Abstract
Under the pressures of global market uncertainty and rapid production changes, the labor-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 acquiring 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.
Highlights
Most labor-intensive manufacturing focuses on the mass production of large varieties of types, colors, and sizes, complicated supply chains, and different manufacturing processes
This paper proposes a flexible internet of things (IoT) architecture for field data collection and cloud-based data storage for the labor-intensive industry
Models for the machine/labor utilization and production prediction are established. Both the IoT architecture and AI modeling techniques are realized in the application field of a labor-intensive footwear manufacturing site
Summary
Most labor-intensive manufacturing focuses on the mass production of large varieties of types, colors, and sizes, complicated supply chains, and different manufacturing processes. In the smart labor-intensive factory paradigm proposed by Kim and Moon [15], the manufacturing information collectors, including the RFID on the products and the digital readers, are applied. These time-dependent data collected from the IoT system should be further analyzed and applied. This paper proposes a flexible IoT architecture for field data collection and cloud-based data storage for the labor-intensive industry. Applying the IoT data, two AI models for the machine/labor utilization and production prediction are established Both the IoT architecture and AI modeling techniques are realized in the application field of a labor-intensive footwear manufacturing site. The conclusion of this paper is given in the last section
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