The role of machine learning in improving resource consumption monitoring: A survey study

Abstract

This research aims to provide a theoretical and applied framework for employing machine learning algorithms in management accounting and costing systems.

The research focuses on the importance of improving resource consumption monitoring, accurately tracking cost behavior, identifying unutilized energy, and supporting decision-making through historical data analysis to enhance the accuracy of production reports.

To achieve the research objective, a descriptive approach was adopted, drawing on available studies. A field study was also used, using a questionnaire to collect data from the research sample (the Electrical Cables and Wires Factory - Ur General Company).

The research also reached a number of conclusions, most notably that employing machine learning algorithms contributes to improving the prediction of quantitative resource consumption, which helps detect deviations and identify their potential causes, and enhances the accuracy and comprehensiveness of production reports.

The research concluded with a set of recommendations, most notably the need to establish an integrated data management system that includes operational data processing to provide real-time solutions and alternatives that contribute to supporting decision-making related to rationalizing resource consumption.