Volume 14, Issue 53 (Winter 2025)
IUESA 2025, 14(53): 13-34 |
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Ghadamgahi S M, Hashemzadeh G, Jamalou F, Fathi Hafshejani K. Adaptive and Intelligentization Model of Manufacturing and Operational Processes in the Automotive Industry with a 4 Industrial Revolution Approach. IUESA 2025; 14 (53) :13-34
URL: http://iueam.ir/article-1-2247-en.html
URL: http://iueam.ir/article-1-2247-en.html
1- , seyedmorteza.ghadamghahi@gmail.com
Abstract: (42 Views)
Abstract:
This study aims to design a conceptual model for adapting manufacturing and operational processes in the automotive industry in line with Industry 4.0, with a focus on intelligentization. From the perspective of objectives in business research and information gathering, this study is descriptive-analytic. The research adopts a mixed exploratory methodology, comprising both qualitative and quantitative phases. In the qualitative phase, a content analysis approach (Strauss–Corbin model) is employed, with semi-structured interviews conducted with elites as the data-collection instrument; participants are selected via snowball sampling, and data collection continues until theoretical saturation is reached. Among the model’s main factors, key resources are identified as the most influential, and its capabilities are identified as the most responsive.
Furthermore, customer-related factors, customer relations, key activities, key partners, and challenges in applying the model are identified as influential factors. In contrast, value proposition, distribution channel, and cost and revenue streams are identified as dependent factors. Data analysis using a fuzzy Analytic Network Process (ANP) indicates the following weights for the main factors: value proposition, key activity, customers, model capabilities, distribution channel, key resources, customer relations, cost and revenue structure, challenges in applying the model, and key partners. Among the sub-factors, the sub-factor “intelligent production” ranks first. Exploiting the capacities of Industry 4.0 key technologies ranks second, the factor of expanding and strengthening process monitoring ranks third, firms active in hardware technologies of Industry 4.0 rank fourth, the possibility of creating fault-tolerant production processes ranks fifth, and, finally, developing the intelligentization dimensions of production processes in the automotive industry ranks sixth among 50 sub-factors, collectively accounting for nearly 30% of the total weight of sub-factors—highlighting the high importance of these sub-factors.
This study aims to design a conceptual model for adapting manufacturing and operational processes in the automotive industry in line with Industry 4.0, with a focus on intelligentization. From the perspective of objectives in business research and information gathering, this study is descriptive-analytic. The research adopts a mixed exploratory methodology, comprising both qualitative and quantitative phases. In the qualitative phase, a content analysis approach (Strauss–Corbin model) is employed, with semi-structured interviews conducted with elites as the data-collection instrument; participants are selected via snowball sampling, and data collection continues until theoretical saturation is reached. Among the model’s main factors, key resources are identified as the most influential, and its capabilities are identified as the most responsive.
Furthermore, customer-related factors, customer relations, key activities, key partners, and challenges in applying the model are identified as influential factors. In contrast, value proposition, distribution channel, and cost and revenue streams are identified as dependent factors. Data analysis using a fuzzy Analytic Network Process (ANP) indicates the following weights for the main factors: value proposition, key activity, customers, model capabilities, distribution channel, key resources, customer relations, cost and revenue structure, challenges in applying the model, and key partners. Among the sub-factors, the sub-factor “intelligent production” ranks first. Exploiting the capacities of Industry 4.0 key technologies ranks second, the factor of expanding and strengthening process monitoring ranks third, firms active in hardware technologies of Industry 4.0 rank fourth, the possibility of creating fault-tolerant production processes ranks fifth, and, finally, developing the intelligentization dimensions of production processes in the automotive industry ranks sixth among 50 sub-factors, collectively accounting for nearly 30% of the total weight of sub-factors—highlighting the high importance of these sub-factors.
Type of Study: Research |
Subject:
Special
Received: 2025/10/29 | Accepted: 2025/12/17 | Published: 2025/12/22 | ePublished: 2025/12/22
Received: 2025/10/29 | Accepted: 2025/12/17 | Published: 2025/12/22 | ePublished: 2025/12/22
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