Workwear Group Drives Innovation and Cuts Costs by 30% with Streamlit and Generative AI

This transcript has been auto-generated.

Workwear Group's been a leading provider, both workwear and uniforms across Australia and New Zealand for nearly a hundred years are a couple of our iconic brands, King G and Hardiaca.

So some of the common challenges we're seeing is the competitive nature of the environment we operate in. It's really putting a lot of emphasis on the need for quality decision making and that needs to be driven by really good data.

Prior to Snowflake, our legacy systems were able to produce data, but it was in a very convoluted way. This led to having multiple sources of data, and people's data across the business wouldn't align. Using Snowflake, all the data comes from one spot, so it's all centralized and everyone's on the same page.

I would say the entire company relies on the data to do their job. For example, if you look at sales coordinators, it's very important for them to look at the orders that are open from customer ends. I think everyone in one way or the other, they depend on the data.

Snowflake's allowed us to achieve a number of outcomes that have been really important to our business. Snowflake is a pure native cloud solution. It has the security that we require and also the ability to scale up and scale down. With our consumer DIFOT reporting.

In consultation with our user base we were able to find that they only needed to refresh it weekly instead of daily and that allowed us to cut our costs by thirty percent. Also we'd be able to create really robust reporting suites that have dramatically reduced manual handling of data. So for our sales and accounts teams, we're saving up to one hundred days a month by provisioning our reporting through Snowflake and then Power BI. 

Snowflake product continues to evolve and continues to add a whole bunch of functionality.

So a good example of that is with Streamlit. We're looking for a vehicle to be able to expose applications to our user base.

Streamlit is a framework where we can build applications that can then transform that data from the user's end rather than from our end. I was really intrigued at how easy it was to use Streamlit. The idea that I had for an app was to be able to upload an image of our products and then generate a description for those products. I integrated Streamlit with a generative AI model.

And then on the back end, I also connected our product master data, and I started building an app. And within a couple of hours, I had probably about eighty percent of a working app.

In the last twelve months, we've been exploring generative AI here at the Workwear Group. We are using GenAI to supplement some of our processes where we have data quality issues, and we foresee there's plenty more opportunities for AI to be integrated within our business.

So Snowflake's a really big enabler for us for our AI journey. We spend a lot of time over the last four or five years getting really well governed secured data and now we're able to start to leverage that and build out AI, particularly for optimization of a lot of our business processes in the DC, supply chain, but also other parts of the business.

From a team viewpoint Snowflake's just so easy to work with, they're really well integrated with our team. The data landscape at Workgroup would look really different without Snowflake and they have been a really critical part of our journey over the last six to seven years. Experience with Snowflake has been really easy, really positive and really supportive.