2024
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.