Journal of Entrepreneurship and Innovation Research

Journal of Entrepreneurship and Innovation Research

The Role of Artificial Intelligence in Factories and Its Impact on Innovative Management: A Structural Analysis

Document Type : Original Article

Authors
Salman Motevaseli 1
Behroz Tahmasebkazemi 2
Mahdi Rajabiun 3
1 Department of Technology Management, Faculty of Management, Islamic Azad University Science and Research Branch, Tehran, Iran
2 Department of Entrepreneurship Department, Faculty of Social Sciences, Razi University, Kermanshah, Iran.
3 Department of Commerce, Customs, and Entrepreneurship, Faculty of Management, North Tehran Branch, Islamic Azad University, Tehran, Iran.
10.22034/eir.2025.495125.1142
Abstract
Objective: This study was conducted with the aim of designing and explaining the role of artificial intelligence in factories and examining its impact on innovative management.

Method: Thematic analysis was used to identify the main indicators and components related to artificial intelligence and interpretive structural modeling (ISM) was used to classify and analyze the relationships between variables. Data were collected and analyzed from 15 expert interviews. In thematic analysis, 39 initial codes were extracted from 170 open codes and categorized into 10 organizing themes, leading to an overarching theme titled "Artificial Intelligence in the Factory and Its Impact on Innovative Management".

Findings: The identified components included industrial artificial intelligence tools, cognitive supply chain, customer service, production process robotization, data management, quality management, productivity optimization, improving production sustainability, increasing flexibility, and workforce development. Structural self-interaction matrix (SSIM) for ISM analysis and variable ranking showed that variables such as industrial AI tools and data management as independent variables had the greatest impact and productivity and improved production sustainability as dependent variables had the greatest impact. Also, cognitive supply chain and quality management were identified as linked variables, and MICMAC analysis introduced independent variables as key factors and linked variables as essential interfaces in system interactions.

Results: Intelligent use of AI-based technologies can lead to improved quality, productivity, cost reduction, supply chain optimization, and increased flexibility in factory production processes. The model presented in this study can be the basis for decision-making by manufacturing industry managers for the effective implementation of related technologies.
Keywords
Keywords: Artificial Intelligence (AI)
Innovative Management
Interpretive Structural Modeling (ISM)
Industrial AI Tools
Production Process Optimization

Subjects

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Journal of Entrepreneurship and Innovation Research
Volume 3, Issue 4 - Serial Number 9
Winter 2025
Pages 111-128