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Four RFID + Edge Computing Use Cases

These are the four RFID Use Cases that use Edge Computing to significantly increase productivity, gain actionable insights from data lakes, and optimize existing processes.  By processing data at the edge, these four use cases are able to enhance their business through edge analytics and machine learning.

Four RFID + Edge Computing Use Cases: Four RFID Use Cases Improved by Edge Computing | Swim Inc.

RFID technologies have been used by industry for almost 50 years, but recently the falling costs of RFID tags and innovations in IOT technologies have seen RFID adoption skyrocket. However, asset tracking systems (RFID, RTLS, bar code) create a significant volume of data, much of which is either redundant or irrelevant. ERP and other business systems often cannot handle the high volumes of data, leading to delays in data processing or costly downtime while ERP and business systems catch up. As more industries continue to implement RFID solutions, edge computing will play an increasingly important role in improving operations and maximizing value from the data created by asset tracking systems. Here are the four RFID Use Cases Improved by Edge Computing:

1. Predictive equipment maintenance

Predictive maintenance is a strategy for decreasing equipment downtime using IOT technologies like machine learning to predict maintenance needs for industrial equipment. According to Deloitte, unplanned downtime is of major concern to organizations, and recent studies have shown that unplanned downtime costs industrial manufacturers $50 billion each year. Furthermore, Deloitte researchers found that poor maintenance strategies can reduce a plant’s overall productive capacity between 5 and 20 percent.

Edge computing, empowered by Machine Learning (ML) algorithms, can use information about past equipment failures, maintenance schedules, and other data to predict maintenance needs and ensure consistent up-time for industrial systems.

2. Receiving and vendor management

Warehouses and manufacturing facilities may receive thousands (or millions) of shipments over the course of a workday. Having business context available at the edge means that RFID readers at docks, bays, and other locations of asset transfer are “aware” of which assets should be within proximity at any time. Having this awareness can greatly reduce accuracy of asset movement and decreases time spent verifying shipments.

In fact, according to RFID solutions provider Cybra, manufacturers & distributors who implement RFID technology into their supply chain see an 90% improvement in receiving time. These efficiency gains can be further extended with the use of ML technologies to improve the accuracy of automated systems. By having context from delivery schedules, vendor manifests, and parts catalogs available at the edge, operators can be alerted immediately if an asset received is incorrect, missing, or in the wrong location.

3. Tool and equipment tracking

Many businesses rely on RFID technologies to keep track of the tools they use during industrial processes. Industries like construction, manufacturing, and warehousing/logistics must keep track of thousands of tools, many of which travel with workers or other equipment during the course of a day. RFID technologies enable equipment operators to quickly locate tools, decreasing operational costs by saving significant time and employee effort.

Edge computing can compound the efficiency gains of RFID-enabled tool tracking systems. With the real-time and location advantages of edge computing, RFID systems can proactively notify workers when tools are stored in the wrong location, or if a policy indicates a tool has moved beyond a set geo-fence. Additionally, RFID tool tracking systems enabled by edge computing architectures can also track employees, so managers always know which employees have a given piece of equipment at any time.

4. Inventory management

One of the oldest and most widely adopted uses of RFID technology is for inventory management. The benefits for RFID adoption in inventory use cases is clear. A Motorola study has found that business that implement an RFID inventory system achieve a 50% reduction of out-of-stock items, and 18% reduction in inventory shrinkage. Furthermore, a study by Auburn University found that business who invest in RFID systems increases inventory accuracy, from 65 percent to more than 95 percent. These efficiency gains are even more impressive when you consider the amount of time saved by eliminating manual counting and inventory reconciliation.

Edge computing architectures ensure that the benefits of RFID investments are realized as issues occur, saving valuable seconds in time-critical environments. Just as RFID systems automate inventory reconciliation, RFID systems that perform edge analytics can continuously compare the flow of inventory with inventory logs, schedules, and manifests. Inventory systems can alert operators instantly if there is a count mismatch, or if inventory is being consumed faster than scheduled. This real-time information can ensure that inventory counts are accurate, and help to eliminate inventory shortages based on errant inventory counts.

Learn More

Learn how Swim ESP™ for Logistics can empower RFID applications with Edge Analytics, to optimize and alleviate your RFID/RTLS data overload. Swim ESP can help transform and manage your RFID data using processing data at the edge. Transform your data lakes into actionable insights with Machine Learning

*Sources: Deloitte, Motorola, Auburn University, Cybra

Posted on Sep 1, 2017 11:10:52 AM by Swim Team in Machine Learning, in Swim Software, in Industrial IOT, in Edge Analytics, in Manufacturing, in Asset Tracking, in Asset Management