Driving Innovation with the Application of Industrial AI in the R&D Domain

• Published in: Distributed, Ambient and Pervasive Interactions pp. 244 - 255
• Main Authors: Xing, Fei, Peng, Guochao (Alex), Zhang, Bingqian, Zuo, Simin, Tang, Jiangfeng, Li, Shuyang
• Format: Book Chapter
• Language: English
• Published: Cham Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Advantage; AI-based R&D; Enterprise application; Industrial AI
• ISBN: 3030503437; 9783030503437
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-50344-4_18

The concept of artificial intelligence (AI) has become increasingly prevalent in the industry, but there is still insufficient understanding about what AI can exactly do for manufacturing companies. This paper focuses on the domain of product research and development (R&D), and aims to depict how AI can assist in industrial R&D activities. Through a comprehensive review of literature, this paper identified three major drawbacks in traditional product R&D approach, namely, low success rate, long research cycle, and difficulty in management. Subsequently, based on the characteristics of AI technology, this paper proposes and discusses a number of scenarios to demonstrate how AI can be applied to support R&D activities. Comparing with traditional product R&D, the advantages of AI-based R&D are proposed and summarized: 1) More objective identification of user requirements to drive enterprise innovation; 2) more precise exploration of market trends; 3) higher efficiency in product design; 4) less risks in R&D process; and 5) improved knowledge sharing ability. This paper will be of interests and value to practitioners and researchers concerned with AI usage in manufacturing contexts.