I’d make it feel much more like an actual Shopra thought-leadership post and less like documentation. I’d also remove the tables, arrows, and excessive bullets so it pastes cleanly into Webflow.

MCP for Beverage Companies: Connecting NetSuite, VIP and Circana Data to AI

Beverage companies don’t have a data problem. They have a data connection problem.

Orders and shipments might live in NetSuite. Distributor depletions might come from Vermont Information Processing (VIP). Retail sales and distribution might come from Circana. Forecasts might live in a planning tool or spreadsheet.

Each system tells you something useful.

The problem is that answering a relatively simple question can require looking across all of them.

Why are we behind forecast?

That might mean checking shipments in NetSuite, depletions in VIP, retail velocity in Circana, inventory from a distributor and then comparing everything back to the forecast.

This is where Model Context Protocol (MCP) gets interesting for beverage companies.

MCP can give AI applications a standardized way to access the tools and data they need to investigate these questions.

But connecting data to MCP is only the first step.

The bigger opportunity is giving AI enough context to understand how all of your beverage data fits together.

What is MCP?

Model Context Protocol, or MCP, is an open standard that allows AI applications to connect to external tools and data sources.

Think of it this way:

APIs helped applications talk to other applications. MCP gives AI a standardized way to discover and use the tools and data available to it.

For a beverage company, an MCP server could give an AI application access to things like shipments, orders, inventory, distributor depletions, retail sales, distribution, velocity, promotions and forecasts.

When someone asks a question, the AI can use the appropriate tools to investigate it.

Instead of telling the AI exactly where to look, you can start with the business question.

Connecting NetSuite to AI with MCP

For many beverage companies, NetSuite is a major source of operational data.

Orders, shipments, customers, inventory and financial information may all live inside the ERP.

NetSuite now supports Model Context Protocol through its AI Connector Service, creating a path for supported AI applications to interact with NetSuite data and functionality while using existing NetSuite permissions.

That makes a NetSuite MCP integration an interesting starting point for beverage teams.

You could ask:

“How are August shipments pacing against forecast?”

“Which distributors have open orders?”

“Which SKUs are behind plan?”

“How much inventory do we have available?”

But here’s where things get more complicated.

NetSuite might tell you that shipments are 15% behind forecast.

It probably can’t tell you the entire reason why.

To understand that, you need more context.

Adding Vermont Information Processing (VIP) Depletion Data

For beverage alcohol companies, Vermont Information Processing (VIP) can provide another important part of the picture.

Your ERP can tell you what you shipped to a distributor.

VIP depletion data can help you understand what is moving through the distributor and into accounts.

Those two views are much more useful together.

Imagine asking:

“Why are shipments to Distributor A behind forecast?”

An AI agent might first check NetSuite.

Shipments are down 12%.

Then it could look at authorized Vermont Information Processing depletion data.

Depletions are actually tracking close to plan.

Next, it could check distributor inventory.

Inventory has been declining for three weeks.

Finally, it could check open orders.

There isn’t currently a replenishment order.

Suddenly the answer isn’t simply:

“Shipments are 12% below forecast.”

It’s closer to:

“Shipments are 12% below forecast, but VIP depletions remain healthy. Distributor inventory has been declining for three weeks and there isn’t currently a replenishment order open.”

That’s a much more useful answer.

And it’s only possible because the AI can understand what’s happening across multiple beverage data sources.

Adding Circana and Retail Data

Now add retail performance to the picture.

Circana and retailer POS data can help beverage companies understand what’s happening at the shelf.

Depending on the data available, that might include sales, units, distribution, velocity, pricing, promotions, category performance and market performance.

That gives AI another layer of context.

Imagine asking:

“Why are retail sales behind plan?”

The answer could require several questions.

Are shipments behind? Check NetSuite.

Are distributor depletions slowing? Check VIP.

Did we lose distribution? Check Circana or retailer POS data.

Are stores carrying the product but selling it more slowly? Check velocity.

Did pricing or promotional activity change? Check retail and promotional data.

Traditionally, an analyst might investigate each of those questions separately.

With MCP, an AI agent can potentially determine which data it needs and work through the investigation.

This Is Where MCP Gets Really Interesting

The opportunity isn’t simply connecting NetSuite to MCP.

Or creating a VIP MCP integration.

Or making Circana data available to an AI application.

The bigger opportunity is allowing AI to understand the relationship between all of them.

Take one of the most common questions in beverage planning:

“What’s driving our forecast variance?”

Maybe shipments are behind.

But why?

Maybe orders slowed down.

Maybe distributor depletions slowed.

Maybe depletions look fine, but inventory is getting low.

Maybe distribution dropped at a major retailer.

Maybe distribution is stable, but velocity declined.

Maybe a promotion didn’t perform as expected.

Or maybe all of those metrics look healthy and the forecast itself was simply too aggressive.

No single system necessarily has the answer.

The answer is usually somewhere between the systems.

That’s why MCP has so much potential for beverage.

The Hard Part Isn’t Connecting MCP

There is an important catch.

Giving an AI application access to NetSuite, Vermont Information Processing and Circana doesn’t automatically mean it understands the data.

Anyone who has worked with beverage data knows why.

The same product might have one SKU in NetSuite, another identifier in VIP, a UPC in retail data and another product code from the distributor.

Customers and accounts can have the same problem.

So can retailers, distributors, markets and even basic metrics.

One platform might call something “sales” when it means shipments.

Another might mean distributor depletions.

Another might mean retail POS sales.

If you simply connect all of those systems to AI without creating context between them, you haven’t really solved the problem.

You’ve just given AI access to more disconnected data.

MCP Needs a Harmonized Beverage Data Layer

This is why we believe the real opportunity is combining MCP with a harmonized beverage data layer.

Your ERP, Vermont Information Processing data, Circana data, retailer POS, forecasts and other sources first need a common understanding of products, customers, distributors, retailers and metrics.

That harmonized layer becomes the source of context.

MCP becomes the way AI interacts with it.

This distinction is important.

MCP solves how AI accesses your data.

Harmonization helps AI understand what that data means.

For many beverage companies, that could still involve a data warehouse such as Snowflake, BigQuery or Databricks.

The warehouse remains responsible for bringing data together, cleaning it and creating a trusted model.

MCP then provides a standardized way for AI applications like ChatGPT, Claude or internal agents to interact with that trusted data.

Don’t Forget About Security and Data Rights

There is another important consideration: just because AI can access data doesn’t mean it should have unrestricted access to it.

This is especially important when working with distributor and syndicated retail data.

Companies need to think about what users are allowed to access, whether AI should have read or write permissions, how data provider agreements apply to AI usage and how interactions are audited.

Metric definitions matter too.

If someone asks for “sales,” does that mean shipments, depletions or retail POS?

Those definitions need to be clear before you put AI on top of the data.

MCP can make enterprise data easier for AI to access.

That makes governance and data quality even more important.

The Shift From Dashboards to Questions

This might ultimately be the most interesting part of MCP for beverage companies.

Today, analytics usually starts with a dashboard.

You open the dashboard.

Select the retailer.

Select the distributor.

Choose the time period.

Find the SKU.

Notice something looks wrong.

Then start opening other systems to figure out why.

AI changes that workflow.

The starting point can simply be:

“What’s driving our forecast variance?”

The AI can investigate the data behind the question.

Maybe shipments are behind because orders slowed down.

Maybe shipments look healthy but VIP depletions are weakening.

Maybe depletions look healthy but Circana shows velocity declining.

Maybe velocity is strong but distribution dropped.

The user doesn’t necessarily need to know which report contains the answer.

They need to know what happened and why.

Where Shopra Fits

This is the problem we’re focused on at Shopra.

We don’t think the future of beverage analytics is simply connecting another chatbot directly to a database.

The harder and more valuable problem is creating context between the systems beverage companies already use.

Shopra creates a harmonized intelligence layer across ERP, distributor, depletion, inventory, retailer, forecast and syndicated data.

That means AI can understand the relationship between shipments, depletions, inventory, distribution, velocity, promotions, retail sales and forecast performance.

MCP creates a powerful standard for making that intelligence available to the AI applications teams want to use.

Instead of asking:

“Which dashboard should I check?”

The starting point becomes:

“What’s driving the variance?”

And the systems behind the scenes help AI figure out the answer.

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