Remote Monitoring and Visualization of Inventory Levels

Examensarbete för masterexamen
Kindlundh, Anna
Larsson, Sanne
Industry 4.0 and mass customization sets high requirements on flexibility of inven tory management. When these requirements are unfulfilled, material shortages can occur, which can result in production stoppages. By monitoring inventory levels, the risk of such shortages occurring can be reduced. The physical inventory must, how ever, correspond to the monitored, digital representation of the inventory. Existing solutions for avoiding inaccuracies of inventory levels are limited, and technologies for register inventory levels of boxes stored in flow racks were therefore explored in this thesis. Further, the aim was to integrate one, or several, of the explored concepts in a real-world system and visualize the data into a digital twin of the system to enable remote monitoring. The methodology was based on a theoretical framework and a mapping of technologies, followed by a concept creation phase and integration and visualization of the concepts in a digital twin. Throughout the project, a case study was used to identifying concept requirements and evaluating the concepts on a real-world system. Technologies found suitable for register inventory levels and enable remote monitoring are barcodes, Radio Frequency Identification (RFID), weight sensors, distance sensors, and contact sensors. The two concepts that fulfill the requirements from the case study are based on a weight sensor, respectively a distance sensor. In the concepts, data from the sensors are sent by WiFi to an Internet of Things (IoT) platform and then integrated and visualized in a digital twin with a low level of data integration. To conclude, there are ways to monitor inventory levels remotely in a flow rack. An ultrasonic distance sensor is proved to satisfyingly measure the correct number of boxes stored in a flow rack. However, the concept requires well-followed procedures to minimize the risk of human errors and thereby avoid inaccuracies.
Inventory Management, Inventory Inaccuracies, Remote Monitoring,, Sensors, Case Study, Digital Twin, Industry 4.0, Visualization
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