Industrial IoT (IIoT) and MES Integration: Enhancing Data-Driven Decision Making

Abstract

Modern manufacturing is increasingly characterized by complex processes and the need for real-time, high-fidelity data to enable effective decision-making. A Manufacturing Execution System (MES) serves as a critical link between enterprise-level planning and shop-floor operations, providing functionalities such as production scheduling, electronic batch recording, and quality management. Nevertheless, traditional MES implementations encounter challenges related to data accuracy, integration complexity, and siloed information. The Industrial Internet of Things (IIoT) addresses these gaps by providing automated, sensor-based data generation and improved contextualization for the MES. This paper proposes a methodology for IIoT–MES integration, focusing on system architecture, data collection procedures, and advanced analytics. Results from pilot projects and literature indicate that integrating IIoT with MES significantly enhances process visibility, equipment maintenance, and review-by-exception workflows, ultimately enabling continuous improvement. Key benefits include fewer manual errors, quicker root-cause analysis, predictive maintenance capabilities, and improved overall equipment effectiveness (OEE). Limitations, such as infrastructure costs and data security concerns, are also highlighted. The paper concludes by outlining potential future directions, including advanced machine learning models and standardized frameworks for broad industry adoption.