In the current manufacturing context, to maximise profitability and increase sustainability, companies are expanding their offerings by adding and diversifying their services. At the same time, the possibility of analysing data and textual information for diagnostic, predictive and prognostic applications, the evolution of digital technologies, and the advancement of Artificial Intelligence (AI) are fostering a significant improvement in service delivery and execution. Despite the mounting interest in the implementation of AI in the manufacturing domain, extant literature predominantly concentrates on its impact on factors such as production efficiency, quality control, and supply chain optimisation. However, a substantial lacuna persists in understanding how AI technologies (such as including Machine Learning, Knowledge-Based Systems, Computer Vision, Robotics, Natural Language Processing, Automated Planning and Scheduling, Optimisation and Generative AI) are contributing to this transformation within the manufacturing sector. Addressing this gap is essential for the development of a comprehensive framework that can enable manufacturing firms to strategically and fruitfully integrate AI technologies, thereby optimising their operational efficiency and competitiveness. This study analyses the use of AI technologies in manufacturing to support service delivery and execution. Therefore, it aims to show where and how AI is used to create value through services. The results outline the main area of applications of AI in support of service delivery and execution in the manufacturing field. For instance, a particularly significant impact of AI in maintenance, with a strong contribution of technologies such as Machine Learning and Robotics, is shown. On the other hand, services such as spare parts and consumables delivery, as well as remanufacturing, refurbishing, cleaning, and safe-keeping, currently exhibit limited applications of AI.

(2025). The Adoption of Artificial Intelligence in Manufacturing Services: Key Insights and Strategic Directions . In ...SUMMER SCHOOL FRANCESCO TURCO. PROCEEDINGS. Retrieved from https://hdl.handle.net/10446/316107

The Adoption of Artificial Intelligence in Manufacturing Services: Key Insights and Strategic Directions

Marinucci, Mariavittoria;Sala, Roberto;Arioli, Veronica;Pirola, Fabiana
2025-01-01

Abstract

In the current manufacturing context, to maximise profitability and increase sustainability, companies are expanding their offerings by adding and diversifying their services. At the same time, the possibility of analysing data and textual information for diagnostic, predictive and prognostic applications, the evolution of digital technologies, and the advancement of Artificial Intelligence (AI) are fostering a significant improvement in service delivery and execution. Despite the mounting interest in the implementation of AI in the manufacturing domain, extant literature predominantly concentrates on its impact on factors such as production efficiency, quality control, and supply chain optimisation. However, a substantial lacuna persists in understanding how AI technologies (such as including Machine Learning, Knowledge-Based Systems, Computer Vision, Robotics, Natural Language Processing, Automated Planning and Scheduling, Optimisation and Generative AI) are contributing to this transformation within the manufacturing sector. Addressing this gap is essential for the development of a comprehensive framework that can enable manufacturing firms to strategically and fruitfully integrate AI technologies, thereby optimising their operational efficiency and competitiveness. This study analyses the use of AI technologies in manufacturing to support service delivery and execution. Therefore, it aims to show where and how AI is used to create value through services. The results outline the main area of applications of AI in support of service delivery and execution in the manufacturing field. For instance, a particularly significant impact of AI in maintenance, with a strong contribution of technologies such as Machine Learning and Robotics, is shown. On the other hand, services such as spare parts and consumables delivery, as well as remanufacturing, refurbishing, cleaning, and safe-keeping, currently exhibit limited applications of AI.
2025
Marinucci, Mariavittoria; Sala, Roberto; Arioli, Veronica; Pirola, Fabiana
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