DecisionX for CPG

Decision AI for FMCG (CPG)

One connected enterprise decision brain for FMCG (CPG), reasoning across NPD, Manufacturing, and Commercial as a single system, not three disconnected tools.

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Built with provenance, auditable, and fully explainable, for industries where decisions carry real-world consequence.

The three pillars

Deep in each function, connected across all three.

Each pillar runs its own full decision lifecycle in depth on its own page, four core use cases, and where DecisionX plugs in.

CPG NPD icon

CPG NPD

New product launch, reformulation, and portfolio decisions grounded in demand and distribution signals.

Launch Demand Forecasting
SKU Performance Diagnosis
Reformulation & Relaunch Prioritization
Distribution & Shelf Readiness
Explore CPG NPD
Manufacturing Excellence icon

Manufacturing Excellence

Line performance, changeover, and yield decisions built on production and quality data.

Line Bottleneck Diagnosis
Yield & Waste Attribution
Changeover & Campaign Sequencing
Make-vs-Buy Sourcing Decisions
Explore Manufacturing Excellence
Commercial Excellence icon

Commercial Excellence

Pricing, trade, and promotion decisions grounded in commercial and retailer signals.

Demand & Margin Reconciliation
Discount & Trade Depth Setting
Trade Spend Calendar Allocation
Retailer Decline Root-Cause Routing
Explore Commercial Excellence

The CPG AI Stack

One connected enterprise brain, purpose-built for CPG.

A state-of-art decision infrastructure, causal, contextual, self-learning, with one reasoning engine sitting underneath every domain.

Where Decisions
SurfaceEvery dashboard and board each function already works in.
CPG NPD
Launch & pipeline dashboards Distribution readiness boards Reformulation trackers
MANUFACTURING
Line performance dashboards Changeover & downtime boards Yield recovery dashboards
COMMERCIAL
Trade spend & promo boards Pricing & margin dashboards Retailer scorecards
Reasoning
AgentsSpecialized agents that reason over the ontology layer below.
Forecasting Agent RCA Agent Optimization Agent Attribution Agent
DecisionX
Reasoning EngineOne brain, reasoning across NPD, Manufacturing, and Commercial at once.
DecisionX Contextual Aware Intelligence Layer

Causal, contextual, and self-learning, every ontology below is reasoned over by the same brain, so a decision in one domain is visible context for the other two.

DecisionX
Ontology LayerThe structured business logic each domain reasons against.
NPD ONTOLOGY
Demand & Forecast Formulation & Recipe Distribution & Shelf Consumer & Panel Competitive & Launch Tracking Regulatory & Labeling
MANUFACTURING ONTOLOGY
Quality & Batch Line & Changeover Asset & Reliability Yield & Waste Network & Make-vs-Buy Supplier & Raw Material
COMMERCIAL ONTOLOGY
Demand & Margin Trade & Promotion Pricing & Assortment Retailer & Channel Media & Channel Mix Category & Space Management
Data & Business
SystemsStructured & unstructured, handwritten scans, messy files, every format in between.
NPD SOURCES
POS & demand signal data Distribution & DC data Consumer & panel data Formulation & recipe data Competitor launch tracking Retailer scorecards
MANUFACTURING SOURCES
MES / line data Changeover logs Process historian Yield / scrap rates Maintenance & OEE data Supplier & raw material data
COMMERCIAL SOURCES
POS / sell-through data Trade promotion data Retailer & channel data Pricing & margin data Media spend data Category management data

How DecisionX reasons

Five layers, one decision.

Every signal above, demand, changeover, or shelf, moves through the same reasoning arc before it becomes a decision.

Signals icon
LAYER 01
Signals
What is happening?
Demand shifts, changeover loss, and shelf gaps are watched continuously, not surfaced once a quarter.
Reason icon
LAYER 02
Reason
What does it mean?
The underperforming SKU, the line's waste driver, and the margin impact are reasoned together, not diagnosed three times.
Decide icon
LAYER 03
Decide
What's the best action?
Reformulate, re-launch, or hold, ranked by margin, changeover cost, and regional demand in one recommendation.
Track icon
LAYER 04
Track
Did it work?
Sell-through and changeover cost are watched after the decision, not assumed to have recovered.
Learn icon
LAYER 05
Learn
What refines next time?
Outcomes feed back into how the next demand signal or changeover pattern gets weighted.

Outcomes feed back into Signals, closing the loop for the next decision.

Why this needs one system

Point solutions vs. one connected brain.

Three point tools, each smart on their own, still leave three separate re-diagnoses. One connected brain doesn't.

The old wayPoint Solutions
The DecisionX wayOne Connected Brain
Re-diagnosis at every handoffThe bottleneck found on the line gets re-investigated by Commercial as if it were a fresh demand problem.
No re-diagnosisOne evidence chain, reused at every handoff, the line bottleneck found in Manufacturing isn't re-investigated as a demand problem in Commercial.
Blind handoffs between functionsCommercial finds out about a changeover cost spike after the re-launch decision is already made.
No blind handoffsEvery function reads the same fact, Commercial sees the changeover cost spike before the re-launch decision, not after.
Three numbers that don't add upReformulation, changeover cost, and demand sit in three separate spreadsheets that never reconcile.
One margin viewReformulation, changeover cost, and demand sit in one view, not three spreadsheets that don't add up.
Insight arrives after the shelf window closesBy the time each tool's answer surfaces, the planogram reset or promo calendar it was meant to inform is already locked.
Reasoning inside the decision windowOne brain, watching demand, plant, and shelf continuously, surfaces the answer while the window is still open.

Built for scale, not a pilot

Most AI pilots die at the second use case. This one is built to scale from the first.

Enterprise AI's real failure point isn't the model, it's what happens after the first pilot succeeds and the next team has to start from zero.

95%
Of enterprise GenAI pilots fail to deliver measurable ROI
MIT, 2026
14%
Of enterprise AI agent pilots ever reach production scale
Enterprise technology leader survey, March 2026

Marginal cost drops with scale

The next function reuses the same ontology and reasoning engine, it doesn't start from zero.

Value compounds over time

A self-learning ontology means every decision made anywhere sharpens the next one, enterprise-wide.

Built on real data, not a curated demo

Structured and unstructured, handwritten scans and messy files, the same data pilots are usually shielded from.

Sponsorship is structural, not incidental

Spanning three functions by design means it requires, and sustains, sponsorship from day one, not one team's budget.

3 functions live today. Each new one reuses the same brain, it ships in weeks, not quarters.

Proof, not promises

What CPG teams actually saw.

NPD · Launch Decisions
Same-cycle
SKU under- or over-performance diagnosed and routed to a reformulate, re-launch, or hold call inside the same planning cycle, not the next one.
DecisionX CPG NPD engagements
Manufacturing · Yield & Waste
Step-level
Yield loss attributed to the actual process step driving it, not spread across a plant-wide average that hides where the margin is leaking.
DecisionX manufacturing engagements
Commercial · Trade & Margin
One number
Demand and margin reconciled into a single trusted figure before any trade or allocation call is made, not argued about after.
DecisionX commercial engagements
Causality
Every recommendation ranked against a matched comparison, not a hunch or a static rule.
Self-learning
Every decision's outcome sharpens how the next signal is triaged.
Unified context
One evidence chain, read the same way by NPD, Manufacturing, and Commercial.

Frequently asked questions

What CPG teams ask us first.

The essentials on scope, data, coverage, and time to first value.

Point solutions each re-diagnose a signal from scratch inside their own function. DecisionX reasons over one shared evidence chain, so a root cause found in NPD or Manufacturing is reused, not re-investigated, at every downstream handoff.
Yes. DecisionX is built to work across structured and unstructured CPG data, POS feeds, retailer scorecards, handwritten line logs, scanned CoAs, and every messy format in between, not just clean warehouse tables.
CPG NPD (concept through launch and reformulation), Manufacturing Excellence (line performance, changeover, yield, and quality), and Commercial Excellence (demand planning, trade spend, and pricing decisions), all on one connected evidence chain.
Most CPG deployments see first-value decisions within 15 days of connecting the relevant data sources.
Demand and consumer data (POS, panel, distribution), manufacturing and quality systems (MES, changeover logs, process historian), and commercial systems (trade, pricing, retailer data), reasoned across all three inside one ontology.

Decision AI for CPG

Stop re-diagnosing the same signal three times.

See DecisionX connect your demand, plant, and shelf data. First value in 15 days.

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