Though we spend ample time examining consumer-based AR endpoints, greater near-term impact is seen in the enterprise. This takes many forms including brands that use AR to promote products in greater dimension, and industrial enterprises that streamline operations.

These industrial endpoints include visual support in areas like assembly and maintenance. The idea is that AR’s line-of-sight orientation can guide front-line workers. Compared to the “mental mapping” they must do with 2D instructions, visual support makes them more effective.

This effectiveness results from AR-guided speed, accuracy, and safety. These micro efficiencies add up to worthwhile bottom-line impact when deployed at scale. Macro benefits include lessening job strain and closing the “skills gap,” which can preserve institutional knowledge.

But how is this materializing today and who’s realizing enterprise AR benefits? Our research arm ARtillery Intelligence tackled these questions in its report: Enterprise AR: Best Practices & Case Studies, Vol 2. We’ve excerpted it below, featuring GE Healthcare’s AR deployment.

Enterprise AR: Best Practices & Case Studies, Volume II

Pick & Pack

Picking up where we left off in last week’s case studies, another developing AR use case is warehousing. AR can empower the process of collecting items – either in manufacturing facilities that need the right part, or eCommerce operations that ship products from fulfillment centers.

Traditional bottlenecks include human error and the cognitive load inherent in warehouse personnel physically running from bay to bay to retrieve items. Like aircraft assembly (last week’s topic), this is an area where AR’s line-of-site guidance can boost accuracy and reduce cost.

And like many areas where AR demonstrates value, traditional methods are outdated. For example, the state of the art in “pick & pack” warehouse work involves paper orders and muscle memory to navigate to a given location. This involves ample strain and “cognitive load.”

AR conversely automates all the above by providing line-of-sight guidance for order details and locations. The benefits this brings aren’t just to reduce cognitive load and boost accuracy, but to free up front-line workers’ hands for manual item retrieval. The result is boosted productivity.

Case Study: Building Spaceships with AR

Line of Sight

Among enterprises deploying AR in this way is GE Healthcare. It processes myriad components for medical imaging equipment such as MRI machinery, including shipping to medical institutions around the world. This makes its warehousing operations quite complex.

For example, its Florence, South Carolina warehouse facility is stacked to the ceiling with hundreds of MRI parts from dozens of component suppliers. Parts are stored throughout the facility but need to come together in various combinations on-demand for a given shipping order.

So how did GE Healthcare apply AR to solve these challenges? Using Google Glass and Upskill’s Skylight AR software, it connects to and integrates with warehouse software systems to provide dynamic line-of-sight vision-picking instructions. This makes things faster and more effective.

As noted, the previous state-of-the-art in warehousing is for workers to flip through printed orders to locate parts as they walk across giant facilities. If they run into issues, such as out-of-stock items, they must walk to a computer terminal to locate an alternative part.

Case Study: Boeing Streamlines Aircraft Assembly with AR

Feedback Loop

Using AR, the same workers can now see line-of-site order details such as part, quantity, location, and alternatives. This reduces the need to visit far-flung computer terminals and carry around paper orders, which adds considerable time to a given shipment and keeps customers waiting.

This all plays out as workers start new orders through a “start new pick” option. They then locate the desired item and confirm the order using voice or touchpad controls on the side of the glasses. Other commands include “scroll forward” or mark an item “not in location.”

As this process carries out, back-end integration informs warehouse systems for an optimal feedback loop. For example, systems can stay up to date on inventory counts, missing items, and other quality-control metrics. That makes it a two-way street of information and optimization.

Beyond theoretical benefits, all the above shows quantifiable results for GE. Specifically, it achieved a 32 percent increase in average speed for its pick & pack orders across the board, and with greater accuracy. It even clocked 46 percent speed boosts in certain situations.

We’ll pause there and circle back in the next report excerpt with another enterprise AR case study. Meanwhile, read the full report here and see our video takeaways below… 

Header image credit: CHUTTERSNAP on Unsplash 

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