Designing a Smart Manufacturing Model in Iran’s Automotive Industry Using the Internet of Things and Artificial Intelligence: A Grounded Theory Approach

Abstract

This study was conducted with the aim of designing a qualitative model of smart manufacturing in Iran’s automotive industry. This qualitative study was carried out using the grounded theory method based on the systematic approach of Anselm Strauss and Juliet Corbin (2015). Data were collected through in-depth semi-structured interviews with 12 experts, including production managers, information technology specialists, and university faculty members. Sampling was conducted using purposive and theoretical methods with a snowball strategy until theoretical saturation was achieved. Data analysis was performed using MAXQDA 2020 software in three stages: open coding, axial coding, and selective coding. To ensure the quality of the study, the criteria proposed by Yvonna Lincoln and Egon Guba (1985) were employed, and inter-coder reliability was calculated using Cohen’s kappa coefficient, which was found to be 0.86. The findings led to the identification of 313 open codes, 176 concepts, and 36 main categories within the paradigm model framework. The core phenomenon (smart manufacturing) consisted of six components: real-time data integration, predictive analytics and maintenance, autonomous robotics, mass customization, supply chain optimization, and product quality enhancement. Causal conditions (5 categories), contextual conditions (6 categories), intervening conditions (7 categories), strategies (6 categories), and consequences (6 categories) were identified. The proposed model provides an integrated and localized framework for managers and policymakers in Iran’s automotive industry and can serve as a roadmap for digital transformation in this sector. Successful implementation of this model requires special attention to technological infrastructure, the development of specialized human resources, and the alignment of supportive policies. Quantitative validation of the model using structural equation modeling is recommended for future studies.