Launch Forecasting
Forecasts for products with no sales history yet. We size the opportunity from patients or shoppers, shape the uptake from comparable launches, and track early results so the plan adjusts fast.
Life Sciences
A launch forecast sets supply, headcount and investment before the first prescription is written. Ours start from the patient funnel, shapes uptake on comparable brands, and sets the peak-year assumptions that feed the long-range plan.
What we factor in
- Prevalence and incidence
- Diagnosis and treatment rates
- Line of therapy
- Payer coverage timing
- Order of entry
- Persistency
Patient-funnel forecast
Epidemiology → diagnosed → treated → addressable
We build demand from prevalence or incidence down through diagnosis, treatment and line of therapy to the addressable patients, then apply share, dosing and compliance to reach units and revenue.
What it gives you: Show every assumption behind peak sales, so brand, medical and finance can each challenge it.
Analog uptake
Normalized months from launch, time to peak
We select comparable launches by indication, mechanism, order of entry and access, align them on months from launch, and use their curves to set time to peak and the shape of the ramp.
What it gives you: Ground the uptake curve in real launches rather than a hockey stick.
Diffusion and access ramp
Bass diffusion and payer coverage timing
Bass diffusion models fitted to analog brands separate innovator from imitator adoption among prescribers, and we layer in when commercial, Medicare and Medicaid coverage actually starts, so the ramp reflects access.
What it gives you: Time the ramp to coverage decisions instead of assuming access on day one.
Launch risk ranges
Monte Carlo P10, P50 and P90
We place distributions on peak share, time to peak, price and persistency, then simulate thousands of launches to show how wide the realistic range is and which assumptions drive it.
What it gives you: Give leadership and the long-range plan a range, with the biggest risks named.
Launch supply and channel fill
Initial stocking and launch safety stock
We translate the launch curve into wholesaler and specialty pharmacy initial stocking, ongoing replenishment and launch safety stock, sized to the uncertainty in the forecast rather than a flat number of months.
What it gives you: Avoid launch stockouts without writing off short-dated product later.
Early-launch tracking
Weekly scripts against the launch curve
As weekly prescription and claims data arrive, we compare new starts, refills and payer approvals against the curve, separate channel fill from real demand, and re-forecast the trajectory.
What it gives you: Spot an under-performing launch within weeks and adjust the plan fast.
Consumer Goods & Retail
New items have no history, but they have relatives. We forecast them from comparable products, trial and repeat, and the distribution plan, then separate pipeline fill from what shoppers actually buy.
What we factor in
- Distribution build
- Trial
- Repeat
- Price point
- Marketing support
- Cannibalization
Analog and short-history models
Like-item S-curves and transfer learning
We forecast new items from like-item histories and S-curves, and from TrailBlazer's transfer learning, which applies patterns learned across 47,000+ datasets to series with only a few weeks of data.
What it gives you: Get a credible forecast from week one instead of waiting for history.
Trial and repeat
Parfitt-Collins: trial × repeat × buying rate
Volume is built from how many households try the product, how many come back, and how often they buy, so you can see whether growth depends on reach or on loyalty.
What it gives you: Tell a slow-trial launch from a weak-repeat one and fix the right problem.
Distribution build
ACV ramp by account
We model how fast the item gains distribution by retailer and channel, using authorization dates and shelf reset timing, because most early volume follows weighted distribution, not consumer demand.
What it gives you: Plan volume around when shelves actually carry the item.
Pipeline fill vs. sell-through
Sell-in spike vs. consumer offtake
Launch shipments include one-time pipeline fill for retailer DCs and shelves. We separate that spike from ongoing sell-through using POS data, so the run rate isn't overstated.
What it gives you: Avoid building production and inventory on a pipeline spike.
Source of volume
Cannibalization of the existing range
We estimate how much of the new item's volume is incremental and how much comes from your existing products, by SKU and channel, so the launch is judged on net portfolio gain.
What it gives you: Size the incremental gain, not just the new item's gross volume.
Product transitions
Phase-in, phase-out and price compression
For short-cycle categories such as consumer electronics, we plan the overlap between outgoing and incoming models and the price compression curve, using what happened in previous product generations.
What it gives you: Hand over between generations without stranded old stock or empty shelves.
Related case studies
Genentech: oncology and specialty therapy planning
Genentech partnered with RoadMap Technologies to unify short-term operational demand forecasts with long-range strategic supply plans across oncology, neurology, and transplant anti-infective therapy portfolios, eliminating the planning disconnects that drove inventory imbalances and capital misallocation.
0–24 mo. + 3–10 yr. · 3 therapy portfoliosDell, GE, and Samsung: forecasting in consumer electronics
RoadMap partnered with leaders in consumer electronics, including Dell, GE, and Samsung, to address the sector's rapid product cycles, price compression curves, and multi-tier channel and service complexity.
Lifecycle, price compression & installed-base serviceMedia & entertainment: forecasting demand across release windows and subscriptions
RoadMap applied its launch and lifecycle forecasting methodology to media and entertainment businesses to handle demand that spikes on release, decays along reception curves, and spreads across physical, digital, licensing, and subscription channels.
Releases, subscribers & multi-window channels
Frequently asked questions
What is launch forecasting?
Launch forecasting estimates demand for a new product before sales history exists. RoadMap Technologies helps life science and consumer goods teams by building patient-funnel and analog-based forecasts, fitting uptake curves to comparable launches, attaching risk ranges, and re-forecasting as early prescriptions or POS data arrive.
How do you forecast a product with no sales history?
From the bottom up. For a medicine that means the patient funnel, from prevalence to treated patients, with share, dosing and persistency applied. For a consumer product it means trial, repeat and distribution. Comparable launches then set the shape and speed of the uptake curve.
How do you choose analog products?
We match on the factors that drive uptake, such as indication or category, mechanism or format, order of entry, price point, and access or distribution. We usually shortlist several analogs, align them on months from launch, and weight them by how closely they match.
How soon after launch can the forecast be updated?
Within weeks. We compare weekly prescription, claims or POS data against the launch curve, separate one-time channel fill from real demand, and re-forecast as soon as the trend is clear rather than waiting for the next quarterly cycle.
How do you plan launch inventory with so much uncertainty?
We size initial stocking and safety stock to the forecast range, not a single number. Monte Carlo ranges on the launch curve show the upside you need to cover and the downside where short-dated stock becomes a write-off risk.
Start with a pilot
Measurable outcomes agreed upfront, a live model on your data in about two weeks, and the ROI proven before you scale.