اولويت بندي تاثير تكنولوژي هاي هوش مصنوعي بر سيستم هاي مديريت منابع سازماني (ERP)

The business lan‎dscape in recent years has undergone fundamental transformations under the influence of rapid technological advancements an‎d the expansion of big data. Organizations, in order to maintain competitiveness an‎d alignment in dynamic an‎d international environments, are compelled to modernize their technological infrastructures an‎d adopt digital innovation across all dimensions of their operations. In this context, Enterprise Resource Planning (ERP) systems, by integrating dispersed processes an‎d data into a shared database, create a systematic framework for organizing an‎d stan‎dardizing business processes an‎d achieving competitive advantage. Despite the key role of ERP, these systems still face limitations; including their reactive an‎d static nature, dependence on manual processes, an‎d lack of predictive capability, which reduces their efficiency in supporting strategic decision-making an‎d digital transformation. Therefore, the evolution of the ERP ecosystem is essential to respond to changing business needs an‎d enhance organizational agility. Emerging technologies, particularly artificial intelligence, have provided new opportunities to overcome the limitations of traditional ERP systems an‎d move toward intelligent systems. Artificial intelligence, with a set of advanced technologies including machine learning, deep learning, natural language processing, neural networks, computer vision, an‎d fuzzy logic, can address many of these limitations. The integration of these technologies with complementary tools such as Robotic Process Automation (RPA) an‎d predictive analytics increases the efficiency of organizational processes an‎d paves the way for transformation in management an‎d operations. In this research, the prioritization of artificial intelligence technologies based on their impacts on ERP systems has been conducted within an industrial context, with a focus on manufacturing environments. To achieve reliable results, in the first stage, after identifying the impacts, the impacts were weighted using the eigenvector method, an‎d then the prioritization of technologies an‎d capabilities was carried out using TOPSIS an‎d PROMETHEE techniques. Among the identified impacts of artificial intelligence are process automation, user interface an‎d support, decision support, data integration, an‎d risk management. The findings of the research showed that machine learning, natural language processing, an‎d predictive analytics are the most important artificial intelligence technologies in improving the performance of ERP systems. Additionally, the significant distance of deep learning an‎d Robotic Process Automation from the decision axis an‎d their orientation contrary to some key criteria in the GAIA plane analysis indicate the complexity of implementation, higher risk, an‎d their incomplete alignment with the current requirements of ERP systems; therefore, they are ranked lower compared to other technologies. In other words, machine learning an‎d predictive analytics, as the main engines of intelligent decision-making, alongside natural language processing for enhancing interaction an‎d knowledge management, create the greatest added value. In contrast, deep learning an‎d RPA play complementary roles that are mainly applied in specialized an‎d operational domains. As a result, this prioritization, in addition to revealing the extent of the impact of each technology on ERP functions, outlines the path for the gradual evolution of these systems toward intelligentization, an‎d the integration of these technologies can transform ERP from a purely managerial system into a transformative tool that not only increases organizational efficiency an‎d productivity, but also significantly enhances the competitiveness of organizations in dynamic an‎d changing environments.

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