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Case Study: Cracking Davinci’s packing code to reduce processing time by 30%

Rabot

Content Contributor

Feb 20, 2024

Key results achieved with Rabot

  • 30% decrease in average processing time

  • System exceptions rate dropped from 7.5% to 2%

  • Achieve optimum capacity utilization of pack stations across all warehouse locations 


The Challenge: Scale best practices

When Davinci Micro Fulfillment reached out to Rabot in 2023, VP of Operations Drake Meyer was on a mission – find out why some of their warehouses were reaching peak performance, while others were not.

Drake had just joined Davinci that spring, and dove head first into things. He began to map the important stuff, like who are Davinci’s typical customers and what do they sell? Who are his strongest operators and how can he motivate the rest of the team to be like them? And how could new technologies help continuously uncover these insights across their warehouses?

He and the crew worked hard to overhaul their inbound and picking processes for optimum inventory control. Once in place, they shifted gears to focus on speed — more specifically, on improving the efficiency of their pack stations.

Initially, the WMS was a good source of macro level metrics like daily order volumes, but uncovering what was actually happening at the pack station was a mystery. Until, that is, Rabot was brought in. 

About Davinci: Small Footprint, Speedy Fulfillment

Davinci’s fulfillment approach strategically places micro fulfillment warehouses (or micro fulfillment centers or MFCs) throughout the country, moving product in and out quickly, with an aim to streamline operations and keep inventory costs at an absolute minimum. Davinci keeps its operations lean, fast, and customer-forward—fulfilling orders for customers faster than other fulfillment models, making it paramount to identify opportunities to eliminate time-suck processes.

Drake Meyer says, “We move product fast. As soon as something hits our dock, we're receiving and shipping it as soon as possible. We try to plan out where we think customers will buy from and get that product there before they buy it, so we can deliver it on time.”

The “Before” pack station at Davinci

Drake has a clear vision for what the ideal pack station looks like: Operators working in an easy and natural flow, using muscle memory, listening to music, sometimes even getting competitive to pump out the most orders. 

But if every order you pack is a puzzle, it can be a “pretty frustrating job and really fry you up.” And as any fulfillment leader will tell you: Time spent to pack each order is precious, and time spent on correcting errors costs money. The objective is to aim for close-to-perfect, with the least amount of time and touchpoints required to pack each order.


The Solution: Standardize and scale operations with Rabot

#1 Eliminate inefficient computer interactions

Rabot’s Vision AI solution laid the foundation to eliminating these frustrations at the pack station. 

Drake: “For every single order, packers had to copy information from one system on the computer to another system, causing a lot of mousing, typing, clicking, and time wasted. 

But after installing Rabot Pulse, the operator facing interface, we were able to say ‘look at how much time we’re wasting on every order.’ Rabot enabled us to give those areas attention and automate steps for a more seamless packout workflow.”

As a result, Rabot cut down Davinci's average processing time by 30%. 


#2 Squash system exceptions 

Before Rabot, they saw a high number of system exceptions that needed a correction, whether address change, carrier change, or other system error. Rabot enabled the team to bring down the unique system exceptions rate from 7.5% to 2%.

Usually, operators resolve these errors independently — acting with the best of intentions — to keep their line moving as quickly as possible. 

But without full visibility into why those system exceptions are happening in the first place, their temporary fixes likely open Davinci up to financial repercussions down the road. Not to mention the slowdown that comes from trying to fix a bottleneck. 

With Rabot, Davinci was able to knock this down to 2%, translating into huge cost savings. Drake Meyer reflects, “I could say, look at how much time we're wasting here. And that got our attention so that we were able to quickly get it fixed.”

“It was a bigger issue than just a few errors here and there. We were never going to get where we wanted to be without Rabot, and that was a big win.”


#3 Confidence with order lookup

Davinci’s micro fulfillment centers may have a small footprint, but they are spread across metropolitan cities in the United States. This level of scaled operation introduces complexity to client management, but Drake explains how Rabot’s order lookup feature helps, especially when it comes to customer complaints: 

“Rabot is super critical for our day-to-day operations. When a customer reports a missing item, it takes a lot of time to investigate. We have to look at all orders that went out that day and cycle count all locations. If that didn’t work, then track down the Picker and Packer to root cause it – what a tremendous disruption and waste of time!

With Rabot, I can simply click on an order, watch the pack video, and confirm that the item went in the box. That's huge. Rabot saves us so much time and gives us confidence when dealing with customers and their inventory.”

“Rabot is super critical; we quickly look up orders when a customer complains – bypassing cycle counts entirely.”



#4 Oversee training rollout

Training is another area where it’s easy to create efficiencies for cost savings quickly and consistently. Davinci uses Rabot extensively to review and retrain operators on errors, not just with the packer but also the rest of the team. They also cover proactive training use cases, such as sharing best practices and encouraging clutter-free packing surfaces. 

Compounding over time, training interventions like these add significant gains in productivity across Davinci’s sites.

Drake Meyer explains, “If I bring on a new packer in a warehouse space like ours, you would put them with probably one of your veteran packers for a day or so, and then you'd ask, Are you ready for your own pack station? It’s speculation at best. But with Rabot, we can actually watch how they're being trained and ensure they're being trained properly. Then you can see how many boxes they pack compared to a normal packer later that day. You can show them the video to help them improve upon their own work. You can gain a lot of confidence as a management team in your packers strengths and your training.” 

Drake again: “If I’m in Jacksonville, I can’t give feedback to a packer in Kentucky, but with Rabot, I can review recorded videos — and I give constructive feedback.”


#5 Maximize pack station utilization

Pack station utilization is another important metric that Rabot illuminates for customers. 

“I could see which stations were operating and which ones were not for that time period. That alone was a big win for us because I think of those pack stations as money machines. If no one is operating a station, then we’re not making money on it!”

Idle stations mean lost revenue for the average fulfillment center. With Rabot, when a site has poor throughput, Davinci is able to pinpoint issues like station downtime and quickly address this with the right staffing allocation.


#6 Meet the demands of peak season

Drake noted that pack station efficiency is vital during peak season — particularly when demand grows up to four times more than the MFC has capacity for. Rabot allows Davinci to maximize its output during this crucial time when holiday orders are rushing in and customer expectations are higher.


What’s next for Davinci?

Davinci is now looking forward to replicating their success in new locations and markets. By removing inefficiencies, aligning operations, and building consistent practices across their MFC network, they’re more than ready to scale. 


CEO Corey Apirian concludes, “The value we saw very early on with Rabot is how it could help us standardize and scale our operations, before we introduce widespread automation into our warehouses. I feel confident about the path that we’re on, and excited about the future!”  

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