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Though we spend ample time examining consumer-based XR endpoints, greater near-term impact is seen in the enterprise. This includes brands that use AR to promote products in greater dimension (B2B2C) and industrial enterprises that streamline their own operations (B2B).
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.
And with VR, employee training and onboarding can be elevated through immersive sequences that boost experiential learning and memory recall. It also scales, given that far-flung employees can get the same quality training, versus costly travel for senior training staff.
Altogether, there are micro and macro benefits to enterprise XR. The above 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 XR benefits? Our ongoing case studies series tackles these questions. We pick things up in this installment with a look at DHL’s AR integrations in its order-picking workflows to boost productivity and reduce errors.
Vision Picking
DHL Supply Chain is the world’s leading logistics company, with about 185,000 employees across 50+ countries. This entails a range of logistical functions, such as warehouse distribution and order fulfillment. These are processes that rely on fast and accurate order picking.
Software that supports order picking has traditionally involved complex and cluttered interfaces, where operators have to mentally translate 2D instructions or warehouse coordinate systems to physical space. This entails levels of cognitive load that breed slow and error-prone work.
With that backdrop, DHL was motivated to streamline its operations and achieve operational efficiencies in its order picking. Based on its scale of operations, any such efficiency gains can create considerable financial impact. So it looked to AR and the art of “vision picking.”
Specifically, it integrated TeamViewer Frontline software. Deployed on RealWear headsets and integrated with DHL’s warehouse management system, the interface guided DHL warehouse personnel using visual cues. This included intuitive line-of-sight instructions.
For example, aisle number, shelf, and bin are displayed. Once the correct item is picked, users get audio and video feedback. Speaking of feedback, this engenders feedback loops as the record is sent back to the warehouse management system for accurate real-time inventory.
Bottom Up
And the results? After implementing AR in all the above ways, DHL was able to achieve a 15 percent boost in overall productivity. It also reduced error rates to .1 percent (one tenth of a percent). It also saved valuable time in onboarding new hires, to the tune of 50-70 percent.
As for strategic takeaways and transferrable lessons, DHL checked many boxes in its AR implementation in terms of best practices. For one, it not only offered gains that satisfy the C-suite, but also end-users. This includes better ergonomics and less mental strain.
Satisfying end users in these ways continues to be a common thread across the case studies we examine. AR deployments will fall flat if there’s cultural resistance or lack of end-user buy-in. It’s all about generating bottom-up interest rather than top-down implementation.
This is why we often say that successful deployments are more about HR than AR. It’s about change management and directly addressing pain points of front-line workers, not just telling them it will be “good for the company.” It needs to be good for the company and good for them.
It’s also notable that DHL’s vision picking brought efficiencies on a few levels. It achieved the primary goal of faster and more accurate picking. But a valuable by-product is real-time synchronization with warehouse management systems, including more accurate inventory data.
We’ll pause there and pick things up in the next case study with more enterprise AR best practices and tactical takeaways…
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