2026
Alarcon, Matias Mauricio Davila; Wicaksono, Hendro
Reinforcement Learning for Circular Manufacturing: A Proximal Policy Optimization Approach for Sustainable Production Planning Journal Article
In: Procedia Computer Science, vol. 277, pp. 1869–1878, 2026.
Links | BibTeX | Tags: artificial intelligence, circular economy, machine learning, manufacturing, reinforcement learning, sustainability
@article{alarcon2026reinforcement,
title = {Reinforcement Learning for Circular Manufacturing: A Proximal Policy Optimization Approach for Sustainable Production Planning},
author = {Matias Mauricio Davila Alarcon and Hendro Wicaksono},
doi = {https://doi.org/10.1016/j.procs.2026.02.225},
year = {2026},
date = {2026-01-01},
urldate = {2026-01-01},
journal = {Procedia Computer Science},
volume = {277},
pages = {1869–1878},
publisher = {Elsevier},
keywords = {artificial intelligence, circular economy, machine learning, manufacturing, reinforcement learning, sustainability},
pubstate = {published},
tppubtype = {article}
}
2025
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.
2024
Ompusunggu, Agusmian P; Tjahjowidodo, Tegoeh; Wicaksono, Hendro
Causal AI-powered Digital Product Passports for enabling a circular and sustainable manufacturing ecosystem Proceedings Article
In: Cranfield University, 2024.
Abstract | Links | BibTeX | Tags: causal AI, causal inference, circular economy, digital product passport
@inproceedings{ompusunggua2024causal,
title = {Causal AI-powered Digital Product Passports for enabling a circular and sustainable manufacturing ecosystem},
author = {Agusmian P Ompusunggu and Tegoeh Tjahjowidodo and Hendro Wicaksono},
doi = {10.57996/cran.ceres-2579},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
publisher = {Cranfield University},
abstract = {Digital product passport (DPP) has been recently introduced by policymakers (e.g., the European Commission) to advance sustainable business practices towards a circular economy (CE). As a newly introduced concept, DPP is still relatively high-level and vague. Therefore, its definition, information flow architecture, what relevant information needs to be stored, and how to use such information in the context of a circular and sustainable manufacturing ecosystem, etc., are still open research questions. This paper addresses these research questions by proposing a novel conceptual framework for DPP, facilitating seamless information exchanges among CE stakeholders, and providing a transparent and trustworthy basis for decision-making. Causal AI utilisation is proposed to extract causal relationships among sustainability/circularity KPIs comprehensively, encompassing raw material supply chain, circularity-compliant product design, manufacturing optimisation on the shop floor, and after-sale product usage optimisation. Seamless information exchange will be achieved through semantic interoperability and a comprehensive model of the whole supply chain by employing an ontology model. The causal AI approach is proposed to identify causalities among KPIs and other factors to predict environmental impacts. This way, a causal model integrating domain expert knowledge and causality discovered from measured data will increase the transparency/explainability of prediction/decision made by machine learning algorithms.},
keywords = {causal AI, causal inference, circular economy, digital product passport},
pubstate = {published},
tppubtype = {inproceedings}
}
Digital product passport (DPP) has been recently introduced by policymakers (e.g., the European Commission) to advance sustainable business practices towards a circular economy (CE). As a newly introduced concept, DPP is still relatively high-level and vague. Therefore, its definition, information flow architecture, what relevant information needs to be stored, and how to use such information in the context of a circular and sustainable manufacturing ecosystem, etc., are still open research questions. This paper addresses these research questions by proposing a novel conceptual framework for DPP, facilitating seamless information exchanges among CE stakeholders, and providing a transparent and trustworthy basis for decision-making. Causal AI utilisation is proposed to extract causal relationships among sustainability/circularity KPIs comprehensively, encompassing raw material supply chain, circularity-compliant product design, manufacturing optimisation on the shop floor, and after-sale product usage optimisation. Seamless information exchange will be achieved through semantic interoperability and a comprehensive model of the whole supply chain by employing an ontology model. The causal AI approach is proposed to identify causalities among KPIs and other factors to predict environmental impacts. This way, a causal model integrating domain expert knowledge and causality discovered from measured data will increase the transparency/explainability of prediction/decision made by machine learning algorithms.
2023
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}
}