Using a flexible IoT architecture and sequential ai model to recognize and predict the production activities in the labor-intensive manufacturing site

Abstract

Under the pressures of global market uncertainty and rapid production changes, the la-bor-intensive industries demand instant manufacturing site information and accurate production forecasting. This research applies sensor modules with noise reduction, information abstracting, and wireless transmission functions to form a flexible internet of things (IoT) architecture for ac-quiring field information. Moreover, AI models are used to reveal human activities and predict the output of a group of workstations. The IoT architecture has been implemented in the actual shoe making site. Although there is a 5% missing data issue due to network transmission, neural network models can successfully convert the IoT data to machine utilization. By analyzing the field data, the actual collaboration among the worker team can be revealed. Furthermore, a sequential AI model is applied to learn to capture the characteristics of the team working. This AI model only requires training by 15 min of IoT data, then it can predict the current and next few days’ productions within 10% error. This research confirms that implementing the IoT architecture and applying the AI model enables instant manufacturing monitoring of labor-intensive manufacturing sites and accurate production forecasting.