Cliniminds Blogs

• 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.

1. Time Series Models:

   • Moving Average and Exponential Smoothing: These are used for short-term forecasts by smoothing fluctuations in historical data.
   • ARIMA (Auto-Regressive Integrated Moving Average): ARIMA is commonly used for forecasting drug demand based on past sales trends, especially when there are seasonal variations.

2. Causal Models:

   • Regression Analysis: This helps identify the relationship between demand and variables like demographics, economic factors, and prescription patterns.
   • Econometric Models: Econometric models are useful for long-term forecasting as they consider macroeconomic factors that impact the pharmaceutical market.

3. Machine Learning and AI Models:

   • Neural Networks and Deep Learning Models: These models are increasingly used to predict demand by recognizing complex patterns in large datasets, such as sales data, patient data, and socio-economic data.
   • Random Forest and Gradient Boosting: These models are useful for scenario-based forecasting, where they can simulate various market conditions to predict outcomes.

4. Judgmental and Consensus Forecasting:

   • Expert Opinion: Often, pharmaceutical companies use expert consensus, particularly for new products, when historical data is unavailable.
   • Delphi Method: This method involves consulting a panel of experts to converge on a forecast through iterative rounds of surveys and discussions.

5. Hybrid Models

   • Combining time-series, causal, and machine learning models: This approach provides more accurate predictions by leveraging the strengths of multiple forecasting approaches.

Examples and Case Studies of Forecasting in Action

1. Pfizer and COVID-19 Vaccine Demand Forecasting:

   • During the COVID-19 pandemic, Pfizer collaborated with BioNTech to develop a vaccine, which required precise demand forecasting due to unprecedented global demand. By using predictive analytics and scenario-based modeling, Pfizer was able to manage supply efficiently, scale production quickly, and meet global demand, achieving a milestone in rapid vaccine distribution.

2. Novartis and Oncology Drug Forecasting:

   • Machine Learning for Demand Forecasting: Novartis has successfully implemented machine learning models to forecast demand for its oncology portfolio. By analyzing patient demographics, treatment regimens, and market trends, Novartis optimized its manufacturing schedules and ensured the availability of critical oncology drugs, enhancing patient outcomes and minimizing stockouts.

3. Roche and Personalized Medicine:

   • Big Data Analytics for Targeted Therapy Demand: Roche, focusing on personalized medicine, leverages big data analytics to forecast demand for targeted therapies. By analyzing patient data and treatment responses, Roche aligns production with patient needs, avoiding overproduction of expensive biologics while ensuring availability for patients who benefit most.

Outsourcing of Forecasting by Pharmaceutical Companies to India

Outsourcing forecasting and data analytics has become a common practice among global pharmaceutical companies, with India emerging as a leading hub for these services. The outsourcing trend is driven by several factors:

• Cost Efficiency: India offers highly skilled data scientists and analysts at a fraction of the cost of Western countries, allowing pharma companies to conduct comprehensive forecasting without significant overheads.
• Availability of Skilled Talent: India has a robust pool of trained professionals in data science, statistics, and life sciences. With a growing emphasis on data analytics in education and the proliferation of specialized training programs, India has become a center for pharmaceutical forecasting talent.
• Advanced Infrastructure and Technology: Many Indian firms have adopted advanced forecasting tools and technologies, including AI and machine learning platforms. This enables Indian outsourcing companies to offer state-of-the-art forecasting solutions.
• Focus on Quality and Compliance: Indian outsourcing firms adhere to strict quality standards and regulatory compliance, which are critical for pharmaceutical forecasting due to the high impact of forecasting accuracy on patient health and company profitability.

Quantum of Outsourcing and Employment Opportunities in India

The outsourcing of pharmaceutical forecasting has led to significant job creation in India, particularly in metropolitan cities like Bangalore, Hyderabad, Mumbai, New Delhi-NCR Region, and Pune. According to industry reports, India is estimated to handle over 40% of the global outsourced data analytics in the pharmaceutical sector. With increasing adoption of data-driven decision-making, employment in this sector is projected to grow by more than 20% annually over the next few years.

Examples of Employment Opportunities:

• Data Scientists and Data Analysts: Responsible for analyzing large datasets, identifying patterns, and creating predictive models.
• Forecasting Specialists: Focus on demand forecasting for specific drug categories, ensuring optimal production planning.
• Clinical Data Analysts: Analyze clinical trial data to support forecasting efforts, especially in areas like oncology and rare diseases.
• Regulatory and Compliance Analysts: Ensure forecasting processes meet global regulatory standards, crucial for compliance.

The growth in forecasting outsourcing not only provides valuable insights to pharmaceutical companies but also fuels employment and skill development in India, contributing to economic growth.

Skilling & Training

Cliniminds offers skilling and training programs in the life sciences sector, including in the field of forecasting, since 2004. These programs are designed to prepare students and professionals for careers as data scientists, data analysts, forecasting specialists, and various other related positions.