Over the last few years, augmented and virtual reality (AR and VR, respectively) have been two of the most talked-about topics in technology. While VR received a lot of consumer attention thanks to Oculus and smartphone applications, commercial and industrial firms have settled on AR as a more practical technology to increase productivity.
And it’s true that AR has incredible potential for changing the way humans work, from hamburger flippers to jet engine mechanics to bulldozer operators. The best current use case involves delivering text- and image-based content to workers who perform manual tasks and access information on a wearable or handheld device.
Hardware devices for industrial settings have made great progress, and all the device makers offer software to display content in various formats, both 2D and 3D.
The benefits can include more rapid job training with better task performance, a higher level of safety in hazardous environments (imagine a worker perched atop a wind turbine), and ideally a superior quality of information that leads to improved productivity.
However, at this point in time, the majority of companies that could profit from AR technology have not seriously considered adopting it. Why is that?
Because content suitable for enterprise-grade AR applications requires an investment of labor and skills that most companies can’t manage. Simple, repeatable tasks, like production line assembly, have fewer objects and variables involved, making it easier to create content. As it turns out, assembly line work is one area where AR has gained a real foothold and delivered positive returns.
Diagnosing problems and providing repair instructions for complex machines is not like this.
Think about how much information needs to be coded simply to create work instructions for tightening a bolt during a maintenance procedure. The object needs to be identified (part ID), along with its context (where is it located, on which machine or assembly), and most likely a proper torque specification.
But what about other contextual data? Should the machine be turned on or off? Are there additional procedures to be performed before or after the bolt is tightened? Does the machine need to be at a certain operating temperature or running at a specific RPM?
As you can see, aggregating and connecting all of this data from operating manuals, repair manuals, engineers’ and mechanics’ tribal knowledge and other sources is not trivial.
When facing a new problem, an experienced technician can access a wealth of data about similar scenarios, alternative workarounds, and “common” sense that may never be replicated by a database.
Our experience after implementing Documoto’s parts catalog software at close to 100 medium-to-large companies is that most don’t have the internal resources at present to tackle augmented reality content development internally, and outside help is too expensive or lacks the necessary industry knowledge.
In fact, legacy data conversion and migration often prove to be one of the biggest resource demands over the course of any enterprise software deployment project.
All of these factors make it safe to say that there’s a lot of work–and technological innovation–to come before we’re at the point where augmented reality is universally adopted.
The future for commercial and industrial service and maintenance operations is to build a content platform and associated hardware that enable someone with little background knowledge to perform a difficult task as efficiently as a seasoned professional. Networked hardware and software will perform predictive maintenance and problem diagnoses using Internet of Things and Big Data technology.
It’s definitely coming, but we are still years away from that scenario.