InventoryIQ — preventing stockouts before they happen.
An AI-powered demand forecasting and automated reorder intelligence platform for retail and distribution businesses. InventoryIQ turns existing sales and inventory history into daily SKU-level forecasts, early stockout-risk signals, and actionable reorder recommendations without requiring a complex integration to get started.
In short
InventoryIQ is an AI-powered demand forecasting and automated reorder intelligence platform built for retail and distribution businesses. It starts from uploaded sales and inventory history, generates daily SKU-level demand forecasts, identifies emerging stockout risk, and converts those forecasts into recommended reorder actions.
- Client Multi-Category Retail & Distribution Enterprise
- Problem Reactive replenishment, stockouts, overstock, and manual spreadsheet reviews
- Solution SKU-level demand forecasting + stockout-risk detection + automated reorder intelligence
- Scale Single warehouse to multi-location, multi-category inventory
Retail teams were reacting to stockouts instead of seeing them form.
Retail and distribution businesses routinely lose revenue to two mirror-image problems: stockouts that send customers to competitors, and overstock that ties up working capital in slow-moving inventory. Most mid-market operators still plan replenishment using static reorder points, spreadsheet-based reviews, or gut-feel judgment calls made under time pressure. These approaches do not account for seasonality, promotional spikes, supplier lead-time variability, or SKU-level demand volatility, and they scale poorly once a catalog grows past a few hundred SKUs across multiple warehouses or stores. By the time a stockout is visible in a sales report, the lost sale has already happened. The client needed a way to see stockout risk forming days or weeks in advance, at the individual SKU level, without requiring a data science team or a lengthy ERP integration project to get started.
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Static reorder points and spreadsheet-driven inventory reviews
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Stockout problems become visible after lost sales have already occurred
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Seasonality, promotions, lead times, and SKU-level volatility are difficult to account for
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Manual processes become difficult to scale across warehouses, stores, and hundreds of SKUs
What InventoryIQ had to achieve.
Forecast demand at SKU level, using historical sales and inventory data the client already had on hand, without demanding a complex system integration up front.
Give operators direct control over risk tolerance, through configurable buffer (safety-stock) days and a selectable forecast horizon, rather than a fixed one-size-fits-all model.
Flag SKUs trending toward stockout early enough for a purchasing team to act, not just report on stockouts after they occur.
Automate the reorder decision, converting a forecast into a specific, actionable reorder trigger and recommended order quantity.
Keep the barrier to entry low, so a business could get from raw CSV export to a working forecast in one sitting, with no coding or BI tooling required.
Build a foundation that scales, from a single warehouse and a few hundred SKUs to a multi-location, multi-category catalog as the business grows.
A self-serve inventory intelligence layer that turns historical data into forecast-led purchasing action.
Daily SKU-level demand intelligence
InventoryIQ processes uploaded sales and inventory history and generates rolling daily demand forecasts incorporating trend, seasonality, day-of-week effects, and recent demand shifts.
Early stockout-risk detection
Projected inventory is evaluated against forecasted demand and configurable buffer requirements, allowing emerging stockout exposure to be identified and prioritized before inventory reaches zero.
Actionable purchasing recommendations
When a SKU crosses its configured risk threshold, the platform converts the forecast into a reorder trigger with recommended quantity and target order date.
Simple CSV-based onboarding
Sales and inventory history can be uploaded directly from an existing point-of-sale, e-commerce platform, or ERP. The ingestion layer maps columns where possible and flags missing dates, duplicate SKUs, negative stock values, and other data-quality issues before forecasting starts.
Configurable planning controls
Operators choose safety-stock buffer days and forecast horizon based on supplier lead times and category type, allowing fast-moving grocery products and multi-month seasonal goods to follow appropriately different planning timelines.
Automated stockout-risk ranking
At-risk SKUs are ranked by urgency so purchasing teams can focus attention on the inventory positions most likely to require intervention within the selected planning window.
Forecast confidence and explainability
Recommendations surface forecast confidence alongside underlying assumptions such as buffer days, supplier lead time, current stock position, and recent demand trend, helping operators understand why a SKU was flagged.
Operator dashboard and exportable actions
A focused dashboard surfaces at-risk SKUs, forecast confidence, and days-of-cover remaining, while reorder recommendations can be exported into purchase-order workflows or existing procurement tools.
Modular architecture for scale
The self-serve design supports growth from a single warehouse and a few hundred SKUs toward multi-location, multi-category inventory operations as the business accumulates more historical sales data.
Five operational barriers, five practical fixes.
Inconsistent and messy historical data
CSV exports can contain different column formats, missing dates, duplicate SKUs, and anomalous stock values.
Automated ingestion and validation
The ingestion layer detects formats, flags gaps and anomalies, and guides operators to correct or accept data before forecasting begins.
One-size-fits-all forecasting does not work across categories
Fast-moving perishables and slow-moving seasonal goods require different planning horizons and risk tolerances.
Configurable buffer days and forecast horizon
Operators can configure planning parameters by SKU category so different product types can be managed using appropriately different timelines.
Forecasts alone do not drive action
A purchasing team still has to interpret a forecast, determine what to buy, and decide when to place the order.
Automated reorder decision support
The platform generates reorder triggers with recommended quantities and target dates, turning forecast output into a specific purchasing action.
Trust in a new, unfamiliar system
Operators need to understand why a SKU was flagged before relying on an automated recommendation.
Confidence and assumption visibility
Forecast confidence and key assumptions such as buffer days, lead time, and recent demand trend are surfaced alongside recommendations.
Fitting into existing purchasing workflows
Teams need recommendations to reach their current purchase-order and procurement processes rather than creating another isolated workflow.
Export and integration paths
Reorder recommendations can flow into existing purchase-order workflows and procurement tools, preserving established operating processes.
"The low-integration approach was central to the platform design: businesses could move from a raw CSV export to a working SKU-level forecast in one sitting, while still retaining control over buffer days, forecast horizon, and the eventual purchasing action."
From reactive inventory reviews to proactive purchasing intelligence.
For purchasing and inventory teams. Early, ranked visibility into which SKUs need attention first, replacing manual spreadsheet reviews with a daily, always-current view of stockout risk.
For finance and operations leadership. Fewer emergency reorders and expedited shipping costs, better working-capital discipline from right-sized order quantities, and a clear audit trail behind every reorder recommendation.
For customers and revenue. Fewer lost sales from out-of-stock popular items, and improved on-shelf or in-stock availability across the catalog during peak and seasonal demand periods.
For the business overall. A self-serve, low-integration-effort system that scales from a single warehouse to a multi-location catalog, and that improves in accuracy as more historical sales data accumulates over time.
Inventory planning becomes proactive, forecast-led, and actionable.
InventoryIQ moves inventory planning from a reactive, spreadsheet-driven exercise to a proactive, AI-guided discipline. By turning a simple CSV upload into daily, SKU-level demand forecasts and automated reorder triggers — governed by buffer days and a forecast horizon the business controls — the platform gives retail and distribution operators early warning on stockout risk and a concrete, ready-to-action reorder recommendation instead of just a report. Its low-integration, self-serve design makes it accessible to mid-market operators immediately, while its modular forecasting architecture positions it to extend into supplier-side lead-time optimization, multi-echelon inventory planning, and deeper procurement automation as the business scales.
Common questions about InventoryIQ.
Find quick answers about CSV onboarding, demand forecasting, stockout-risk detection, reorder recommendations, and procurement workflows.
How does InventoryIQ get started without an ERP integration?
The platform starts with a CSV export of sales and inventory history from an existing point-of-sale, e-commerce platform, or ERP. InventoryIQ validates the file, maps columns where possible, and flags data-quality issues before forecasting begins, so a complex integration is not required for the initial deployment.
Can operators control the forecast horizon and safety-stock buffer?
Yes. Operators can configure buffer days and select a forecast horizon appropriate to supplier lead times and category type. This allows fast-moving and seasonal products to be planned using different risk tolerances and planning timelines.
How does InventoryIQ identify SKUs that are likely to stock out?
The platform compares projected on-hand inventory with forecasted demand and the configured buffer. SKUs projected to approach zero stock within the selected forecast horizon are automatically flagged and ranked by urgency.
Does the system only provide forecasts, or does it recommend what to reorder?
InventoryIQ closes the loop from forecast to purchasing action. When a SKU crosses its risk threshold, the system generates a reorder trigger with a recommended order quantity and target order date based on demand, stock position, buffer settings, and supplier lead time.
How does InventoryIQ fit into an existing procurement workflow?
Reorder recommendations can be exported into purchase-order workflows or existing procurement tools. This allows teams to use InventoryIQ for forecasting and decision support without requiring them to abandon their established purchasing processes.
Still planning inventory with spreadsheets?
Our architects can map a low-integration forecasting and reorder intelligence workflow for your catalog — starting with the sales and inventory data you already have, rather than requiring a lengthy ERP integration before you can see value.
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