Publication

@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}

}

@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}

}

@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}

}

@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}

}

@article{susanty2025mapping,

title = {Mapping barriers to digital adoption in Batik MSMEs: insights from DEMATEL-ISM integration},

author = {Aries Susanty and Nia Budi Puspitasari and Selvrida Eka Junaidi and Hendro Wicaksono},

doi = {https://doi.org/10.1007/s41870-025-02839-9},

year  = {2025},

date = {2025-11-21},

urldate = {2025-01-01},

journal = {International Journal of Information Technology},

pages = {1–26},

publisher = {Springer},

abstract = {This study aims to identify and map the key barriers affecting the readiness of batik Micro and Small Medium Enterprises (MSMEs) to adopt digital technology and to provide actionable recommendations to enhance adoption. Using a mixed-method approach, including systematic literature review, Content Validity Method (CVM), integrated Decision Making Trial and Evaluation Laboratory-Interpretive Structural Modeling (DEMATEL-ISM) modeling, and the Delphi method, the study refines 84 initial barriers into 14 core factors. The integrated DEMATEL-ISM analysis highlights two dominant barriers: lack of digital awareness and inadequate Information Technology (IT) infrastructure. Delphi-based recommendations include awareness campaigns and financial support mechanisms. While expert-based analysis may limit generalizability and overlook cultural or demand-side dynamics, the study offers valuable insight into overcoming systemic barriers. Practical implications emphasize the need for training, institutional support, and fintech-enabled strategies, while social benefits include digital empowerment and cultural preservation. This research is original in its use of DEMATEL-ISM to structure digital transformation challenges in the batik sector, offering a scalable, evidence-based framework for policy and practice.},

keywords = {innovation management, multi criteria decision making, technology adoption},

pubstate = {published},

tppubtype = {article}

}

@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}

}

@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}

}

@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}

}

@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}

}

@article{boroukhian2025integrating,

title = {Integrating 3D object detection with ontologies for accurate digital twin creation in manufacturing systems},

author = {Tina Boroukhian and Kritkorn Supyen and Jhealyn Bautista Samson and Atit Bashyal and Hendro Wicaksono},

doi = {https://doi.org/10.1007/s00170-025-16548-x},

year  = {2025},

date = {2025-10-01},

urldate = {2025-10-01},

journal = {The International Journal of Advanced Manufacturing Technology},

volume = {140},

number = {9},

pages = {4679–4711},

publisher = {Springer London},

abstract = {The digitization of manufacturing resources through digital twins (DTs) enhances operational efficiency and resource management. Ontologies play a key role in maintaining semantic consistency within DT systems. However, existing ontology-based approaches face challenges, including limited adaptability, integration of heterogeneous data—such as 3D images—and high manual effort in ontology development. These limitations hinder the scalability of DT implementations. Traditional 2D imaging often lacks spatial accuracy in complex manufacturing environments, causing inefficiencies and higher costs. Integrating richer data with intelligent frameworks is crucial for improving production and adaptability. The proposed study addresses these challenges by introducing a methodology that integrates existing ontologies with advanced 3D object detection models. The proposed approach employs two fully automated pipelines: one for detecting manufacturing resources from 3D images and another for mapping them into ontologies, ensuring seamless integration into DT frameworks. By leveraging established ontologies, the methodology enhances interoperability, reduces implementation complexity, and facilitates scalable deployment of DT systems across various industrial applications. Additionally, a comparative analysis of multiple advanced 3D detection models provides valuable insights to guide the selection of optimal solutions for diverse industrial settings. Experimental results show that YOLOv8 achieved the highest performance, with 91% classification accuracy, 86% precision, 81% recall, and the fastest inference time of 0.66 s. For ontology population, four machine labels—Robot, MillingMachine, BandSaw, and Lathe—were successfully integrated using a semantic similarity-based mapping strategy, enabling automated class creation and merging. This innovative framework sets a new benchmark for DT implementations, offering enhanced accuracy, efficiency, and semantic coherence in modern manufacturing.},

keywords = {data science, deep learning, digital twins, image processing, industry 4.0, machine learning, manufacturing, object detection, ontologies, semantic web},

pubstate = {published},

tppubtype = {article}

}

@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}

}

@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}

}

@article{gupta2025comparative,

title = {A comparative assessment of causal machine learning and traditional methods for enhancing supply chain resiliency and efficiency in the automotive industry},

author = {Ishansh Gupta and Adriana Martinez and Sergio Correa and Hendro Wicaksono},

doi = {https://doi.org/10.1016/j.sca.2025.100116},

year  = {2025},

date = {2025-06-01},

urldate = {2025-06-01},

journal = {Supply Chain Analytics},

volume = {10},

pages = {100116},

publisher = {Elsevier},

abstract = {Efficient supplier escalation is crucial for maintaining smooth operational supply chains in the automotive industry, as disruptions can lead to significant production delays and financial losses. Many companies still rely on traditional escalation methods, which may lack the precision and adaptability offered by modern technologies. This study presents a comparative analysis of decision-making strategies for supplier escalation, evaluating causal machine learning (CML), traditional machine learning (ML), and current escalation practices in a leading German automotive company. The study employs an explanatory sequential mixed method, integrating the Analytical Hierarchy Process (AHP) with in-depth interviews with 25 industry experts. These methods are assessed based on several performance metrics: accuracy, business impact, explanation capability, human bias, stress test, and time-to-recover. Findings reveal that CML outperforms traditional ML and existing approaches, offering superior risk prediction, interpretability, and decision-making support Additionally, the research explores the internal acceptance of these technologies through the Technology Acceptance Model (TAM). The results highlight the transformative potential of CML in enhancing supply chain resilience and efficiency. By bridging the gap between predictive analytics and explainable AI, this research offers valuable guidance for firms seeking to optimize supplier management using advanced analytics.},

keywords = {artificial intelligence, causal AI, causal inference, data science, decision support systems, industry 4.0, industry 5.0, machine learning, multi criteria decision making, resillience, supply chain management, technology adoption},

pubstate = {published},

tppubtype = {article}

}

@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}

}

@article{priyandari2025desain,

title = {Desain Dashboard untuk Penyajian Informasi Publik Rumah Sakit berbasis Indikator Kinerja dan Evaluasi Dashboard Menggunakan Short-User Experience Questionnaire.},

author = {Yusuf Priyandari and Rio Detri Setyoko and Eko Pujiyanto and Hendro Wicaksono},

doi = {10.62411/tc.v24i2.12804},

year  = {2025},

date = {2025-05-01},

urldate = {2025-01-01},

journal = {Techno. com},

volume = {24},

number = {2},

abstract = {Law Number 14 of 2008 on Public Information Disclosure mandates that every public institution, including government hospitals, provide access to public information. Unfortunately, hospital performance information is only available in PDF and spreadsheet formats through internal hospital websites and certain provincial-level websites, and it is not well-organized. Therefore, this study proposes a standardized dashboard design for all hospitals in a province. The design process consists of four stages: system diagnosis, needs analysis, dashboard development, and evaluation of the design. The design methodology follows the dashboard development approach using Power BI, with the Short-User Experience Questionnaire (UEQ-S) specifically employed in the evaluation phase to assess the design outcomes. The resulting dashboard is capable of visualizing six groups of hospital performance indicators as public information for three user categories: university academics, executives/legislative staff, and independent researchers/media. Based on user experience responses, the dashboard successfully fulfills its purpose as a public information presentation tool, with users expressing satisfaction and comfort while using the dashboard. Overall, users have a very positive impression of the dashboard. UEQ-S results indicate that the dashboard's pragmatic quality is rated as good, its hedonic quality is rated as excellent, and its overall rating is excellent.},

keywords = {data science, decision support systems, healthcare},

pubstate = {published},

tppubtype = {article}

}

@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}

}

@article{angreani2025ontomat,

title = {OntoMat 4.0: An Ontology Framework for Enhanced Industry 4.0 Maturity Assessment},

author = {Linda S Angreani and Annas Vijaya and Hendro Wicaksono},

doi = {https://doi.org/10.1109/ACCESS.2025.3561229},

year  = {2025},

date = {2025-04-15},

urldate = {2025-01-01},

journal = {IEEE Access},

volume = {13},

pages = {68801-68819},

publisher = {IEEE},

abstract = {Industry 4.0 (I4.0) is expected to revolutionize the manufacturing process and business model and offer a significant competitive advantage. The Industry 4.0 Maturity Model (I4.0MM) is applied to guide organizations in I4.0 adoption. Nevertheless, most present models have certain limitations, including the absence of standardization, limited scope or narrow focus, and difficulty of use, which makes them less efficient. To address these gaps, this study presents OntoMat 4.0, an ontology that enables interoperability, knowledge sharing, and I4.0 maturity assessment. It was created following a four-step iterative process based on well-established ontology development frameworks, such as LOT and NeOn. The novelty of Ontomat 4.0 includes its innovative functionality through the ability to work alongside established I4.0 ontologies, which enables users to navigate multidimensional I4.0 aspects and extend interoperability while promoting the reuse of relevant and widely recognized ontologies. It also delivers prioritized actionable insights that guide strategic decisions during I4.0 transformation initiatives. Ontomat 4.0 was evaluated and tested through real-world strategic planning and benchmarking applications, and the effectiveness of Ontomat 4.0 was demonstrated in aiding organizations in making informed decisions. It brings the possibility of integrating the technical and nontechnical factors of I4.0 and can be a standard solution for measuring I4.0 maturity. Despite its limitations, this ontology has been shown to fill in the gaps in current models and promote consistency and interoperability.},

keywords = {digital transformation, industry 4.0, interoperability, manufacturing, ontologies, semantic web, supply chain management},

pubstate = {published},

tppubtype = {article}

}

@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}

}

@article{nokey,

title = {Artificial Intelligence in Open Innovation Project Management: A Systematic Literature Review on Technologies, Applications, and Integration Requirements},

author = {Moonita Limiany Prasetyo and Randall Aginta Peranginangin and Nada Martinovic and Mohammad Ichsan and Hendro Wicaksono},

url = {https://www.sciencedirect.com/science/article/pii/S2199853124002397},

doi = {https://doi.org/10.1016/j.joitmc.2024.100445},

year  = {2025},

date = {2025-03-01},

urldate = {2025-03-01},

journal = {Journal of Open Innovation: Technology, Market, and Complexity},

volume = {11},

number = {100445},

issue = {1},

abstract = {This study aims to provide insights to support organizations in building effective strategies for adopting Artificial Intelligence (AI) and improving project management processes. It focuses on open innovation projects. It employs a comprehensive and systematic literature review (SLR). A total of 365 publications have been chosen from a pool of 1265 papers in the IEEE and Scopus databases. The study develops a framework for literature synthesis guided by five research questions. Those questions address AI technologies, project management tasks, industries adopting AI, and requirements for successful adoption. The analysis reveals that Machine Learning is widely employed in project management, especially for predicting analytics, optimizing resources, and managing risks. AI improves open innovation project management by integrating diverse knowledge sources, enhancing collaboration, and providing strategic insights for decision-making. This study also found that AI adoption depends not only on technical infrastructure, integration with existing systems, and data readiness but also on leadership support, strategic alignment, financial resources, skills development, and organizational culture. The findings also highlight the importance of aligning AI initiatives with open innovation requirements, where collaboration, agility, and external knowledge integrations are crucial. The construction sector is at the forefront of adopting AI. This study fills a significant gap in previous research by identifying the technical and non-technical prerequisites for effectively incorporating AI into open innovation project management methodologies.},

keywords = {artificial intelligence, industry 4.0, innovation management, project management},

pubstate = {published},

tppubtype = {article}

}

@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}

}

@article{nokey,

title = {Evaluating risk factors in automotive supply chains: A hybrid fuzzy AHP-TOPSIS approach with extended PESTLE framework},

author = {Ishansh Gupta and Seyed Taha Raeisi and Sergio Correa and Hendro Wicaksono},

url = {https://www.sciencedirect.com/science/article/pii/S2199853125000241},

doi = {https://doi.org/10.1016/j.joitmc.2025.100489},

year  = {2025},

date = {2025-03-01},

journal = {Journal of Open Innovation: Technology, Market, and Complexity},

volume = {11},

issue = {1},

pages = {100489},

abstract = {The purpose of this study is to evaluate Exogenous Risk Factors (ERFs) affecting Key Performance Indicators (KPIs) in automotive supply chains, aiming to enhance resilience against global disruptions. The primary research question focuses on identifying and prioritizing ERFs that pose the greatest threat to operational performance. A hybrid decision-making framework integrating Fuzzy Analytical Hierarchy Process (FAHP) and Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (FTOPSIS) is employed. Validation is ensured through insights from 18 supply chain professionals with diverse roles and a combined 318 years of experience. The study identifies 34 ERFs, including semiconductor shortages, pandemics, and information infrastructure disruptions, and evaluates their impact on KPIs such as missing parts, backlogs, special transports, and wrong deliveries. By extending the traditional PESTLE framework with Transportation and Material dimensions, this study provides actionable strategies to mitigate risks and strengthen supply chain resilience in volatile environments.},

keywords = {multi criteria decision making, PESTLE, supply chain management},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Digital Product Passport (DPP) technological advancement and adoption framework: A systematic literature review},

author = {Hendro Wicaksono and Abel Mengistu and Atit Bashyal and Tamas Fekete},

url = {https://www.sciencedirect.com/science/article/pii/S1877050925003655},

doi = {https://doi.org/10.1016/j.procs.2025.02.022},

year  = {2025},

date = {2025-02-25},

journal = {Procedia Computer Science},

volume = {253},

pages = {2980-2989},

abstract = {This research investigates the integration of Digital Product Passports (DPPs) into the Circular Economy (CE) paradigm. DPPs are digital papers that accompany products and contain detailed lifecycle data on materials, manufacturing processes, distribution networks, environmental effects, and end-of-life treatment. They improve industry openness, traceability, and sustainability by closing information gaps and encouraging sustainable product management. Despite the growing interest in DPPs, there is a significant gap in understanding the practical challenges and scalability of DPP adoption across the industry. This paper digs into technology developments and their adoptions for efficient DPP implementation within the CE framework through a systematic literature review (SLR). It investigates how DPPs promote resource efficiency, improve lifecycle assessments, and strengthen end-of-life management techniques. It also looks at the economic and legal consequences of integrating DPP into existing supply chains, stressing potential cost issues and the need for regulatory frameworks. The findings highlight DPPs’ significance in facilitating long-term product management decisions by providing openness and accountability across the product lifecycle. This paper also underlines the importance of stakeholder collaboration in realizing DPPs’ revolutionary potential for advancing the CE agenda. It proposes a conceptual model illustrating the technical architecture of DPPs, adoption framework, and DPP adoption ecosystem. Finally, this paper discusses the future research directions around DPPs based on the research gap identified through the SLR.

},

keywords = {artificial intelligence, digital product passport, technology adoption},

pubstate = {published},

tppubtype = {article}

}

@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}

}

@article{nokey,

title = {Demand response based industrial energy management with focus on consumption of renewable energy: a deep reinforcement learning approach},

author = {Atit Bashyal and Hani Alnahas and Tina Boroukhian and Hendro Wicaksono},

url = {https://www.sciencedirect.com/science/article/pii/S1877050925002145},

doi = {https://doi.org/10.1016/j.procs.2025.01.206},

year  = {2025},

date = {2025-02-25},

journal = {Procedia Computer Science},

volume = {253},

pages = {1442-1451},

abstract = {Integrating Renewable Energy Resources (RESs) into power grids requires effective Demand Response (DR) programs. Despite high DR potential in industrial sectors, adoption lags behind that of residential and commercial sectors due to diverse operations and production continuity requirements. This paper explores a reinforcement learning (RL)-based DR scheme for energy-intensive industries, promoting the consumption of distributed Renewable Energy (RE) generation. Our approach introduces modifications to the existing Markov Decision Process (MDP) framework. It proposes a flexible reward structure that provides flexibility in balancing production requirements and promotes the consumption of RE. This study addresses the gap in industrial DR literature, emphasizing tailored DR solutions for industrial settings. The key highlight of our RL-based DR solution is its ability to facilitate a price-based DR scheme while promoting the integration of RE into the smart grid.

},

keywords = {artificial intelligence, demand response system, energy management, manufacturing, reinforcement learning},

pubstate = {published},

tppubtype = {article}

}

@article{chawalitanont2025uncertainty,

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},

year  = {2025},

date = {2025-01-01},

journal = {Journal of Manufacturing Systems},

volume = {83},

pages = {713–735},

publisher = {Elsevier},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{liandra2025interplay,

title = {Interplay of critical factors in effective agile projects},

author = {Tasya Fitriah Liandra and Mohammad Ichsan and Ayupitha Tiara and Hendro Wicaksono},

doi = {https://doi.org/10.1016/j.procs.2025.07.064},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

journal = {Procedia Computer Science},

volume = {263},

pages = {530–538},

publisher = {Elsevier},

abstract = {Agile project management (APM), which was previously used among software development companies has recently gained prominence in various industries due to its ability to better cope with frequent changes in requirements as organizations seek strategies to enhance project outcomes in uncertainty and rapidly evolving business landscape. While the benefits have been well discussed in many papers, there is still a lack of studies about the factors that might influence the effectiveness of APM during its implementation. Therefore, the purpose of this paper is to expand on recent SLR findings, by examining the effects of identified factors, which are customer characteristics, digital leadership, and project management methodologies towards the effectiveness of Agile Project Management (APM) within the Indonesian context. This research employed a Structural Equation Modelling with Partial Least Squares (SEM-PLS) with Smart PLS 4.0 software as an analytical tool. A survey was conducted among Indonesian professionals who possess expertise in APM using a quantitative approach. From the collected data of 156 respondents, the findings showed that customer characteristics have a positive effect on digital leadership and an indirect effect on APM Effectiveness, digital leadership which serves as a mediating factor positively affects APM effectiveness. Only project management methodology does not moderate the relationship between customer characteristics and APM effectiveness. This proved that the identified factors could influence the effectiveness of APM and should be considered to achieve effective agile projects.},

keywords = {digital transformation, project management, technology adoption},

pubstate = {published},

tppubtype = {article}

}

@article{ghribi2025comparative,

title = {Comparative Analysis of Statistical and Machine Learning Models for Enhancing Demand Forecasting Accuracy in the Medical Device Industry},

author = {Youssef Ghribi and Ega Rudy Graha and Hendro Wicaksono},

doi = {https://doi.org/10.1016/j.procir.2025.02.209},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

journal = {Procedia CIRP},

volume = {134},

pages = {849–854},

publisher = {Elsevier},

abstract = {Demand forecasting is a crucial instrument in the business strategy. The medical devices in the healthcare system are further significant as critical roles. Multiple businesses rely on traditional forecasting techniques due to their simplicity and understandable algorithm’s easy-to-use nature characteristics. The research conducted for each model analyzes how traditional statistical, Machine Learning (ML), and Deep Learning (DL) models can be used to make demand forecasting more accurate and valuable in the medical device industry. The work expands beyond prior research to demonstrate the enhanced effectiveness of DL models compared to statistical and ML models across multiple areas. However, research still needs to identify studies that adopt a business-centric perspective on the practical applicability of these models. Research utilizing SARIMAX, Exponential Smoothing, Linear Regression, Average, Support Vector Regression (SVR), Random Forest (RF), K-Nearest Neighbour Regression (KNR), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Convolution 1D (CONV1D) models to forecast what people demand to order. The data comes from a German medical device manufacturer’s past sales record. We evaluated the model’s performance using the weighted Mean Absolute Percentage Error (wMAPE) method. These showed that DL models needed a lot of knowledge and preprocessing, but they were the most accurate at predicting what would happen. The LSTM model exhibited outstanding performance, achieving an average wMAPE of 0.3102, surpassing all other models. The research results for more sophisticated models surpass traditional statistical models despite limited datasets, recommending that medical device businesses consider investing in advanced demand forecasting models.



  publisher={Elsevier}

}},

keywords = {artificial intelligence, data science, deep learning, demand forecasting, healthcare, machine learning, manufacturing, supply chain management},

pubstate = {published},

tppubtype = {article}

}

@article{nasrabadi2024shopping,

title = {Shopping marketplace analysis based on customer insights using social media analytics},

author = {Negar Nasrabadi and Hendro Wicaksono and Omid Fatahi Valilai},

url = {https://www.sciencedirect.com/science/article/pii/S2215016124003200},

doi = {https://doi.org/10.1016/j.mex.2024.102868},

year  = {2024},

date = {2024-12-01},

urldate = {2024-01-01},

journal = {MethodsX},

volume = {13},

pages = {102868},

publisher = {Elsevier},

abstract = {Using the recent advances in data analytics, Companies can leverage sentiment analysis to identify trends and areas for improvement of their market strategy and operation planning. This analysis allows them to understand the sentiments expressed by customers and accurately predict customer behavior. Social media platforms have fundamentally altered the field of digital marketing. The findings of this research try to provide:

•

Potential implications for businesses aiming to optimize their product offerings and enhance customer satisfaction within specific cultural contexts.



•

A case study has been designed for understanding customers' perceptions and satisfaction levels toward various shops, including local ethnic stores and big chain stores.



The paper has conducted a literature review around main pillars like application of data analytics on social media, and sales strategies for establishing a marketplace using data analytics. Exploring the utilization of social media and customer feedback, the paper has proposed a conceptual model for creating insights for extraction of shopping experiences and factors used for customer purchasing decision making.},

keywords = {data science, sentiment analysis, text analytics},

pubstate = {published},

tppubtype = {article}

}

@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}

}

@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}

}

@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}

}

@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}

}

@article{angreani2024enhancing,

title = {Enhancing strategy for Industry 4.0 implementation through maturity models and standard reference architectures alignment},

author = {Linda Salma Angreani and Annas Vijaya and Hendro Wicaksono},

url = {https://www.emerald.com/insight/content/doi/10.1108/jmtm-07-2022-0269/full/html},

doi = {https://doi.org/10.1108/JMTM-07-2022-0269},

year  = {2024},

date = {2024-09-27},

urldate = {2024-01-01},

journal = {Journal of Manufacturing Technology Management},

volume = {35},

issue = {4},

pages = {848-873},

publisher = {Emerald Publishing Limited},

abstract = {Purpose

A maturity model for Industry 4.0 (I4.0 MM) with influencing factors is designed to address maturity issues in adopting Industry 4.0. Standardisation in I4.0 supports manufacturing industry transformation, forming reference architecture models (RAMs). This paper aligns key factors and maturity levels in I4.0 MMs with reputable I4.0 RAMs to enhance strategy for I4.0 transformation and implementation.



Design/methodology/approach

Three steps of alignment consist of the systematic literature review (SLR) method to study the current published high-quality I4.0 MMs, the taxonomy development of I4.0 influencing factors by adapting and implementing the categorisation of system theories and aligning I4.0 MMs with RAMs.



Findings

The study discovered that different I4.0 MMs lead to varied organisational interpretations. Challenges and insights arise when aligning I4.0 MMs with RAMs. Aligning MM levels with RAM stages is a crucial milestone in the journey toward I4.0 transformation. Evidence indicates that I4.0 MMs and RAMs often overlook the cultural domain.



Research limitations/implications

Findings contribute to the literature on aligning capabilities with implementation strategies while employing I4.0 MMs and RAMs. We use five RAMs (RAMI4.0, NIST-SME, IMSA, IVRA and IIRA), and as a common limitation in SLR, there could be a subjective bias in reading and selecting literature.



Practical implications

To fully leverage the capabilities of RAMs as part of the I4.0 implementation strategy, companies should initiate the process by undertaking a thorough needs assessment using I4.0 MMs.



Originality/value

The novelty of this paper lies in being the first to examine the alignment of I4.0 MMs with established RAMs. It offers valuable insights for improving I4.0 implementation strategies, especially for companies using both MMs and RAMs in their transformation efforts.},

keywords = {industry 4.0, multi criteria decision making},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Characterization of Structural Building Damage in Post-Disaster Using GLCM-PCA Analysis Integration},

author = {Agung Teguh Wibowo Almais and Adi Susilo and Agus Naba and Moechammad Sarosa and Alamsyah Muhammad Juwono and Cahyo Crysdian and Muhammad Aziz Muslim and Hendro Wicaksono},

url = {https://ieeexplore.ieee.org/abstract/document/10697160},

doi = {https://doi.org/10.1109/ACCESS.2024.3469637},

year  = {2024},

date = {2024-09-27},

urldate = {2024-09-27},

journal = {IEEE Access},

volume = {12},

abstract = {Objective: To determine the characteristics of a building after a natural disaster using image input through the integration of image analysis techniques. Methods: Several image analysis techniques, including GLCM and PCA, were employed. The GLCM process converts image input into numerical values using 8 different angles and pixel distances of 1 and 0.5 pixels. The numerical values from GLCM are then processed by PCA to extract information stored in the images of buildings post-disaster. Results: The PCA process revealed different information between images processed with GLCM at 1 pixel distance and those at 0.5 pixel distance. Validation by surveyors confirmed that the accurate information corresponding to real images was obtained from GLCM with a 0.5 pixel distance, indicating severe damage. The PCA results using GLCM at 0.5 pixel distance produced 2D and 3D visualizations with dominant coordinates in the severely damaged cluster, with a value range (n) of n ≥ 2. Conclusion: Based on these findings, the integration of image analysis techniques, specifically GLCM and PCA, can be used to determine the level of damage to buildings after a natural disaster.},

keywords = {artificial intelligence, data science, machine learning},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Interdependencies in Industry 4.0 Maturity: Fuzzy MCDA Analysis for Open Innovation in Developing Countries},

author = {Linda Salma Angreani and Faris Dzaudan Qadri and Annas Vijaya and Rana Manahil and Isabella Marquez Petrone and Nabilah and Ahmad Fauzi and Tasya Santi Rahmawati and Hendro Wicaksono},

url = {https://www.sciencedirect.com/science/article/pii/S2199853124001768},

doi = {https://doi.org/10.1016/j.joitmc.2024.100382},

isbn = {2199-8531},

year  = {2024},

date = {2024-09-25},

urldate = {2024-09-25},

journal = {Journal of Open Innovation: Technology, Market, and Complexity},

abstract = {The emergence of Industry 4.0 (I4.0) is reshaping industries worldwide, driven by rapid technological progress and the need for open innovation. This study focuses on understanding the interdependencies of driving factors of I4.0 maturity in developing countries using Fuzzy Multi-Criteria Decision Analysis (MCDA) methods. By analyzing Indonesia, Pakistan, and Venezuela, the research aims to foster open innovation and address the unique challenges these nations face in adopting I4.0 technologies. I4.0 maturity models are essential for evaluating current maturity levels and identifying areas for improvement. However, the complexity and interdependence of various factors—ranging from data science and technology to policy, governance, and open innovation dynamics, such as social open innovation and the role of SMEs—complicate this process. This study employs Fuzzy TOPSIS and Fuzzy DEMATEL to identify the critical factors influencing I4.0 maturity and analyze their interdependencies and prioritization. The results indicate that 'Data and Information' and 'Willingness to Change' are crucial across all countries, while strategic differences between large enterprises and SMEs highlight the need for tailored approaches. This research highlights the importance of continuous IT investment, digital leadership, collaborative ecosystems, and agile strategies in fostering open innovation and driving I4.0 adoption. This research contributes to the theoretical and practical understanding of I4.0 maturity, offering valuable insights for practitioners and academics to explore the dynamic interactions of I4.0 factors and their impact on operational efficiency.},

keywords = {industry 4.0, innovation management, multi criteria decision making, TOPSIS},

pubstate = {published},

tppubtype = {article}

}

@article{mechai2024causal,

title = {Causal Inference in Supply Chain Management: How Does Ever Given Accident at the Suez Canal Affect the Prices of Shipping Containers?},

author = {Nadhir Mechai and Hendro Wicaksono},

url = {https://www.sciencedirect.com/science/article/pii/S1877050924003119},

doi = {https://doi.org/10.1016/j.procs.2024.02.133},

year  = {2024},

date = {2024-09-20},

urldate = {2024-01-01},

journal = {Procedia Computer Science},

volume = {232},

pages = {3173–3182},

publisher = {Elsevier},

abstract = {In March 2021, the Ever Given, a colossal 4000-meter-long container ship with a capacity of 20,000 TEUs (Twenty-foot equivalent units), became lodged in the Suez Canal for six days, causing a significant disruption. This accident blocked approximately 400 ships, impacting not only the vessel itself and the canal but also global trade and the already strained global supply chain post-pandemic. Our research leverages causal inference techniques to rigorously assess and quantify the causal effects of the Ever Given accident on the World Container Index (WCI). We conducted experiments using time series data from eight major global shipping routes, achieving statistically significant results with a confidence level of 99.89%. This research conclusively demonstrates that the Ever Given incident at the Suez Canal had a substantial impact on shipping container prices, quantifying the effect as a remarkable 40% price increase post-exposure. By offering companies the ability to apply causal inference in understanding cause-and-effect dynamics within their supply chain networks, this study equips them with the knowledge needed to make well-informed decisions, especially in times of disruption, thus facilitating the optimization of their supply chain configuration and operations.

},

keywords = {causal AI, causal inference, supply chain management},

pubstate = {published},

tppubtype = {article}

}

@article{thapaliya2024power,

title = {Power Consumption and Processing Time Estimation of CNC Machines Using Explainable Artificial Intelligence (XAI)},

author = {Suman Thapaliya and Omid Fatahi Valilai and Hendro Wicaksono},

url = {https://www.sciencedirect.com/science/article/pii/S1877050924000863},

doi = {https://doi.org/10.1016/j.procs.2024.01.086},

year  = {2024},

date = {2024-03-20},

urldate = {2024-03-20},

journal = {Procedia Computer Science},

volume = {232},

pages = {861–870},

publisher = {Elsevier},

abstract = {Due to environmental issues such as climate change, companies are required to optimize their resource and energy consumption in their production process. Predicting power consumption and processing time of all production facilities is essential for manufacturing to develop mechanisms to prevent energy and resource waste and optimize their use. Machine learning is a powerful tool for prediction tasks using data in digitalized environments. In this paper, we present power consumption and processing time prediction of CNC milling machines using five machine learning regression models, i.e., decision tree, random forest, support vector regression (SVR), extreme gradient boosting (XGBoost), and artificial neural network (ANN). Since most of those models are black-box, we applied two explainable artificial intelligence (XAI) approaches, SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME), to give post-hoc explanations of the predictions given by the machine learning models. Our experiments indicated that random forest regression performed the best in predicting power consumption and processing time. The explanation showed that the number of axis rotations and the number of travels to the machine's zero point in rapid traverse were the most important factors that affected the processing time and power consumption. The companies using CNC milling machines can use our prediction models to optimally plan and schedule the operation of the milling machines in a time and energy-efficient manner. They can also optimize the factors that affect power consumption and processing time the most.

},

keywords = {energy management, explainable AI, machine learning, manufacturing},

pubstate = {published},

tppubtype = {article}

}

@article{habtemichael2024nft,

title = {NFT based Digital Twins for Tracing Value Added Creation in Manufacturing Supply Chains},

author = {Noah Habtemichael and Hendro Wicaksono and Omid Fatahi Valilai},

url = {https://www.sciencedirect.com/science/article/pii/S1877050924002771},

doi = {https://doi.org/10.1016/j.mex.2024.102868},

year  = {2024},

date = {2024-03-20},

urldate = {2024-01-01},

journal = {Procedia Computer Science},

volume = {232},

pages = {2841–2846},

publisher = {Elsevier},

abstract = {The globalization of products and markets increases the distance between the origin of products and consumers. This leads to a condition where customers don't have information about the origins of their products. Thus, traceability has become an essential sub-system of manufacturing supply chain management. However, due to globalization and complexity of supply chain interactions among the suppliers and manufacturing enterprises, it is hard to pinpoint the exact contributions of different actors in a supply chain. Integrated supply network structure with suitable visibility and usage of real time data transfer is another area of great importance. This paper focuses on how NFT (Non-Fungible Token) coupled with smart contracts could utilize blockchain to make it easier to track the products in a supply chain. Explaining how NFT's could help in tracking the contributions of different stakeholders in a supply chain by tracking the product throughout the entire process of sourcing, production, and sale by using a digital twin. In a manufacturing supply chain enabled by NFT Technology, whenever raw materials are transferred and processed through the supply chain, an NFT would be attached to its digital twin which will capture the created values. Each NFT can easily and uniquely be known by its data stored. Data would be updated based on real time information and will enable the stakeholders to trace the product information about how much each company has contributed to the produced products. The data stored in the form of a smart contract in the blockchain prevents the data being entered from being destroyed, eliminated, or changed without permission. Thus, there is a secure data flow among different stakeholders.

},

keywords = {blockchain, digital twins, manufacturing, supply chain management},

pubstate = {published},

tppubtype = {article}

}

@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}

}

@article{rahmawati2024investment,

title = {Investment Decision-Making to Select Converted Electric Motorcycle Tests in Indonesia},

author = {Tasya Santi Rahmawati and Wahyudi Sutopo and Hendro Wicaksono},

url = {https://www.mdpi.com/2032-6653/15/8/334},

doi = {https://doi.org/10.3390/wevj15080334},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {World Electric Vehicle Journal},

volume = {15},

number = {8},

pages = {334},

publisher = {MDPI AG},

abstract = {The issue of carbon emissions can be addressed through environmentally friendly technological innovations, which contribute to the journey towards achieving net-zero emissions (NZE). The electrification of transportation by converting internal combustion engine (ICE) motorcycles to converted electric motorcycles (CEM) directly reduces the number of pollution sources from fossil-powered motors. In Indonesia, numerous government regulations support the commercialization of the CEM system, including the requirement for conversion workshops to be formal entities in the CEM process. Every CEM must pass a test to ensure its safety and suitability. Currently, the CEM testing process is conducted at only one location, making it inefficient and inaccessible. Therefore, most conversion workshops in Indonesia need to take investment steps in procuring CEM-type test tools. This research aims to determine the best alternative from several investment alternatives for CEM-type test tools. In selecting the investment, three criteria are considered: costs, operations, and specifications. By using the investment decision-making model, a hierarchical decision-making model is obtained, which is then processed using the analytical hierarchy process (AHP) and the technique for order of preference by similarity to the ideal solution (TOPSIS). Criteria are weighted to establish a priority order. The final step involves ranking the alternatives and selecting Investment 2 (INV2) as the best investment tool with a relative closeness value of 0.6279. Investment 2 has the shortest time process (40 min), the lowest electricity requirement, and the smallest dimensions. This research aims to provide recommendations for the best investment alternatives that can be purchased by the conversion workshops.

},

keywords = {e-mobility, multi criteria decision making, technology adoption, TOPSIS, transportation},

pubstate = {published},

tppubtype = {article}

}

@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}

}