Hi I'm Matt Schmidt,
Senior Sales Engineer at Snowflake.
In this demo, we're going to look at
one of the burning topics in the industry today--supply chain.
Let's take a deep dive into Snowflake's Retail Data Cloud
and see how retailers can optimize inventory and fulfillment processes.
Establishing a strong data foundation is key. In case that you do not have a data model, or
you're not getting everything out of your data model, Snowflake has made a strategic investment
in Robling. This can help you accelerate your journey to analytics, spanning the major functional
areas, such as customer, store ops, merchandising, omnichannel, finance, and supply chain. This is
especially crucial in the area of supply chain. Having full visibility into other functional
areas ensures that retailers can make data-driven decisions to optimize inventory and fulfillment.
Who else's porch has looked like this in the past year? The challenge with inventory and fulfillment,
today, particularly since the pandemic, is the sheer volume of shipments from purchases and returns
as consumers pivoted to online transactions. This type of volume can make it challenging for
retailers to plan for fulfillment and logistics. Orders may be split into multiple shipments,
or products may need to be shipped across country to accommodate demand in certain regions.
This can be a costly proposition at scale and lead to losses with low margin items.
By having all of our data in one place on Snowflake, we can start exploring our data
to identify problem areas to address. We can start by identifying our loss leaders to rank
our low margin items quickly and efficiently, aggregating all of our inventory/SKU-level data.
From here, we can start to look into our orders and fulfillment data.
How many multi-product orders did we observe? How many of those resulted in split shipments?
These are questions we can ask our data, and automate for reporting purposes.
But before we make any decisions on this data, let's take a look at the Snowflake
Data Marketplace to enrich our analysis. The Data Marketplace contains over 1,000 live,
ready-to-query data listings across many different categories, including demographic data, weather,
COVID, and more. All datasets that retailers can use to improve their models. For this scenario,
we'll look into how macroeconomic trends affect inventory-demand planning. Factset is a financial
data provider who has listings for economic data, including CPI, employment, and more.
They can deliver live data listings to any Snowflake account. No need for FTPs or cumbersome APIs.
This Factset data is now completely ready to query, and we can start exploring it.
Here we have CPI and unemployment data for North
America available for analysis and it can be joined with our first-party data.
With all this data available to them, the retailer can now involve other groups
for advanced analytics, making all inventory/SKU- level data available to data scientists
to inform inventory demand prediction.
Bringing the Factset data into model training, we can see that CPI
in unemployment data is highly correlated and results in better inventory demand forecast overall.
We can see this in aggregate, projecting demand across North America and diving deeper
into state-level demand forecasts.
With this demand forecast, the retailer can now accurately predict demand
by item, per region, to inform inventory and fulfillment.
Most machine-learning models never make it into production.
With Snowpark for Python, data scientists can now operationalize their insights,
automating their inventory forecasts for business users to leverage.
All this data can now be visualized
and distributed enterprise-wide, providing live data to any user or stakeholder
Visualizing aggregated KPIs, geographic maps, and the same
inventory demand prediction that was performed earlier by SKU and geography.
In this demo, you saw me wear lots of hats, from a data analyst drilling into the data to a data
engineer harmonizing first- and second-party data to a data scientist getting that data
and building predictive models on that, giving it back into our data analyst hands to make
decisions based on those insights. This can all only be done with the Snowflake Retail Data Cloud.
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