The Impact of Artificial Intelligence Applications on Cost Prediction Accuracy in Industrial Establishments: An Applied Study on an Egyptian Industrial Company (Elsewedy SEDCO, Elsewedy Electric Group)
DOI:
https://doi.org/10.56830/IJAMSSI202610Keywords:
Artificial Intelligence, Machine Learning, Deep Learning, Big Data Analytics, Cost Prediction AccuracyAbstract
Objective: This study aims to investigate the impact of Artificial Intelligence (AI) applications — represented by Machine Learning (ML), Deep Learning (DL), and Big Data Analytics (BDA) — on cost prediction accuracy in industrial establishments, focusing on two key dimensions: the accuracy of preliminary cost estimates and the speed of forecast preparation. Problem Statement: Industrial establishments frequently experience significant deviations between estimated and actual costs, coupled with slow cost-planning cycles, which weaken the quality of strategic decisions and elevate operational risks. This raises a fundamental question: Can the deployment of AI applications effectively address these challenges and enhance cost prediction precision? Methodology: The study adopts a descriptive-analytical approach. Primary data were collected through a purpose-built questionnaire distributed to a purposive sample of financial managers and specialists in a number of industrial establishments. Multiple statistical techniques were employed for data analysis and hypothesis testing. Key Findings: (a) Machine Learning demonstrated high effectiveness in constructing accurate predictive cost models from historical data, substantially reducing cost deviations. (b) Deep Learning exhibited a distinctive capacity to detect non-linear patterns in complex cost structures, improving the accuracy of multi-stage production cost estimates. (c) Big Data Analytics significantly reduced the time required to prepare cost forecasts and provided early proactive indicators of potential budget overruns. Recommendations: The study recommends that industrial establishments adopt AI applications within their cost-management systems, invest in improving the quality and completeness of their databases, and build human-capital competencies in using these advanced technologies.
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