FMCG — Fast-Moving Consumer Goods
AI that senses demand, optimises promotions and gets the right SKU to the right shelf
You get demand sensing agents, trade promotion optimisation, route-to-market AI, quality assurance automation, shelf analytics, and SKU rationalisation workflows. All running on your cloud, connected to your ERP and POS systems, with the governance your commercial teams need.
Built on your AWS, Azure or GCP tenant. Data stays in your environment. Fully auditable.
Proof of concept
To measurable ROI
AWS, Azure or GCP
Your forecasts are wrong. Your promotions leak margin. Your route-to-market is inefficient. AI fixes all three.
FMCG companies lose 2 to 4 percent of revenue through demand forecast errors. Trade promotions deliver negative ROI 60 percent of the time. Route-to-market inefficiencies add 15 to 20 percent to distribution costs in emerging markets. These are not planning problems. They are data intelligence problems.
We build AI agents that sit between your data and your commercial decisions — sensing demand shifts in real time, optimising promotion spend before it is committed, rationalising SKU portfolios, and routing distribution fleets to maximise in-store availability.
improvement in demand forecast accuracy with AI-driven sensing (McKinsey, 2024)
improvement in trade promotion ROI with ML-based optimisation (BCG, 2024)
reduction in distribution costs through AI-optimised route-to-market (McKinsey QuantumBlack, 2024)
annual global trade spend in FMCG, most of it poorly optimised (BCG, 2024)
From POS data to promotion plan in one governed workflow
We connect your ERP, POS feeds, syndicated data, trade spend systems, and DSD platforms to AI agents that forecast demand, optimise promotions, rationalise SKUs, and generate the distribution plans your commercial teams need to act on
POS, DSD, ERP, trade spend, syndicated data
- Approval gates
- Human-in-loop
- Audit logging
AWS, Azure or GCP
- Demand forecasts
- Promotion plans
- Distribution routes
What we deliver for FMCG and consumer goods
From demand sensing to shelf analytics. Governed, auditable, commercially precise.
Demand sensing and forecasting
Predict demand at SKU-store-week level, weeks ahead.
ROI
Process
Ingest demand signals
POS, retailer inventory, weather, promotional calendars, and external signals streamed into the sensing engine
Generate granular forecasts
ML models produce SKU-store-week forecasts with confidence intervals and demand driver attribution
Feed planning systems
Forecasts pushed to S&OP, production scheduling, and replenishment systems with automated variance tracking
Sources
McKinsey QuantumBlack, 'AI-powered demand sensing in consumer goods,' 2024; BCG, 'The AI-driven supply chain,' 2024 — reproduced for illustrative purposes
Trade promotion optimisation
Predict promotion ROI and recommend the optimal spend mix.
ROI
Process
Analyse historical promotions
Every past promotion decomposed into baseline, incremental, cannibalisation, and pull-forward effects
Predict and optimise
Ensemble models forecast ROI for proposed promotions and recommend optimal mechanics, depth, and duration
Track and learn
Post-event analysis automated. Model retrained continuously with actual sell-through and margin data
Sources
BCG, 'Reinventing trade promotion with AI,' 2024; McKinsey, 'Revenue growth management in CPG,' 2024 — reproduced for illustrative purposes
Route-to-market AI
Design routes, allocate loads, and adapt to real-time changes.
ROI
Process
Map distribution network
Store locations, demand profiles, fleet capacity, road networks, and delivery windows modelled
Optimise routes
AI calculates optimal van loads, visit sequences, and delivery routes that minimise cost while maximising coverage
Adapt in real time
Routes adjusted dynamically based on traffic, order changes, and out-of-stock alerts from field sales
Sources
McKinsey QuantumBlack, 'AI in last-mile distribution,' 2024; BCG, 'Route-to-market excellence in emerging markets,' 2024 — reproduced for illustrative purposes
Quality assurance automation
Detect defects on the line before products reach consumers.
ROI
Process
Capture quality data
High-speed cameras, spectroscopy, and inline sensors capture product attributes at line speed
Detect and classify
Computer vision models identify defects, contamination, and packaging faults with sub-second latency
Reject and root-cause
Defective products auto-rejected. Pattern analysis identifies upstream root causes to prevent recurrence
Sources
McKinsey, 'AI-powered quality in manufacturing,' 2024; BCG, 'Digital quality assurance in CPG,' 2024 — reproduced for illustrative purposes
Shelf analytics and execution
Audit shelf compliance and flag execution gaps in real time.
ROI
Process
Capture shelf images
Field sales reps or in-store cameras capture shelf images. AI processes thousands of SKUs per image
Analyse and score
Models measure share of shelf, planogram compliance, pricing accuracy, and out-of-stock conditions
Alert and action
Execution gaps flagged to field teams in real time with prioritised corrective actions and competitor intelligence
Sources
BCG, 'AI in retail execution,' 2024; McKinsey, 'Perfect store execution with computer vision,' 2024 — reproduced for illustrative purposes
SKU rationalisation
Find which SKUs drive growth and which add cost.
ROI
Process
Profile every SKU
Revenue, margin, velocity, cannibalisation, supply chain cost, and strategic role assessed for every SKU
Model portfolio scenarios
AI simulates rationalisation scenarios showing revenue at risk, margin impact, and supply chain savings
Execute with governance
Recommended delists, reformulations, and pack size changes presented with full business case and approval workflows
Sources
McKinsey, 'Portfolio simplification in CPG,' 2024; BCG, 'SKU rationalisation with AI,' 2024 — reproduced for illustrative purposes
Revenue growth management
Optimise pricing and pack mix to maximise revenue per unit.
ROI
Process
Model price elasticities
Cross-price elasticities, channel preferences, and competitive dynamics modelled at SKU-channel-market level
Optimise pricing and mix
AI recommends optimal price points, pack sizes, and channel allocation to maximise revenue and margin
Monitor and adjust
Continuous tracking of market response with automated recommendations for price and promotion adjustments
Sources
McKinsey, 'Revenue growth management in CPG,' 2024; BCG, 'AI-powered pricing in consumer goods,' 2024 — reproduced for illustrative purposes
Chairman & CEO, The Coca-Cola Company
James Quincey
"We have invested heavily in AI-powered demand sensing across our bottling network. The ability to predict what consumers want at a local level, weeks in advance, has transformed how we plan production and allocate marketing spend. It is the difference between reacting to demand and shaping it."
Originally published: Coca-Cola Company Media Center — reproduced for illustrative purposes
How we work in FMCG and consumer goods environments
FMCG AI must deliver commercial results, not science projects. We start by identifying the one use case that delivers the most measurable impact on your P&L, then build it with the data governance, commercial rigour, and audit trails your finance and commercial teams require from day one.
Everything runs on your cloud tenant. Your POS data, your trade spend records, your distribution routes. Nothing leaves your perimeter. Your internal audit and data teams can inspect the system directly.
01
Commercial and data assessment
We map your ERP, POS integrations, trade spend systems, and distribution infrastructure. Then identify the highest-value use case that delivers measurable P&L impact.
02
30-day proof of concept
A working AI agent on your cloud, connected to your commercial systems, producing actionable output in 30 days.
03
Governance and accuracy built in
Data quality checks, model validation, audit trails, and approval workflows structured to meet your commercial governance and finance sign-off requirements.
04
Scale in 12-18 weeks
From one validated workflow to measurable commercial ROI across demand planning, trade spend, distribution, or revenue management.
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