During the webinar: Level Up Your FP&A in Pharma & Biotech we discussed that. Pharma finance teams are managing a planning problem that EPM (Enterprise Performance Management) solves and Excel never will. Not because Excel fails at arithmetic, but because coordinating 50,000 products across 50 countries through emailed spreadsheets is not a planning process. It is a data collection exercise with a planning calendar bolted on.
The Planning Maturity Model: Where Pharma Organisations Actually Are
Finext maps integrated business planning maturity across eight levels. Knowing where you sit determines which problem to fix first.
Level 0: Spreadsheet-only. Planning lives entirely in Excel. No shared model, no version control, no audit trail.
Level 1: Database-backed. Budget outcomes land in a central database, but input is still manual and fragmented across teams.
Level 2: Process-structured. Planning follows a calendar. Reviews happen. The model still lacks analytical intelligence.
Level 3: Driver-based planning. Multi-dimensional, driver-based models replace static spreadsheets. Most pharma organisations struggle to reach this level. The jump from Level 2 to Level 3 is not a software upgrade. It requires rethinking data ownership across the organisation.
Level 4: Predictive forecasting. Data quality is high enough that historical patterns drive automated baselines.
Level 5: Business-integrated. Sales, supply, workforce, and finance share one connected model. Operational and financial data move together.
Levels 6 to 8: Extended and chain-integrated. Plans reach outside the organisation to customers and suppliers. A retailer's replenishment signal feeds the supplier's production schedule in real time.
Most pharma organisations that come to Finext sit between Level 1 and Level 2. Some are at Level 3, but with gaps that make the driver model more theoretical than operational.
Pharma Demand Planning: The Distressed Inventory Problem
Pharmaceutical demand planning carries a constraint most industries don't face: shelf life. When a product nears expiry unsold, companies choose between a full write-off or bulk sales at zero margin. Neither outcome was in the forecast.
The root cause is a structural disconnect between sales and supply. Sales teams optimise for volume. Supply teams optimise for production efficiency. Neither team sees the other's numbers early enough to reconcile them.
Integrated business planning fixes this by putting constrained supply capacity and unconstrained sales ambition into the same planning process, with clear accountability for resolving the gap before inventory expires.
One Finext implementation covered a pharma organisation with 50,000 local products, 900 sales channels, and operations across more than 50 countries. Over 1,000 people were involved in the planning process. Every local team was running isolated Excel files. The global team's entire job was consolidation. They had no time left for analysis.
The solution connected the EPM tool to the central data warehouse. High-priority products get manual local input, where teams have genuine market knowledge. Low-priority, stable products run on predictive baseline algorithms. Local users can override those baselines when they have specific intelligence. The global team gets a consolidated view the moment local inputs arrive, without waiting for everyone to submit.
R&D and Vaccine Funding: Planning for Portfolio Risk
CEPI (the Coalition for Epidemic Preparedness Innovations) funds vaccine development for diseases commercial pharma doesn't prioritise. During COVID-19, government funding scaled rapidly, and a team built for careful long-term portfolio management had to track billions across dozens of active programmes simultaneously.
Finext built a two-track EPM model for CEPI.
The first track covers contributions: money in from governments, foundations, and multilateral funds. The model integrates with Salesforce to separate contracted from uncontracted commitments, map expected inflows by disease programme, and flag funding gaps before they become programme risks.
The second track covers R&D investment: money out. Risk and delay factors are embedded directly. If a programme spends €10 million against a €20 million budget, the model reads the underspend as a schedule risk, not a saving. Portfolio managers see whether a programme is ahead, on track, or in trouble. Not just whether the line balances.
Before the implementation, that read took two weeks. After: one to two days.
Multi-Country Workforce Planning and OPEX
Pharma organisations operating across multiple countries face a workforce planning problem that multiplies with every entity added. Pension schemes, national insurance rates, holiday entitlements, and contract structures differ by country. When a government changes a rate mid-budget cycle, someone has to find every affected row across every local spreadsheet.
That rarely happens cleanly. Numbers drift. Reforecasts take days.
The alternative is a global workforce planning model where an administrator updates one parameter. Every entity, every currency, every country recalculates instantly. Finance sees accurate numbers without chasing HR. Business units input their own headcount data directly, because they hold that knowledge, not finance.
Finance business partnering only works when finance is not the data bottleneck. The model needs to put ownership where knowledge sits, while giving finance the visibility to act on it.
Driver-Based Cash Flow
Manual cash flow forecasting in pharma typically means treasury working from assumptions about collections, payments, and inventory in a spreadsheet that runs separately from the P&L.
Finext replaces that with a cash conversion cycle model built on three drivers: DSO (Days Sales Outstanding), DPO (Days Payable Outstanding), and DIO (Days Inventory Outstanding). Cash flow derives from actuals and the forecasted P&L. When the forecast moves, cash flow updates with it. Treasury does not wait for finance to send a file.
What xP&A Means for Pharma Finance
Traditional FP&A produces financial outputs. xP&A (Cross-Departmental Planning) treats those outputs as the result of drivers that live across the business: commercial forecasts, demand plans, supply constraints, workforce costs, and R&D pipelines.
For pharma, this matters because the financial outcome is downstream of decisions made in sales, supply chain, manufacturing, and R&D. An FP&A team working only from financial inputs is always reacting. An xP&A model gives finance the levers to contribute before the numbers are set.
The S&OP processes that work in large pharma organisations are almost always built on top of a functioning xP&A integration. The ones that stall are usually missing it.
AI in Pharma Planning: What's Arriving and What It Requires
AI integration in EPM platforms is moving from pilot to production feature. The near-term capability is scenario planning by natural language prompt.
Instead of navigating templates to model a December promotional uplift by adjusting volume, checking capacity, and reviewing production line utilisation, a planner types: "Apply a 10% promotion in December for this product and show me the impact on capacity at this production line." The model runs the scenario.
This does not replace the planner. It removes the distance between a business question and an answer.
Organisations at Level 3 and above, with clean data and working driver models, are positioned to use these tools as they land. Organisations still on spreadsheets will spend the next two years building the foundation before AI becomes a relevant option.
FAQs: EPM and Integrated Planning in Pharma
What is EPM in pharma, and why does it matter?
EPM (Enterprise Performance Management) connects financial planning, budgeting, forecasting, and reporting into one model. For pharma, that means demand planning, workforce planning, supply chain constraints, and R&D portfolio management feeding a single version of the numbers rather than being reconciled manually at month-end. The alternative is fragmented planning across systems and spreadsheets, with finance spending its time consolidating instead of analysing.
We already have an ERP system. Do we need EPM on top?
ERP systems record transactions. EPM tools plan and forecast. Your ERP tells you what happened. Your EPM model tells you what will happen, what the variance drivers are, and how different scenarios change the outcome. Most pharma finance teams need both, running in parallel.
At what scale does driver-based planning make sense for pharma?
Driver-based planning starts paying back as soon as the manual overhead of maintaining a spreadsheet model outweighs the cost of a proper tool. For pharma organisations with multiple markets or product lines, that threshold arrives earlier than most teams expect. Complexity of planning drivers matters more than headcount or revenue size.
How long does an EPM implementation take in pharma?
Scope drives timeline more than company size. A focused workforce planning or cash flow module can go live in a few months. A global demand planning model covering 50,000 SKUs across 50+ countries is a multi-year programme. Implementations that run into trouble are almost always the ones that tried to solve everything at once rather than phasing by use case.
