Leading consumer brands are using Alloy.ai to take Walmart Scintilla’s analytic capabilities to the next level. Discover how Alloy.ai enhances Walmart Scintilla with automated replenishment recommendations, cross-retailer data normalization, improved forecasting, and deeper e-commerce insights, ERP data integrations and more — helping consumer brands act quickly to identify problems and ultimately add millions in new revenue.
How Alloy.ai takes Walmart sales and inventory analytics to the next level
Walmart Scintilla arms consumer brands with crucial sales and inventory insights, but analyzing and making sense of that data — across channels and even across all of your retail partners — remains a challenge. That’s where Alloy.ai comes in. Alloy.ai automates data processing and provides advanced analytics on top of Scintilla, helping brands optimize replenishment, improve forecasting, gain a clearer view of e-commerce trends, spot cross-retailer trends and even leverages predictive analytics to help you stay ahead of out-of-stocks, phantom inventory and more. Here are seven ways Alloy.ai enhances Walmart Scintilla to help brands make better, faster decisions.
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Flexible and powerful ad-hoc analysis
Walmart Scintilla provides many essential metrics, but what if you want the answer to a question that falls outside of its prescribed metrics? Today, that means downloading reports from different systems or portals, cleaning the data and then finally doing a bit of analysis (if you have any time left over). Imagine doing all of that with a couple of clicks.
Scintilla is best-in-class in-terms of retailer portals, but it isn’t designed to support the need for flexible deep, customized analysis. Most sales, insights, category, and supply chain teams still need to work outside of Scintilla to answer questions beyond the preconfigured insights Walmart provides.
Alloy.ai eliminates this manual work by enabling ad-hoc analysis within the platform. Instead of being confined to pre-built charts and static reports, users can:
Combine data from multiple reports for deeper insights
Create custom visualizations and derived metrics with a few clicks
Quickly drill down into trends and variances without relying on exporting CSVs from different systems
With Alloy.ai, teams don’t have to rely only on pre-built reports. They can flexibly analyze data to answer specific business questions, making decision-making faster and more precise. In short, it’s built for the way analysts like to analyze.
Automatic retail replenishment order recommendations
Walmart gives their suppliers certain tools to help augment their replenishment systems. These include store-specific orders and push orders. If used correctly, these tools can improve on-shelf availability for the consumer, build trusting retailer relationships, and crucially, increase sales for your business. But figuring out when to use them and how often takes a lot of manual data crunching.
You need to combine data from multiple different reports in Scintilla, calculate the need and then make adjustments to meet any supply chain constraints. Simply pulling all the latest data and stitching it all together can take significant time — and acting fast is critical to avoid missing the opportunities or avoiding stockouts. You also need to be careful with your calculations, to avoid unnecessary overstocks and losing your Replenishment Manager’s trust.
Alloy.ai’s Retail Replenishment Recommendations for Walmart Suppliers module supplements Walmart’s Scintilla and NOVA systems by alerting you to low inventory and delivering automated replenishment order recommendations, helping brands optimize inventory flow based on demand signals, sales trends, and Walmart’s replenishment logic. Then it automatically spins up an order form in NOVA.
Alloy.ai’s replenishment recommendations enable brands to:
Maintain optimal stock levels to prevent lost sales
Avoid overstocking, reducing carrying costs
Respond proactively to fluctuations in demand
By automating and improving replenishment decisions, Alloy.ai helps brands ensure Walmart shelves remain stocked with the right products at the right time. Alloy.ai customers have identified, submitted and had Walmart RM’s approve millions of dollars in incremental replenishment orders — per month.
See our replenishment recommendations workflow in action here:
Normalizing data across retailers and channels
Most consumer brands don’t just sell through Walmart — they operate across multiple retailers and e-commerce channels. However, each retailer provides data in different formats, making it difficult to compare performance across retailers.
Alloy.ai automatically integrates and normalizes data from Walmart Scintilla alongside data from all of your other retailers, transforming fragmented reports into a unified view.
With your data normalized by product ID, units of measure, location and time, brands can easily:
Compare sales and inventory performance across retailers
Spot trends that might be hidden in siloed data
Make more informed decisions with a complete picture of their supply chain
By providing a consistent data structure, Alloy.ai removes the manual effort of aligning reports from different retailers and enables brands to gain cross-retailer insights that drive better sales and inventory strategies.
See the kinds of cross-retailer insights you can easily surface in Alloy.ai:
Forecast accuracy and versioning
Walmart provides two forecasts that impact replenishment decisions — the POS store forecast and the GRS forecast, which drives automatic replenishment. If these forecasts are inaccurate, products can either go out of stock or be overstocked. Alloy.ai helps brands quickly detect when forecasts are underestimating or overestimating demand. Because even best-in-class forecasts like Walmart’s aren’t right all the time. Alloy.ai stores historical forecasts and all changes, to give brands a measure of the retailer’s accuracy vs. forecasts from all other retailers:
POS Retail Forecast Accuracy Dashboard in Alloy.ai
Additionally, Alloy.ai allows for versioning of forecasts, helping brands compare different forecast scenarios over time. You will be alerted when changes happen — something that Walmart does not flag for suppliers today — so you can look into root causes. Is it a promotion or an error?
With Alloy.ai, brands can:
- Spot problems with forecast accuracy, which can be used to get ahead of replenishment problems and OTIF fines
- Compare multiple forecast versions to see how projections evolve
- Adjust production and inventory strategies proactively
Handling data quirks and restatements
Retail data often requires interpretation due to occasional adjustments, delayed updates, or reporting shifts. Walmart Scintilla provides extensive data, but keeping up with changes can be challenging. Alloy.ai helps brands detect and adjust for these changes more efficiently.
Examples of data “quirks” Alloy.ai can identify and help manage include:
Restated Sales Data: Sometimes sales numbers are updated after initial reports due to late transactions or adjustments. Alloy.ai tracks and highlights these restatements to ensure reporting accuracy over time.
- Shifting Metrics: Sometimes important metrics change, and not knowing can leave you in the dark. Take the MaxShelf metric, which represents the maximum number of units that can fit on a store shelf based on the current modular setup. This is a crucial metric for brands, especially in high-traffic categories like checkout aisle products. However, this metric can sometimes change after the fact, making historical analysis difficult. Alloy.ai helps track and freeze MaxShelf values as they were originally reported, ensuring consistency in analysis and preventing discrepancies when assessing past inventory needs.
- Phantom Inventory: A product may appear in stock according to data but be unavailable on the shelf. Alloy.ai can highlight discrepancies between sales velocity and inventory levels to flag potential phantom inventory issues.
By automatically detecting these quirks and normalizing the data, Alloy.ai ensures that brands have an accurate and reliable view of their business performance. This allows teams to act confidently on the insights derived from Scintilla without the need for constant manual data reconciliation.
Understanding ecommerce trends
Alloy.ai enhances e-commerce insights by integrating all fulfillment methods, including buy online, pick up in-store (BOPIS), and ship-to-home. By analyzing these fulfillment options alongside in-store sales, brands gain a deeper understanding of consumer purchasing behavior.
By offering more configurable metrics and analysis options than pre-configured dashboards, Alloy.ai allows brands to go deeper into e-commerce data, ensuring they capture emerging trends and optimize omnichannel performance.
Key benefits include:
Nil Pick Analysis: Identify where products are frequently unavailable for online orders and in-store pickup, helping brands address stock availability issues.
Custom Weeks of Supply Metrics: Alloy.ai enables brands to analyze store supply levels with or without online sales factored in, providing greater flexibility in understanding inventory dynamics.
Lost Sales Correlation: Track lost sales in relation to out-of-stock incidents and fulfillment method trends, enabling proactive supply chain adjustments.
Connecting ERP and retail POS data to unlock new insights
There is no better way to align supply and demand than to analyze POS sell-through data alongside shipment and order data from your ERP. Unfortunately there is no way to do this within Scintilla, which will slow down any analyst trying to look for answers, trends or insights.
Alloy.ai bridges this gap by allowing brands to seamlessly combine their ERP data with retail insights, unlocking powerful new capabilities for decision-making:
Inventory constraints & order allocation: Brands can determine whether they have enough inventory to fulfill Walmart orders before committing, preventing unfillable purchase orders. If inventory is limited, Alloy.ai can help allocate available stock strategically across different fulfillment channels and retailers.
Drill into inventory issues: Instead of jumping between systems — or, gasp, exporting everything into Excel — brands can now analyze store-level supply issues alongside their ERP-stored fill rates and inventory availability, making it easier to diagnose and address inventory shortages or overhangs.
Avoid OTIF fines and improve service levels: Meet your supply chain KPIs by making informed decisions on production ordering, expediting production, allocation or transfers with visibility into ERP movements and retail supply levels in one place.
By centralizing ERP and retail data within Alloy.ai, brands break down data and organizational silos, speed up analysis, and make more data-driven decisions that directly impact their bottom line.