Forecasting plays a critical role in the pharmaceutical industry by enabling companies to predict demand, plan resources, manage inventory, allocate budgets, and drive strategic decisions. Accurate forecasting not only enhances operational efficiency but also ensures that essential medicines are available to patients without delay. In an industry that operates under strict regulatory oversight and is driven by innovation, forecasting provides a means to anticipate challenges and stay ahead in a competitive market.
Demand and Supply Optimization: Accurate demand forecasting ensures that drugs and pharmaceutical products are manufactured in the right quantities. This prevents stockouts, minimizes overproduction, reduces waste, and saves on storage costs. For instance, during the COVID-19 pandemic, Pfizer used predictive analytics to forecast vaccine demand, enabling it to manage production and supply effectively and meet global demand without stockouts that could have disrupted vaccinations.
Strategic Planning: Forecasting supports informed decisions on drug development, market entry, expansion, and resource allocation. Strategic forecasting considers factors like patient demographics, disease prevalence, and treatment trends. Novartis, for example, applies machine learning models for oncology forecasting, aligning production with demand and ensuring critical oncology medications are available without costly overproduction.
Cost Management and Profit Maximization: Predicting demand helps companies optimize production schedules and manage costs, especially for rare or specialty drugs with high production costs. Roche uses big data analytics to forecast demand for personalized medicine, avoiding overproduction of biologics, reducing costs, and maximizing the efficiency of its high-cost therapies.
Inventory Management: With products that have limited shelf lives, forecasting aids in managing inventory to reduce waste, especially for vaccines, biologics, and temperature-sensitive items. Johnson & Johnson applies advanced forecasting to manage biologics inventory across regions, minimizing wastage and ensuring timely delivery of products with short shelf lives.
Regulatory Compliance: Forecasting helps companies prepare for regulatory changes by anticipating shifts in compliance requirements, market demands, and health trends. Amgen, for example, uses forecasting to adjust to regulatory changes globally, ensuring quick adaptation to maintain compliance in regions with stringent drug approval processes.
Market Trends Analysis: Forecasting provides insights into changing market dynamics, helping companies adapt to new trends like biosimilars, precision medicine, and patient-centric care. GSK, for instance, uses forecasting models to analyze market trends, allowing it to position itself effectively in the growing patient-centric treatment landscape.
Different forecasting models help in predicting demand and sales, each with its advantages based on the specific goals and data availability. Here are some common types:
Moving Average and Exponential Smoothing: Used for short-term forecasts by smoothing fluctuations in historical data.
ARIMA (Auto-Regressive Integrated Moving Average): Commonly used for forecasting drug demand based on past sales trends, especially when there are seasonal variations.
Regression Analysis: Identifies the relationship between demand and variables like demographics, economic factors, and prescription patterns.
Econometric Models: Useful for long-term forecasting, considering macroeconomic factors that impact the pharmaceutical market.
Neural Networks and Deep Learning Models: Predict demand by recognizing complex patterns in large datasets, such as sales data, patient data, and socio-economic data.
Random Forest and Gradient Boosting: Useful for scenario-based forecasting, simulating various market conditions to predict outcomes.
Expert Opinion: Often used for new products when historical data is unavailable.
Delphi Method: Involves consulting a panel of experts to converge on a forecast through iterative surveys and discussions.
Combining time-series, causal, and machine learning models: Provides more accurate predictions by leveraging multiple forecasting approaches.
During the COVID-19 pandemic, Pfizer collaborated with BioNTech to develop a vaccine, using predictive analytics and scenario-based modeling to manage supply efficiently and meet global demand.
Novartis implemented machine learning models to forecast demand for oncology drugs, optimizing manufacturing schedules and ensuring availability.
Roche leverages big data analytics to forecast demand for targeted therapies, aligning production with patient needs and avoiding overproduction of biologics.
Outsourcing forecasting and data analytics has become a common practice among global pharmaceutical companies, with India emerging as a leading hub for these services.
Cost Efficiency: India offers highly skilled data scientists at a fraction of the cost of Western countries.
Availability of Skilled Talent: A robust pool of trained professionals in data science, statistics, and life sciences.
Advanced Infrastructure and Technology: Indian firms use AI and machine learning platforms for state-of-the-art forecasting solutions.
Focus on Quality and Compliance: Indian firms adhere to strict standards critical for pharmaceutical forecasting.
India handles over 40% of global outsourced pharmaceutical data analytics, driving job creation and economic growth. Employment opportunities include:
Data Scientists and Data Analysts: Analyze large datasets and create predictive models.
Forecasting Specialists: Focus on demand forecasting for specific drug categories.
Clinical Data Analysts: Analyze clinical trial data to support forecasting efforts.
Regulatory and Compliance Analysts: Ensure compliance with global regulatory standards.
Cliniminds offers training programs in life sciences forecasting, preparing professionals for roles like data scientists, forecasting specialists, and more.
