Developing a Structural Model of Supply Chain Management at Iran Khodro Based on Artificial Intelligence Capabilities with Emphasis on Resilience and Bullwhip Effect Reduction

Marzban Abbasabadi, Ali; Fegh-hi Farahmand, Nasser; Mahmoudzadeh, Morteza

doi:10.22075/svcm.2026.40140.1081

Developing a Structural Model of Supply Chain Management at Iran Khodro Based on Artificial Intelligence Capabilities with Emphasis on Resilience and Bullwhip Effect Reduction

Document Type : Original Article

Authors

Ali Marzban Abbasabadi ¹

Nasser Fegh-hi Farahmand ²

Morteza mahmoudzadeh ³

¹ Ph.D. Candidate in Industrial Management, Ta. C., Islamic Azad University, Tabriz, Iran.

² Associate Prof. Department of Management, Ta. C., Islamic Azad University, Tabriz, Iran.

³ Assistant Prof. Department of Management, Ta. C., Islamic Azad University, Tabriz, Iran.

10.22075/svcm.2026.40140.1081

Abstract

The rapid growth of businesses has increased the complexity and vulnerability of supply chains, highlighting the need to strengthen resilience strategies and mitigate the bullwhip effect. The present study aims to develop a structural model for managing the supply chain of Iran Khodro and to explain the role of artificial intelligence in enhancing resilience and controlling the bullwhip effect. This research is applied in purpose and descriptive–library based in terms of data collection. The statistical population includes academic and organizational experts at Iran Khodro Company. In the qualitative phase, using snowball sampling, 15 experts were selected, and data were collected through in-depth interviews and questionnaires. Qualitative analysis was conducted using thematic analysis with the help of MAXQDA software. Furthermore, Interpretive Structural Modeling, supported by MATLAB and MICMAC analysis, was employed to classify and examine the relationships among the identified dimensions. Findings revealed six key dimensions influencing supply chain management in Iran Khodro. At the highest level, supplier development and resilience, supply chain integration and coordination, and resource and demand management were positioned as driving factors with the strongest influence on the system. The intermediate level consisted of the use of artificial intelligence, acting as a connecting link between managerial drivers and final outcomes. At the first level, supply chain resilience and bullwhip effect reduction emerged as dependent variables and the ultimate outcomes of the model. This structure demonstrates that achieving resilience and mitigating the bullwhip effect require strengthening managerial drivers and strategically leveraging artificial intelligence capabilities.

Keywords

Supply Chain

Resilience

Bullwhip Effect

Artificial Intelligence

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