IOT-BASED REAL TIME QUALITY MONITORING IN THE STITCHING DEPARTMENT OF SHOE MANUFACTURING

Abstract

This research proposal investigates the application and impact of the Internet of Things (IoT) a system of interconnected devices and sensors capable of real-time data collection, communication, and analysis on improving quality control processes in the shoe manufacturing industry. IoT technologies such as Radio Frequency Identification (RFID) tags, force sensors, 3D vision systems, thermal cameras, and flex sensors are strategically deployed at critical stages of production, including raw material quality tracking, stitching and assembly line monitoring, sole bonding, and final product inspection. By integrating these technologies, the study enables continuous monitoring, real-time defect detection, and predictive maintenance, thereby enhancing product quality, reducing waste, and improving operational efficiency. The research methodology employs a mixed-methods design that combines quantitative data from IoT sensor networks with qualitative data gathered through interviews and direct observations of factory personnel. Data analytics, including machine learning techniques, interpret sensor data to detect anomalies, predict machine failures, and support data-driven decision-making within the manufacturing process. This study examines significant barriers to IoT integration, such as the substantial upfront costs of deploying advanced sensor networks and supporting infrastructure, challenges in integrating new IoT systems with legacy manufacturing equipment, and critical concerns about data security and privacy in increasingly connected production environments. This study contributes valuable insights to both academic scholarship and industrial practice, facilitating the modernization of shoe manufacturing quality control and positioning the industry for future technological advancements.