How do you forecast biosimilar uptake in the UK?

UK biosimilar uptake follows unusually fast S-curves because the NHS actively drives switching: national commissioning guidance has pushed best-value biological medicines expectations, and regional tenders can move the bulk of a molecule's volume in months. A credible biosimilar forecast models the molecule market first, then share transfer between originator and biosimilars, anchored to comparable historical launches, tender timings and realistic price erosion — and is corrected against monthly actuals from the first cycle.

What data does a pharmaceutical forecast need?

Typically a blend of IQVIA sell-in and sell-out data, internal sales and tender wins, and public NHS prescribing sources such as hospital and primary care dispensing datasets. The unglamorous work is harmonising them — aligning molecule and pack mappings, deduplicating parallel feeds and converting everything into consistent therapy units — before any modelling starts. That data foundation determines whether the forecast can be trusted.

Should we forecast in units, days of therapy or value?

Model demand in therapy units or days of therapy at molecule level, then convert to packs and value. Forecasting revenue directly hides what is actually happening: pack-mix changes, price erosion and volume shifts all blur into one line. Working in therapy units keeps market size, share and price visible as separate assumptions you can challenge and update independently.

Why track forecast versus actuals every month?

Because forecasts are only useful if they are corrected. A monthly forecast-versus-actuals cycle exposes variance early, reveals persistent bias (systematic over- or under-forecasting), and forces assumptions to be revisited while there is still time to act. It is also what makes a forecast credible with finance and leadership — they can see how it performed, not just what it predicts.

Can't we just keep forecasting in Excel?

Excel is where most pharma forecasts start, and where most of them quietly break — one workbook per brand, formulas that only one person understands, and a full rebuild every cycle. A forecasting platform runs the monthly refresh automatically, holds assumptions in one governed place, tracks actuals against every prior forecast, and scales across the whole portfolio. We typically deliver one in weeks, and you own it outright.

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Pharmaceutical Forecasting

Pharmaceutical forecasting that survives contact with actuals

Portfolio demand forecasts built from molecule-level data, biosimilar uptake curves and UK market dynamics — refreshed automatically every month and scored against what actually happened. Not another spreadsheet that only one person understands.

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Most pharmaceutical forecasts fail the same way: a revenue line extrapolated in Excel, assumptions buried in formulas, and no systematic check against what the market actually did. The forecast gets built once a year, argued over once a quarter, and quietly ignored the rest of the time.

Pharmaceutical forecasting done properly works the other way round. Demand is modelled in therapy units at molecule level, market growth and share are separated, biosimilar erosion and tender outcomes are explicit assumptions — and every month the model is rerun against fresh IQVIA and sales actuals, with variance reported automatically.

It is one of the most valuable places to apply AI in the pharmaceutical industry, because the inputs are data-heavy, the logic is repeatable, and the cost of a bad number — to supply, finance and credibility — is high.

What the model must handle

What a pharma forecast has to get right

Pharmaceutical demand doesn't behave like consumer demand — the model has to reflect how the market actually works.

Portfolio, not one brand

A real pharma forecast covers every molecule and pack in the portfolio in one consistent model — not a different workbook, owner and logic per brand.

Biosimilar & LoE dynamics

Loss of exclusivity rewrites a molecule's economics in months. Uptake S-curves, switching speed and price erosion have to be explicit assumptions, not afterthoughts.

Market growth vs share

Volume moves for two different reasons — the market growing and your share changing. A forecast that blends them can't explain itself when the number misses.

NHS market structure

UK demand is shaped by commissioning policy, regional tenders and formulary decisions. Forecasts that ignore how the NHS actually buys drift fast.

Price & tender dynamics

Winning or losing a tender steps volume overnight; list-versus-net price gaps distort value forecasts. Both need to live in the model as first-class inputs.

Forecast vs actuals

Every monthly run is compared against what actually happened — variance surfaced, bias measured, assumptions corrected. That discipline is what makes the number credible.

Why it matters

What a forecasting platform is worth

Oneversion of the number

Supply, finance and commercial planning against the same forecast, built from the same assumptions — not three competing spreadsheets.

Monthlyautomated refresh

New actuals land, the model reruns, the outputs update. No two-week manual rebuild before anyone can look at the numbers.

Earlyvariance warning

When a market, brand or account moves off plan, it shows up in the next cycle — while there is still time to respond in-year.

How we build it

Forecasting built for pharma, owned by you

Demand first, value second

We model therapy-unit demand at molecule level, then layer share, pack mix and price — so every driver of the number stays visible and challengeable.

Assumptions you control

Uptake curves, growth caps, tender outcomes and pricing sit in governed, editable inputs — the team owns the assumptions, the platform does the arithmetic.

Actuals wired in

IQVIA and internal sales feeds are integrated so every forecast is scored against reality each month, with variance and bias reported automatically.

Owned by you

You keep the model, the code and the data. No black-box licence — a forecasting platform your analysts can open, understand and extend.

Governed · Owned by you

Start with one portfolio

Bring us the portfolio your team struggles to forecast today. We'll build the demand model, wire in the actuals and hand over a platform that reruns itself every month — see how we did it in our automated forecasting case study, or explore our wider AI & data consulting services.

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FAQ

Common questions

Pharmaceutical forecasting is the process of projecting demand, volume and revenue for a pharmaceutical portfolio — by molecule, brand and market — so supply, finance and commercial teams are planning against the same number. A good forecast starts from patient-level or therapy-unit demand, layers on market share and uptake assumptions, and converts to packs and value, rather than extrapolating last year's sales line.

Part of our guide to AI in the pharmaceutical industry. See also pharma commercial analytics.