The Role of Modern Technologies (Mixed-Integer Linear Programming, Big Data Analytics, and Artificial Intelligence) in Improving Resource Allocation, Cost Accounting Accuracy, and Production Scheduling in Iraqi Industrial Companies: An Informationalism Approach - Alameri , Ammar; Rezazade, Javad; Sepasi, Sahar (Business, Marketing, and Finance Open, 2027)
This study aims to examine the role of three modern technologies—mixed-integer linear programming, big data analytics, and artificial intelligence—in improving cost accounting calculations in Iraqi industrial companies by adopting informationalism as a key strategy. The research employed an applied design based on mathematical modeling and the analysis of real-world data obtained from four industrial units—the Kufa Cement Plant, the Najaf Cement Plant, the Baghdad Soft Drinks Company, and the Iraqi Carpet Company—during the 2020–2024 period. The findings indicate that applying the mixed-integer linear programming model reduced unit costs by between 4% and 88% and optimized resource allocation. Big data analytics reduced cost fluctuations by up to 82%, improved forecasting accuracy by up to 86%, and contributed to cumulative profit growth of up to 64%. Artificial intelligence also reduced unplanned downtime by between 4% and 13%, resulting in a reduction of up to 10.6% in unit costs. The results demonstrate that the effects of these technologies differed across industries. In capital-intensive industries, particularly cement production, their effects were more evident in cost reduction, whereas in labor-intensive industries, particularly carpet manufacturing, their effects were more pronounced in reducing cost variability and improving measurement accuracy. Overall, the systematic integration of these three technologies within an informationalism framework enhances cost accounting accuracy, strengthens the efficiency of managerial decision-making, and is recommended as a practical strategy for industrial organizations in developing economies.