Implementation of AI Technologies in manufacturing - success factors and challenges

Abstract

There is a broad consensus on the potential of smart services for production and the added value their use offers. Industrial artificial intelligence (AI) has several advantages. AI technologies, for example, can strengthen resilience, support work processes, increase product quality and thus improve competitiveness. Many companies have recognised these potentials and are developing AI solutions. There are many successful proof-of-concepts (PoC) and pilot projects, but AI technologies successfully implemented in the real environment are scarce. Successful implementation of smart services based on industrial AI in production operations can be understood as its repetitive use and integration into operational business, which is a prerequisite for exploiting the potentials. Currently, little is known about how to achieve successful implementation. In contrast, there is much evidence that the implementation and operation of AI in manufacturing is associated with extensive challenges and barriers. The factors that positively influence the roll-out of AI technologies in manufacturing, however, are little explored. Therefore, this paper focuses on the identification of success factors and barriers for the implementation and operation of AI solutions in manufacturing. Furthermore, it is analysed whether and how the identified success factors and barriers differ from each other in order to subsequently derive initial recommendations for action. The methodology is based on explorative qualitative research. First, 10 semi-structured interviews were conducted with AI experts from a German Original Equipment Manufacturer (OEM). In an expert workshop, the main findings were validated, and possible solution and support options were discussed. Our findings confirm the results found in the literature and complement them with new insights. Success factors and challenges can be found on the technical, organisational, and human side and relate most often to "data", "development and operational processes" and "stakeholder engagement".

Keywords: "Industrial AI", "success factors", "challenges", "implementation", "manufacturing"

DOI: 10.54941/ahfe1002565