Analytics & Forecasting

The Intelligence Console for Modern Commerce

Move beyond retrospective reporting. Alloy.ai applies proprietary commerce logic to derive the brand-specific metrics and SKU-location forecasts required to stay ahead of market shifts.

TRUSTED BY THE WORLD’S TOP BRANDS

Metric Intelligence

Decision-Ready Metrics for Operational Velocity

Alloy.ai automatically derives hundreds of advanced metrics using proprietary formulas honed by a decade of commerce expertise. By blending partner POS with internal ERP feeds and sales targets, we provide the prescriptive insights required to manage your business from production to the shelf.

Some of the 100s of derived metrics we generate:

Unconstrained Demand
A more accurate baseline of demand, calculated by adjusting historical sales to account for volume lost during out-of-stock events.
Lost Sales
The quantified revenue, left on the table due to localized inventory gaps, identifies exactly where supply failed to meet demand.
Weeks of Supply
Eliminate guesswork with a real-time view of your inventory runway based on current consumption velocity across every location.
Phantom Inventory
Proactively flag products showing positive on-hand levels but zero sales—a leading indicator of misplaced stock or localized reporting errors.

Total Inventory
A comprehensive view of your position that adds partner on-hand units to your internal data for In Transit, On Order, and Allocated stock.
Sales vs. Plan
Maintain accountability by tracking real-time POS performance against your internal sales targets or shipment projections.
Digital Penetration
Visibility into the fulfillment mix of your business across direct-to-consumer, marketplaces, and dot-com channels.
Forecast Accuracy
Continuous assessment of planning performance by measuring variance and systematic bias between actual consumption and projections.

Metric Configuration

Dynamic Logic for Bespoke Business Rules

Alloy.ai metrics are live calculations, not static reports. Because every partner ecosystem is unique, our engine allows you to configure parameters at runtime—standardizing global logic while giving teams the flexibility to pivot analysis instantly to meet specific partner needs.

Tailor your metrics with a few clicks:

Inventory & Pipeline Inclusion
Choose exactly which inventory states—On Hand, In Transit, or Allocated—to define your true supply position.
Flexible OOS Definitions
Define out-of-stock triggers based on zero-unit counts or advanced thresholds like presentation minimums.
Demand Signal Integration
Toggle between partner forecasts, seasonally-adjusted historical averages, or ML models to power forward-looking metrics.
Fulfillment Granularity
Filter performance by channel (such as in-store vs. online) to pinpoint where growth and fulfillment costs originate.
Accounting Logic (Net vs. Gross)
Switch between Net and Gross units or dollars to account for returns and clearance, ensuring accurate margin analysis.
Temporal & Network Perspective
Measure stock health via ending inventory or period averages at a specific location or across your entire network.

Demand Forecasting

Predictive Demand Sensing at the
SKU-Location Level

Traditional planning relies on shipments. Alloy.ai shifts the focus to consumption forecasts—using store-level POS and fulfillment signals to sense demand in real-time and align your supply chain with the reality of the shelf.

POS-Based Forecasts Matter:

Accelerated Response
Identify and adjust to demand shifts in days rather than waiting weeks or months for shipment patterns to surface.
Hyper-Granular Visibility
Generate forecasts at the SKU-location level to drive precise replenishment and localized inventory optimization.
Superior Accuracy
Integrating POS signals with historical shipments has been shown to improve forecast accuracy by 10%, on average.
High-Frequency Updates
Transition from rigid monthly planning to daily or weekly updates that reflect the true pace of modern commerce.

Predictive Models

Models for Complex Demand Patterns

Move beyond basic projections with a library of advanced models tailored for modern commerce. From proprietary seasonal logic to sophisticated machine learning, our engine ensures accuracy across every product lifecycle and category.

Advanced Modeling Capabilities:

Seasonal Historical Average (SHA)
A proprietary model that enhances recent sales averages with a week-indexed seasonality profile. It is engineered for stable seasonal growth, providing robustness against short-term market noise.
Generalized Additive Model (GAM)
A sophisticated ML model that decomposes sales into trends, complex seasonality, and external features—using Fourier series to capture multiple seasonal cycles and holiday impacts.

Historical Intelligence

Intelligence That Heals Your History

Retail data is inherently noisy. Promotions, stockouts, and new launches can “pollute” your historical data, leading to skewed projections. Alloy.ai uses advanced optimizations purpose-built for consumer goods to clean and normalize your data, ensuring your models are built on a true baseline of demand.

Depromo History
Isolate your true baseline demand by automatically removing the sales spikes and "noise" caused by promotions or temporary events.
New Product Introduction
Accelerate accuracy for new launches with less than 12 months of data by intelligently applying seasonality patterns from similar, established products.
Store Count Normalization
Ensure distribution changes don't skew velocity by modifying forecasts based on the actual number of stores carrying a SKU over time.
Product Chaining
Use AI and vector embeddings to link relaunched or replacement products with discontinued ones, creating a continuous, high-fidelity historical record.

Forecast Accuracy

Accuracy and Alignment Monitoring

A forecast is only as valuable as its reliability. Alloy.ai automatically archives and snapshots every projection, allowing you to monitor shifts in confidence over time and ensure your plan remains perfectly aligned with retailer expectations.

Retailer Forecast Monitoring
Compare partner POS forecasts side-by-side to identify shifts in expectations and proactively anticipate changes in future ordering behavior.
Historical Snapshotting
Access any previous forecast version to calculate lagged-accuracy measures and identify systematic bias in your planning process.
Custom Accuracy Metrics
Evaluate performance using industry standards like MAPE and WAPE, or leverage our proprietary Rolling WAPE—specifically engineered to account for the timing variance of CPG demand spikes.

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The Intelligence Console for Modern Commerce