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Let us demystify the Internet of Things and help you manage
your networks and grow your business along the way.
[2]Hall, K. D., Guo, J., Dore, M., & Chow, C. C. 2009. The progressive increase of food waste in America and its environmental impact. PLoS One 4(11): e7940.
[3] FAO. 2011. Global food losses and food waste – extent, causes and prevention. Rome: United Nations Food and Agriculture Organization.
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Billions of dollars are wasted in the food-chain, from production to supply. Roughly 40% of the United States food supply is never eaten due to lack of coordination within the supply chain .
The AXON Platform reduces waste and provides better insight and real-time feedback from critical actors in the supply chain.
The majority of the costs for wind turbines are triggered by unplanned downtime. AXON Predict operates in real-time to help these critical systems reduce unplanned downtime.
We constantly monitor for anomalies and signs of failure using advanced techniques based on machine learning at the edge.
[1] Peter Wright, ABB Limited, Predictive Maintenance Gains Ground in Wind power Industry, 2014
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Predictive maintenance allows you to run edge analytics on a vehicle to reduce data cost, provide real-time feedback and reduce unplanned downtime .
Fleet managers are turning to predictive analytics to stay on top of maintenance and mitigate part failures before they happen. AXON Predict helps you manage large amounts of data generated by vehicles and sensors.
AXON Predict can help beyond the track-and-trace management for containers.
We can help securely connect and analyze the content of each container to optimize
the supply chain in the transportation industry.
This is a smarter way smarter way to improve operational
efficiency through anomaly detection and real-time alerts.
Turbine
Maintenance
Container
tracking
Fleet
Management
Silo Sensing
Container
tracking
Silo
Sensing
Fleet
Management
Turbine
Maintenance
Silo
Sensing
Turbine
Maintenance
Container
tracking
Fleet
Management
Silo
Sensing
Turbine
Maintenance
Fleet
Management
Container
tracking
AXON Predict Sample
Use Cases
Device/
Sensor
Cloud Infrastructure
Network
Switch
On-Premise
Server
IoT Gateway
Orchestrate analytics across all devices though our
Cloud API in any public cloud service
Leverage the open Internet
Run the entire analytics infrastructure
in a private cloud
Leverage additional processing power to analyze
all connected devices
Unique engine analyzes, identifies and takes action on the device as well
as runs arbitrary self-contained analytics at the edge
Cloud
Edge
Latency in the cloud is too high for real-time.
*http://www-03.ibm.com/press/us/en/pressrelease/46453.wss
90% of edge data never makes
it to the cloud – important data
is simply not available*.
Batch processing
Real-time processing
Roll over on the icons to find out more.
It is expensive to transmit and store large amounts of raw data.
Intelligence is required at the edge regardless of the device.
Chain of Command for Edge Analytics
Why Analytics at the Edge
TM
®
Greenwave’s AXON Predict brings real-time visual Industrial IoT analytics to the edge, while other solutions get data “from” the edge and push it to the cloud. As part of the AXON Platform family, AXON Predict performs analytics “at” the edge, on the device, in real-time.
Alive at
the Edge
