1. Introduction:
The emergence of cognitive IOT and edge native intelligence solutions presents a unique opportunity for companies within the manufacturing industry to improve and gain value in nearly every facet of their business. Between realtime data analytics, predictive automated maintenance, and improved response time, our patented edge native solution yields a number of benefits that will help to revolutionize the industrial manufacturing industry, regardless of the company’s scale.
2. Key Benefits:
A. Realtime Data Analytics
Historically, those working in the manufacturing industry had to rely on incomplete, out-of-date, and manually gathered data in order to monitor their operational performance. With the power of Ranial’s real time data analytics offerings, companies will have access to one centralized source of real time operating information and consistent KPI measurements. In turn, companies will receive a reliable and unbiased overview of performance that factory managers can use to identify everything from macroscopic factory-level performance information to more granular asset-based metrics. Subsequent corrective actions can be made with full confidence of the root cause of the performance issues.
B. Predictive Automated Maintenance
Ranial’s patented CognitIoT™ solution enables the deployment of AI learning models directly at the edge. Traditionally, those running machine learning models on performance information would need to send data back into their cloud management platform, an expensive process and one that introduces a latency into any action that the model might recommend. Ranial’s solution enables our models to be trained at the edge in real time and, based on historical performance data, predict system and machine failures before they happen, taking corrective measures when appropriate. With our solution in place, factoryies will minimize costly operational downtime, ensure optimal system-level output, and eliminate time-intensive human corrective interventions with machines.
3. Potential Use Cases:
A. Automotive Manufacturing Plant
Automotive Manufacturing Plant Managers making use of Ranial’s suite of offerings will have a variety of advantages that will enable them to ensure their plant is performing optimally and that any inefficiencies in the assembly line are eliminated.
- After logging in to Ranials client home page, a plant manager will have access to an Asset Dashboard that offers a comprehensive, real time overview of the performance of every asset under their control and any inefficiencies that are occurring.
- Plant manager’s will have the ability to set operating parameters for every asset under their control. For example, if a plant manager wanted to ensure that an engine machining station doesn’t exceed a certain temperature, that “rule” can be issued from the Ranial client home page. The plant manager can also specify whether they’d like to receive an SMS, E-mail, or application native alert if the machine falls outside of its operating parameters. Alternatively, the plant manager would also be able to specify an action to be automatically taken by the machine, or an accompanying one, should this scenario play out (i.e. stop engine machining station for 30 seconds if the specified temperature is exceeded).
B. Food Packaging Facility
Food Packaging Facility Managers making use of Ranial’s suite of offerings will have a variety of advantages that will enable them to ensure their plant is performing optimally and that any inefficiencies in the assembly line are eliminated.
- After logging in to Ranials client home page, a facility manager will have access to an Asset Dashboard that offers a comprehensive, real time overview of the performance of every asset under their control and any inefficiencies that are occurring.
- Facility manager’s will see significant productivity and yield improvements when making use of Ranial’s predictive maintenance capabilities. With AI models deployed and continuously learning at the edge, our technology will be able to detect patterns in a machine’s performance and take autonomous corrective actions. For example, if a conveyor belt was given a series of data readings that our models detected as being a likely indicator of future performance degradation or failure, our system will automatically issue a corrective action, or alternatively, send a notification to the facility manager.