Industrial IoT Platform

IIoT Solutions to Truly Maximize on your Data

Machine-to-machine connectivity for enhanced business intelligence and process improvements. As an experienced Industrial IoT company, we put your data to work for you!

Connecting many systems, databases, equipment and machines benefits manufacturing operations. Use the power of the IIoT to increase productivity.
Connected equipment helps improve data integrity, measure efficiency and identify problems. With Radley Industrial IoT solutions, almost anything can be a connected device!
Industrial Internet of Things companies

Communication & Visibility


  • PLCs
  • RFID
  • Automated Retrieval Systems
  • Scales, Palletizers, Transport Sleds, Tanks
  • Automated Handling Equipment
  • Proximity Sensors
  • Outside Databases: Quality, Maintenance and more
As an experienced Industrial IoT company, Radley brings the IIoT together to help you:
  • Calculate key performance indicators from your shop floor in real-time
  • Track machine and user activities and events without the complexity of multiple systems
  • Cut waste and decrease downtime for a more efficient manufacturing facility
  • View OEE and OLE metrics from the dashboard—without the need for manual calculations

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What’s the Use of the IIoT?

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Unlike other Industrial IoT companies, Radley puts 40 years of experience to work for you! Talk to a Product Specialist to learn about connected equipment for industrial manufacturers. Start your digital transformation today!

The IIoT Has Arrived. Are You Benefitting?


Click below for answers to some frequently asked questions.

What is the difference between IoT and IIoT?
First, the IoT and IIoT are both part of what’s called Industry 4.0. Since the industrial revolution began in the 1700’s, there have been four versions of transformation.
Industry 1.0 was the transition of hand production to the use of steam powered machines. Then, in the late 1800’s, Industry 2.0 included technology such as the telegraph and electricity.
Next, Industry 3.0 occurred during the second half of the 20th century. It saw the creation of digital advancements like widespread computer use. Finally, Industry 4.0 is the current stage of industrial transformation. It includes connected devices and cloud computing.
Both the IoT and the IIoT connect physical objects to collect data. But the Internet of Things (IoT) refers to internet-connected consumer devices. For example, billions of devices are IoT connected. These IoT devices include phones, fitness trackers, or even some smart home appliances.
Also, some smart cities are coming on-line. These cities are using sensors to collect data. In turn, they use the data to manage assets and resources.
In contrast, the IIoT refers to the Industrial Internet of Things. The IIoT focuses on industrial applications.
Connected machines and systems provide vast amounts of data. The purpose is to improve business outcomes. Namely, IIoT technologies improve connectivity and efficiency or add time or cost savings.  These can include sensors or intelligent machines used to gain data analytics. 
How is IIoT transforming the supply chain?
Many factors present challenges to today’s complex supply chains. For example, theft, IT failures and more can work to undermine production and raise costs. But, the addition of control systems through an Industrial IoT platform can curtail these risks.
IIoT connected devices generate large amounts of data. When analyzed, this “big data” can improve processes. Big data analytics is the process of examining this data. Ultimately, the goal is to discover information to make informed business decisions.
For example, sensors added to shipments can track temperature for perishable products. Also, sensors aide in keeping track of reliability and condition of production equipment. Then, companies use this data to track downtime, maintenance schedules and more.
What is the difference between machine learning and IoT?
In short, the IIoT gathers real-time data from connected devices. In contrast, machine learning analyzes that data to identify patterns. Then, it is able to make predictions based on those patterns.


For example, machine learning algorithms may perform “predictive maintenance.” As a result, the data can predict device failure or remaining equipment life.
Learn More
Continue reading about the IIoT on Radley’s Blog
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