Machine Learning for Predicting Complex Solutions in Production, Cost, and Financial Performance of Companies

Abstract

In recent years, competition among manufacturing companies regarding the provision of goods and products required by customers has been increasing. At present, customers tend to receive their required goods in the shortest possible time and at the lowest possible cost. In this regard, companies that are able to reduce their costs and, at the same time, deliver goods to customers on time and without delay, have a greater ability to attract more customers. Therefore, it can be stated that production planning and scheduling has gained increasing importance compared to the past, and today it plays a significant role in achieving competitive advantage for companies. Accordingly, the present study seeks to utilize machine learning to predict complex solutions. From the perspective of purpose, this research is applied, and in terms of methodology, it is correlational. Regarding data collection, this study is descriptive–analytical and relies on library studies for gathering information. Descriptive research is used to examine current conditions for better understanding in order to support the decision-making process. The statistical population of the present study consists of the Mobarakeh Steel Company of Isfahan. Considering that the research data is quantitative, the data was extracted from existing documents and records. As a result, the sample under study consists of data related to production, cost, and financial performance during the years 2016–2020. The sampling method in this research is purposive. To collect information, both library and field methods were used. Library studies were employed as a foundation for developing the theoretical framework of the research, and the field method was used to obtain data from Mobarakeh Steel Company.