Artificial Intelligence in the Factory and Its Effect on Innovative Management

Objective:

This study aims to investigate the impact of AI-based tools and technologies on innovation management in factories and industrial environments. In today's competitive world with dynamic markets, organizations that can quickly adapt to environmental changes and strengthen their innovation capabilities are more successful. One of the main drivers of change in modern industries is AI technology, which has the ability to analyze big data, learn from patterns, and provide intelligent solutions and can play a role in all stages of the innovation process. This study aims to identify key areas of AI's impact on innovation, examine the relationship between management and technological factors, and ultimately provide a conceptual model to explain these effects. The proposed model helps managers optimize their organization's innovative strategies by more accurately understanding the challenges, benefits, and success factors in the field of AI.

Method:

This research is of an applied type and was conducted using a descriptive-survey method. The statistical population consisted of managers, experts, and specialists working in various industries in Iran who have experience in the field of organizational innovation and digital technologies. To collect this data, a standard questionnaire with confirmed validity and reliability was used, which was distributed and completed by 317 people. In data analysis, SPSS software was first used for descriptive analysis, and then SmartPLS software was used for structural equation modeling based on the partial least squares method (PLS-SEM). To evaluate the validity of the final model, indicators such as composite reliability, Cronbach's alpha, convergent and divergent validity, average variance extracted (AVE), and coefficient of determination (R²) were examined.

Findings:

The findings of this study show that artificial intelligence tools have very positive and significant effects on innovative management in industrial environments and factories. Among the most important of these effects are the improvement of decision-making speed and accuracy, faster identification of market needs, improvement of human resource productivity, and reduction of product development costs. In addition, the use of intelligent algorithms increases the flexibility of production systems in the face of demand fluctuations and has paved the way for faster innovation in product design. The coefficient of determination of the model was calculated to be 31%, which indicates the relatively favorable power of the model in explaining the dependent variables. The relationships between the variables are statistically significant and the reliability of the constructs has also been confirmed at an acceptable level. The results indicate that the combination of intelligent technologies with creative management can be the driving force of innovation in factories.

Conclusion:

The present study emphasizes the strategic role of artificial intelligence as one of the main transformative factors in innovation management. In today's industrial world, which is witnessing the transition from the fourth to the fifth era, organizations need to rethink their traditional approaches to survive and be competitive, and the use of new technologies, especially artificial intelligence, is a good way to start this transformation. The results of this research show that the successful integration of artificial intelligence technology with management policies can lead to increased flexibility, creativity, and even the ability of the organization to respond quickly to environmental changes. In conclusion, it is suggested that managers should look at the issue of artificial intelligence and innovation with a strategic, forward-looking and long-term perspective and should consider investment in this area as one of their key priorities.