2025
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.
Almashaleh, Omaymah; Wicaksono, Hendro; Valilai, Omid Fatahi
A framework for social media analytics in textile business circularity for effective digital marketing Journal Article
In: Journal of Open Innovation: Technology, Market, and Complexity, vol. 11, iss. 2, pp. 100544, 2025.
Abstract | Links | BibTeX | Tags: circular economy, data science, decision support systems, sustainability
@article{almashaleh2025framework,
title = {A framework for social media analytics in textile business circularity for effective digital marketing},
author = {Omaymah Almashaleh and Hendro Wicaksono and Omid Fatahi Valilai},
doi = {https://doi.org/10.1016/j.joitmc.2025.100544},
year = {2025},
date = {2025-05-01},
urldate = {2025-01-01},
journal = {Journal of Open Innovation: Technology, Market, and Complexity},
volume = {11},
issue = {2},
pages = {100544},
publisher = {Elsevier},
abstract = {In the contemporary era of digital transformation, organizations are increasingly aligning their operations with sustainability objectives, particularly within the framework of circular economy (CE) principles in production and consumption systems. While the concept of circularity has been extensively explored through theoretical research, a notable gap remains in empirical studies that analyze user-generated content related to circularity in the textile industry. This study aims to bridge the gap by proposing a novel Instagramⓒ analytics framework that seamlessly integrates content and network analyses. A mixed-method approach is adopted, merging qualitative insights derived from unstructured data with quantitative techniques. To analyze content, unsupervised machine learning methods, including topic modeling and sentiment analysis, are employed. In parallel, Social Network Analysis (SNA) and hashtag co-occurrence analysis are applied to investigate the dynamics within the network. The findings demonstrate a significant level of interest and engagement in discussions surrounding Textile Circularity (TC). Moreover, consumer responses to sustainability initiatives show considerable variation, underscoring the necessity of strategies that foster meaningful interactions. Notably, content emphasizing positive sentiments and tangible benefits, such as cost savings and environmental improvements, consistently achieves higher engagement levels. This paper contributes to the field by integrating social media data with advanced data analytics techniques. Together, these approaches offer an unparalleled opportunity to investigate customer drivers within the context of TC. Additionally, the study presents a comprehensive analytical model and delivers actionable insights. These findings hold the potential to refine digital marketing strategies and enhance customer engagement, particularly by deepening the understanding of factors that motivate consumers to TC.},
keywords = {circular economy, data science, decision support systems, sustainability},
pubstate = {published},
tppubtype = {article}
}
In the contemporary era of digital transformation, organizations are increasingly aligning their operations with sustainability objectives, particularly within the framework of circular economy (CE) principles in production and consumption systems. While the concept of circularity has been extensively explored through theoretical research, a notable gap remains in empirical studies that analyze user-generated content related to circularity in the textile industry. This study aims to bridge the gap by proposing a novel Instagramⓒ analytics framework that seamlessly integrates content and network analyses. A mixed-method approach is adopted, merging qualitative insights derived from unstructured data with quantitative techniques. To analyze content, unsupervised machine learning methods, including topic modeling and sentiment analysis, are employed. In parallel, Social Network Analysis (SNA) and hashtag co-occurrence analysis are applied to investigate the dynamics within the network. The findings demonstrate a significant level of interest and engagement in discussions surrounding Textile Circularity (TC). Moreover, consumer responses to sustainability initiatives show considerable variation, underscoring the necessity of strategies that foster meaningful interactions. Notably, content emphasizing positive sentiments and tangible benefits, such as cost savings and environmental improvements, consistently achieves higher engagement levels. This paper contributes to the field by integrating social media data with advanced data analytics techniques. Together, these approaches offer an unparalleled opportunity to investigate customer drivers within the context of TC. Additionally, the study presents a comprehensive analytical model and delivers actionable insights. These findings hold the potential to refine digital marketing strategies and enhance customer engagement, particularly by deepening the understanding of factors that motivate consumers to TC.