX | Tags: causal AI, interoperability, logistics, ontologies, reinforcement learning, semantic web, sustainability
@article{nokey,
title = {Investigating the Potential of Causal Reinforcement Learning in Collaborative Urban Logistics: A Systematic Literature Review},
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, interoperability, logistics, ontologies, reinforcement learning, semantic web, 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
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.
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}
}