Publication

@article{nokey,

title = {Artificial Intelligence in Open Innovation Project Management: A Systematic Literature Review on Technologies, Applications, and Integration Requirements},

author = {Moonita Limiany Prasetyo and Randall Aginta Peranginangin and Nada Martinovic and Mohammad Ichsan and Hendro Wicaksono},

url = {https://www.sciencedirect.com/science/article/pii/S2199853124002397},

doi = {https://doi.org/10.1016/j.joitmc.2024.100445},

year  = {2025},

date = {2025-03-01},

urldate = {2025-03-01},

journal = {Journal of Open Innovation: Technology, Market, and Complexity},

volume = {11},

number = {100445},

issue = {1},

abstract = {This study aims to provide insights to support organizations in building effective strategies for adopting Artificial Intelligence (AI) and improving project management processes. It focuses on open innovation projects. It employs a comprehensive and systematic literature review (SLR). A total of 365 publications have been chosen from a pool of 1265 papers in the IEEE and Scopus databases. The study develops a framework for literature synthesis guided by five research questions. Those questions address AI technologies, project management tasks, industries adopting AI, and requirements for successful adoption. The analysis reveals that Machine Learning is widely employed in project management, especially for predicting analytics, optimizing resources, and managing risks. AI improves open innovation project management by integrating diverse knowledge sources, enhancing collaboration, and providing strategic insights for decision-making. This study also found that AI adoption depends not only on technical infrastructure, integration with existing systems, and data readiness but also on leadership support, strategic alignment, financial resources, skills development, and organizational culture. The findings also highlight the importance of aligning AI initiatives with open innovation requirements, where collaboration, agility, and external knowledge integrations are crucial. The construction sector is at the forefront of adopting AI. This study fills a significant gap in previous research by identifying the technical and non-technical prerequisites for effectively incorporating AI into open innovation project management methodologies.},

keywords = {artificial intelligence, industry 4.0, innovation management, project management},

pubstate = {published},

tppubtype = {article}

}

@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}

}

@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}

}

@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}

}

@article{wicaksono2020contributions,

title = {Contributions of muslim scientists to the 4th industrial revolution},

author = {Hendro Wicaksono},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

publisher = {OSF Preprints},

keywords = {digital transformation, industry 4.0},

pubstate = {published},

tppubtype = {article}

}