Critical Analysis for Artificial Intelligence in Manufacturing Automation

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Abstract

cessible to all manufacturing entities, particularly small and medium-sized enterprises (SMEs). This digital divide, as implicitly mentioned in the studies by Parschau & Hauge (2020) and Johansen & Rönnbäck (2021), suggests a potential bias towards larger, more technologically equipped organizations. Additionally, the focus on high-tech solutions may overshadow simpler, yet effective, approaches that can provide value in certain contexts.Limitations of Current Methodologies: The methodologies employed, while innovative, carry limitations. The complexity and cost of implementing AI-driven solutions can be prohibitive for some organizations. Furthermore, the need for substantial data inputs to train AI models presents challenges in data collection, privacy, and security. The reliance on specific technologies, such as blockchain and IoT, also introduces dependencies that may affect system robustness and flexibility.Gaps and Inconsistencies: A critical gap in the current literature is the limited exploration of the socio-technical aspects of AI implementation in manufacturing. There is a need for more comprehensive studies on the impact of AI on workforce dynamics, skill requirements, and job displacement. Moreover, inconsistencies in the reported effectiveness of AI applications across different manufacturing contexts suggest a need for more nuanced, context-specific researc

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h. The potential for AI to contribute to sustainable manufacturing practices, as suggested by Johansen & Rönnbäck (2021), remains underexplored, indicating a significant area for future inquiry.References<ul><li>Ashima, R., Haleem, A., Bahl, S., Javaid, M., Mahla, S. K., & Singh, S. (2021). Automation and manufacturing of smart materials in Additive Manufacturing technologies using Internet of Things towards the adoption of Industry 4.0. Materials Today: Proceedings, 45, 5081–5088.</li><li>Le Chau, N., Dang, M. P., Prakash, C., Buddhi, D., & Dao, T. P. (2022). Structural optimization of a rotary joint by hybrid method of FEM, neural-fuzzy and water cycle–moth flame algorithm for robotics and automation manufacturing. Robotics and Autonomous Systems, 156, 104199.</li><li>Mo, F., Querejeta, M. U., Hellewell, J., Rehman, H. U., Rezabal, M. I., Chaplin, J. C., … & Ratchev, S. (2023). PLC orchestration automation to enhance human–machine integration in adaptive manufacturing systems. Journal of Manufacturing Systems, 71, 172–187.</li><li>Parschau, C., & Hauge, J. (2020). Is automation stealing manufacturing jobs? Evidence from South Africa’s apparel industry. Geoforum, 115, 120–131.</li><li>Johansen, K., & Rönnbäck, A. Ö. (2021). Small automation technology solution providers: facilitators for sustainable manufacturing. Procedia CIRP, 104, 677–682.</li></ul></article></body>

Critical Analysis for Artificial Intelligencein Manufacturing Automation

The critical analysis of Artificial Intelligence (AI) in Manufacturing Automation, as presented in various studies within the Journal of Manufacturing Systems and related literature, reveals a multifaceted landscape of technological advancements, methodologies, strengths, weaknesses, biases, and knowledge gaps.

Strengths and Methodologies: The integration of AI in manufacturing automation has notably improved operational efficiency, resilience, and innovation. Ashima et al. (2021) and Mo et al. (2023) highlight the deployment of AI-driven process automation and cognitive engagement significantly enhancing both planned and adaptive resilience among manufacturers. This indicates a strong alignment with Industry 4.0 paradigms, where interoperability, real-time operation, and service orientation are key principles. The use of advanced methodologies, including machine learning algorithms, neural-fuzzy systems, and hybrid optimization methods (Le Chau et al., 2022), underpins these achievements, offering robust frameworks for addressing complex manufacturing challenges.

Weaknesses and Biases: Despite these strengths, the literature also points to several weaknesses and biases. One notable concern is the heavy reliance on advanced digital technologies, which may not be accessible to all manufacturing entities, particularly small and medium-sized enterprises (SMEs). This digital divide, as implicitly mentioned in the studies by Parschau & Hauge (2020) and Johansen & Rönnbäck (2021), suggests a potential bias towards larger, more technologically equipped organizations. Additionally, the focus on high-tech solutions may overshadow simpler, yet effective, approaches that can provide value in certain contexts.

Limitations of Current Methodologies: The methodologies employed, while innovative, carry limitations. The complexity and cost of implementing AI-driven solutions can be prohibitive for some organizations. Furthermore, the need for substantial data inputs to train AI models presents challenges in data collection, privacy, and security. The reliance on specific technologies, such as blockchain and IoT, also introduces dependencies that may affect system robustness and flexibility.

Gaps and Inconsistencies: A critical gap in the current literature is the limited exploration of the socio-technical aspects of AI implementation in manufacturing. There is a need for more comprehensive studies on the impact of AI on workforce dynamics, skill requirements, and job displacement. Moreover, inconsistencies in the reported effectiveness of AI applications across different manufacturing contexts suggest a need for more nuanced, context-specific research. The potential for AI to contribute to sustainable manufacturing practices, as suggested by Johansen & Rönnbäck (2021), remains underexplored, indicating a significant area for future inquiry.

References

Ashima, R., Haleem, A., Bahl, S., Javaid, M., Mahla, S. K., & Singh, S. (2021). Automation and manufacturing of smart materials in Additive Manufacturing technologies using Internet of Things towards the adoption of Industry 4.0. Materials Today: Proceedings, 45, 5081–5088.

Le Chau, N., Dang, M. P., Prakash, C., Buddhi, D., & Dao, T. P. (2022). Structural optimization of a rotary joint by hybrid method of FEM, neural-fuzzy and water cycle–moth flame algorithm for robotics and automation manufacturing. Robotics and Autonomous Systems, 156, 104199.

Mo, F., Querejeta, M. U., Hellewell, J., Rehman, H. U., Rezabal, M. I., Chaplin, J. C., … & Ratchev, S. (2023). PLC orchestration automation to enhance human–machine integration in adaptive manufacturing systems. Journal of Manufacturing Systems, 71, 172–187.

Parschau, C., & Hauge, J. (2020). Is automation stealing manufacturing jobs? Evidence from South Africa’s apparel industry. Geoforum, 115, 120–131.

Johansen, K., & Rönnbäck, A. Ö. (2021). Small automation technology solution providers: facilitators for sustainable manufacturing. Procedia CIRP, 104, 677–682.