Using IoT and ML in ERP: A method for optimising decisions (#820)

Authors

Mamani De La Cruz, Alexander Miguel

Urbiola Huaylla, José Alfredo

Cruz Arpi, Fredy Nelio

Abstract

Today, industries face major challenges in resource management and decision making. The integration of emerging technologies, such as the Internet of Things (IoT) and Machine Learning (ML), into Enterprise Resource Planning (ERP) systems offers an opportunity to improve real-time data collection and analysis. However, this integration faces challenges related to technical compatibility, handling large volumes of data and data protection. This study conducts a systematic literature review to analyse how the combination of IoT and ML optimises ERP functionality in industries. The PRISMA method was used to select relevant articles published between 2019 and 2024 in recognised databases. The findings indicate that the adoption of these technologies facilitates process automation, failure prediction and improved strategic decisions. However, significant challenges are recognised, such as lack of advanced infrastructure, high implementation costs and organisational resistance to change. It is concluded that the combination of IoT and ML in ERP systems represents a significant advance in operational efficiency and business competitiveness. However, its success will depend on strategies that ensure scalability, interoperability and cybersecurity. This analysis lays the groundwork for future research and practical applications in the digitisation of the industrial sector.