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Demand OS for CPG

Demand planning AI software built for CPG

Trade promotions, cannibalization, and SKU proliferation break generic forecasting tools. The CPG template ships with promotional baseline separation, cannibalization hierarchies, and retailer-ready explainability on day one.

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What does demand planning AI software do for CPG companies?

Demand planning artificial intelligence software for CPG manufacturers models trade promotional uplift, cannibalization, and halo effects at the SKU-retailer-week level, keeping the statistical baseline separate from promotional lift so post-event measurement is automatic instead of a spreadsheet reconciliation. It ingests daily POS and retailer sell-through data to sharpen near-term forecasts, and ships every number with ranked factor attribution planners can defend in a joint business planning session. The result is a category management and retailer collaboration number CPG teams can trust and explain, not one they have to negotiate line by line.

The CPG Problem

Trade promotions and SKU proliferation break averaged forecasts

Trade promotion accuracy nobody trusts
The promotional forecast is built by layering uplift estimates on top of a baseline — often in a spreadsheet, often by a different team than the one that runs the promotion. When the lift misses, post-event reconciliation takes weeks.
Cannibalization and halo are invisible
When a promoted SKU lifts, sibling SKUs often drop. When a new product launches, the older SKU in the same segment erodes. Neither effect appears in a standard volume forecast unless someone models it explicitly.
SKU proliferation lengthens every planning cycle
New flavors, pack formats, and private-label variants add to the planning workload without a proportional increase in headcount. The result is less time per SKU and more reliance on copy-paste.
Retailer collaboration needs a defensible number
Major retailers expect a joint business planning number that reflects current sell-through, not last year’s shipment data. Building that number in a spreadsheet turns every collaborative session into a negotiation over the data, not the plan.

What the CPG Template Includes

Capabilities mapped to CPG failure modes

CPG FAILURE MODE
The promotional forecast is layered on top of a baseline in a spreadsheet by a different team than the one running the promotion — when the lift misses, reconciliation takes weeks.
Trade Promotion Demand Modeling
The AI Forecasting Engine models promotional uplift, cannibalization, and halo effects at the SKU-retailer-week level, with baselines separated from promotional lifts so post-event measurement is automatic.
CPG FAILURE MODE
When a promoted SKU lifts, sibling SKUs often drop — and neither cannibalization nor halo shows up in a standard volume forecast unless someone models it explicitly.
Cross-SKU Cannibalization & Halo
Configurable product hierarchies define which SKUs share demand. When a promotion runs on one SKU, estimated cannibalization coefficients apply automatically to siblings in the same segment.
CPG FAILURE MODE
New flavors, pack formats, and private-label variants add planning workload every year without a proportional rise in headcount — less time per SKU, more copy-paste.
New Product Forecasting
Configurable reference-curve logic borrows from analogous SKUs at the category or subcategory level. Planners review and adjust the curve before the new SKU enters live planning.
CPG FAILURE MODE
"Why is your number different from ours?" — a retailer buyer asking for a defensible joint business planning number turns the session into a negotiation over data.
Sell-Through Sensing + Explainable AI
Demand Sensing ingests daily POS and retailer sell-through data to sharpen the near-term forecast; every SKU-level number ships with ranked factor attribution planners can explain in the room.

In Practice

A personal care CPG cuts post-promotion reconciliation from 3 weeks to 2 days

The Situation
A personal care manufacturer ran 180+ promotional events per year across 12 major retail accounts. Post-event reconciliation was a manual 3-week process each month, with the promotional baseline maintained separately from the statistical forecast in a shared spreadsheet.
What Demand OS Did
The AI Forecasting Engine ingested 24 months of promotional history, segmented by event type and retailer, and separated baselines from promotional lifts automatically. Cannibalization coefficients were estimated across product siblings, and Demand Sensing ingested daily sell-through feeds from the top-8 accounts.
The Outcome
Post-event measurement became a 48-hour automated process. Promotional accuracy improved by 7%. The joint business planning number was presented with factor attribution the retailer buyer could verify against their own sell-through data — cutting negotiation time in half.
CPG grocery shelves stocked by AI-driven demand planning

Measurable CPG Outcomes

What CPG teams measure after go-live

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

6–8%average improvement in promotional forecast accuracy from statistical baseline/promotional separation (Gartner Supply Chain)55%of CPG demand planning time currently spent on promotional forecasting, reclaimed as the model takes over the reconciliation work (Gartner Supply Chain Survey)5–7 wksto a live pilot on one brand or category family, including promotional event configuration and product hierarchy setup
Related

Explore Demand OS for CPG

Scenario Planning

Model optimistic, pessimistic, and base-case outcomes side-by-side before committing to a plan.

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Explainable AI

Ranked, plain-language factor attribution for every forecast — no black-box model outputs.

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Demand OS vs SAP IBP

Native SAP S/4HANA and ECC integration without the consultant-dependent IBP rollout.

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Frequently Asked Questions

CPG demand planning, answered

How does Demand OS model trade promotional uplift?+
The AI Forecasting Engine separates the promotional baseline from uplift using historical promotion data, event type, retailer, and channel. Lift factors are estimated per promotion type and updated with each completed event, distinguishing baseline demand from the incremental volume attributable to the promotion.
Can Demand OS handle promotional cannibalization across sibling SKUs?+
Yes. Cross-SKU cannibalization and halo effects are modeled by configuring product hierarchies that define which SKUs share demand. When a promotion runs on one SKU, the system applies estimated cannibalization coefficients to sibling SKUs in the same segment.
How does Demand OS handle new product introductions?+
New products without sales history use configurable reference-curve logic — the system borrows from analogous SKUs at the category or subcategory level. Planners can review and adjust the reference curve before the new SKU enters live planning.
Does Demand OS connect to retailer collaboration portals or EDI?+
Demand OS connects to EDI feeds and retailer data platforms for sell-through data ingestion. Direct integrations for major retailer data portals are available, and forecast output can be published in retailer-specified formats for joint business planning.
What is the typical implementation timeline for CPG?+
A CPG pilot on one brand or category family typically goes live in 5–7 weeks. The additional time relative to simpler verticals accounts for promotional event configuration and product hierarchy setup, with full portfolio rollout following in phased blocks.

Get Started

See the CPG template on your own SKUs

A scoped pilot with your brands, your trade promo calendar, and your retailer POS feed — live in 5–7 weeks. You leave with accuracy benchmarks, a rollout plan, and published pricing.

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