2025
Priyandari, Yusuf; Sutopo, Wahyudi; Nizam, Muhammad; Wicaksono, Hendro
In: Scientific Reports, vol. 15, no. 1, pp. 36613, 2025.
Abstract | Links | BibTeX | Tags: automotive industry, product service system, resillience, supply chain management, sustainability, transportation
@article{priyandari2025vulnerability,
title = {Vulnerability assessment model integrating outcome and characteristic-based metrics for electric motorcycle battery swapping and charging stations},
author = {Yusuf Priyandari and Wahyudi Sutopo and Muhammad Nizam and Hendro Wicaksono},
doi = {https://doi.org/10.1038/s41598-025-20325-x},
year = {2025},
date = {2025-10-21},
urldate = {2025-10-21},
journal = {Scientific Reports},
volume = {15},
number = {1},
pages = {36613},
publisher = {Nature Publishing Group UK London},
abstract = {Battery swapping and charging stations are essential for increasing the adoption of electric motorcycles. The stations address the range anxiety issue and quickly obtain a fully recharged battery. However, operational issues with swapping and charging activities drive operational vulnerability. Therefore, this study proposes a vulnerability assessment model utilizing the IoT Platform data of electric motorcycle battery swapping and charging stations. The model computes a vulnerability score by integrating vulnerability indicator metrics of the system outcome and characteristic. The system outcome uses performance data representing vulnerability impact. The system characteristic uses data from the vulnerability driver and exposure factors. The driver factor represents mitigation ability, and the exposure factor represents conditions that may affect both the mitigation ability and performance. The model also classifies the vulnerability of stations in four categories: not vulnerable, potentially vulnerable, moderately vulnerable, and vulnerable. The model was implemented in a case in Jakarta. The result reveals significant differences in vulnerability among stations, although most stations fall into the not vulnerable to moderately vulnerable categories. The findings facilitate identifying station characteristics that potentially affect performance quantitatively.},
keywords = {automotive industry, product service system, resillience, supply chain management, sustainability, transportation},
pubstate = {published},
tppubtype = {article}
}
Battery swapping and charging stations are essential for increasing the adoption of electric motorcycles. The stations address the range anxiety issue and quickly obtain a fully recharged battery. However, operational issues with swapping and charging activities drive operational vulnerability. Therefore, this study proposes a vulnerability assessment model utilizing the IoT Platform data of electric motorcycle battery swapping and charging stations. The model computes a vulnerability score by integrating vulnerability indicator metrics of the system outcome and characteristic. The system outcome uses performance data representing vulnerability impact. The system characteristic uses data from the vulnerability driver and exposure factors. The driver factor represents mitigation ability, and the exposure factor represents conditions that may affect both the mitigation ability and performance. The model also classifies the vulnerability of stations in four categories: not vulnerable, potentially vulnerable, moderately vulnerable, and vulnerable. The model was implemented in a case in Jakarta. The result reveals significant differences in vulnerability among stations, although most stations fall into the not vulnerable to moderately vulnerable categories. The findings facilitate identifying station characteristics that potentially affect performance quantitatively.
Gupta, Ishansh; Martinez, Adriana; Correa, Sergio; Wicaksono, Hendro
In: Supply Chain Analytics, vol. 10, pp. 100116, 2025.
Abstract | Links | BibTeX | Tags: artificial intelligence, causal AI, causal inference, data science, decision support systems, industry 4.0, industry 5.0, machine learning, multi criteria decision making, resillience, supply chain management, technology adoption
@article{gupta2025comparative,
title = {A comparative assessment of causal machine learning and traditional methods for enhancing supply chain resiliency and efficiency in the automotive industry},
author = {Ishansh Gupta and Adriana Martinez and Sergio Correa and Hendro Wicaksono},
doi = {https://doi.org/10.1016/j.sca.2025.100116},
year = {2025},
date = {2025-06-01},
urldate = {2025-06-01},
journal = {Supply Chain Analytics},
volume = {10},
pages = {100116},
publisher = {Elsevier},
abstract = {Efficient supplier escalation is crucial for maintaining smooth operational supply chains in the automotive industry, as disruptions can lead to significant production delays and financial losses. Many companies still rely on traditional escalation methods, which may lack the precision and adaptability offered by modern technologies. This study presents a comparative analysis of decision-making strategies for supplier escalation, evaluating causal machine learning (CML), traditional machine learning (ML), and current escalation practices in a leading German automotive company. The study employs an explanatory sequential mixed method, integrating the Analytical Hierarchy Process (AHP) with in-depth interviews with 25 industry experts. These methods are assessed based on several performance metrics: accuracy, business impact, explanation capability, human bias, stress test, and time-to-recover. Findings reveal that CML outperforms traditional ML and existing approaches, offering superior risk prediction, interpretability, and decision-making support Additionally, the research explores the internal acceptance of these technologies through the Technology Acceptance Model (TAM). The results highlight the transformative potential of CML in enhancing supply chain resilience and efficiency. By bridging the gap between predictive analytics and explainable AI, this research offers valuable guidance for firms seeking to optimize supplier management using advanced analytics.},
keywords = {artificial intelligence, causal AI, causal inference, data science, decision support systems, industry 4.0, industry 5.0, machine learning, multi criteria decision making, resillience, supply chain management, technology adoption},
pubstate = {published},
tppubtype = {article}
}
Efficient supplier escalation is crucial for maintaining smooth operational supply chains in the automotive industry, as disruptions can lead to significant production delays and financial losses. Many companies still rely on traditional escalation methods, which may lack the precision and adaptability offered by modern technologies. This study presents a comparative analysis of decision-making strategies for supplier escalation, evaluating causal machine learning (CML), traditional machine learning (ML), and current escalation practices in a leading German automotive company. The study employs an explanatory sequential mixed method, integrating the Analytical Hierarchy Process (AHP) with in-depth interviews with 25 industry experts. These methods are assessed based on several performance metrics: accuracy, business impact, explanation capability, human bias, stress test, and time-to-recover. Findings reveal that CML outperforms traditional ML and existing approaches, offering superior risk prediction, interpretability, and decision-making support Additionally, the research explores the internal acceptance of these technologies through the Technology Acceptance Model (TAM). The results highlight the transformative potential of CML in enhancing supply chain resilience and efficiency. By bridging the gap between predictive analytics and explainable AI, this research offers valuable guidance for firms seeking to optimize supplier management using advanced analytics.
Angreani, Linda S; Vijaya, Annas; Wicaksono, Hendro
OntoMat 4.0: An Ontology Framework for Enhanced Industry 4.0 Maturity Assessment Journal Article
In: IEEE Access, vol. 13, pp. 68801-68819, 2025.
Abstract | Links | BibTeX | Tags: digital transformation, industry 4.0, interoperability, manufacturing, ontologies, semantic web, supply chain management
@article{angreani2025ontomat,
title = {OntoMat 4.0: An Ontology Framework for Enhanced Industry 4.0 Maturity Assessment},
author = {Linda S Angreani and Annas Vijaya and Hendro Wicaksono},
doi = {https://doi.org/10.1109/ACCESS.2025.3561229},
year = {2025},
date = {2025-04-15},
urldate = {2025-01-01},
journal = {IEEE Access},
volume = {13},
pages = {68801-68819},
publisher = {IEEE},
abstract = {Industry 4.0 (I4.0) is expected to revolutionize the manufacturing process and business model and offer a significant competitive advantage. The Industry 4.0 Maturity Model (I4.0MM) is applied to guide organizations in I4.0 adoption. Nevertheless, most present models have certain limitations, including the absence of standardization, limited scope or narrow focus, and difficulty of use, which makes them less efficient. To address these gaps, this study presents OntoMat 4.0, an ontology that enables interoperability, knowledge sharing, and I4.0 maturity assessment. It was created following a four-step iterative process based on well-established ontology development frameworks, such as LOT and NeOn. The novelty of Ontomat 4.0 includes its innovative functionality through the ability to work alongside established I4.0 ontologies, which enables users to navigate multidimensional I4.0 aspects and extend interoperability while promoting the reuse of relevant and widely recognized ontologies. It also delivers prioritized actionable insights that guide strategic decisions during I4.0 transformation initiatives. Ontomat 4.0 was evaluated and tested through real-world strategic planning and benchmarking applications, and the effectiveness of Ontomat 4.0 was demonstrated in aiding organizations in making informed decisions. It brings the possibility of integrating the technical and nontechnical factors of I4.0 and can be a standard solution for measuring I4.0 maturity. Despite its limitations, this ontology has been shown to fill in the gaps in current models and promote consistency and interoperability.},
keywords = {digital transformation, industry 4.0, interoperability, manufacturing, ontologies, semantic web, supply chain management},
pubstate = {published},
tppubtype = {article}
}
Industry 4.0 (I4.0) is expected to revolutionize the manufacturing process and business model and offer a significant competitive advantage. The Industry 4.0 Maturity Model (I4.0MM) is applied to guide organizations in I4.0 adoption. Nevertheless, most present models have certain limitations, including the absence of standardization, limited scope or narrow focus, and difficulty of use, which makes them less efficient. To address these gaps, this study presents OntoMat 4.0, an ontology that enables interoperability, knowledge sharing, and I4.0 maturity assessment. It was created following a four-step iterative process based on well-established ontology development frameworks, such as LOT and NeOn. The novelty of Ontomat 4.0 includes its innovative functionality through the ability to work alongside established I4.0 ontologies, which enables users to navigate multidimensional I4.0 aspects and extend interoperability while promoting the reuse of relevant and widely recognized ontologies. It also delivers prioritized actionable insights that guide strategic decisions during I4.0 transformation initiatives. Ontomat 4.0 was evaluated and tested through real-world strategic planning and benchmarking applications, and the effectiveness of Ontomat 4.0 was demonstrated in aiding organizations in making informed decisions. It brings the possibility of integrating the technical and nontechnical factors of I4.0 and can be a standard solution for measuring I4.0 maturity. Despite its limitations, this ontology has been shown to fill in the gaps in current models and promote consistency and interoperability.
Gupta, Ishansh; Raeisi, Seyed Taha; Correa, Sergio; Wicaksono, Hendro
Evaluating risk factors in automotive supply chains: A hybrid fuzzy AHP-TOPSIS approach with extended PESTLE framework Journal Article
In: Journal of Open Innovation: Technology, Market, and Complexity, vol. 11, iss. 1, pp. 100489, 2025.
Abstract | Links | BibTeX | Tags: multi criteria decision making, PESTLE, supply chain management
@article{nokey,
title = {Evaluating risk factors in automotive supply chains: A hybrid fuzzy AHP-TOPSIS approach with extended PESTLE framework},
author = {Ishansh Gupta and Seyed Taha Raeisi and Sergio Correa and Hendro Wicaksono},
url = {https://www.sciencedirect.com/science/article/pii/S2199853125000241},
doi = {https://doi.org/10.1016/j.joitmc.2025.100489},
year = {2025},
date = {2025-03-01},
journal = {Journal of Open Innovation: Technology, Market, and Complexity},
volume = {11},
issue = {1},
pages = {100489},
abstract = {The purpose of this study is to evaluate Exogenous Risk Factors (ERFs) affecting Key Performance Indicators (KPIs) in automotive supply chains, aiming to enhance resilience against global disruptions. The primary research question focuses on identifying and prioritizing ERFs that pose the greatest threat to operational performance. A hybrid decision-making framework integrating Fuzzy Analytical Hierarchy Process (FAHP) and Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (FTOPSIS) is employed. Validation is ensured through insights from 18 supply chain professionals with diverse roles and a combined 318 years of experience. The study identifies 34 ERFs, including semiconductor shortages, pandemics, and information infrastructure disruptions, and evaluates their impact on KPIs such as missing parts, backlogs, special transports, and wrong deliveries. By extending the traditional PESTLE framework with Transportation and Material dimensions, this study provides actionable strategies to mitigate risks and strengthen supply chain resilience in volatile environments.},
keywords = {multi criteria decision making, PESTLE, supply chain management},
pubstate = {published},
tppubtype = {article}
}
The purpose of this study is to evaluate Exogenous Risk Factors (ERFs) affecting Key Performance Indicators (KPIs) in automotive supply chains, aiming to enhance resilience against global disruptions. The primary research question focuses on identifying and prioritizing ERFs that pose the greatest threat to operational performance. A hybrid decision-making framework integrating Fuzzy Analytical Hierarchy Process (FAHP) and Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (FTOPSIS) is employed. Validation is ensured through insights from 18 supply chain professionals with diverse roles and a combined 318 years of experience. The study identifies 34 ERFs, including semiconductor shortages, pandemics, and information infrastructure disruptions, and evaluates their impact on KPIs such as missing parts, backlogs, special transports, and wrong deliveries. By extending the traditional PESTLE framework with Transportation and Material dimensions, this study provides actionable strategies to mitigate risks and strengthen supply chain resilience in volatile environments.
Ghribi, Youssef; Graha, Ega Rudy; Wicaksono, Hendro
Comparative Analysis of Statistical and Machine Learning Models for Enhancing Demand Forecasting Accuracy in the Medical Device Industry Journal Article
In: Procedia CIRP, vol. 134, pp. 849–854, 2025.
Abstract | Links | BibTeX | Tags: artificial intelligence, data science, deep learning, demand forecasting, healthcare, machine learning, manufacturing, supply chain management
@article{ghribi2025comparative,
title = {Comparative Analysis of Statistical and Machine Learning Models for Enhancing Demand Forecasting Accuracy in the Medical Device Industry},
author = {Youssef Ghribi and Ega Rudy Graha and Hendro Wicaksono},
doi = {https://doi.org/10.1016/j.procir.2025.02.209},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
journal = {Procedia CIRP},
volume = {134},
pages = {849–854},
publisher = {Elsevier},
abstract = {Demand forecasting is a crucial instrument in the business strategy. The medical devices in the healthcare system are further significant as critical roles. Multiple businesses rely on traditional forecasting techniques due to their simplicity and understandable algorithm’s easy-to-use nature characteristics. The research conducted for each model analyzes how traditional statistical, Machine Learning (ML), and Deep Learning (DL) models can be used to make demand forecasting more accurate and valuable in the medical device industry. The work expands beyond prior research to demonstrate the enhanced effectiveness of DL models compared to statistical and ML models across multiple areas. However, research still needs to identify studies that adopt a business-centric perspective on the practical applicability of these models. Research utilizing SARIMAX, Exponential Smoothing, Linear Regression, Average, Support Vector Regression (SVR), Random Forest (RF), K-Nearest Neighbour Regression (KNR), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Convolution 1D (CONV1D) models to forecast what people demand to order. The data comes from a German medical device manufacturer’s past sales record. We evaluated the model’s performance using the weighted Mean Absolute Percentage Error (wMAPE) method. These showed that DL models needed a lot of knowledge and preprocessing, but they were the most accurate at predicting what would happen. The LSTM model exhibited outstanding performance, achieving an average wMAPE of 0.3102, surpassing all other models. The research results for more sophisticated models surpass traditional statistical models despite limited datasets, recommending that medical device businesses consider investing in advanced demand forecasting models.
publisher={Elsevier}
}},
keywords = {artificial intelligence, data science, deep learning, demand forecasting, healthcare, machine learning, manufacturing, supply chain management},
pubstate = {published},
tppubtype = {article}
}
Demand forecasting is a crucial instrument in the business strategy. The medical devices in the healthcare system are further significant as critical roles. Multiple businesses rely on traditional forecasting techniques due to their simplicity and understandable algorithm’s easy-to-use nature characteristics. The research conducted for each model analyzes how traditional statistical, Machine Learning (ML), and Deep Learning (DL) models can be used to make demand forecasting more accurate and valuable in the medical device industry. The work expands beyond prior research to demonstrate the enhanced effectiveness of DL models compared to statistical and ML models across multiple areas. However, research still needs to identify studies that adopt a business-centric perspective on the practical applicability of these models. Research utilizing SARIMAX, Exponential Smoothing, Linear Regression, Average, Support Vector Regression (SVR), Random Forest (RF), K-Nearest Neighbour Regression (KNR), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Convolution 1D (CONV1D) models to forecast what people demand to order. The data comes from a German medical device manufacturer’s past sales record. We evaluated the model’s performance using the weighted Mean Absolute Percentage Error (wMAPE) method. These showed that DL models needed a lot of knowledge and preprocessing, but they were the most accurate at predicting what would happen. The LSTM model exhibited outstanding performance, achieving an average wMAPE of 0.3102, surpassing all other models. The research results for more sophisticated models surpass traditional statistical models despite limited datasets, recommending that medical device businesses consider investing in advanced demand forecasting models.
publisher={Elsevier}
}
publisher={Elsevier}
}
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}
}
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.
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}
}
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.
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.
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}
}
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.
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}
}
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.
2023
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}
}
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.
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}
}
Raza, Arif; Wicaksono, Hendro; Valilai, Omid 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.
Abstract | Links | BibTeX | Tags: blockchain, data management, logistics, supply chain management
@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}
}
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.
2020
Wicaksono, Hendro
Data analytics in supply chain management Journal Article
In: 2020.
BibTeX | Tags: data science, machine learning, supply chain management
@article{wicaksono2020data,
title = {Data analytics in supply chain management},
author = {Hendro Wicaksono},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
publisher = {OSF},
keywords = {data science, machine learning, supply chain management},
pubstate = {published},
tppubtype = {article}
}