A Vendor-Managed Inventory Mechanism Based on SCADA of Internet of Things Framework

In recent years, with the rise of the Internet of Things (IoT) and artificial intelligence (AI), intelligent applications in various fields, such as intelligent manufacturing, have been prioritized. The most important issue in intelligent manufacturing is to maintain a high utilization rate of production. On the one hand, for maintaining high utilization, the production line must have enough materials at any time; on the other hand, too many materials in stock would greatly increase the operating cost of the factory. Therefore, maintaining sufficient inventory while avoiding excessive inventory is an important key issue in intelligent manufacturing. After the factory receives the order, it would issue the manufacturing order to the production line for manufacturing. The capacities of different production lines are different. If the Supervisory Control And Data Acquisition (SCADA) system based on the IoT framework can be used to monitor the capacity of each production line, in addition to estimating the capacity, the usage of key materials can also be accurately estimated through AI; when the quantity of key materials is below the safety stock, the manufacturer can actively notify the supplier and request for replenishment. This is a Customer-to-Business (C2B) safety stock management model (i.e., the vendor-managed inventory, VMI), which combines AI and IoT. In particular, in the case of consumer electronics, because their life cycles are short and they are vulnerable to market fluctuations, the manufacturer must adjust the production capacity. This study will propose to construct a SCADA system based on the IoT, including the capacity of the production line, materials inventory, and downstream order requirements, and use the Artificial Neural Network (ANN) to accurately predict inventory requirements. In this study, through the factory, a SCADA system based on AI and IoT will be constructed to monitor the factory's manufacturing capacity and predict the product sales of downstream manufacturers, for the purpose of facilitating the analysis and decision-making of safety stock. In addition to effectively reducing the inventory level, in essence, the purpose of this study is to enhance the competitiveness of the overall production and sales ecosystem, and to achieve the goal of digital transformation of manufacturing with AI and IoT.