If you're a Walmart supplier, your Monday morning probably looks something like this:
You log into Retail Link. You navigate to Scintilla. You download 13 files — sales and inventory, eComm sales, eComm inventory, vendor scorecard, tender analysis, demand forecast, order forecast, DC metrics, store MUMD, future valid items, modular plan metrics, eComm instock, and item tables.
That's 13 Excel files. Every week. For every supplier ID you manage.
Then the real work begins.
The 2.5-Hour Ritual
You open each file. You reformat columns. You build pivot tables. You copy numbers into last week's template. You update your charts. You write a summary for your team. You format a deck for your buyer meeting.
If you're fast, this takes about 2.5 hours. If you manage multiple supplier IDs or work at a broker, multiply that by 3, 5, or 15.
By Wednesday, you have a rough picture of last week's performance. By Thursday, you're in the buyer meeting presenting data that's already 5 days old.
The Signals You're Missing
Here's the problem with this workflow: it's backward-looking by design. You're spending all your time figuring out what happened instead of figuring out what's about to happen.
While you were building pivot tables, these signals were sitting in your data, unnoticed:
DC Ordering Gaps — Distribution center 6023 hasn't ordered your #2 SKU in two weeks. By the time stores run out and it shows up in your instock numbers, you've already lost 2-3 weeks of sales. The signal was in your order forecast file on Sunday. Nobody looked.
Instock Drops on Key Items — Your overall instock is 94%. Looks fine. But three of your top-10 items dropped below 80% this week. The average masked the problem. Your buyer sees item-level data in Luminate — they already know.
Forecast Misalignment — Your demand forecast shows 50,000 units, but actual orders are tracking at 32,000. That's a 36% gap. Either the forecast is wrong (and you'll get blamed for excess inventory) or orders aren't being placed (and you'll get blamed for out-of-stocks). Either way, you need to know now — not next Wednesday.
Ghost Inventory — Store 4521 has been showing 24 units of your product for 9 consecutive weeks with zero sales. That inventory isn't real. It's sitting in a back room, miscounted, or damaged. It inflates your instock number while customers can't actually buy your product.
Your Buyer Already Has This Data
Here's what most suppliers don't realize: your Walmart buyer has Luminate. They have real-time POS data. They see trends across your entire category — not just your brand.
When you walk into that meeting with a spreadsheet you built on Tuesday from last week's data, you're already behind. They already know your instock dropped. They already see which items are trending down.
The question they're asking isn't "what happened?" It's "what are you doing about it?"
The Suppliers Who Win
The suppliers winning at Walmart in 2026 aren't the ones with the best spreadsheet skills. They're the ones who see the data sooner, spot exceptions faster, and show up to buyer meetings with answers instead of excuses.
They know which DCs stopped ordering before stores run out. They know which items are losing distribution before the line review. They know where their forecast is diverging from reality before it becomes a fill rate problem.
They're not working harder. They're seeing the data differently.
What Does Your Monday Morning Look Like?
If your team is still spending hours every week turning raw Scintilla files into something usable, there's a better way.
We built SupplySense to solve exactly this problem. Upload your 13 weekly files — or let us pull them automatically — and within seconds you have a complete picture: sales trends, instock analysis, DC ordering gaps, forecast exceptions, ghost inventory detection, and buyer-ready reports.
No pivot tables. No reformatting. No stale data by Thursday.
Your Monday morning should start with answers, not Excel files.
SupplySense is built in Bentonville by people who've lived the Walmart supplier experience. If you're spending more time preparing data than acting on it, let's talk.