2025
Susanty, Aries; Puspitasari, Nia Budi; Junaidi, Selvrida Eka; Wicaksono, Hendro
Mapping barriers to digital adoption in Batik MSMEs: insights from DEMATEL-ISM integration Journal Article
In: International Journal of Information Technology, pp. 1–26, 2025.
Abstract | Links | BibTeX | Tags: innovation management, multi criteria decision making, technology adoption
@article{susanty2025mapping,
title = {Mapping barriers to digital adoption in Batik MSMEs: insights from DEMATEL-ISM integration},
author = {Aries Susanty and Nia Budi Puspitasari and Selvrida Eka Junaidi and Hendro Wicaksono},
doi = {https://doi.org/10.1007/s41870-025-02839-9},
year = {2025},
date = {2025-11-21},
urldate = {2025-01-01},
journal = {International Journal of Information Technology},
pages = {1–26},
publisher = {Springer},
abstract = {This study aims to identify and map the key barriers affecting the readiness of batik Micro and Small Medium Enterprises (MSMEs) to adopt digital technology and to provide actionable recommendations to enhance adoption. Using a mixed-method approach, including systematic literature review, Content Validity Method (CVM), integrated Decision Making Trial and Evaluation Laboratory-Interpretive Structural Modeling (DEMATEL-ISM) modeling, and the Delphi method, the study refines 84 initial barriers into 14 core factors. The integrated DEMATEL-ISM analysis highlights two dominant barriers: lack of digital awareness and inadequate Information Technology (IT) infrastructure. Delphi-based recommendations include awareness campaigns and financial support mechanisms. While expert-based analysis may limit generalizability and overlook cultural or demand-side dynamics, the study offers valuable insight into overcoming systemic barriers. Practical implications emphasize the need for training, institutional support, and fintech-enabled strategies, while social benefits include digital empowerment and cultural preservation. This research is original in its use of DEMATEL-ISM to structure digital transformation challenges in the batik sector, offering a scalable, evidence-based framework for policy and practice.},
keywords = {innovation management, multi criteria decision making, technology adoption},
pubstate = {published},
tppubtype = {article}
}
This study aims to identify and map the key barriers affecting the readiness of batik Micro and Small Medium Enterprises (MSMEs) to adopt digital technology and to provide actionable recommendations to enhance adoption. Using a mixed-method approach, including systematic literature review, Content Validity Method (CVM), integrated Decision Making Trial and Evaluation Laboratory-Interpretive Structural Modeling (DEMATEL-ISM) modeling, and the Delphi method, the study refines 84 initial barriers into 14 core factors. The integrated DEMATEL-ISM analysis highlights two dominant barriers: lack of digital awareness and inadequate Information Technology (IT) infrastructure. Delphi-based recommendations include awareness campaigns and financial support mechanisms. While expert-based analysis may limit generalizability and overlook cultural or demand-side dynamics, the study offers valuable insight into overcoming systemic barriers. Practical implications emphasize the need for training, institutional support, and fintech-enabled strategies, while social benefits include digital empowerment and cultural preservation. This research is original in its use of DEMATEL-ISM to structure digital transformation challenges in the batik sector, offering a scalable, evidence-based framework for policy and practice.
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.
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}
}
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.
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.
2024
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}
}
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.
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.
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}
}
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.
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}
}
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.
2023
Angreani, Linda Salma; Vijaya, Annas; Wicaksono, Hendro
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.
Abstract | Links | 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 = {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}
}
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.
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}
}
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.
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}
}