The world of manufacturing is changing fast. In fact, the impact of the COVID-19 pandemic is accelerating a change that was already well underway as manufacturers moved to an “Industry 4.0” model that embraced a pervasive digital approach.
Infographic courtesy of the OECD
The technologies underpinning the Industrial Internet of Things (IIoT) are key to the success of Industry 4.0. I recently had the honor of hosting a panel during IIoT World Days virtual conference looking at the role and power of analytics in IIoT.
Each of the five guests on the panel had insights into what they called the “Manufacturing Analytics Journey” – taking a detailed look at how analytics impacts profitability, powers prediction, informs intelligent optimization and leverages big data.
The insights they offered about the importance of data and analytics got me to thinking about the important role that AR, AI and mobile devices can play in actually making use of that data on the front line.
As it happens, integration with industrial IIoT infrastructures is something that our team has spent a great deal of time working on over the last several years. Since the first release of our “Transforming the Enterprise” white paper back in late 2018, we have been clear about the relationship between IIoT, AR and AI.
In the latest release of that White Paper, we spelled out exactly how we saw the connections between AR. AI, IIoT and machine learning. We start with the context of the frontline team member in an industrial setting who is servicing a piece of equipment.
This context could leverage data about:
- the work identity profile of the frontline team member
- the skill set data of the frontline team member
- historical data covering the work instructions they may have previously worked with in relation to a particular piece of equipment they are servicing
- the remote experts or colleagues they typically work with
- and what level of certification and training they may have in undertaking the job they’re about to do.
Once we have that foundational context, we can combine it with information about location, time and date (all drawn from the mobile device itself) – and then start using relevant industrial IoT data to provide:
- very specific assistance that is relevant to the task at hand,
- insights into how the equipment that the frontline team member is working on may relate to other useful IoT data from similar equipment
- live diagnostic data from the equipment itself.
We believe that front line teams need to be able to use their mobile devices (including smart glasses, tablets and smartphones) to get information from machines, sensors, and the IIoT infrastructure and see the the data flow into their field of vision.
The IoT data can come from the frontline team member’s immediate work environment – with QR code or object recognition scans being used to perhaps draw information about when a piece of equipment was last serviced, provide immediate access to all relevant service records, work instructions and performance data for the equipment itself.
And the utility of having these technologies linked doesn’t stop there. Context is also a vital component of helping systems become more intelligent (though ML and AI technologies) and predictive.
Leveraging both edge computing and AR technologies, enhanced by machine learning and artificial intelligence, creates a platform that can anticipate what members of the extended enterprise will need to do next – sometimes before they know it themselves.
It builds on the idea that an organization has the capability, with the simple introduction of something like our Front Line OS (powered by AR and AI), to hold up a mirror to itself – and its supply chain – to gain true predictive insight in both the specific and broad collaborations of the extended enterprise.
If you want to see this in action, we’d be happy to show you how it works.