2025
Prasetyo, Moonita Limiany; Peranginangin, Randall Aginta; Martinovic, Nada; Ichsan, Mohammad; Wicaksono, Hendro
In: Journal of Open Innovation: Technology, Market, and Complexity, vol. 11, iss. 1, no. 100445, 2025.
Abstract | Links | BibTeX | Tags: artificial intelligence, industry 4.0, innovation management, project management
@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}
}
Vijaya, Annas; Meisterknecht, Johanne Paula Sophia; Angreani, Linda Salma; Wicaksono, Hendro
Advancing sustainability in the automotive sector: A critical analysis of environmental, social, and governance (ESG) performance indicators Journal Article
In: Cleaner Environmental Systems, vol. 16, 2025.
Abstract | Links | BibTeX | Tags: automotive industry, ESG, multi criteria decision making, sustainability
@article{nokey,
title = {Advancing sustainability in the automotive sector: A critical analysis of environmental, social, and governance (ESG) performance indicators},
author = {Annas Vijaya and Johanne Paula Sophia Meisterknecht and Linda Salma Angreani and Hendro Wicaksono},
url = {https://www.sciencedirect.com/science/article/pii/S2666789424000862},
doi = {https://doi.org/10.1016/j.cesys.2024.100248},
year = {2025},
date = {2025-03-01},
journal = {Cleaner Environmental Systems},
volume = {16},
abstract = {ESG (Environment, Social, Governance) is becoming increasingly important as sustainability concerns in the industry increase. The automotive industry is one that receives significant attention and pressure on sustainability, with the ever-growing regulations pushing it towards sustainability. However, ESG improvement could be more effective due to the many factors. Although previous studies have revealed the evaluation and prioritization of ESG key performance indicators (KPIs) in the automotive sector, there is still a need for other approaches to identify the priorities and interdependencies between critical factors that enhance organizational strategic improvement measures. The study aims to address the gaps by identifying critical indicators in ESG reporting standards and utilizing Fuzzy DEMATEL and Fuzzy TOPSIS methodologies to explore the priorities and causal relationships of ESG KPIs in the automotive industry. The findings indicate that the top three of 17 identified factors are the top priorities that influence others in improving ESG performance, including corporate governance, air emissions, and sustainable product development. The importance of addressing social sustainability issues in strengthening stakeholder relationships is also highlighted in the research findings, such as human rights and labor practices. Businesses in the automotive sector can use the study's insights to enhance their sustainability strategies, determine critical opportunities for improvement, and rank their priorities to achieve sustainability objectives. Policymakers can use it to promote industry-wide efforts for sustainable development and create regulatory frameworks.},
keywords = {automotive industry, ESG, multi criteria decision making, sustainability},
pubstate = {published},
tppubtype = {article}
}
2024
Hidayat, Rahmat; Ourairat, Apivut; Wicaksono, Hendro
Explainable Artificial Intelligence in Agrifood Supply Chain: State of the Art Review Proceedings Article
In: Lecture Notes in Mechanical Engineering : Flexible Automation and Intelligent Manufacturing: Manufacturing Innovation and Preparedness for the Changing World Order, Springer, 2024.
Abstract | Links | BibTeX | Tags: agrifood, artificial intelligence, explainable AI, supply chain management
@inproceedings{nokey,
title = {Explainable Artificial Intelligence in Agrifood Supply Chain: State of the Art Review},
author = {Rahmat Hidayat and Apivut Ourairat and Hendro Wicaksono
},
doi = {https://doi.org/10.1007/978-3-031-74485-3_33},
year = {2024},
date = {2024-12-13},
booktitle = {Lecture Notes in Mechanical Engineering : Flexible Automation and Intelligent Manufacturing: Manufacturing Innovation and Preparedness for the Changing World Order},
publisher = {Springer},
abstract = {The increasing pressure to feed a growing population of humans for food security, with constantly changing food consumption behavior, as well as in recent light of livestock treatment and awareness for food sustainability both economically and ecologically, also due to the challenges of climate change, lead to challenges for the industries operating in the Agrifood Supply Chain (ASC). Recent technological strides in Data Analysis, Internet of Things (IoT), Machine Learning (ML), and Artificial Intelligence (AI) have ushered in a digitized and intelligent era within the ASC, reshaping production quality, sustainability, and food longevity. However, the nascent stage of AI and ML methods within the ASC raises questions about their reliability, value, transparency, and understandability. The prevalent use of black box methods underscores the need for more explainable methodologies, as the opacity of current approaches restricts widespread applicability. This paper presents a State-of-the-Art Review of Explainable AI (XAI) and ML methods in the ASC, delving into operations spanning “Farm-to-Fork,” encompassing agriculture production, processes, quality assurance, tracking, warehousing, distribution, packaging, retailing, safety, and sustainability. The research identifies challenges and proposes research directions, offering researchers an overview of opportunities to implement XAI methods in the ASC. The exploration of coexisting problems and their solutions enhances our understanding of intelligent systems in the ASC, providing valuable insights for stakeholders’ decision-making processes.
},
keywords = {agrifood, artificial intelligence, explainable AI, supply chain management},
pubstate = {published},
tppubtype = {inproceedings}
}
Fan, Xiaotong; Valilai, Omid Fatahi; Wicaksono, Hendro
Integrating Economic, Technological, and Consumer Factors for Enhanced Accuracy in Electric Vehicle Demand Forecasting: A Case Study in Germany Proceedings Article
In: Lecture Notes in Mechanical Engineering (LNME): Flexible Automation and Intelligent Manufacturing: Manufacturing Innovation and Preparedness for the Changing World Order , Springer, 2024.
Abstract | Links | BibTeX | Tags: automotive industry, demand forecasting, machine learning, supply chain management, sustainability, timeseries analysis
@inproceedings{nokey,
title = {Integrating Economic, Technological, and Consumer Factors for Enhanced Accuracy in Electric Vehicle Demand Forecasting: A Case Study in Germany},
author = {Xiaotong Fan and Omid Fatahi Valilai and Hendro Wicaksono
},
doi = {https://doi.org/10.1007/978-3-031-74485-3_50},
year = {2024},
date = {2024-12-13},
booktitle = {Lecture Notes in Mechanical Engineering (LNME): Flexible Automation and Intelligent Manufacturing: Manufacturing Innovation and Preparedness for the Changing World Order },
publisher = {Springer},
abstract = {The development of the electric vehicle industry has the potential to reduce CO
emissions significantly and overcome energy supply challenges. However, manufacturing electric cars is a complex process consisting of procurement, logistics, and assembly. Accurate demand forecasting plays an essential role in the industry’s long-term development because it effectively fulfills customer needs while mitigating the risks of overproduction. Forecasting electric vehicle demand presents a significant challenge due to limited data availability and multiple factors influencing it. Comprehensive research integrating economic, technological, and consumer dynamics into demand forecasts remains notably deficient. The main goal of this research is to develop demand forecasting of electric vehicles within Germany, considering those factors, including variables like gasoline prices, the count of installed charging stations, and the Google search index. The research involves experimentation with various models, including Prophet, SARIMA, and their variants, incorporating different combinations of exogenous variables. The results demonstrate that SARIMA and its variants outperform other models regarding predictive accuracy. This research equips electric vehicle manufacturing companies with invaluable insights into market trends and the potential impact of diverse influencing variables. With this knowledge, companies can adapt their production strategies to align with market dynamics, enhancing their competitiveness in the rapidly evolving electric vehicle landscape.},
keywords = {automotive industry, demand forecasting, machine learning, supply chain management, sustainability, timeseries analysis},
pubstate = {published},
tppubtype = {inproceedings}
}
emissions significantly and overcome energy supply challenges. However, manufacturing electric cars is a complex process consisting of procurement, logistics, and assembly. Accurate demand forecasting plays an essential role in the industry’s long-term development because it effectively fulfills customer needs while mitigating the risks of overproduction. Forecasting electric vehicle demand presents a significant challenge due to limited data availability and multiple factors influencing it. Comprehensive research integrating economic, technological, and consumer dynamics into demand forecasts remains notably deficient. The main goal of this research is to develop demand forecasting of electric vehicles within Germany, considering those factors, including variables like gasoline prices, the count of installed charging stations, and the Google search index. The research involves experimentation with various models, including Prophet, SARIMA, and their variants, incorporating different combinations of exogenous variables. The results demonstrate that SARIMA and its variants outperform other models regarding predictive accuracy. This research equips electric vehicle manufacturing companies with invaluable insights into market trends and the potential impact of diverse influencing variables. With this knowledge, companies can adapt their production strategies to align with market dynamics, enhancing their competitiveness in the rapidly evolving electric vehicle landscape.
Nasrabadi, Negar; Wicaksono, Hendro; Valilai, Omid Fatahi
Shopping marketplace analysis based on customer insights using social media analytics Journal Article
In: MethodsX, vol. 13, pp. 102868, 2024.
Abstract | Links | BibTeX | Tags: data science, sentiment analysis, text analytics
@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}
}
•
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.
Yuniaristanto,; Sutopo, Wahyudi; Hisjam, Muhammad; Wicaksono, Hendro
Estimating the market share of electric motorcycles: A system dynamics approach with the policy mix and sustainable life cycle costs Journal Article
In: Energy Policy, vol. 195, pp. 114345, 2024.
Abstract | Links | BibTeX | Tags: e-mobility, sustainability, system dynamics, technology adoption, transportation
@article{yuniaristanto2024estimating,
title = {Estimating the market share of electric motorcycles: A system dynamics approach with the policy mix and sustainable life cycle costs},
author = {Yuniaristanto and Wahyudi Sutopo and Muhammad Hisjam and Hendro Wicaksono},
url = {https://www.sciencedirect.com/science/article/pii/S0301421524003653},
doi = {https://doi.org/10.1016/j.enpol.2024.114345},
year = {2024},
date = {2024-12-01},
urldate = {2024-01-01},
journal = {Energy Policy},
volume = {195},
pages = {114345},
publisher = {Elsevier},
abstract = {Introducing electric vehicles is critical to maintaining air quality and reducing carbon emissions. The Indonesian government has issued several regulations to stimulate the diffusion of electric vehicles. This research attempts to forecast the electric vehicle market share, especially electric motorcycles, by involving the policy mix and sustainable life cycle costs. We propose a system dynamics approach that takes into account a policy mix including 0% down payment without credit interest subsidies, tax abolition, expansion of charging station network, and sustainable life cycle costs, i.e., total cost of ownership, social, and environment. The system dynamics model has four modules: the electric motorcycle cost, the conventional motorcycle cost, the economy module, and the consumer market. The simulation results show that the electric motorcycle market share will increase positively in 2021–2030, reaching 5.7% in 2030. Based on the scenario simulation results, providing more charging stations and vehicle tax abolition can significantly boost the market share of electric motorcycles in Indonesia. The study provides valuable insights for policymakers in formulating more appropriate policy instruments to promote electric vehicle diffusion in Indonesia.
},
keywords = {e-mobility, sustainability, system dynamics, technology adoption, transportation},
pubstate = {published},
tppubtype = {article}
}
Shah, Muhammad Abdullah; Wicaksono, Hendro
Leveraging Machine Learning for Power Consumption Prediction of Multi-Step Production Processes in Dynamic Electricity Price Environment Journal Article
In: Procedia CIRP, vol. 130, pp. 226-231, 2024.
Abstract | Links | BibTeX | Tags: energy management, machine learning, manufacturing, sustainability
@article{Shah2024,
title = {Leveraging Machine Learning for Power Consumption Prediction of Multi-Step Production Processes in Dynamic Electricity Price Environment},
author = {Muhammad Abdullah Shah and Hendro Wicaksono},
url = {https://www.sciencedirect.com/science/article/pii/S2212827124012332},
doi = {https://doi.org/10.1016/j.procir.2024.10.080},
year = {2024},
date = {2024-11-27},
urldate = {2024-11-27},
journal = {Procedia CIRP},
volume = {130},
pages = {226-231},
abstract = {Rising energy costs drive a compelling demand for energy-efficient manufacturing across sectors, paralleled by increasing consumer preferences for eco-friendly products. To remain competitive, companies are actively enhancing their energy efficiency. Integrating dynamic pricing in manufacturing, aimed at optimizing renewable energy use, requires strategic adjustments in production planning for sustainability. This research highlights the importance of incorporating dynamic pricing into production planning, emphasizing the need to shift processes to time slots when the energy prices are low or optimal. This study focuses on predicting the power consumption of multi-step CNC machine operations within a production cycle. Utilizing advanced Machine Learning (ML), including neural networks, statistical, and additive models, this research found unique time series characteristics influencing model performance across production steps. A practical use case within a German manufacturing Small and Medium Enterprises (SME) demonstrates how prediction results can optimize production processes in a dynamic pricing environment, providing a blueprint for diverse machinery forecasting models. This research’s insights extend to any industry managing production schedules for multiple machines with various steps in a process cycle. Industries with high energy consumption will benefit significantly through aligning operational efficiency with environmental sustainability goals.},
keywords = {energy management, machine learning, manufacturing, sustainability},
pubstate = {published},
tppubtype = {article}
}
Nesro, Vahid Menu; Fekete, Tamas; Wicaksono, Hendro
Leveraging Causal Machine Learning for Sustainable Automotive Industry: Analyzing Factors Influencing CO2 Emissions Journal Article
In: Procedia CIRP, vol. 130, pp. 161-166, 2024.
Abstract | Links | BibTeX | Tags: causal AI, causal inference, sustainability
@article{nokey,
title = {Leveraging Causal Machine Learning for Sustainable Automotive Industry: Analyzing Factors Influencing CO2 Emissions},
author = {Vahid Menu Nesro and Tamas Fekete and Hendro Wicaksono},
url = {https://www.sciencedirect.com/science/article/pii/S2212827124012241},
doi = {https://doi.org/10.1016/j.procir.2024.10.071},
year = {2024},
date = {2024-11-27},
urldate = {2024-11-27},
journal = {Procedia CIRP},
volume = {130},
pages = {161-166},
abstract = {Integrating artificial intelligence (AI) and machine learning (ML) in the industry has become increasingly essential with the rise of Industry 4.0. These technologies can revolutionize product development, decision-making, and operational optimization in industrial processes. However, most modern ML tools focus on identifying associations between data points rather than causal relationships, a crucial limitation for industries. This paper explores the potential for causal machine learning to overcome this limitation. The investigation involves selecting a focus area within the automotive sector, analyzing a dataset, and modeling, identifying, and estimating causal associations. The chosen case study investigates the potential causes of CO2 emissions from vehicles, which is a critical area in terms of sustainability. Furthermore, the applied dataset is suitable for demonstrating the proposed approach as an alternative to engineering techniques that explain factors influencing CO2 emissions. In the future, a more detailed model based on the case study can help learn more about the potential causes of environmental harm caused by vehicles. This paper provides valuable insights into how this technology can be leveraged to optimize industrial processes and improve outcomes in reducing CO2 emissions in product creation processes to achieve environmental sustainability goals.
},
keywords = {causal AI, causal inference, sustainability},
pubstate = {published},
tppubtype = {article}
}
Prayitno, Kutut Aji; Wicaksono, Hendro
Leveraging Causal Machine Learning for Sustainable Automotive Industry: Analyzing Factors Influencing CO2 Emissions Journal Article
In: Procedia CIRP, vol. 130, pp. 1070-1076, 2024.
Abstract | Links | BibTeX | Tags: causal AI, logistics, ontologies, reinforcement learning, sustainability
@article{nokey,
title = {Leveraging Causal Machine Learning for Sustainable Automotive Industry: Analyzing Factors Influencing CO2 Emissions},
author = {Kutut Aji Prayitno and Hendro Wicaksono},
url = {https://www.sciencedirect.com/science/article/pii/S2212827124013659},
doi = {https://doi.org/10.1016/j.procir.2024.10.208},
year = {2024},
date = {2024-11-27},
urldate = {2024-11-27},
journal = {Procedia CIRP},
volume = {130},
pages = {1070-1076},
abstract = {Efficiently managing logistics operations is crucial in elevating sustainability and tackling the challenges urbanization brings in today’s urban environment. Collaborations among the public and private sectors in urban logistics are essential to minimize environmental impacts. This study aims to create a novel conceptual framework for collaborative logistics designed explicitly for sustainable metropolitan areas. The framework aims to enable collaborative data-driven sustainability optimization in urban logistics. It comprises ontologies to facilitate interoperability among stakeholders by providing a shared understanding of the exchanged data. The framework utilizes causal artificial intelligence to enable traceability and transparency of data-driven decisions compared to conventional machine learning working based on correlations. Furthermore, the framework also employs causal reinforcement learning that enables agents to learn what actions lead to targeted outcomes and why those actions are effective. The developed framework optimizes vehicle routes and conveyance selection while considering several operational constraints such as time windows, split-load scenarios, and commodity-specific requirements. Moreover, the system integrates the distinctive features of public transport networks. The suggested strategy minimizes fuel use and overall delivery costs, promoting a more sustainable logistics environment in metropolitan areas measured using Environmental, Social, and Governance (ESG) indicators. This study contributes to the theoretical understanding of collaborative logistics. It underscores the importance of environmental stewardship and societal well-being in logistics planning and implementation by utilizing a data-driven approach.
},
keywords = {causal AI, logistics, ontologies, reinforcement learning, sustainability},
pubstate = {published},
tppubtype = {article}
}
Angreani, Linda Salma; Vijaya, Annas; Wicaksono, Hendro
Enhancing strategy for Industry 4.0 implementation through maturity models and standard reference architectures alignment Journal Article
In: Journal of Manufacturing Technology Management, vol. 35, iss. 4, pp. 848-873, 2024.
Abstract | Links | BibTeX | Tags: industry 4.0, multi criteria decision making
@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}
}
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.
Almais, Agung Teguh Wibowo; Susilo, Adi; Naba, Agus; Sarosa, Moechammad; Juwono, Alamsyah Muhammad; Crysdian, Cahyo; Muslim, Muhammad Aziz; Wicaksono, Hendro
Characterization of Structural Building Damage in Post-Disaster Using GLCM-PCA Analysis Integration Journal Article
In: IEEE Access, vol. 12, 2024.
Abstract | Links | BibTeX | Tags: artificial intelligence, data science, machine learning
@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}
}
Angreani, Linda Salma; Qadri, Faris Dzaudan; Vijaya, Annas; Manahil, Rana; Petrone, Isabella Marquez; Nabilah,; Fauzi, Ahmad; Rahmawati, Tasya Santi; Wicaksono, Hendro
Interdependencies in Industry 4.0 Maturity: Fuzzy MCDA Analysis for Open Innovation in Developing Countries Journal Article
In: Journal of Open Innovation: Technology, Market, and Complexity, 2024, ISBN: 2199-8531.
Abstract | Links | BibTeX | Tags: industry 4.0, innovation management, multi criteria decision making, TOPSIS
@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}
}
Mechai, Nadhir; Wicaksono, Hendro
Causal Inference in Supply Chain Management: How Does Ever Given Accident at the Suez Canal Affect the Prices of Shipping Containers? Journal Article
In: Procedia Computer Science, vol. 232, pp. 3173–3182, 2024.
Abstract | Links | BibTeX | Tags: causal AI, causal inference, supply chain management
@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}
}
Supyen, Kritkorn; Mathur, Abhishek; Boroukhian, Tina; Wicaksono, Hendro
Streamlining Manufacturing Resource Digitization for Digital Twins Through Ontologies and Object Detection Techniques Proceedings Article
In: International Conference on Dynamics in Logistics, pp. 419–430, Springer Nature Switzerland Cham 2024.
Abstract | Links | BibTeX | Tags: computer vision, digital twins, machine learning, ontologies, semantic web
@inproceedings{supyen2024streamlining,
title = {Streamlining Manufacturing Resource Digitization for Digital Twins Through Ontologies and Object Detection Techniques},
author = {Kritkorn Supyen and Abhishek Mathur and Tina Boroukhian and Hendro Wicaksono},
url = {https://link.springer.com/chapter/10.1007/978-3-031-56826-8_32},
doi = {https://doi.org/10.1007/978-3-031-56826-8_32},
year = {2024},
date = {2024-04-03},
urldate = {2024-04-03},
booktitle = {International Conference on Dynamics in Logistics},
pages = {419–430},
organization = {Springer Nature Switzerland Cham},
abstract = {Digital twins play an essential role in manufacturing companies to adopt Industry 4.0. However, their uptake has been lagging, especially in European manufacturing firms. This can be attributed to the absence of automated methods for digitizing physical manufacturing resources and creating digital representations accessible and processable by both humans and computers. Our research addresses this challenge by automating the digitization of manufacturing resources captured on the shop floor. We employ object detection techniques on a set of images and align the results with an ontology that standardizes the semantic description of digital representations. This research aims to accelerate digital transformation for manufacturing companies, providing digital representations to their physical resources. The ontology-based digital representation fosters interoperability among diverse equipment and machines from various vendors. It enables the automated deployment of digital twins, improving the efficiency of planning and control of manufacturing systems.
},
keywords = {computer vision, digital twins, machine learning, ontologies, semantic web},
pubstate = {published},
tppubtype = {inproceedings}
}
Wicaksono, Hendro; Trat, Martin; Bashyal, Atit; Boroukhian, Tina; Felder, Mine; Ahrens, Mischa; Bender, Janek; Groß, Sebastian; Steiner, Daniel; July, Christoph; others,
Artificial-intelligence-enabled dynamic demand response system for maximizing the use of renewable electricity in production processes Journal Article
In: The International Journal of Advanced Manufacturing Technology, pp. 1–25, 2024.
Abstract | Links | BibTeX | Tags: artificial intelligence, demand response system, energy management, machine learning, manufacturing, ontologies, reinforcement learning
@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 = {2024},
date = {2024-03-22},
urldate = {2024-01-01},
journal = {The International Journal of Advanced Manufacturing Technology},
pages = {1–25},
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, demand response system, energy management, machine learning, manufacturing, ontologies, reinforcement learning},
pubstate = {published},
tppubtype = {article}
}
Thapaliya, Suman; Valilai, Omid Fatahi; Wicaksono, Hendro
Power Consumption and Processing Time Estimation of CNC Machines Using Explainable Artificial Intelligence (XAI) Journal Article
In: Procedia Computer Science, vol. 232, pp. 861–870, 2024.
Abstract | Links | BibTeX | Tags: energy management, explainable AI, machine learning, manufacturing
@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}
}
Habtemichael, Noah; Wicaksono, Hendro; Valilai, Omid Fatahi
NFT based Digital Twins for Tracing Value Added Creation in Manufacturing Supply Chains Journal Article
In: Procedia Computer Science, vol. 232, pp. 2841–2846, 2024.
Abstract | Links | BibTeX | Tags: blockchain, digital twins, manufacturing, supply chain management
@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}
}
Yuniaristanto,; Sutopo, Wahyudi; Hisjam, Muhammad; Wicaksono, Hendro
Exploring the determinants of intention to purchase electric motorcycles: the role of national culture in the UTAUT Journal Article
In: Transportation research part F: traffic psychology and behaviour, vol. 100, pp. 475–492, 2024.
Links | BibTeX | Tags: data science, sustainability, technology adoption
@article{sutopo2024exploring,
title = {Exploring the determinants of intention to purchase electric motorcycles: the role of national culture in the UTAUT},
author = {Yuniaristanto and Wahyudi Sutopo and Muhammad Hisjam and Hendro Wicaksono},
url = {https://www.sciencedirect.com/science/article/pii/S1369847823002772},
doi = {https://doi.org/10.1016/j.trf.2023.12.012},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Transportation research part F: traffic psychology and behaviour},
volume = {100},
pages = {475–492},
publisher = {Pergamon},
keywords = {data science, sustainability, technology adoption},
pubstate = {published},
tppubtype = {article}
}
Ompusunggu, Agusmian P; Tjahjowidodo, Tegoeh; Wicaksono, Hendro
Causal AI-powered Digital Product Passports for enabling a circular and sustainable manufacturing ecosystem Proceedings Article
In: Cranfield University, 2024.
Abstract | Links | BibTeX | Tags: causal AI, causal inference, circular economy, digital product passport
@inproceedings{ompusunggua2024causal,
title = {Causal AI-powered Digital Product Passports for enabling a circular and sustainable manufacturing ecosystem},
author = {Agusmian P Ompusunggu and Tegoeh Tjahjowidodo and Hendro Wicaksono},
doi = {10.57996/cran.ceres-2579},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
publisher = {Cranfield University},
abstract = {Digital product passport (DPP) has been recently introduced by policymakers (e.g., the European Commission) to advance sustainable business practices towards a circular economy (CE). As a newly introduced concept, DPP is still relatively high-level and vague. Therefore, its definition, information flow architecture, what relevant information needs to be stored, and how to use such information in the context of a circular and sustainable manufacturing ecosystem, etc., are still open research questions. This paper addresses these research questions by proposing a novel conceptual framework for DPP, facilitating seamless information exchanges among CE stakeholders, and providing a transparent and trustworthy basis for decision-making. Causal AI utilisation is proposed to extract causal relationships among sustainability/circularity KPIs comprehensively, encompassing raw material supply chain, circularity-compliant product design, manufacturing optimisation on the shop floor, and after-sale product usage optimisation. Seamless information exchange will be achieved through semantic interoperability and a comprehensive model of the whole supply chain by employing an ontology model. The causal AI approach is proposed to identify causalities among KPIs and other factors to predict environmental impacts. This way, a causal model integrating domain expert knowledge and causality discovered from measured data will increase the transparency/explainability of prediction/decision made by machine learning algorithms.},
keywords = {causal AI, causal inference, circular economy, digital product passport},
pubstate = {published},
tppubtype = {inproceedings}
}
Rahmawati, Tasya Santi; Sutopo, Wahyudi; Wicaksono, Hendro
Investment Decision-Making to Select Converted Electric Motorcycle Tests in Indonesia Journal Article
In: World Electric Vehicle Journal, vol. 15, no. 8, pp. 334, 2024.
Abstract | Links | BibTeX | Tags: e-mobility, multi criteria decision making, technology adoption, TOPSIS, transportation
@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}
}
2023
Llanos, Alan Francisco Caraveo Gomez; Vijaya, Annas; Wicaksono, Hendro
Rating ESG key performance indicators in the airline industry Journal Article
In: Environment, Development and Sustainability, vol. 26, pp. 27629–27653, 2023.
Abstract | Links | BibTeX | Tags: ESG, multi criteria decision making, sustainability
@article{caraveo2023rating,
title = {Rating ESG key performance indicators in the airline industry},
author = {Alan Francisco Caraveo Gomez Llanos and Annas Vijaya and Hendro Wicaksono},
url = {https://link.springer.com/article/10.1007/s10668-023-03775-z},
doi = {https://doi.org/10.1007/s10668-023-03775-z},
year = {2023},
date = {2023-08-30},
urldate = {2023-01-01},
journal = {Environment, Development and Sustainability},
volume = {26},
pages = {27629–27653},
publisher = {Springer Netherlands},
abstract = {The environmental, social, and governance (ESG) integration finds itself in a transition with rapid developments worldwide, given that the pandemic incentivized companies and investors to focus on other social and governance measures such as ESG ratings. However, the divergence of ratings from the ESG and a lack of transparency lead the companies to report voluntary indicators without standardization. This study aimed to identify the ESG criteria and the most suitable set of key performance indicators (KPIs) in the airline industry after the impact of COVID-19. Furthermore, the second objective was to determine the appropriate weights and ranking of the identified criteria. The multi-criteria decision-making analytical hierarchical process was applied for this purpose. Additionally, the use of intuitionistic variables delivers a comprehensive model for rating the airlines according to their ESG performance. The most relevant criteria found in the study were critical risk management, greenhouse gas emissions, and systemic risk management. Regarding the KPIs, the top-3 weights were the number of flight accidents, jet fuel consumed and sustainable aviation used, and the number of digital transformation initiatives.
},
keywords = {ESG, multi criteria decision making, sustainability},
pubstate = {published},
tppubtype = {article}
}
Aikenov, Temirlan; Hidayat, Rahmat; Wicaksono, Hendro
Power Consumption and Process Cost Prediction of Customized Products Using Explainable AI: A Case in the Steel Industry Proceedings Article
In: International Conference on Flexible Automation and Intelligent Manufacturing, pp. 1183–1193, Springer Nature Switzerland Cham 2023.
Abstract | Links | BibTeX | Tags: energy management, explainable AI, machine learning, manufacturing, sustainability
@inproceedings{aikenov2023power,
title = {Power Consumption and Process Cost Prediction of Customized Products Using Explainable AI: A Case in the Steel Industry},
author = {Temirlan Aikenov and Rahmat Hidayat and Hendro Wicaksono},
url = {https://link.springer.com/chapter/10.1007/978-3-031-38165-2_135},
doi = {https://doi.org/10.1007/978-3-031-38165-2_135},
year = {2023},
date = {2023-08-25},
urldate = {2023-01-01},
booktitle = {International Conference on Flexible Automation and Intelligent Manufacturing},
pages = {1183–1193},
organization = {Springer Nature Switzerland Cham},
abstract = {Production shifted from a product-centered perspective (mass production of one article) to a customer-centered perspective (mass customization of product variants). It also happens in energy-intensive industries, such as steel production. Mass customization companies face a challenge in accurately estimating the total costs of an individual product. Furthermore, 20% to 40% of the costs related to steel products come from energy. Increasing the product variety can cause an inevitable loss of sustainability. This paper presents machine-learning approaches to improve the sustainability of the steel production industry. It is done by finding the most accurate way to predict the power consumption and the costs of customized products. Moreover, this research also finds the most energy-efficient machine mix based on the predictions. The method is validated in a steel manufacturing Small Medium Enterprise (SME). In this research, experiments were conducted with different machine learning models, and it was found that the most accurate results were achieved using regularization-based and random forest regression models. Explainable AI (XAI) is also used to clarify how product properties influence process costs and power consumption. This paper also discusses scenarios on how the prediction of costs and power consumption can assist production planners in performing workstation selection. This research improves the production planning of customized products by providing a trustable decision support system for machine selection based on explainable machine learning models for process time and power consumption predictions.
},
keywords = {energy management, explainable AI, machine learning, manufacturing, sustainability},
pubstate = {published},
tppubtype = {inproceedings}
}
Almais, Agung Teguh Wibowo; Susilo, Adi; Naba, Agus; Sarosa, Moechammad; Crysdian, Cahyo; Basid, Puspa Miladin NSA; Hariyadi, Mokhamad Amin; Tazi, Imam; Arif, Yunifa Miftachul; Wicaksono, Hendro
SASSD: A Smart Assessment System For Sector Damage Post-Natural Disaster Using Artificial Neural Networks Proceedings Article
In: 2023 2nd International Conference on Computer System, Information Technology, and Electrical Engineering (COSITE), pp. 96–101, IEEE 2023.
Abstract | Links | BibTeX | Tags: data science, machine learning
@inproceedings{almais2023sassd,
title = {SASSD: A Smart Assessment System For Sector Damage Post-Natural Disaster Using Artificial Neural Networks},
author = {Agung Teguh Wibowo Almais and Adi Susilo and Agus Naba and Moechammad Sarosa and Cahyo Crysdian and Puspa Miladin NSA Basid and Mokhamad Amin Hariyadi and Imam Tazi and Yunifa Miftachul Arif and Hendro Wicaksono},
url = {https://ieeexplore.ieee.org/abstract/document/10249540},
doi = {https://doi.org/10.1109/COSITE60233.2023.10249540},
year = {2023},
date = {2023-08-02},
urldate = {2023-08-02},
booktitle = {2023 2nd International Conference on Computer System, Information Technology, and Electrical Engineering (COSITE)},
pages = {96–101},
organization = {IEEE},
abstract = {Smart Assessment System Sector Damage (SASSD) is an intelligent system for assessing the level of sector damage after natural disasters based on Machine Learning (ML) by applying the Artificial Neural Network (ANN) method. SASSD uses forward propagation in ANN. To measure the level of accuracy of the forward propagation algorithm, it is necessary to have a trial method using data pattern modelling. The optimal accrual level value can be achieved by applying 15 data pattern models and changing the structural values of the forward propagation, namely the hidden layer, and epoch. We used 100 training data and 50 testing data at the experimental stage. The training data is the processed data from Decision Support System (DSS), while the training data contains the level of damage to the sector after natural disasters collected by surveyors. The trial results demonstrate the E5 data pattern model’s ideal accuracy rate of 97 percent with a Mean Squared Error (MSE) value of 0.06 and a Mean Absolute Percentage error (MAPE) of 3 percent. This model uses five hidden layers and 125 epochs. The trial results demonstrate the E5 data pattern model’s ideal accuracy rate of 97 % with an MSE value of 0.06 and a MAPE of 3 %. This model uses five hidden layers and 125 epochs. Thus, the SASSD can use the 15th data pattern model (E5) to obtain optimal and accurate results.
},
keywords = {data science, machine learning},
pubstate = {published},
tppubtype = {inproceedings}
}
Beibit, Rauan; Valilai, Omid Fatahi; Wicaksono, Hendro
Estimating the COVID-19 Impact on the Semiconductor Shortage in the European Automotive Industry using Supervised Machine Learning Proceedings Article
In: Proceedings of the 2023 10th International Conference on Industrial Engineering and Applications, pp. 302–308, 2023.
Abstract | Links | BibTeX | Tags: machine learning, supply chain management, timeseries analysis
@inproceedings{beibit2023estimating,
title = {Estimating the COVID-19 Impact on the Semiconductor Shortage in the European Automotive Industry using Supervised Machine Learning},
author = {Rauan Beibit and Omid Fatahi Valilai and Hendro Wicaksono},
doi = {https://doi.org/10.1145/3587889.3588215},
year = {2023},
date = {2023-06-09},
urldate = {2023-01-01},
booktitle = {Proceedings of the 2023 10th International Conference on Industrial Engineering and Applications},
pages = {302–308},
abstract = {The COVID-19 pandemic impacted different industrial sectors. It causes semiconductor shortage and, subsequently, on the industries downstream, such as the automotive industry. It is because of factory shutdown, increasing consumer electronic demands due to working from home, shifted focus of companies to consumer electronics, and limited logistic capacity. This research aims to analyze the influencing factors and estimate the extent of the impact of COVID-19 on the semiconductor and automotive industry in Europe using machine learning. We developed five regression models to predict the semiconductor sales and number of new passenger car registrations that reflect the development of sales in the automotive industry. Our research revealed that random forest regression is the best machine learning model for analyzing the relationship between COVID-19, semiconductor sales, and passenger car registrations. However, overall, our research found that the COVID-19 pandemic is not the only factor that impacts the semiconductor shortage in the automotive industry. The geopolitical landscape and the world’s reliance on Chinese exports are also essential influencing factors in many supply chains, including in the semiconductor and automotive sectors.
},
keywords = {machine learning, supply chain management, timeseries analysis},
pubstate = {published},
tppubtype = {inproceedings}
}
Krstevski, Stefan; Valilai, Omid Fatahi; Wicaksono, Hendro
Integrating Real-Time Dynamic Electricity Price Forecast into Job Shop Production Scheduling Model with Multiple Machine Environments Proceedings Article
In: Proceedings of the 2023 10th International Conference on Industrial Engineering and Applications, pp. 98–106, 2023.
Abstract | Links | BibTeX | Tags: energy management, manufacturing, operation research
@inproceedings{krstevski2023integrating,
title = {Integrating Real-Time Dynamic Electricity Price Forecast into Job Shop Production Scheduling Model with Multiple Machine Environments},
author = {Stefan Krstevski and Omid Fatahi Valilai and Hendro Wicaksono},
doi = {https://doi.org/10.1145/3587889.3587905},
year = {2023},
date = {2023-06-09},
urldate = {2023-01-01},
booktitle = {Proceedings of the 2023 10th International Conference on Industrial Engineering and Applications},
pages = {98–106},
abstract = {One of the challenges in the transition towards green electricity is the intermittence of power generated by renewable sources. Thus, power consumers, including the manufacturing industry, must adapt their activities and processes to green electricity supply. Real-time dynamic pricing is an approach to encourage electricity consumers to change their consumption patterns by lowering prices when the availability of green electricity in the grid is high. Due to the introduction of real-time electricity pricing, manufacturing companies must adapt their production planning by integrating dynamic price information into their production scheduling. Our research focuses on extending the basic production scheduling mathematical model by introducing real-time power pricing in the model. The prices are built based on the current proportion of green electricity in the grid represented in the green electricity index (GEI) with one-hour intervals. This paper also illustrates a scenario of how to use the model. Our future research will further extend the model addressing the flexibility of manufacturing shop floors (e.g. adding buffer, retooling, and setup time) and validate the model in two small and medium manufacturing enterprises.
},
keywords = {energy management, manufacturing, operation research},
pubstate = {published},
tppubtype = {inproceedings}
}
Almais, Agung Teguh Wibowo; Susilo, Adi; Naba, Agus; Sarosa, Moechammad; Crysdian, Cahyo; Tazi, Imam; Hariyadi, Mokhamad Amin; Muslim, Muhammad Aziz; Basid, Puspa Miladin Nuraida Safitri Abdul; Arif, Yunifa Miftachul; others,
Principal Component Analysis-Based Data Clustering for Labeling of Level Damage Sector in Post-Natural Disasters Journal Article
In: IEEE Access, vol. 11, pp. 74590-74601, 2023.
Abstract | Links | BibTeX | Tags: data science, machine learning
@article{almais2023principal,
title = {Principal Component Analysis-Based Data Clustering for Labeling of Level Damage Sector in Post-Natural Disasters},
author = {Agung Teguh Wibowo Almais and Adi Susilo and Agus Naba and Moechammad Sarosa and Cahyo Crysdian and Imam Tazi and Mokhamad Amin Hariyadi and Muhammad Aziz Muslim and Puspa Miladin Nuraida Safitri Abdul Basid and Yunifa Miftachul Arif and others},
url = {https://ieeexplore.ieee.org/abstract/document/10123944},
doi = {https://doi.org/10.1109/ACCESS.2023.3275852},
year = {2023},
date = {2023-05-12},
urldate = {2023-01-01},
journal = {IEEE Access},
volume = {11},
pages = {74590-74601},
publisher = {IEEE},
abstract = {Post-disaster sector damage data is data that has features or criteria in each case the level of damage to the post-natural disaster sector data. These criteria data are building conditions, building structures, building physicals, building functions, and other supporting conditions. Data on the level of damage to the post-natural disaster sector used in this study amounted to 216 data, each of which has 5 criteria for damage to the post-natural disaster sector. Then PCA is used to look for labels in each data. The results of these labels will be used to cluster data based on the value scale of the results of data normalization in the PCA process. In the data normalization process at PCA, the data is divided into 2 components, namely PC1 and PC2. Each component has a variance ratio and eigenvalue generated in the PCA process. For PC1 it has a variance ratio of 85.17% and an eigenvalue of 4.28%, while PC2 has a variance ratio of 9.36% and an eigenvalue of 0.47%. The results of data normalization are then made into a 2-dimensional graph to see the data visualization of the results of each main component (PC). The result is that there is 3 data cluster using a value scale based on the PCA results chart. The coordinate value (n) of each cluster is cluster 1 ( $text{n} < 0$ ), cluster 2 ( $0le text{n} < 2$ ), and cluster 3 ( $text{n}ge 2$ ). To test these 3 groups of data, it is necessary to conduct trials by comparing the original target data, there are two experiments, namely testing the PC1 results based on the original target data, and the PC2 results based on the original target data. The result is that there are 2 updates, the first is that the distribution of PC1 data is very good when comparing the distribution of data with PC2 in grouping data, because the eigenvalue of PC1 is greater than that of PC2. While second, the results of testing the PC1 data with the original target data produce good data grouping, because the original target data which has a value of 1 (slightly damaged) occupies the coordinates of group 1 (n < 0), the original target data which has a value of 2 (moderately damaged) occupies group 2 coordinates ( $0le text{n} < 2$ ), and for the original target data the value 3 (heavily damaged) occupies group 3 coordinates ( $text{n}ge 2$ ). Therefore, it can be concluded that PCA, which so far has been used by many studies as feature reduction, this study uses PCA for labeling unsupervised data so that it has appropriate data labels for further processing.},
keywords = {data science, machine learning},
pubstate = {published},
tppubtype = {article}
}
Sukoco, Badri Munir; Putra, Rizky Ananda; Muqaffi, Humam Nur; Lutfian, Muhammad Vinka; Wicaksono, Hendro
Comparative study of ASEAN research productivity Journal Article
In: Sage Open, vol. 13, no. 1, pp. 21582440221145157, 2023.
Abstract | Links | BibTeX | Tags: innovation management, research management
@article{sukoco2023comparative,
title = {Comparative study of ASEAN research productivity},
author = {Badri Munir Sukoco and Rizky Ananda Putra and Humam Nur Muqaffi and Muhammad Vinka Lutfian and Hendro Wicaksono},
url = {https://journals.sagepub.com/doi/10.1177/21582440221145157},
doi = {https://doi.org/10.1177/21582440221145157},
year = {2023},
date = {2023-01-03},
urldate = {2023-01-01},
journal = {Sage Open},
volume = {13},
number = {1},
pages = {21582440221145157},
publisher = {SAGE Publications Sage CA: Los Angeles, CA},
abstract = {Research productivity has become one of the main indicators used by higher education institutions (HEIs) as well as the country to support their innovation capability. This study purposely describes the research productivity among ASEAN countries, which is considered to be the world’s current economic hotspot. By using SciVal database to examine the literature over the last 10 years, we describe productivity, citation impact, and economic impact metrics. The findings indicate that Singapore is superior in terms of publication quality (citation) and patents while Malaysia is leading in terms of the number of scientific research. Interestingly, Indonesia’s scientific publication growth has the highest percentage. Furthermore, Engineering & Technology and Life Sciences & Medicine are the two major contributors to ASEAN research productivity. These subjects could be the major locomotives for ASEAN countries to use to sustain their competitiveness if the leaders can transform it into successful commercialization.
},
keywords = {innovation management, research management},
pubstate = {published},
tppubtype = {article}
}
Wicaksono, Hendro
Pencapaian Profesor Indonesia di Jerman Presentation
01.01.2023.
@misc{wicaksono2023pencapaian,
title = {Pencapaian Profesor Indonesia di Jerman},
author = {Hendro Wicaksono},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
publisher = {OSF Preprints},
keywords = {education},
pubstate = {published},
tppubtype = {presentation}
}
Hamsal, Mohammad; Ichsan, Mohammad; Wicaksono, Hendro
The impact of environmental turbulence on business sustainability through organisational resilience and dynamic capabilities Journal Article
In: International Journal of Business Environment, vol. 14, no. 4, pp. 417–439, 2023.
BibTeX | Tags: innovation management
@article{hamsal2023impact,
title = {The impact of environmental turbulence on business sustainability through organisational resilience and dynamic capabilities},
author = {Mohammad Hamsal and Mohammad Ichsan and Hendro Wicaksono},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
journal = {International Journal of Business Environment},
volume = {14},
number = {4},
pages = {417–439},
publisher = {Inderscience Publishers (IEL)},
keywords = {innovation management},
pubstate = {published},
tppubtype = {article}
}
Yuniaristanto, Yuniaristanto; Sutopo, Wahyudi; Hisjam, Muhammad; Wicaksono, Hendro
Factors Influencing Electric Motorcycle Adoption: A Logit Model Analysis Proceedings Article
In: E3S Web of Conferences, pp. 02035, EDP Sciences 2023.
BibTeX | Tags: data science, sustainability, technology adoption
@inproceedings{yuniaristanto2023factors,
title = {Factors Influencing Electric Motorcycle Adoption: A Logit Model Analysis},
author = {Yuniaristanto Yuniaristanto and Wahyudi Sutopo and Muhammad Hisjam and Hendro Wicaksono},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
booktitle = {E3S Web of Conferences},
volume = {465},
pages = {02035},
organization = {EDP Sciences},
keywords = {data science, sustainability, technology adoption},
pubstate = {published},
tppubtype = {inproceedings}
}
Yuniaristanto, Wahyudi Sutopo; Hisjam, Muhammad; iD, Hendro Wicaksono
Electric Motorcycle Adoption Research: A Bibliometric Analysis Check for updates Proceedings Article
In: Proceedings of the 6th Asia Pacific Conference on Manufacturing Systems and 4th International Manufacturing Engineering Conference: APCOMS-IMEC 2022, Surakarta, Indonesia, pp. 131, Springer Nature 2023.
BibTeX | Tags: sustainability, technology adoption
@inproceedings{yuniaristanto2023electric,
title = {Electric Motorcycle Adoption Research: A Bibliometric Analysis Check for updates},
author = {Wahyudi Sutopo Yuniaristanto and Muhammad Hisjam and Hendro Wicaksono iD},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
booktitle = {Proceedings of the 6th Asia Pacific Conference on Manufacturing Systems and 4th International Manufacturing Engineering Conference: APCOMS-IMEC 2022, Surakarta, Indonesia},
pages = {131},
organization = {Springer Nature},
keywords = {sustainability, technology adoption},
pubstate = {published},
tppubtype = {inproceedings}
}
Angreani, Linda Salma; Vijaya, Annas; Wicaksono, Hendro
Identifying Essential Driving Factors of Industry 4.0 Maturity Models Using Fuzzy MCDM Methods Journal Article
In: Procedia CIRP, vol. 120, pp. 1582–1587, 2023.
BibTeX | Tags: industry 4.0, multi criteria decision making
@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}
}
Sarafanov, Egor; Valilai, Omid Fatahi; Wicaksono, Hendro
Causal Analysis of Artificial Intelligence Adoption in Project Management Proceedings Article
In: Intelligent Systems Conference, pp. 245–264, Springer Nature Switzerland Cham 2023.
BibTeX | Tags: artificial intelligence, causal inference, data science, project management, technology adoption
@inproceedings{sarafanov2023causal,
title = {Causal Analysis of Artificial Intelligence Adoption in Project Management},
author = {Egor Sarafanov and Omid Fatahi Valilai and Hendro Wicaksono},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
booktitle = {Intelligent Systems Conference},
pages = {245–264},
organization = {Springer Nature Switzerland Cham},
keywords = {artificial intelligence, causal inference, data science, project management, technology adoption},
pubstate = {published},
tppubtype = {inproceedings}
}
Pidikiti, Vamsi Sai; Vijaya, Annas; Valilai, Omid Fatahi; Wicaksono, Hendro
An Ontology Model to Facilitate the Semantic Interoperability in Assessing the Circular Economy Performance of the Automotive Industry Journal Article
In: Procedia CIRP, vol. 120, pp. 1351–1356, 2023.
BibTeX | Tags: circular economy, ontologies, semantic web, sustainability
@article{pidikiti2023ontology,
title = {An Ontology Model to Facilitate the Semantic Interoperability in Assessing the Circular Economy Performance of the Automotive Industry},
author = {Vamsi Sai Pidikiti and Annas Vijaya and Omid Fatahi Valilai and Hendro Wicaksono},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
journal = {Procedia CIRP},
volume = {120},
pages = {1351–1356},
publisher = {Elsevier},
keywords = {circular economy, ontologies, semantic web, sustainability},
pubstate = {published},
tppubtype = {article}
}
Priyandari, Yusuf; Sutopo, Wahyudi; Nizam, Muhammad; Wicaksono, Hendro
Vulnerability Indicators on The Operation of Electric Motorcycle - Battery Swapping Station Proceedings Article
In: 4th Asia Pacific International Conference on Industrial Engineering and Operations Management, Vietnam, 2023.
BibTeX | Tags: supply chain management
@inproceedings{priyandari2023vulnerability,
title = {Vulnerability Indicators on The Operation of Electric Motorcycle - Battery Swapping Station},
author = {Yusuf Priyandari and Wahyudi Sutopo and Muhammad Nizam and Hendro Wicaksono},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
booktitle = {4th Asia Pacific International Conference on Industrial Engineering and Operations Management, Vietnam},
keywords = {supply chain management},
pubstate = {published},
tppubtype = {inproceedings}
}
Sukaridhoto, Sritrusta; Prayudi, Agus; Rasyid, Muhammad Udin Harun Al; Wicaksono, Hendro
Internet of Things Platform as a Service for Building Digital Twins and Blockchain Proceedings Article
In: Intelligent Systems Conference, pp. 616–635, Springer Nature Switzerland Cham 2023.
BibTeX | Tags: blockchain, digital twins
@inproceedings{sukaridhoto2023internet,
title = {Internet of Things Platform as a Service for Building Digital Twins and Blockchain},
author = {Sritrusta Sukaridhoto and Agus Prayudi and Muhammad Udin Harun Al Rasyid and Hendro Wicaksono},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
booktitle = {Intelligent Systems Conference},
pages = {616–635},
organization = {Springer Nature Switzerland Cham},
keywords = {blockchain, digital twins},
pubstate = {published},
tppubtype = {inproceedings}
}
Reinhold, Y; Valilai, O Fatahi; Wicaksono, H
Will Industry 4.0 Applications Help in Designing Sustainable Forest Management? A Conceptual Framework of Connected Networks in Novel Sectors Proceedings Article
In: 2023 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), pp. 0918–0922, IEEE 2023.
BibTeX | Tags: artificial intelligence, design science research, digital twins, industry 4.0
@inproceedings{reinhold2023will,
title = {Will Industry 4.0 Applications Help in Designing Sustainable Forest Management? A Conceptual Framework of Connected Networks in Novel Sectors},
author = {Y Reinhold and O Fatahi Valilai and H Wicaksono},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
booktitle = {2023 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)},
pages = {0918–0922},
organization = {IEEE},
keywords = {artificial intelligence, design science research, digital twins, industry 4.0},
pubstate = {published},
tppubtype = {inproceedings}
}
Angreani, LS; Vijaya, A; Wicaksono, H
Evaluating the Interrelationships of Driving Factors of Industry 4.0 Maturity Models in Developing Countries Using Fuzzy DEMATEL Proceedings Article
In: 2023 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), pp. 1662–1666, IEEE 2023.
BibTeX | Tags: industry 4.0, multi criteria decision making
@inproceedings{angreani2023evaluating,
title = {Evaluating the Interrelationships of Driving Factors of Industry 4.0 Maturity Models in Developing Countries Using Fuzzy DEMATEL},
author = {LS Angreani and A Vijaya and H Wicaksono},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
booktitle = {2023 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)},
pages = {1662–1666},
organization = {IEEE},
keywords = {industry 4.0, multi criteria decision making},
pubstate = {published},
tppubtype = {inproceedings}
}
Raza, A; Wicaksono, H; Valilai, O Fatahi
Blockchain Technologies for Sustainable Last Mile Delivery: Investigating Customer Awareness and Tendency Using NFT Reward Mechanisms Proceedings Article
In: 2023 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), pp. 0021–0026, IEEE 2023.
BibTeX | Tags: blockchain, logistics
@inproceedings{raza2023blockchain,
title = {Blockchain Technologies for Sustainable Last Mile Delivery: Investigating Customer Awareness and Tendency Using NFT Reward Mechanisms},
author = {A Raza and H Wicaksono and O Fatahi Valilai},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
booktitle = {2023 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)},
pages = {0021–0026},
organization = {IEEE},
keywords = {blockchain, logistics},
pubstate = {published},
tppubtype = {inproceedings}
}
Wicaksono, H; Nisa, M Un; Vijaya, A
Towards Intelligent and Trustable Digital Twin Asset Management Platform for Transportation Infrastructure Management Using Knowledge Graph and Explainable Artificial Intelligence (XAI) Proceedings Article
In: 2023 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), pp. 0528–0532, IEEE 2023.
BibTeX | Tags: digital twins, explainable AI, ontologies
@inproceedings{wicaksono2023towards,
title = {Towards Intelligent and Trustable Digital Twin Asset Management Platform for Transportation Infrastructure Management Using Knowledge Graph and Explainable Artificial Intelligence (XAI)},
author = {H Wicaksono and M Un Nisa and A Vijaya},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
booktitle = {2023 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)},
pages = {0528–0532},
organization = {IEEE},
keywords = {digital twins, explainable AI, ontologies},
pubstate = {published},
tppubtype = {inproceedings}
}
Vorrink, Nicklas; Wicaksono, Hendro; Valilai, Omid Fatahi
Analyzing VR/AR Technology Capabilities for Enhancing the Effectiveness of Learning Processes with Focus on Gamification Proceedings Article
In: Proceedings of SAI Intelligent Systems Conference, pp. 806–817, Springer Nature Switzerland Cham 2023.
BibTeX | Tags: augmented reality, virtual reality
@inproceedings{vorrink2023analyzing,
title = {Analyzing VR/AR Technology Capabilities for Enhancing the Effectiveness of Learning Processes with Focus on Gamification},
author = {Nicklas Vorrink and Hendro Wicaksono and Omid Fatahi Valilai},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
booktitle = {Proceedings of SAI Intelligent Systems Conference},
pages = {806–817},
organization = {Springer Nature Switzerland Cham},
keywords = {augmented reality, virtual reality},
pubstate = {published},
tppubtype = {inproceedings}
}
Sukaridhoto, Sritrusta; Hanifati, Kirana; Fajrianti, Evianita Dewi; Haz, Amma Liesvarastranta; Hafidz, Ilham Achmad Al; Basuki, Dwi Kurnia; Budiarti, Rizqi Putri Nourma; Wicaksono, Hendro
Web-Based Extended Reality for Supporting Medical Education Proceedings Article
In: Proceedings of SAI Intelligent Systems Conference, pp. 791–805, Springer Nature Switzerland Cham 2023.
BibTeX | Tags: augmented reality, education, industry 4.0, virtual reality
@inproceedings{sukaridhoto2023web,
title = {Web-Based Extended Reality for Supporting Medical Education},
author = {Sritrusta Sukaridhoto and Kirana Hanifati and Evianita Dewi Fajrianti and Amma Liesvarastranta Haz and Ilham Achmad Al Hafidz and Dwi Kurnia Basuki and Rizqi Putri Nourma Budiarti and Hendro Wicaksono},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
booktitle = {Proceedings of SAI Intelligent Systems Conference},
pages = {791–805},
organization = {Springer Nature Switzerland Cham},
keywords = {augmented reality, education, industry 4.0, virtual reality},
pubstate = {published},
tppubtype = {inproceedings}
}
2022
Khaturia, Roshaali; Wicaksono, Hendro; Valilai, Omid Fatahi
Srp: a sustainable dynamic ridesharing platform utilizing blockchain technology Proceedings Article
In: International Conference on Dynamics in Logistics, pp. 301–313, Springer International Publishing Cham 2022.
BibTeX | Tags: blockchain, logistics, operation research, sustainability
@inproceedings{khaturia2022srp,
title = {Srp: a sustainable dynamic ridesharing platform utilizing blockchain technology},
author = {Roshaali Khaturia and Hendro Wicaksono and Omid Fatahi Valilai},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {International Conference on Dynamics in Logistics},
pages = {301–313},
organization = {Springer International Publishing Cham},
keywords = {blockchain, logistics, operation research, sustainability},
pubstate = {published},
tppubtype = {inproceedings}
}
Navendan, Karthikeyan; Wicaksono, Hendro; Valilai, Omid Fatahi
Enhancement of Crowd Logistics Model in an E-Commerce Scenario Using Blockchain-Based Decentralized Application Proceedings Article
In: International Conference on Dynamics in Logistics, pp. 26–37, Springer International Publishing Cham 2022.
BibTeX | Tags: blockchain, logistics
@inproceedings{navendan2022enhancement,
title = {Enhancement of Crowd Logistics Model in an E-Commerce Scenario Using Blockchain-Based Decentralized Application},
author = {Karthikeyan Navendan and Hendro Wicaksono and Omid Fatahi Valilai},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {International Conference on Dynamics in Logistics},
pages = {26–37},
organization = {Springer International Publishing Cham},
keywords = {blockchain, logistics},
pubstate = {published},
tppubtype = {inproceedings}
}
Ganesan, Santhosh; Wicaksono, Hendro; Valilai, Omid Fatahi
Enhancing Vendor Managed Inventory with the Application of Blockchain Technology Proceedings Article
In: International Conference on System-Integrated Intelligence, pp. 262–275, Springer International Publishing Cham 2022.
BibTeX | Tags: blockchain, logistics
@inproceedings{ganesan2022enhancing,
title = {Enhancing Vendor Managed Inventory with the Application of Blockchain Technology},
author = {Santhosh Ganesan and Hendro Wicaksono and Omid Fatahi Valilai},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {International Conference on System-Integrated Intelligence},
pages = {262–275},
organization = {Springer International Publishing Cham},
keywords = {blockchain, logistics},
pubstate = {published},
tppubtype = {inproceedings}
}
Lobo, Carol Riona; Wicaksono, Hendro; Valilai, Omid Fatahi
Implementation of Blockchain Technology to Enhance Last Mile Delivery Models with Sustainability Perspectives Journal Article
In: IFAC-PapersOnLine, vol. 55, no. 10, pp. 3304–3309, 2022.
BibTeX | Tags: blockchain, logistics
@article{lobo2022implementation,
title = {Implementation of Blockchain Technology to Enhance Last Mile Delivery Models with Sustainability Perspectives},
author = {Carol Riona Lobo and Hendro Wicaksono and Omid Fatahi Valilai},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {IFAC-PapersOnLine},
volume = {55},
number = {10},
pages = {3304–3309},
publisher = {Elsevier},
keywords = {blockchain, logistics},
pubstate = {published},
tppubtype = {article}
}
Wicaksono, Hendro; Yuce, Baris; McGlinn, Kris; Calli, Ozum
Smart cities and buildings Book Section
In: Buildings and Semantics, pp. 25, CRC Press, 2022.
BibTeX | Tags: machine learning, ontologies, semantic web, smart cities, sustainability
@incollection{wicaksono2022smart,
title = {Smart cities and buildings},
author = {Hendro Wicaksono and Baris Yuce and Kris McGlinn and Ozum Calli},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {Buildings and Semantics},
pages = {25},
publisher = {CRC Press},
keywords = {machine learning, ontologies, semantic web, smart cities, sustainability},
pubstate = {published},
tppubtype = {incollection}
}
Wicaksono, Hendro
Production Planning and Control: Theories and Best Practices Journal Article
In: 2022.
BibTeX | Tags: data science, manufacturing
@article{wicaksono2022production,
title = {Production Planning and Control: Theories and Best Practices},
author = {Hendro Wicaksono},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
publisher = {OSF Preprints},
keywords = {data science, manufacturing},
pubstate = {published},
tppubtype = {article}
}
Istiqomah, Silvi; Sutopo, Wahyudi; Hisjam, Muhammad; Wicaksono, Hendro
Optimizing Electric Motorcycle-Charging Station Locations for Easy Accessibility and Public Benefit: A Case Study in Surakarta Journal Article
In: World Electr. Veh. J., vol. 13, no. 12, pp. 232, 2022.
BibTeX | Tags: operation research, sustainability, transportation
@article{istiqomah2022optimizing,
title = {Optimizing Electric Motorcycle-Charging Station Locations for Easy Accessibility and Public Benefit: A Case Study in Surakarta},
author = {Silvi Istiqomah and Wahyudi Sutopo and Muhammad Hisjam and Hendro Wicaksono},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {World Electr. Veh. J.},
volume = {13},
number = {12},
pages = {232},
keywords = {operation research, sustainability, transportation},
pubstate = {published},
tppubtype = {article}
}
Yuniaristanto,; Sutopo, Wahyudi; Hisjam, Muhammad; Wicaksono, Hendro
Electric Motorcycle Adoption Research: A Bibliometric Analysis Proceedings Article
In: Proceedings of the International Manufacturing Engineering Conference & The Asia Pacific Conference on Manufacturing Systems, pp. 131–138, Springer Nature Singapore Singapore 2022.
BibTeX | Tags: innovation management, technology adoption
@inproceedings{yuniaristanto2022electric,
title = {Electric Motorcycle Adoption Research: A Bibliometric Analysis},
author = {Yuniaristanto and Wahyudi Sutopo and Muhammad Hisjam and Hendro Wicaksono},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {Proceedings of the International Manufacturing Engineering Conference & The Asia Pacific Conference on Manufacturing Systems},
pages = {131–138},
organization = {Springer Nature Singapore Singapore},
keywords = {innovation management, technology adoption},
pubstate = {published},
tppubtype = {inproceedings}
}