Japanese firm embraces edge computing after experiment

The Internet of Things (IoT) market is estimated to reach 75 billion devices worldwide by 2025. With the big increase in connected devices, it’s becoming more critical than ever for manufacturers to leverage new technologies, such as edge computing, to gather, process and manage IoT data.

Edge computing is growing in adoption and popularity for IoT deployments to manage the data generated by assets on the ground. Edge technologies bring intelligence to raw machine data through AI and machine learning, resulting in improved operational insights.

Whether it is building CNC machines or assembling motherboards, real-time alerts to operators or machines through edge-enabled solutions result in substantial cost savings and new revenue streams.

The edge is the next big lever for digital transformation: The computational demands of emerging applications have increased significantly through the rise of complex data streams, such as streaming video and audio, acoustic or vibration data. Transporting the data sets between edge and cloud defeats the purpose of low-level latency applications—and the cost of transport and storage is prohibitive. Lastly, industrial enterprises would prefer to retain their data in-house.

Edge computing equips factory operations with:

• Operational intelligence: Every component of the value-chain generates an increasing amount of data, which advanced analytics can quickly turn into actionable insights instantly communicated across the relevant parts of the system. Edge computing transforms machine data into real-time actionable insight related to production efficiency and quality metrics that can be used by plant managers to reduce unplanned downtime and increase machine utilization.
• Condition-based monitoring: With edge computing, industrial organizations can supervise asset health more effectively. By monitoring conditions in real-time, they can save on expensive asset downtime by enabling early warning indicators, which automatically trigger inventory refreshes. Moreover, asset manufacturers can explore ‘X as a Service’ offerings—where condition monitoring applications are pre-bundled with existing or new resources.
• Worker safety: Adopting or refining predictive maintenance models lets operations personnel get ahead of potentially dangerous or costly issues with machinery. Edge solutions ingest streaming data from machine sensors and alert operations staff before damage to the machine or personnel occurs. Also, a signal can be sent to the central system to automatically shut down a machine to prevent damage.

Daihen faced a challenge to make its digital transformation a reality by modernizing factory operations. Leaders at the Japanese industrial electronics firm’s Osaka factory sought faster ways to analyze sensor data from devices measuring material conditions and reduce the need for manual monitoring.

Daihen deployed edge computing software in collaboration with Energia Communications to automate monitoring of its industrial transformers and harness the power of its industrial data to improve efficiency in factory operations. Daihen leveraged an RFID infrastructure to track productivity and installed condition-monitoring sensors.

Within six months of deployment, the Osaka plant achieved 70% coverage with the RFID-based tracking system. Daihen so far has saved about 1,800 hours per year in the manual logging process in addition to reducing levels of inaccurate planning within the manufacturing process. It projects savings of 5,000 hours/year in the Osaka plant.

A nationwide rollout is now set.