The Role of Strategic AI Management in Improving Operational Forecasting and Increasing Supply Chain Efficiency in Industrial Companies: A Case Study of the National Cement Company and its Factories.

Abstract

This study aimed to examine the impact of strategic management of artificial intelligence on enhancing supply chain efficiency, as well as the mediating role of operational forecasting in this relationship, applied to The National Cement Company across its factories and administrative complex. To achieve the study's objectives and test its hypotheses, a descriptive-analytical approach was utilized, and a questionnaire was developed as the primary data collection tool from a purposive sample of (66) individuals comprising administrative leaders, engineering and technical staff, and heads of operational departments within the company. The data were processed and statistically analyzed using the Statistical Package for the Social Sciences (SPSS), relying on arithmetic means, standard deviations, simple and multiple regression analysis, Path Analysis, and the Sobel Test.

The study results revealed several main conclusions, most notably: the existence of a statistically significant impact at the significance level (α ≤ 0.05) of the strategic management of artificial intelligence on improving supply chain efficiency at The National Cement Company. It was also found that there is a statistically significant impact of the strategic management of artificial intelligence on improving the accuracy of operational forecasting across all its dimensions (demand forecasting, predictive maintenance, and capacity and production planning). In addition, path analysis and Sobel test results demonstrated that operational forecasting plays a partial mediating role in the relationship between the strategic management of artificial intelligence and supply chain efficiency, as predictive practices accounted for (53.63%) of the total impact of artificial intelligence on supply chain performance. Furthermore, the "inventory management efficiency" dimension ranked first in terms of importance among the dimensions of supply chain efficiency, with a mean score of (4.25 - Very High), while the "predictive equipment maintenance" dimension topped the operational forecasting dimensions with a mean score of (4.18), reflecting the utmost importance of monitoring the condition of cement kilns and production lines to prevent breakdowns and operational risks.

Based on these results, the study provided a set of actionable recommendations, most prominently: the necessity of expanding the company's investments in artificial intelligence infrastructure and integrating it with the Enterprise Resource Planning (ERP) system, a complete transition toward smart predictive maintenance systems supported by Internet of Things (IoT) sensors for all production lines and kilns, as well as establishing a unified digital operations center for logistical connection between raw material warehouses (clinker and gypsum), distribution outlets, and transport fleets to ensure greater flexibility and rapid responsiveness to market demand.