About the client
Our client is one of the world’s leading pharmaceutical companies, focused on the discovery, development, and commercialization of prescription medicines across Oncology, Rare Disease, Cardiovascular, Renal & Metabolism, and Respiratory & Immunology.
The challenge
The clients’ demand planning function was operating under a fragmented, manual, and one-size-fits-all model that could not keep pace with the company’s growth ambitions. With a significant increase in product launches expected between 2025 and 2030, and a projected sixfold increase in SKU complexity, the existing setup was no longer sustainable.
At its core, the challenge was a structural mismatch between the forecasting process and the scale, complexity, and criticality of the business.
Key challenges included:
- High manual workload: Over 200 Excel uploads per month, with more than 95% of markets relying on spreadsheet-based forecasting and manual data reconciliation across legacy systems.
- Limited use of statistical forecasting: Forecasts were largely driven by manual inputs and commercial judgment, with minimal use of advanced models.
- Inefficient and inconsistent processes: Over 350 forecasters across markets with ~50% annual turnover, leading to low process standardization and knowledge retention.
- No scenario planning capability: Limited ability to model demand variability or share scenarios with supply teams.
- Weak NPI forecasting: No standardized process for new product introductions, increasing supply risk at launch.
Several factors made transformation urgent:
- A rapidly accelerating growth trajectory and increasing portfolio complexity
- The S/4HANA transformation creating a natural re-design moment
- High patient impact, particularly in rare disease, where forecasting errors can directly affect treatment availability
The project
We partnered to re-design the demand planning capability into a scalable, data-driven, and future-ready model aligned with transformation ambitions – structured around 4 pillars.
1. Demand Planning Operating Model redesign
A segmentation-driven forecasting approach replaced the single-mode model, introducing three differentiated planning strategies:
- Streamlined: Fully automated statistical forecasting for stable, mature products
- Agile: Statistical baseline enriched with structured market intelligence for growth brands
- High Focus (Non-Stat): Collaborative, manual planning for NPIs, tenders, and high-growth or complex products
2. Forecast as a Service (FaaS) – Proof of Value
To validate the new model, a Proof of Value was launched in selected markets. To kickstart the process, change and improve organization readiness for the APS transformation to come.
- Centralized statistical baseline forecasting
- Automated data cleansing and quality management
- Local market enrichment via structured workflows
- Power BI dashboards for KPI tracking and FVA analysis
- Structured forecasting cycles with clearly defined roles across central and local teams
3. Implementation of OMP as the new demand planning platform
OMP is introduced as the enterprise Advanced Planning System, replacing legacy tools. Key capabilities include:
- Automated statistical model selection and parameter tuning
- Advanced outlier detection and data cleansing
- Integrated product lifecycle management (NPI, phase-in/phase-out, end-of-life)
- Event-based forecasting with standardized categories
- Scenario planning (upside/downside demand)
4. New roles, governance, and ways of working
A new operating structure was introduced, defining responsibilities between:
- A central demand planning team, responsible for segmentation, modelling, and data governance
- Local market teams, responsible for commercial insights and demand validation
In addition, a Centre of Excellence (CoE) and Business Process Owner function were established to drive continuous improvement, governance, and maturity development.
Results
The transformation is currently in progress, with early outcomes focused on building a scalable and future-ready demand planning foundation:
- Establishment of a segmentation-driven planning model aligned with product complexity and business needs
- Significant reduction in manual effort and reliance on Excel-based processes
- Introduction of advanced statistical forecasting and scenario planning capabilities
- Improved data quality, governance, and process standardization across markets
- Clear definition of roles, responsibilities, and decision-making structures
- Enhanced visibility through KPI tracking and Forecast Value Add (FVA) analysis
- A scalable demand planning framework aligned with OMP and S/4HANA transformation
These improvements are laying the groundwork for a more efficient, data-driven, and resilient demand planning function capable of supporting long-term growth ambitions.
Scaling demand planning or preparing for an Advanced Planning System transformation?
Transforming demand planning in a highly complex and fast-growing pharmaceutical environment requires more than just implementing a new system. It demands a fundamental redesign of operating models, processes, and ways of working. More information on this topic, get in contact with our expert Bart Paridaen.
By partnering with EyeOn, the client has taken a critical step toward building a scalable, insight-driven demand planning capability that supports both business growth and patient needs. EyeOn can help you design and implement a future-ready solution. Get in touch with our experts to learn more.