Current Volume 9
Abstract
Enterprise resource planning (ERP) systems serve as the backbone of modern organizational operations, yet traditional costing mechanisms within these systems mostly suffer from delays, manual errors, and limited visibility across value chains. This paper proposes solutions involving the integration of artificial intelligence (AI) and machine learning (ML) technologies in ERP environments so as to increase actual costing accuracy, responsiveness, and real-time decision-making. With the automation of data ingestion, anomaly detection, and cost allocation functions, the AI-enabled ERP systems promise to improve cost transparency and operational efficiency manifold. This study proposes a hybrid methodology that combines supervised learning algorithms with ERP data pipelines running in real-time for continuous cost analysis. A prototype-based implementation using Python-based machine learning models proves to be practically feasible for this purpose. From the comparative analysis with conventional costing models, it could be seen that there are significant gains in processing speed and predictive accuracy. The results indicate that AI-assisted actual costing has the potential to transform cost management by presenting information that is precise, timely, and strategic for decision-making in the manufacturing and service industries. The contribution of this research is an original framework to introduce real-time costing intelligence toward agile and data-driven financial planning and control.
Keywords
AI-enhanced ERP, actual costing, real-time cost transparency, machine learning, cost optimization, enterprise resource planning, predictive costing, automated accounting, data-driven finance, real-time analytics.
Citations
IRE Journals:
Abhishek P. Sanakal
"AI-Enhanced Actual Costing in ERP: A Path Toward Real-Time Cost Transparency" Iconic Research And Engineering Journals Volume 5 Issue 10 2022 Page 365-372
IEEE:
Abhishek P. Sanakal
"AI-Enhanced Actual Costing in ERP: A Path Toward Real-Time Cost Transparency" Iconic Research And Engineering Journals, 5(10)
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