An artificial intelligence approach to support knowledge management on the selection of creativity and innovation techniques

Abstract

Purpose Creativity is an important skill for design teams to reach new and useful solutions. Designers often use one or more of creativity and innovation techniques (CITs) to achieve the desired creative potential during new product development (NPD). The selection of adequate CITs requires considerable expertise, given the multiple application contexts and the extensive number of techniques available. The purpose of this study is to present a creativity support system able to manage this amount of information and provide valuable knowledge to improve NPD. Design/methodology/approach This study presents a knowledge-based system prototype using artificial intelligence (AI) to support knowledge management on the selection of CITs for design. CITs assertion is modelled through a double inference process using five categories, correlating over 500 different entry scenarios to 24 implemented CITs. The techniques are classified according to: design stage, innovation focus, team relationship, execution method and difficult of use. Prototype outputs explanations on the inference process and chosen techniques information. Findings To demonstrate the system scope, two opposite design cases are presented. The system was validated by experts in knowledge management and mechanical engineering design. The validation process demonstrates relevance of the approach and improvement directions for future developments. Originality/value Though literature contains toolkits and taxonomy for CITs, no work applies AI to identify design scenarios, select best CITs and instruct about their use. Validators reported to know less than half of the available techniques, showing a clear knowledge gap among design experts.

Analysis of Collaborative Driving Effect of Artificial Intelligence on Knowledge Innovation Management

“…By multiple linear regression, the research focuses on testing the influencing factors of knowledge flow management, knowledge stock management, knowledge flow management, and knowledge network management and empirically analyzing the mechanism of artificial intelligence on knowledge innovation management. As shown in Table 4, in the regression results of model (1), IPTR is 0.726, which is significant at the 5% level. e results show that the application of artificial intelligence has a substantial impact on the surface runoff and deep runoff of knowledge flow management, which is conducive to enhancing the internal driving force of intellectual property application.…”

“…Botega and Da Silva [1] proposed that knowledge innovation management could affect the operation law of all kinds of scientific and technological innovation, and its influencing factors were extremely diverse and complex, which mainly played a leading role in the process of knowledge network and flow [1]. Vikingur et al [2] adopted a regression model to show that the ability of knowledge flow had attracted the researchers' attention in the empirical regression analysis and dynamic investigation of knowledge network.…”

Toward advancing theory on creativity in marketing and artificial intelligence

“…"brand*," and "product*." Indeed, Botega and da Silva (2020) stated that design teams need to be creative if they are to come up with innovative and beneficial ideas.…”

Product Creativity as an Identity Issue: Through the Eyes of New Product Development Team Members

“…Standardized technology (de Vries and Verhagen, 2016 ; Xie et al, 2016 ), technology orientation (Aloulou, 2019 ), and preliminary technology assessment (Florén et al, 2018 ), all of these, improve the innovation and creativity of NPD. The current trend in research also addressed the use of artificial intelligence and its supportive function for creativity of NPD team members (Botega and da Silva, 2020 ).…”