Investigating the role of artificial intelligence-based risk management in increasing the agility and capabilities of supply chain reengineering

Document Type : Original research

Authors

1 Associate Professor, Department of Business Management, Faculty of Social Sciences, Mohaghegh Ardabili University, Ardabil, Iran.

2 PhD Student in Marketing, Department of Business Management, Faculty of Social Sciences, Mohaghegh Ardabili University, Ardabil, Iran

Abstract

In a dynamic business environment, organizations use an agile supply chain as a key strategy to deal with volatility. Therefore, the agility of a knowledge-based company indicates the responsiveness of that company when facing internal and external changes, and agile knowledge-based companies have the ability to compete with other companies in providing services to the target market. Therefore, in current research, the impact of risk management based on artificial intelligence is being investigated. This study is practical in terms of purpose, and from the point of view of data collection, this study is in the field of descriptive survey research. The statistical population of the current research consists of employees of knowledge-based companies, 280 sample members were available and the questionnaires were completed. In this research, two library and field methods were used to collect data, and the variables investigated in the present study were measured through polling using "localized electronic questionnaire". Data was analyzed using spss software and partial least squares method and Smart PLS software were analyzed. Examining the results of this research showed that risk management based on artificial intelligence affects the agility and ability to reengineer the company's supply chain. Also, in the indirect relationship between the ability to reengineer the supply chain, the relationship between risk management based on artificial intelligence agility and the ability to reengineer the supply chain mediates the company.

Keywords

Main Subjects

References

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History

  • Receive Date: 22 December 2022
  • Revise Date: 05 February 2023
  • Accept Date: 22 February 2023

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