Speaker
Description
"How Artificial Intelligence and Machine Learning Can Revolutionize Production Systems: Opportunities, and Challenges"
J. Frick *,
University of Stavanger, Norway
- jan.frick@uis.no
Abstract.
In the contemporary manufacturing landscape, advanced production systems epitomize the pinnacle of efficiency, integrating avant-garde technologies such as artificial intelligence (AI) and machine learning (ML) to re-engineer the way we produce. These systems aim not only for increased productivity but also to foster an eco-friendly production environment, while addressing the ever-evolving demands of the consumer market. The role of AI and ML in these setups cannot be overstated; they offer a plethora of opportunities, from predictive maintenance and quality assurance to real-time demand forecasting. However, the marriage of AI and ML with production is not without challenges. Concerns over data privacy, the need for skilled manpower, and the risk of over-automation highlight the nuances that industries need to negotiate. Yet, with best practices in place, the integration of these technologies can lead to reduced production costs, heightened product quality, and greater flexibility. As the horizon of the digital age expands, it's evident that the convergence of AI, ML, and production systems has immense untapped potential, emphasizing the need for judicious planning and deployment in the industry's march forward.
- Introduction
Advanced production systems refer to highly efficient and automated manufacturing processes that incorporate advanced technologies and digitalization to optimize production and resource allocation. These systems are designed to reduce waste, increase productivity, and enhance the overall quality of products while also minimizing costs and environmental impact. They typically involve the use of artificial intelligence, machine learning, robotics, and other cutting-edge technologies to automate and optimize various aspects of the production process, from product design and development to manufacturing and distribution. The goal of advanced production systems is to create a more agile and adaptable production environment that can quickly respond to changing market demands and deliver high-quality products to consumers in a timely and cost-effective manner.
Outline of main part of paper:
• Importance of incorporating artificial intelligence and machine learning
• Opportunities for AI and ML in production systems
• Challenges and considerations
• Best practices for implementing AI and ML in production systems
• The most common benefits
• IRecap of opportunities and challenges of AI and ML in production systems
• Careful planning and implementation of AI and ML in production systems is crucial for several reasons
• Future potential for further integration of advanced technologies in production systems.
References:
Bertolini, M., Mezzogori, D., Neroni, M., & Zammori, F. (2021). Machine Learning for industrial applications: A comprehensive literature review. Expert Systems with Applications, 175, 114820. https://doi.org/10.1016/j.eswa.2021.114820
Frick, J. (2023) AI and Machine Learning in Industrial Asset Management: Insights from CIAM Meetings. SunText Reviews of Economics & Business, 4(3) 10.51737/2766-4775.2023.089
Frick, J. (2023) Future of Industrial Asset Management: A Synergy of Digitalization, Digital Twins, Maintenance 5.0/Quality 5.0, Industry 5.0 and ISO55000. https://orcid.org/0000-0002-3204-1574
Frick, J. (2023). Facilitating Data Sovereignty and Digital Transformation in Municipalities and Companies: An Examination of the Data for All Initiative. International Journal of Business Administration, 14(3): https://doi.org/10.5430/ijba.v14n3p1
Frick, J., Gertsen, F., Hansen, P. H. K., Riis, J. O., & Sun, H. (1992). Evolutionary CIM Implementation: An Empirical Study of Technological-organizational Development and Market Dynamics. In Computer Integrated Manufacturing. Proceedings of the 8th CIM-Europe Annual Conference.
Netland, T. H., & Frick, J. (2017). Trends in manufacturing strategies: A longitudinal investigation of the International Manufacturing Strategy Survey. International manufacturing strategy in a time of great flux, 1-16.
Frick, J., & Laugen, B. T. (2012). Advances in Production Management Systems: Value Networks: Innovation, Technologies, and Management. Springer.
Timenes Laugen, B., Acur, N., Boer, H., & Frick, J. (2005). Best manufacturing practices: what do the best‐performing companies do? International Journal of Operations & Production Management, 25(2), 131-150.
| Conference Topic Areas | Track7: Smart Operations and Maintenance |
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