New Product Launch Forecast
This guide explains how to build a new product launch forecast for consumer goods, FMCG and CPG manufacturers in Model Reef.
You will:
Represent new SKUs and variants in the model structure.
Model ramp up, distribution build and rate of sale by channel.
Link launch assumptions to BOM, cost, margin and trade spend.
Use scenarios to test launch timing, pricing, mix and support plans.
Model Reef is not a market research tool. It applies structured launch assumptions you define or import, then connects them to full financial statements and scenarios.
When to use this pattern
Use this pattern when:
You are planning a new SKU, range extension or format.
Launch decisions depend on revenue, margin and payback expectations.
You want a consistent launch modelling framework across brands.
You need to test alternative launch timings, support and distribution plans.
It builds on:
SKU Manufacturing Cost Model
Promotional Lift and Discount Impact
Retailer and Channel Margin Modelling
Build a Unit Economics Model
Architecture overview
New product launch forecasting uses the following components:
Structure
New SKU branches under existing categories and brands.
Channel and retailer allocation for new SKUs.
Launch drivers
Listing dates and distribution build by channel.
Initial rate of sale and ramp curves.
Promotional and trade support plans.
Cost and margin
BOM and manufacturing cost for the new SKU.
Trade terms and pricing by channel.
Launch and ongoing marketing and support cost.
Scenarios
Alternative launch windows and roll out strategies.
Different price, promotion and support levels.
Cannibalisation and portfolio mix effects.
Add new SKUs and channels to the structure
In the branch tree, create new SKU entries under the relevant brand and category, for example:
Brand - Sparkle Drinks
SKU - Sparkle Zero 330ml Can
SKU - Sparkle Zero 6 Pack
Under each SKU, you may distinguish channels or retailers via:
Sub branches for major retailers or channels, or
Separate variables distinguished by channel in their names and categories.
This ensures new SKU volume, revenue and cost can be analysed per channel and at brand and category level.
Define launch timing, distribution and ramp up
In the Data Library, build launch drivers including:
Launch Date per SKU per channel.
Speed of Distribution Build, for example percentage of stores or distribution points over time.
Rate of Sale per store or per outlet, including ramp curves from launch to steady state.
Event or support based spikes, such as media bursts or retailer features.
Combine these into volume drivers such as:
Volume per Period = Number of Stores × Rate of Sale per Store × Pack Size.
Use flags to distinguish:
Pre launch periods with zero or minimal volume.
Launch periods with steep ramp.
Steady state and maturity phases.
Attach price, trade terms and promotional plans
For each new SKU and channel, define:
List Price per Unit and per pack.
Everyday Net Price after standard discounts.
Promotional Discount levels and calendars.
Trade spend and retailer support, such as listing fees, launch fees and co funded media.
Use Revenue and Opex variables to represent:
Gross and net revenue per channel.
Trade spend and launch support cost.
Ongoing promotional activity.
This allows you to produce a full gross to net revenue view for the launch.
Configure BOM, cost and margin for the new SKU
Using the SKU Manufacturing Cost Model, set up:
BOM components and quantities per unit for the new SKU.
Material costs and any expected differences from existing SKUs.
Labour and overhead costs, especially if the new SKU uses different lines or packaging formats.
Derive:
Cost per Unit and per pack.
Gross margin per unit by channel and retailer.
Fully loaded contribution after trade spend and logistics when combined with channel margin modelling.
Make sure that any new line or plant implications are captured in capacity and cost assumptions.
Represent cannibalisation and portfolio effects
New launches often replace or cannibalise existing SKUs. To reflect this, use drivers for:
Cannibalisation Percentages by existing SKU or SKU family.
Incremental versus diverted volume assumptions.
Delisting or phase out calendars for replaced SKUs.
Adjust existing SKU volume drivers downwards where cannibalisation is expected, and if appropriate, reduce related COGS and trade spend. This allows you to see:
Net portfolio impact of the launch.
Shift in mix and margins within the category.
Impact on manufacturing footprint and capacity usage.
Use scenarios for launch timing, pricing and support
Clone the base model into scenario models to explore:
Launch now versus later, or phased by channel and region.
Different price points and everyday net prices.
Different levels and timing of promotional and media support.
More or less aggressive distribution and rate of sale assumptions.
Different cannibalisation patterns and SKU rationalisation strategies.
In each scenario, adjust:
Launch and distribution drivers.
Pricing, trade and promotional support drivers.
BOM or cost assumptions if you expect different scale or sourcing.
Cannibalisation and portfolio mix drivers.
Compare scenarios using:
Revenue and margin for the new SKU alone.
Net portfolio level P&L and contribution.
Manufacturing capacity and cost implications.
Cashflow and payback profile for launch investment.
Check your work
Launch volumes and ramp curves are realistic given historical launches and customer feedback.
Pricing and trade terms are aligned with retailer expectations and strategy.
Cannibalisation and portfolio effects are plausible and documented.
Scenario outputs are used in decision forums for go or no go and launch design.
Troubleshooting
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