Course / Course Details
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By - Ashish Modi
This course provides a practical and comprehensive guide to leveraging Microsoft Excel for pharmaceutical forecasting, equipping participants with the essential skills to build, analyze, and interpret robust forecast models tailored to the unique demands of the pharma industry. Designed for professionals in commercial analytics, market access, and business intelligence, the curriculum blends foundational Excel techniques with advanced forecasting methodologies relevant to pharmaceutical products and markets.
Master Excel Fundamentals for Forecasting: Learn to organize, clean, and structure large datasets using Excel’s core functions, including sorting, filtering, and conditional formatting for pharmaceutical data analysis.
Build Forecast Models: Develop hands-on expertise in constructing time-series and scenario-based models using Excel’s built-in formulas (e.g., SUM, AVERAGE, IF), pivot tables, and advanced charting tools to visualize trends and outcomes.
Apply Forecasting Techniques: Utilize Excel’s forecasting functions—such as FORECAST.LINEAR, moving averages, and exponential smoothing—to project sales, demand, and market growth for pharma products.
Scenario Planning and Sensitivity Analysis: Implement what-if analysis, data tables, and Monte Carlo simulations to assess uncertainty and variability in pharmaceutical forecasts.
Integrate External Data: Import and link external datasets, automate data updates, and use Power Query for efficient data transformation and integration.
Communicate Insights: Create dynamic dashboards and visualizations that clearly present forecast results to stakeholders, supporting data-driven decision-making.
Interactive modules with step-by-step Excel exercises based on real-world pharma datasets
Case studies focusing on demand estimation, sales projections, and market access scenarios
Practical assignments and quizzes to reinforce learning
Final project: Build and present a comprehensive pharma forecast model in Excel
By the end of this course, participants will be able to confidently apply Excel as a powerful tool for pharmaceutical forecasting, supporting strategic planning and commercial success in a dynamic industry
Upon completing the program on applying Excel for pharmaceutical forecasting, learners will achieve the following outcomes:
Confidently use Excel’s core and advanced functions to organize, clean, and analyze large pharmaceutical datasets.
Build and automate robust forecast models tailored to pharma industry needs.
Construct time-series, scenario-based, and sensitivity analysis models using Excel’s built-in tools.
Apply forecasting techniques such as moving averages, exponential smoothing, and the FORECAST.LINEAR function for sales and demand projections.
Import, transform, and integrate external data sources using Power Query and other Excel features, streamlining the forecasting process.
Conduct what-if analyses, create data tables, and run Monte Carlo simulations to evaluate uncertainty and variability in forecasts.
Design dynamic dashboards and visualizations to clearly present forecast results, supporting data-driven decision-making for stakeholders.
Tackle real-world case studies and assignments focused on demand estimation, market access, and sales projections within the pharmaceutical sector.
Complete a capstone project, demonstrating the ability to build and present a comprehensive pharma forecast model in Excel.
Gain practical skills valued in commercial analytics, business intelligence, and market access roles within the pharmaceutical industry.
Position themselves as proficient Excel users capable of supporting strategic planning and commercial success in pharma organizations.
Graduates of this program will be equipped to make immediate, impactful contributions to forecasting and analytics teams, driving better business outcomes through data-driven insights and advanced Excel modeling.
With over 7 years of experience specialising in pharmaceutical forecasting, Ashish works closely with client teams to address key forecasting challenges, from drug acquisition valuations to long-term portfolio planning.
His expertise spans the full forecasting lifecycle, from designing patient funnels and developing models to presenting findings to executives. He also has an extensive experience in implementing cloud-based forecasting solutions at pharmaceuticals to enhance their overall process.
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