Implementation Strategies for Intelligent Systems to Support Manufacturing Planning: Recommended Actions to Avoid Failure

Abstract

In many manufacturing enterprises, manufacturing planning for the production of complex components is carried out by using CAM systems (CAM: Computer-Aided Manufacturing) (Bi and Wang, 2020). The increasing complexity and individualization of components, tools and machines lead to new requirements for manufacturing planning and CAM systems (Suhl and Isenberg, 2019; Jayasekara et al., 2019). Providers of CAx systems and researchers are currently working on the further development of conventional support systems by incorporating artificial intelligence (AI) applications (e.g. Dripke et al., 2017).AI-based, intelligent support systems are intended to enable employees to perform the increasingly complex process of manufacturing planning quickly and efficiently (cf. Burgert et al., 2022). At the same time, studies in Germany (e.g. Lundborg and Gull, 2021; Merkel-Kiss and von Garrel, 2022) indicate that available AI-based systems are generally rather used with restraint, especially by SMEs, or not used effectively, e.g., due to acceptance issues. Since a successful implementation of these systems requires appropriate strategies (Kletti, 2007; cf. Bellantuono et al., 2021; cf. Kovrigin and Vasiliev, 2020), insufficient implementation strategies could be a reason for the restraint. However, existing implementation strategies within the application context of manufacturing planning do not specifically focus on intelligent support systems, but rather on conventional digital ones in general. This paper addresses the research question of how to design an implementation strategy for intelligent support systems for manufacturing planning to ensure a successful implementation for the long term.First, a systematic literature review was conducted to identify success factors and corresponding recommendations for action in the context of implementation strategies for digital support systems in manufacturing. The recommendations for action were aggregated into 27 recommendations within the categories organization, people, technology, and data. Second, 31 experts with experience in implementing support systems in a corporate context were asked to assess the importance of these recommendations for action for the successful implementation of intelligent support systems for manufacturing planning in an online questionnaire. The questionnaire also included the assignment of the recommendations for action to five phases of a generic implementation model. Additional suggestions based on the participants' own professional experience could be added.In this paper, the methodological approaches and the results of the literature review as well as the empirical study within the context of intelligent support systems for manufacturing planning are presented. The results show, e.g., that most of the recommendations concern the interaction with the employees affected. Furthermore, many of the recommended actions are important for most or even all phases of an implementation process. Finally, the resulting recommendations for action concerning the implementation of intelligent support systems for manufacturing planning and related limitations are discussed.