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Brick by Brick: How the LEGO Group Scaled Analytics

Bill Shube, Sr. Manager, Decision Support Tools, at the LEGO Group, shares data challenges at the LEGO Group and their solutions.

Technology   |   Gib Bassett   |   Apr 28, 2022

Editor’s Note: This blog features an interview with Bill Shube, Sr. Manager, Decision Support Tools, at the LEGO Group.

How are analytics organized at the LEGO Group?

Shube: From an organizational standpoint, high level, IT and BI are very much global organizations and very centralized to our base in Denmark. From a local standpoint, here in the Americas, we don’t have a lot of BI or IT resources; it’s always people in Denmark who we’re working with. And as such, they tend to have global priorities, and those priorities often don’t mesh well with some of the local stuff that we have to deal with. So what we’ve learned is that we have to learn to help ourselves sometimes. 

We were two demand planners and a supply planner. We really had to learn about the rest of the business to understand people’s needs. But I’d say the advantage that we had is that we’ve been working in supply chain ops for a long time, and we knew all the right people.
We spent hours just kind of virtually looking over people’s shoulders to understand what they did and what they needed.

What are some of the analytics challenges you faced?

Shube: I’d say two of the main analytics challenges at the LEGO Group are data accessibility and data latency.

Data accessibility

From an accessibility standpoint, we use a business warehouse (BW). It was set up years ago, and at the time that it was implemented, it was a new thing, and we never really had a clear idea of how people would end up putting it to use. And as such, it grew organically over the years. One of the significant issues that this organic growth has caused is that the data that we need to do our jobs on a daily basis isn’t all in one place. I don’t know anyone in the company who can go to a single query and get all the answers they need. We’re always going to multiple queries. We’re splicing them together and doing a whole bunch of manipulations.

Data latency

Another challenge with our current systems is that it’s Excel-based. So until recently, we’ve only been able to access this data via Excel. So there are a lot of limitations that come along with that, just from the size of the data set perspective and the kind of flexibility that you get.

Excel is great for many things, but at this point, what we have is a hodgepodge of Excel files that get emailed all over the place, and as our business has grown, the size of those files has grown. They get bigger and bigger, more complicated, and they crash frequently. And no one file can hold everything that everybody needs. And so what you end up with is a bunch of people looking at a whole bunch of different Excel files, trying to do their job. They can’t just look at one place and get the answers they need.

Our team is dedicated to daily execution work in supply chain operations. And the business warehouse takes snapshots of the systems they work in daily. These people are working live in systems with live data – they need to know what that data looks like on a minute-to-minute basis.
Those snapshots are generally taken overnight, and by the time we check in the morning, it’s already out of date. And that’s not very helpful.
For order management, most of our orders come in at five in the morning, so when they walk in, they wouldn’t see any of the orders from the BW that they have to action today.

So what this has caused is a suite of shadow reporting. People are downloading data directly out of SAP, doing a bunch of manual lookups, and then distributing it via email. So it’s very unstandardized, it’s very manual, it’s very time-consuming. And as the pace of our business is increasing, it’s increasingly unsustainable.

LEGO Group + Alteryx: It Just Clicked

Shube: When I first started using Alteryx in 2019, I was working in demand planning and we were a relatively new group and didn’t really have any reporting tools at our disposal. And so I was tasked with developing new reporting. I asked around and one of the guys on the local BI team told me about Alteryx.

And after just working with it for a few days, a month here and there, I was able to pull together, I’d say, 90% of the data that my group needed on a daily basis to do our jobs.

I was able to get it all into one place, put it into Tableau, and all of a sudden, it changed the way that we did our work. We were doing almost no data prep anymore, and we had everything we needed at our fingertips.

Today, we’re trying to apply it to other groups like order management and our distribution team, supply planning, and basically everyone within supply chain ops.