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Demand OS for Retail

Demand planning AI software built for retail

Promotions, seasonality, and store-level replenishment break generic forecasting tools. The retail template ships with promotion-aware models, cannibalization math, and store/DC hierarchies on day one.

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What does demand planning AI software do for retail?

Demand planning artificial intelligence software for retail forecasts sales at store/SKU grain, models promotional uplift and cannibalization, and converts those forecasts into replenishment orders — automatically, with every number explained. The measurable outcomes are fewer stockouts on promoted and seasonal items, less capital tied up in slow movers, and planner hours shifted from spreadsheet reconciliation to exception handling.

The Retail Problem

Retail demand punishes averaged forecasts

Promotions distort everything
A promoted week is not a demand signal — it is a marketing decision. Averaging it into the baseline inflates next quarter and starves the promo period itself.
Seasonality is store-shaped
The same SKU peaks in different weeks in different regions. Chain-level seasonality curves miss both peaks and leave stores swapping stock in transit.
Replenishment at store grain
A forecast that is right at DC level and wrong at store level still produces empty shelves. Store-level accuracy is where retail lives or dies.

What the Retail Template Includes

Capabilities mapped to retail failure modes

RETAIL FAILURE MODE
Flat percentage uplift applied to every promotion, regardless of mechanic, depth, or timing.
Promotion Planning
Models uplift, cross-SKU cannibalization, and halo effects per event, with confidence intervals planners can override with a logged reason.
RETAIL FAILURE MODE
Weekly cycles miss a demand spike until the stockout has already happened.
Demand Sensing
Ingests daily POS and order signals, flags the spike, explains the likely driver, and recommends a replenishment adjustment within days.
RETAIL FAILURE MODE
New seasonal lines launch with zero history and gut-feel initial buys.
New Product Forecasting
Analogous product matching by attribute, category, and channel plus Bayesian priors — planners see which analogues were used and why.
RETAIL FAILURE MODE
One safety-stock rule across 40,000 SKU-locations overstocks basics and misses volatile fashion items.
Inventory Optimization
Service-level–driven safety stock by SKU, store, and season, balancing shelf availability against working capital.
RETAIL FAILURE MODE
Merchants, planners, and finance argue from three different spreadsheets.
Consensus Forecasting
One structured workflow for merchant, planning, and finance inputs — one number with full traceability.
RETAIL FAILURE MODE
"Why did the forecast jump 30%?" takes a day of analyst time to answer.
Explainable AI + Copilot
Every forecast ships its ranked drivers (seasonality, promo, weather); the copilot answers the question in the workbench in seconds.

A Week in the Retail Workbench

From Monday's POS feed to Friday's approved orders

MON
POS lands, baseline sharpens
Weekend sell-through updates the near-term forecast. Anomalies are flagged with their likely driver.
TUE–WED
Promo & exception review
Planners review flagged SKUs and upcoming promo forecasts, overriding with logged reasons where they know better.
THU
Consensus & scenarios
Merchant and finance inputs reconcile into one number; scenario view shows the range before commitment.
FRI
Orders write back
Approved replenishment pushes to the ERP as POs, store-by-store, with a full audit trail.

Retail Formats

One retail template, tuned per format

Grocery demand behaves nothing like fashion demand. The demand planning AI software adapts its models and replenishment cadence to the format you run.

Grocery & Convenience
Grocery & Convenience
High-velocity perishables with daily replenishment: freshness-aware forecasts, waste reduction, and promo-driven basket effects.
Fashion & Apparel
Fashion & Apparel
Short seasons, size-curve allocation, and markdown timing: cold-start forecasting for lines with no history and clean sell-through tracking.
Big-Box & DC-Led
Big-Box & DC-Led
Store/DC hierarchies with multi-echelon safety stock: right inventory at the right echelon, not just the right chain total.
Omnichannel & D2C
Omnichannel & D2C
Store, web, and marketplace demand planned together: channel-mix what-ifs and returns-adjusted net demand.
Grocery store shelves stocked by AI-driven replenishment

Measurable Retail Outcomes

What retail teams measure after go-live

Demand planning artificial intelligence software earns its keep in three numbers retail leadership already tracks. Demand OS reports all three continuously, from the pilot's first cycle.

20–50%lower forecast error vs. traditional methods on promoted and seasonal SKUs (McKinsey benchmark for AI-driven forecasting)6–15%of inventory value released as working capital through service-level–driven safety stock by store and season4–8 wksto a live pilot on a defined store/SKU scope — results measured before any chain-wide rollout
Related

Explore Demand OS for Retail

AI Forecasting Engine

Ensemble of statistical and ML models auto-selected per SKU, with every choice logged and explainable.

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Demand Sensing

Daily signal detection that sharpens the near-term forecast before replenishment decisions are made.

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Demand OS vs o9 Solutions

Planner-first design vs an enterprise IT platform, with published positioning up front.

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Frequently Asked Questions

Retail demand planning, answered

What is the best demand planning artificial intelligence software for retail?+
The best retail demand planning AI models promotions natively — uplift, cannibalization, halo — forecasts at store/SKU grain, and explains every number so planners can override with confidence. Demand OS ships all of that in its retail template, with a scoped pilot live in 4–8 weeks and published pricing before any sales call.
How does AI forecasting handle retail promotions?+
Demand OS decomposes demand into baseline and promoted volume, then models each promotion's uplift, cross-SKU cannibalization, and post-event dip from your historical promo calendar. Planners see the estimate with confidence intervals and can override any assumption with a logged reason.
Can it forecast new products with no sales history?+
Yes. New Product Forecasting matches the launch item to analogous SKUs by attribute, category, and channel, and blends them with Bayesian priors. Planners see exactly which analogues were used, which matters for fashion and seasonal lines where most SKUs are new each season.
Does it replenish at store level or DC level?+
Both. Forecasts are generated at store/SKU grain and aggregated up the hierarchy, and safety stock is set per echelon, so the DC and the shelf are planned as one network rather than two separate spreadsheets.
What data does a retail pilot need to start?+
Two years of sales history (POS or order lines), your promo calendar, and current inventory positions — via native ERP connectors (SAP, Oracle, Dynamics 365, NetSuite) or CSV/SFTP. A defined store/SKU pilot scope goes live in 4–8 weeks.

Get Started

See the retail template on your own SKUs

A scoped pilot with your stores, your promo calendar, and your POS feed — live in 4–8 weeks. You leave with accuracy benchmarks, a rollout plan, and published pricing.

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