Forecasting is an important aspect of every business. However, for companies in the pharmaceutical industry, it is even more critical, as it involves not just finance but also other functions understanding the drugs and the markets in which they are competing.
A leading pharmaceutical company approached us to support its yearly forecasting cycle, as well as the development of new patient-based models in different therapy areas.
By combining their domain expertise in forecasting and analytics with their broad knowledge across several therapy areas and primary and secondary research skills, Evalueserve successfully developed robust AI-powered, forecast models.
A global pharmaceutical enterprise sought support around forecasting drugs based on patient-based models.
They wanted Evalueserve to support and build its global strategic net sales forecasts for specific brands/portfolios for its annual long-term strategic forecasting process and utilize Evalueserve’s powerful analytical abilities and extensive experience in the epidemiology-based forecasting space.
It was important to the client that Evalueserve maintain open, clear communication across all stakeholders and departments that needed to be aligned on the project.
Evalueserve formulated an approach dividing the project into three parts:
- Forecast Planning: The forecast execution exercise was broken down into critical milestones along with their timelines and a deep dive through the existing forecast models to fully understand their data, what it measured, and why it was relevant
- Forecast Development: By collaborating with relevant stakeholders and liaising with Global Marketing teams, we developed AI-powered forecast models and scenarios that met everyone’s needs.
- Forecast Review: In developing interactive visualizations for higher management, we assessed various scenarios and presented crucial insights to all key stakeholders for approval. This step was crucial in ensuring the accuracy and relevance of our visualizations.
The exercise brought all stakeholders to a common, AI-enhanced platform to discuss the forecasts, provide their inputs and suggestions, and increase collaboration, leading to highly accurate forecast models. Regular check-ins and planning reduced last-minute chaos, and minimal manual intervention was required, reducing errors, effort, and troubleshooting time by ~40%. Interactive visualizations helped generate key messages and insights for top-level management to make crucial investment decisions related to the brand.
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