Publication

@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{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{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{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{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 = {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{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 = {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{angreani2023identifying,

title = {Identifying Essential Driving Factors of Industry 4.0 Maturity Models Using Fuzzy MCDM Methods},

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

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {Procedia CIRP},

volume = {120},

pages = {1582–1587},

publisher = {Elsevier},

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

pubstate = {published},

tppubtype = {article}

}

@article{wicaksono2020contributions,

title = {Contributions of muslim scientists to the 4th industrial revolution},

author = {Hendro Wicaksono},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

publisher = {OSF Preprints},

keywords = {digital transformation, industry 4.0},

pubstate = {published},

tppubtype = {article}

}