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RFID Brings Visibility, Analytics to Conditions and Status of Electronics Under Assembly

DAIHEN Corp. is already saving approximately 1,800 hours annually with an IoT-based system that employs RFID and sensor data to understand conditions and work-in-progress for each product being built.
By Claire Swedberg

The solution consists of wireless sensors deployed in specific areas, as well as RFID tags affixed to products. DAIHEN has declined to indicate what kind of RFID technology it is using or the technology provider it utilizes. The sensors capture environmental conditions and forward that data to a server, while the tags transmit their own unique ID numbers, linked to the products to which they are attached, and this information is then paired with the sensor data for each location. As the sensor and RFID data is captured, FogHorn's Lightning Edge ML provides complex machine learning, says Yuta Endo, FogHorn's VP, general manager and head of APAC operations.

DAIHEN installed an infrastructure of RFID readers to capture the location of WIP. RFID tags are applied to each product being assembled, and the unique ID number on each tag transmits data to readers to provide a WIP record of every item, based on its location within the assembly process.

The FogHorn Lightning solution is unique, the company reports, in that it manages data "on the edge," identifies the content of value and then forwards only that useable data to the back-end software. This contrasts with traditional IoT solutions, Endo explains, in which big data goes to a server, where the software must then extract and analyze the valuable information. The FogHorn Lightning system is designed to reduce the level down to only that which is of greatest value. "If you process data at the edge," he says, "it can immediately be used for operations." In fact, analytics can be available in near-real time.

The real-time delivery of data is important not only for planning, but also for sharing information between assembly stations or teams. "All of this information is visualized and shared among teams" as it is collected, Yamano says, "so all teams can be in synch without having inaccurate, verbal-based communication."

The project launched in April 2017, and the FogHorn condition-monitoring feature was fully deployed last May. Two months later, the RFID component for WIP was taken live. Initially, at the first plant, the company reports that it already sees increased accuracy of data, as well as labor reduction when it comes to data input. "Since implementation, DAIHEN expects to save an estimated 1,800 hours in manual logging processes" at the first facility, Yamano says. "DAIHEN can now make faster and more accurate business decisions with better intelligence of the factory manufacturing operation, with detailed, contextualized manufacturing process data."

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