Consumer goods planning that absorbs promotions and omnichannel swings
Promotional spikes, retail, e-commerce and DTC channels, SKU proliferation, freshness requirements — Convect models your distribution network end to end, and its agents keep service high and inventory lean as demand moves.
The challenge
What makes cpg planning hard
Promotions whipsaw demand
Promotions can spike demand 3–5×. Build too little and you miss sales; build too much and it sits in the warehouse.
Every channel behaves differently
Big-box retail, grocery, e-commerce marketplaces and DTC each have their own demand patterns and service rules.
SKU proliferation
Product lines keep expanding — hundreds of SKUs become thousands, and planning complexity explodes.
Freshness and retailer compliance
Retailers enforce minimum remaining shelf life and OTIF targets; fines follow when you miss.
Agents at work
What your planning agents handle in cpg
Real situations your planners deal with every week — picked up, analyzed and turned into an explained recommendation without anyone having to notice them first.
Sell-through runs 40% above forecast
Demand Sensing Agent
Detects the deviation early, re-forecasts, and proposes rebalancing stock between DCs before stores run out.
A key retailer tightens its OTIF window
Coordination Agent
Updates the service rule once in the model; deployment and production plans adjust everywhere it matters.
A co-packer misses a run
Disruption Response Agent
Finds alternate capacity, re-sequences internal lines and shows which channels get prioritized — and at what cost.
Industry ontology
An ontology built for cpg
On top of the core model of products, lines, materials and orders, Convect adds the entities and constraints specific to cpg — so agents understand your business from day one.
What this means for you
Channels + promo calendar + freshness — once these relationships are in the model, the impact of any change (a rush order, a shortage, a breakdown) is traced automatically and handed to the optimizer as constraints.
Channel & promotion calendar
Promotions by channel, historical lift and demand elasticity
Multi-echelon network
Plant → regional DC → forward DC → distributor / retailer
SKU lifecycle
Launch, growth, maturity and decline curves with matching inventory policies
Freshness constraints
Minimum remaining shelf life, FEFO rules and turn targets
The decisions we optimize
From promotion planning to the line: five decisions
From long-range planning to the shop floor, each layer’s output becomes the next layer’s constraint.
Promotion forecasting
Decision
Forecast promo lift with a backtested ensemble of 12+ models
Output
Promotion forecast accuracy of 85%+
Multi-plant allocation
Decision
Which products run on which plant and line, balancing cost and service
Output
Balanced utilization across plants
Materials & replenishment
Decision
Ingredient and packaging quantities and timing; safety stock and reorder points
Output
Fewer shortages, leaner stock
Cross-plant coordination
Decision
Coordinate schedules and transfers across plants, balancing freshness and freight
Output
Faster inventory turns
Daily line schedule
Decision
Group similar products, re-sequence quickly for promotion surges
Output
Fewer changeovers, higher utilization
Upstream decisions constrain downstream ones. When execution drifts, agents trace back up the chain and assess the impact automatically.
Capabilities
Planning capabilities built for cpg
Promotion-driven production
Promotion calendars and forecasts combined to pre-build without overstocking
Omnichannel inventory optimization
Cross-channel visibility and smart rebalancing
SKU segmentation
Differentiated strategies by volume, margin and lifecycle
Freshness management
Safety and max stock set automatically to keep FEFO
| Production type | Key constraints | Planning granularity | BOM complexity | Rush-order frequency | Ontology highlights |
|---|---|---|---|---|---|
| Process / batch | Promotions, freshness | Day / week | Shallow (packaging-driven) | High (promotion-driven) | Channels + promo calendar + freshness |
See also: Production planning & scheduling · How the platform works
Customer results
Results from the industry
Unilever
Safety stock optimized across a multi-echelon network, reducing total inventory while holding service level steady.
10%+
Lower inventory
Decathlon
Automatic model selection for new and seasonal products, giving planning a forecast it can trust.
55% → 85%+
Forecast accuracy
Standards
Configured for cpg requirements
Few hard regulatory requirements apply to CPG planning, but we configure for:
GS1
Product identification standards
Retailer OTIF programs
On-time, in-full compliance windows
Cold chain standards
For temperature-controlled products
Put planning agents to work in cpg
Get the Consumer Goods (CPG) Planning Playbook, or talk to a specialist who knows cpg planning.