Sponsored by: Domo | Eliminating the Warehouse “Tail Problem”: AI-Powered Optimization

Summary

Domo integrates with the Databricks Data and AI platform to create a self-adaptive warehouse system that continuously analyzes live palletizer data and recommends lane rebalancing actions as conditions diverge from plan.
The system replaces reactive firefighting with proactive optimization by combining Domo's five-pillar agentic platform—connectors, transformations, visualizations, AI workflows, and governance—with Databricks-powered analytics.
Supervisors interact through a Domo application to execute AI-driven lane recommendations, simulate proposed changes, and trigger automated evacuation workflows when a palletizer lane fails or goes offline.

Chapters

FAQs

What is the warehouse tail problem?

The warehouse tail problem occurs when real-world conditions diverge from static planning assumptions near the end of a shift, causing bottlenecks across palletizer lanes that require constant manual intervention. This video shows how one logistics operator solved it by combining Domo with the Databricks Data and AI platform to build a continuously adapting system.

How does Domo integrate with the Databricks Data and AI platform?

Domo connects to Databricks through a library of approximately 1,500 pre-built connectors to load data into the lakehouse. The platform uses Databricks AI query pipelines to generate recommendations, then surfaces them through Domo's visualization and workflow layers so supervisors can act on them immediately.

What actions can supervisors take through the Domo warehouse optimization app?

Supervisors can execute AI-driven lane rebalancing suggestions, manually override recommendations, simulate proposed changes before committing, and trigger automated evacuation workflows when a palletizer lane fails. The system also tracks real-time moves and records historical performance data for future optimization.

What is Domo's agentic platform?

Domo describes itself as the agentic platform for intelligent enterprise work, built around five pillars: data connectors, a transformation layer, visualization, AI and workflow automations, and governance. This video demonstrates how these pillars work together with Databricks to deliver a self-aware warehouse optimization system.

Full transcript

[00:08] My name is Mason Crane and it's awesome to be here today. I am representing Domo. Just quick in the crowd, have you guys all heard of Domo? Ever heard of Domo? Can you guys Everybody can hear? Okay, cool. Cool. All right, so I work
[00:21] for Domo. I am a senior solution engineer here at Domo and specifically what we're going to talk about today is how Domo integrates with Databricks and how we kind of what we do is we take data from,
[00:33] you know, all of the engineers, data scientists, the really smart people doing all the cool data things and we empower the end users to be able to access that information. We kind of decouple or take away those roadblocks.
[00:45] We let the smart people keep working and we let the business people get those insights and access. Specifically, we're going to be operating with a demo. What is Domo? And we're going to talk about the Let me
[00:58] flip back a slide. The um eliminating the warehouse tail problem. Are you guys all in logistics, warehousing? Is that why you're here? Did you guys just want to hear about Domo? Looking for Just want to hear about it. That's totally fine.
[01:12] So, what is Domo? Right? So, Domo is the agentic platform for intelligent enterprise work. And the way that that works is, you know, you can think about Domo as the fastest path to getting data inside of the lakehouse. Why we do that and how we do that, I really think of
[01:24] kind of Domo as five key pillars. Pillar number one is the ability to grab your data from all of its different systems via our connector library. We have about 1,500 different pre-built connectors to load data into Databricks. We have our
[01:37] Pillar number two is our um transformation layer where I know that a lot of you are data scientists, you're SQL experts, Python experts, things like that. The where Domo comes in is we help out in that more end user last mile
[01:50] transform type of business analytics. And then our Pillar number three is our visualization layer. It's how we It's how we start visualizing the data and and get those insights to the right people. The way that we do that is through a
[02:03] plethora of different options. We can do our typical dashboarding opportunity. We can integrate with PowerPoint, Google Slides, things like that. We can embed our platform as a white glove approach to
[02:16] however you want to consume it and keep it governed. And then we also, you know, we can email it out and schedule reports and things like that. So, getting data to your end users, however that might be, is one of the areas that Domo is very strong. A fourth layer that we have, a fourth
[02:29] pillar, is our AI and workflow automations. So, the cool part here is is that you've got AI integrated with Databricks. And because we sit right on top of Databricks, we can take all of that computing and AI capabilities and
[02:41] integrate it with Domo. Some of the cool AI capabilities that we have in that regard, one is our Agent Catalyst, which is essentially like vibe coding inside of Domo using Databricks AI models. We
[02:54] also have our AI chat for business user questions and problems that they might be trying to solve. And then like I mentioned, the workflow automations, we have AI agents inside so you can essentially create an agent for different business problems that you
[03:06] want to solve. And then those agents will run, kick off different processes, and solve those problems. And then the fifth piece is or fifth pillar is our governance layer. So, it's really just how do we keep the data secure. And the cool part there is that
[03:18] we can integrate all the securities and policies that live inside of Databricks. We can also, you know, if you have single sign-on and things like that, we can integrate it. Or if you're just looking for governance around permissioning of the dashboards and the
[03:30] data sets within those dashboards, row-level security, things like that, we have a pillar for that as well. Um Now, let's see here. So, from there, I really just kind of wanted you know,
[03:42] help you guys to understand like Domo Domo's really we're here to operationalize the data. You know, you guys are doing all the hard work prepping the data, getting it into a format that's digestible and needed for the end users, and then we are going to
[03:54] help get that to the right people with the right govern capabilities. Um with all that being said, um I'm going to talk to you guys a little bit about a product that I developed for um warehouse optimization, okay? So, to
[04:06] paint the a bit of a picture for those that are not familiar with warehouse work and distribution and things like that. Um this client operates with uh they're they're called palletizers, and it's basically an automated machine that
[04:18] picks product, puts it on a lane, loads it on a truck, and gets it out the door. And what ends up happening is the upstream system that they have is basically operating with perfection. It says, "Hey, for a given day we're uh
[04:30] we're going to operate 100 pallets every hour in every lane until we're out of product for the day." The problem is that upstream system doesn't understand that perfection is is essentially failing the moment that the day starts.
[04:42] Uh um a human could make a mistake, a palletizer could slow down, something could break, go offline, things like that. And those upstream systems, they're it it's not set up to adapt accordingly. So, this app is actually
[04:55] built to do that. Um and this is just a little bit more context. I'm going to kind of click through these um And so, what we did is we we built out um some AI models, we generated an app.
[05:07] So, this again is is highlighting Domo's capabilities of that end-to-end solution. But the other cool part is is that if you guys are operating with different tool sets and you love your integration tool or your um ingestion tool, but you're looking for a new
[05:19] visualization tool, you could use Domo. Or you love your visualization tool, but you want to use an integration tool, you can use Domo as a kind of modular pillared approach. Grab what you want, use what you want, that's totally up to you. So, just for the sake of time here, I'm
[05:32] going to um pop out of this screen, and I'm going to share my app. Okay, I'll bump it up into a full screen here. Okay. So, what am I looking at?
[05:44] What's going on on this page? Right now, we have a set of um different sites, different locations, warehouse distribution centers, essentially. And we've got insights around them. Different ones have different amounts of of palletizers. I'm just checking if you
[05:58] guys can read that. It's it's a little small, but um what it's doing right now is it's kind of telling you the starting state. It's telling you a bit of what's going on, the imbalancedness of the the palletizers.
[06:10] Um we can filter to different sites. We can do different things here. Um and if I just to show you guys cuz this is going to work, if I come to this report section right now, you're not going to see anything, and that's that's by design because we actually haven't run anything in the model. But inside of
[06:23] the app, we have the ability to understand what the suggested moves are. We have the ability to see if you want to make a manual move. And so for those that are in warehousing or logistics or things like that, you know that there are people that have been in the industry for 20, 30 years, and they they
[06:37] don't want data to come in and tell them how to forecast. How many boxes of Cheerios do I need to order today? Put it on a shelf. They don't need that. And so they want the ability to put a manual move in. And so we can score those moves, we can give them that capability.
[06:50] But what I found was really important was explaining why a move was a good move. So you'll see down here, we have explanations on why they should make this move. And that helps kind of bridge the gap between not I don't want to say
[07:02] egos, but more of like the the industry knowledge that they have that they can adjust and adapt and go, you know what, I get this. Like this makes sense. I can take my hands off the off the the wheel a little bit and let this run. Additionally, we also have the statuses
[07:15] of the palletizer. So these can go offline, like I mentioned. They could be working perfectly fine for a given day, and then they go offline or for an hour or something like that. So being able to toggle these on and off is super important. Additionally,
[07:27] um we do have we have this AI engine. And so what this is doing is it's tracking through all the different kind of processes where Domo's integrating with Databricks. It's walking through Lake House. It's walking through the AI query, the data sets, the client
[07:40] analytics, things like that. And so, it's giving all of that high-level tech information that you need to be aware of for the right users. And that's another cool part about the platform. Right now, as the presenter, I can see everything,
[07:52] but you can actually govern and maybe only the IT team is allowed to see this page. You know, you can choose what content is digestible by the end users. Um So, just to kind of kick this off here,
[08:04] I'm going to go back to overview and we're going to notice, just to call out a couple of things, we're sitting at 41% optimized. And then, if we scroll down here, we have our starting state and our current balance. Now, I haven't done anything yet, so the current balance is staying exactly the way that it is. But,
[08:17] if I come up here and I click auto optimize, what we're going to see is the model is actually kicking off different decisions and it's moving product across the different lanes or palletizers. And
[08:29] so, while this is operating, you're seeing the, you know, the starting balance was 41%. It's making all of the moves and it's actually tracking all of this historical data for you, so that you can actually get smarter with the model as well. And you'll get insights,
[08:42] and I'll show you this in a moment. But, so we we actually we wrapped it up and we went from 41% to 86% optimized. So, we we did really, really well. And the way that we can see that, if I scroll down here,
[08:54] and it's a a little hard to see on the screen, but there are kind of the starting state was these kind of this big red bar here and now it's gray because you can see that it it it normalized. It brought the the information down. And to show this in a
[09:07] different way, if I filter to Atlanta as a site, you can see that it was starting at a starting balance of 18% and it increased to 73%. So, we really normalized that data and got it to a place where, you know, it's going to be
[09:20] operating more efficiently. Why does that matter? With these types of machines, if you're running a machine constantly over and over and over again and you're burning it out at the end of the day, it's going to wear out more more and that's going to have costs
[09:32] associated with it to maintain it, fix it, things like that. So, being able to use them for all roughly the same amount of hours in a day keeps the maintenance um um on you know, you understand what's going on there. So, now I mentioned, you know,
[09:44] we can come over here and if I click the reports, this is showing up now. And so, we're seeing the amount of moves that were executed, the amount of product that was moved. We can see the uh the total moves, the units um rebalanced,
[09:57] and then we can see a breakdown of the different facilities, which ones had the biggest increase, which ones had the lowest increase. And then these are actual metrics, you know, where the the the client when we were working with them, it was costing them
[10:09] $1,200 a day to not be efficient with their product. And so, with um with an implementation and building this out and scaling it with Domo and Databricks, you know, we're we're making them or we're saving them $3.5 million a year by being
[10:22] more efficient with getting the product where it needs to go. Part of that is because you've got SLA agreements. When you put it on top of the you know, you put it on the truck and it gets out, it's got to get there on time, you know, all those different pieces. Then you have the maintenance costs and all that
[10:35] jazz. So, if I come down here, we can see a little bit more information. We can see kind of the total moves. We can see the score distribution. And then we have some AI insights here that can help you with the upstream analytics and the downstream analytics of what you could
[10:47] do to try to potentially improve this process. And then additionally, we're tracking all the different moves. And so, this is really really important because we're essentially using the app and all the existing data to create downstream data, data that we can
[11:00] actually use to get better, smarter, and then you know, take that all of that logic, apply it back, and then hopefully keep improving with the model um day after day. So, um I actually realized I burned through
[11:13] this pretty quick. I did want to show you a couple of other cool parts. Um So, if I come up here, I'm going to reset the simulation for a moment and I want to walk through another really important piece and I touched on it briefly, but it is the um it's the
[11:27] palletizer status. So, as an example, let's say that your your day is going great. You've got your different product. They're moving, but then this palletizer goes offline. So, what's going to happen if I click auto optimize
[11:39] now, you're going to see in the top there that this lane has all of this product and we have to get the product off because it's gone offline. So, what that means is, you know, this product it was scheduled to go through this lane.
[11:52] The lane is no longer operating. Let's move it, redistribute it accordingly, and then see see how it optimizes after that. And you know, when you have multiple warehouses, multiple palletizers, things going on offline, online all the time,
[12:06] being able to use the system to also alert end users and let them know things are going on, that's another aspect of the Domo platform where we can get those real-time analytics to your phone, um to your tablet, to the people on the
[12:18] floor, whoever needs to consume this content they're going to be able to get it in real time. So, if I pop out of here and I come back into the overview page, we can see that um you know, this lane it was on, it's now gone offline,
[12:31] and then we redistributed as best we could based on the the situation that was going on. Um and actually we we got more optimized because we turned one off. That's surprising. Um
[12:43] so let me see here. I mean, honestly, I I uh I joke I'm from New York. I talk very quickly. I managed to do this faster than I expected to. Um you know, like I mentioned uh I'm with Domo. We're in booth 205. So, if you
[12:57] guys have any questions, I uh originally I didn't realize we couldn't do questions. So, um please come over. We've got a bunch of folks that are um demo or prepped on this demo, can have conversations. I'm happy to answer any questions that you have. Um
[13:12] I mean, I'm I'm really good at rambling. So, like I'm not going to do that to you all. So, thank you so much for coming out and listening. Really appreciate your time today. And again, come over to 205. Uh really excited to talk to you all. Thank you.

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