Sager Creek Vegetable Company sees supply chain insights with Tableau

At Sager Creek Vegetable Company, producing and distributing fruits and vegetables involves data at every step of the process. In this video, Matthew Hughes, Senior Manager of Business Intelligence at Sager Creek discusses how Tableau has helped them connect to data from two huge production environments in 8 hours—an effort that had been ongoing for two years. Today, Sager Creek Vegetables can:

  • Analyze millions of rows of data
  • Give execs “an immense amount of control” over the environment
  • Plan for variables that affect their business with predictive analytics

Sager Creek Vegetable Company (Sager Creek) is a division of Del Monte Foods, Inc. The manufacturer produces canned fruit and vegetable products for many well-known brands, including Popeye™ and Freshlike™.

Before Tableau:

  • Insight into the database required complex data blending across several data languages
  • Efforts to connect to data from two huge production environments took two years

Now with Tableau and Alteryx Sager Creek was able to:

  • Provide a view across multiple environments in just 8 hours
  • Give decision makers the ability to quickly identify top-performing areas & those areas that need work
  • Analyze millions of rows of data
  • Understand and control everyday processes
  • Plan for the future with predictive analyses

Tableau: What goals have you achieved with Tableau?
Matthew Hughes, Sr. Manager of Business Intelligence: We try to tell a story of many, many, many people's actions all at one time and identify where we could do better and where we're doing well.

You just want to understand what behavior caused the result. And at the end of the day, what was the result? What was it yesterday compared to today? And what do we expect tomorrow?

But at a transaction level, can I predict what that asset is going to do? Can I predict what that asset is going to do when I put this person on it? Can I predict what it will do when I put this person on it during this shift?

Right there you're talking 20,000 lines of transactional data over just a couple days. And to do that for an entire plant where there are 300 people and 100 assets, three shifts—it's amazing. It just gets me excited to think we can actually crunch that and answer that question.

Tableau: How has Tableau inspired your team?
Matthew: You connected two, you know, huge production environment systems in eight hours with a few thousand dollars and three people, and suddenly you've got stuff that they couldn't do for two years with an entire IT team. And that's what's inspiring about it.

Tableau: What questions have you been able to answer with Tableau?
Matthew: So much has to do with the volume of transactions. If you can record the abilities of your assets and say what happens historically when I change these things about these assets? Then you really empower them.

But then you step back and you say, "Now, what combination of that with the timing of our commodities coming in at the back of the plant, all these vegetables pouring off of trucks." You know, what happens when we start to put those together and say, "Now, really, predictively, what can we do when the timing is this and our assets are running like this?"

And that's the stuff you can hand over to a plant manager or a C-level executive and say, "You've got an immense amount of information control over the environment."

You connected two huge production environment systems in eight hours…and suddenly you've got stuff that they couldn't do for two years with an entire IT team.

Tableau: How did you integrate Tableau into the company?
Matthew: We had to fight that battle for a long time. It wasn't until we demonstrated Tableau had full capability to go to the detail and all the way back up to the aggregate and planning processes for a billion-dollar company that they thought, "Oh, okay, this is actually what we want—this is how we want to tell our story, this is how we want to learn about our own company."

Tableau is a hundred-percent scalable for anything you throw at it, right? Now, your visualization that told a great story tells it in a way that can change the way everybody does business. That company now has a competitive advantage

You always feel like the hero. You know, “How did you do that?” I just did this. It's not that hard; I'll train you. And you make friends that way, right?

Partnering with Alteryx to tell great data stories

Tableau: How would you describe Tableau and Alteryx?
Matthew Hughes, Sr. Manager of Business Intelligence: Tableau is the great communicator. Alteryx is what gets that communicator where it needs to be.

Tableau: How do you use Tableau to make discoveries?
Matthew: Production environment data requires complex blending and aggregation across so many different languages of data in a company.

You can tell great stories in each vertical, in each area with Tableau, and we did that. But what it did was it showed us there's a great story to tell here, and here is the story in Tableau, but we see this great story, we don't know how they work together. What if we use Tableau to tweak this story?

Tableau: How do Tableau and Alteryx work together to address your data challenges?
Matthew: So you start to take these big chunks. And you've told these great stories. But now you've taken Alteryx and you've said, "We're going to tell all those stories together at one time." And this is how you operate it. And Tableau can just so purely display it in a simple way. But not until, for us, until Alteryx took seven systems that don't communicate, that took data cubes with millions of rows and took all this and put it together and said, "Now, go tell a story about all these different pieces."

So I think that that powerfulness was, for us, was you know, we had Tableau and we had great stories. But we had so many of them. How do you tell it as one great story? And we did it with Alteryx.

Tableau: That’s great. Is Tableau having a personal impact on people?
Matthew: Just a few weeks ago, the VP that we work for, she didn't have her Sunday mornings. She was spending four hours every Sunday morning normalizing Excel data in reports to answer questions she needs to answer. And so you can just step in and give some time back.

But when you know you can do stuff so quickly and you can build this infrastructure around it that it almost manages itself. You just have to turn the little knobs. And you can give that to somebody else real quick and be, like, "Hey, here's your mornings back." That feels really good.

We're really going to change the way we look at our manufacturing processes. What we've really tried to do is just step in and say, okay, we can give you all that, just we need to build this culture of capture, blend it, compress it, and tell the story.

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