Developing a Systemic Model of Intelligent Industrial Control Using a Grounded Theory Approach

Mohammad  Najafi; Mohammad Ali Keramati; Hossein  Moinzad; Safiyeh  Mehri Nejad

Authors

Mohammad Najafi Department of Industrial Management, CT.C., Islamic Azad University, Tehran, Iran Author https://orcid.org/0009-0007-4777-6841
Mohammad Ali Keramati Department of Industrial Management, CT.C., Islamic Azad University, Tehran, Iran Author https://orcid.org/0000-0002-6339-6562
Hossein Moinzad Department of Industrial Management, CT.C., Islamic Azad University, Tehran, Iran Author https://orcid.org/0000-0003-2640-0427
Safiyeh Mehri Nejad Department of Financial Management, CT.C., Islamic Azad University, Tehran, Iran Author

Keywords:

control system, intelligent industrial control, grounded theory

Abstract

The present study aimed to develop a systemic model of intelligent industrial control in ore mines using a grounded theory approach. In terms of purpose, the study was applied, and in terms of methodology, it was qualitative. The research population consisted of academic experts, managers, and specialists in mining, industrial control, automation, information technology, and artificial intelligence. Using purposive and snowball sampling, 15 participants were selected until theoretical saturation was reached. The data were collected through semi-structured interviews and analyzed according to the systematic grounded theory approach of Strauss and Corbin through open, axial, and selective coding. During the analysis process, 180 initial codes were extracted and, following refinement, organized into 144 final codes, 24 subcategories, and six main categories. The findings indicated that the final model comprised six dimensions: causal conditions, the central phenomenon, contextual conditions, intervening conditions, strategies, and consequences. Within the causal conditions, equipment deterioration, inadequate data infrastructure, operational risks, and the need for intelligent decision-making were identified as the most important influencing factors. The central phenomenon was a data-mining-based intelligent industrial control system, which develops within a context of technological infrastructure, organizational readiness, and specialized human resources and is influenced by intervening conditions such as economic constraints, technical challenges, legal requirements, and human factors. Strategies such as implementing intelligent monitoring systems, applying data mining and artificial intelligence, developing predictive maintenance, and establishing data governance were also identified as the principal actions required to realize this system. The implementation of these strategies leads to consequences such as increased operational productivity, enhanced safety, reduced maintenance costs, improved equipment reliability, higher-quality decision-making, and the realization of smart mining. By integrating technological, managerial, organizational, and environmental dimensions, the proposed model provides a comprehensive framework for designing and implementing intelligent industrial control systems in ore mines. It can serve as a roadmap for digital transformation, productivity enhancement, and sustainability improvement in the mining industry for managers, policymakers, and researchers.

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Developing a Systemic Model of Intelligent Industrial Control Using a Grounded Theory Approach

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