Industrial Internet of Things (IIoT)- What To Do With Big Data

Everyone is talking about the Internet of Things – but what do you do with it?

Last August, motion control expert Kristin Lewotsky presented MCMA's webinar on “How to Turn Big Data Into Actionable Information." She discussed a central thought on so many minds: How do you translate the capabilities of the Internet of Things into efficiency, effectiveness, and profit?

The Industrial Internet of Things (IIoT) refers to the merger of online capabilities with hardware that allows novel approaches to capturing data: For example, embedded sensors, actuators, and controllers. These make it possible to gather data during manufacturing operations.

IIoT illuminates opportunities across the value chain:

• Greater efficiency;
• Reduced downtime;
• Shorter lead times;
• Lower on-hand inventory;
• Reduced production costs;
• Easier personalization.

The Industrial Internet of Things is Poised for Explosive Growth – OEE is Why

By 2025, experts estimate 5.4 billion IoT devices will ship annually, serving an installed base of nearly 50 billion. Industrial applications will dwarf non-industrial ones, with early adopters yielding outsized benefits well into the technology’s maturity.

This will allow businesses to monetize Big Data by overcoming its challenges: Volume, Variety, Velocity, and Veracity. As implementation continues, industrial concerns will link IoT directly to improvements in equipment effectiveness.

Overall equipment effectiveness (OEE) is a measurable performance metric that refers to equipment availability multiplied by quality – that is, the percentage of good parts produced. IoT will facilitate faster, more proactive strategies for raising OEE.

Making Use of Big Data Means Having a Plan in Place

Lewotsky shares a plan for Big Data success – how to focus on actionable, relevant information for decision support.

Plan Data Capture

Data capture takes time, storage, and bandwidth – and requires data management practices to be in place. Every form of data you choose to capture should have a complete rationale: A clear owner, repository, and benefit.

Identify Problems & Set Goals

Begin with a clear, localized problem you can tackle as a test case – for example, variance in how similar machines perform. Then, roll out a larger plan over time. Set measurable goals in areas like energy use or downtime.

Get Instrumentation & Storage in Place

Sensors can provide data like load diagnostics, alarm status, and application activity. To be effective, all data must be stored until it can be used. Dedicated data loggers offer configurability and performance advantages here.

To learn more, watch MCMA's archived webinar: How to Turn Big Data into Actionable Information.

Related Blog Articles

Understanding Embedded Systems in Industrial Vision Applications, Pt. 1

Read

A Brief Introduction to the Industrial Robotics Market

Read