Publication

@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}

}

@inproceedings{reinhold2023will,

title = {Will Industry 4.0 Applications Help in Designing Sustainable Forest Management? A Conceptual Framework of Connected Networks in Novel Sectors},

author = {Ylva Reinhold and Omid Fatahi Valilai and Hendro Wicaksono},

doi = {https://doi.org/10.1109/IEEM58616.2023.10406763},

year  = {2023},

date = {2023-12-18},

urldate = {2023-01-01},

booktitle = {2023 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)},

pages = {0918–0922},

organization = {IEEE},

abstract = {Using Industry 4.0 technologies creates new opportunities in many fields. This paper examines the potential of such technologies for the forest sector. Existing research mainly proposes solutions to collect and analyze data on specific topics. This research aims to create a model combining different data inputs to draw a comprehensive picture of forest conditions by closing the gap between science and policymaking. With the help of a pre-defined set of indicators, the output is communicable across sectors and countries while maintaining practicability on a local level. The model evaluation has been completed according to the Design Science Research (DSR) Guidelines proposed by Hevner, et al., which prospected good chances of adoptability. With the successful implementation of the model, ways of decision-making for sustainable forest management could be revolutionized.

},

keywords = {artificial intelligence, design science research, digital twins, industry 4.0, sustainability},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{angreani2023evaluating,

title = {Evaluating the Interrelationships of Driving Factors of Industry 4.0 Maturity Models in Developing Countries Using Fuzzy DEMATEL},

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

doi = {https://doi.org/10.1109/IEEM58616.2023.10406637},

year  = {2023},

date = {2023-12-18},

urldate = {2023-01-01},

booktitle = {2023 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)},

pages = {1662–1666},

organization = {IEEE},

abstract = {Many companies strive to incorporate Industry 4.0 into their strategies, considering it the upcoming major trend with new benefits. As it continues to evolve, industrial growth and competitive advantage must understand the key factors driving Industry 4.0's development. Despite the benefits of Industry 4.0, its impact on employment, wealth and distribution in developing countries is not fully understood. In emerging economies, companies often possess restricted capabilities and typically function at a considerable distance from the frontier. Furthermore, businesses, especially small and medium enterprises (SMEs), do not understand the tangible benefits of Industry 4.0. At the same time, the relationships of key driving factors could differ in the context of developing countries regarding limited infrastructure, workforce skills, or different regulations. This paper examines the interrelationship of Industry 4.0 maturity models driving factors. 16 driving factors were extracted from 30 references primary studies through a structured literature review and taxonomy development and grouped into 6 domains. By using expert opinions from developing countries, the study employs a linguistic fuzzy Decision Making Trial and Evaluation Laboratory (DEMATEL) method to quantify the interdependencies of each factor. This study found that although categorized as an affecting factor, strategy for I4.0 has the most relation to others. The findings presented in this study offer insights into the relative importance of I4.0MM driving factors, enabling policymakers, industry practitioners, and researchers to prioritize their efforts and allocate resources effectively as part of their adoption and growth strategies.

},

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

pubstate = {published},

tppubtype = {inproceedings}

}

@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 = {Hendro Wicaksono and Mehr Un Nisa and Annas Vijaya},

doi = {https://doi.org/10.1109/IEEM58616.2023.10406401},

year  = {2023},

date = {2023-12-18},

urldate = {2023-01-01},

booktitle = {2023 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)},

pages = {0528–0532},

organization = {IEEE},

abstract = {In the transportation sector, implementing digital twins is part of the digitization measure to improve resource efficiency in infrastructure management. However, the use of digital twins is still limited due to challenges such as a lack of shared understanding of digital twin models, complex model integration, security issues, lack of access to essential data, and high costs due to inefficient business models. This research develops an asset management platform suitable for Small and Medium Enterprises (SMEs) for the cross-company, secure, and intuitive collaborative management of digital twin assets. It can be achieved by developing an ontology-based semantic model of the assets, explainable machine learning (XAI), and a scenario-based intelligent search and discovery mechanism.},

keywords = {digital twins, explainable AI, interoperability, ontologies, semantic web, transportation},

pubstate = {published},

tppubtype = {inproceedings}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@inproceedings{raza2023blockchain,

title = {Blockchain Technologies for Sustainable Last Mile Delivery: Investigating Customer Awareness and Tendency Using NFT Reward Mechanisms},

author = {Arif Raza and Hendro Wicaksono and Omid Fatahi Valilai},

doi = {https://doi.org/10.1109/IEEM58616.2023.10406357},

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},

abstract = {Last mile delivery as a challenging area of supply chain is facing growing challenges for e-commerce firms. One of the most important aspects of these challenges is related to the sustainability of last mile delivery. This study has targeted customers' awareness and eagerness to play an effective role for sustainable last mile delivery. The paper has also focused on the capabilities of blockchain technology for enabling the tracking and tracing of the contributions of the customers through NFT (non-fungible tokens) tokens. The paper has designed surveys to examine the awareness and tendency of customers for sustainable last mile delivery. It has been found that there is a significant gap when it comes to completely understanding the blockchain and its potential benefits in terms of reducing CO2 emissions concludes. The results show that a high proportion of respondents who indicated a willingness to delay and consolidate deliveries if offered an NFT token incentive. Finally, it has been concluded that there is high potential for blockchain technology to promote sustainability in the last mile delivery industry.

},

keywords = {blockchain, data management, logistics, supply chain management},

pubstate = {published},

tppubtype = {inproceedings}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@inproceedings{ahmadi2021extending,

title = {Extending the last mile delivery routing problem for enhancing sustainability by drones using a sentiment analysis approach},

author = {Elham Ahmadi and Hendro Wicaksono and O Fatahi Valilai},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

booktitle = {2021 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)},

pages = {207–212},

organization = {IEEE},

keywords = {data science, logistics, operation research},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{gumilar2020analysis,

title = {Analysis curve of maximum power and torque turbine generated by vertical axis wind turbine based on number of blades},

author = {Langlang Gumilar and Muhammad Afnan Habibi and Dwi Prihanto and Hendro Wicaksono and Jade Rosida Larasati and Achmad Gunawan},

year  = {2020},

date = {2020-01-01},

booktitle = {AIP Conference Proceedings},

volume = {2217},

number = {1},

organization = {AIP Publishing},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{habibi2020study,

title = {The study of sweep frequency response analysis for inspecting the performance of transformer},

author = {MA Habibi and AA Zain and FAP Sudarto and A Kusumawardana and S Wibawanto and L Gumilar and H Wicaksono},

year  = {2020},

date = {2020-01-01},

booktitle = {Journal of Physics: Conference Series},

volume = {1595},

number = {1},

pages = {012016},

organization = {IOP Publishing},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}