What is Pharma Demand Forecasting?
Pharmaceutical demand forecasting is defined as the systematic estimation of the demand for pharmaceutical products in different markets, channels, and time periods. This is the foundation on which different business operations are performed.
The demand forecasting of pharmaceutical products is different from other industries because of the high degree of uncertainty involved. This uncertainty arises because of different factors like regulatory approval, pricing and reimbursement, competitors, etc. A reliable framework for demand forecasting helps in reducing the risks involved in the business even in uncertain conditions.
Accurate demand forecasting for pharmaceutical products is helpful in different ways, like better market access, ensuring the timely availability of medicines to patients, and avoiding overproduction or stockouts. It also benefits the commercial operations of the company by guiding the marketing, sales, and pricing strategies.
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The data required for the forecasting process may be scattered across several functions in the organisation. These may include the commercial team, the medical team, the market access team, and the supply chain team. Without a single framework in place, this leads to inconsistencies in the data.
Another challenge in the forecasting process has been the use of manual adjustments. These adjustments are not easy to track and explain. This leads to a lack of transparency in the process. This becomes even more complicated if the organisation has operations in several markets with different adoption rates and pricing. Without a data-driven approach in the forecasting process, the forecasts may be overly optimistic or conservative.
Despite the significant investment in the forecasting process, the accuracy and alignment of the forecasts continue to be challenges for several organisations. One of the key challenges in the forecasting process has been the lack of reliable historical data, especially in the case of new product launches.
Pharma organisations are changing the way they approach demand planning by focusing on transparency, integration, and flexibility. A successful forecasting model clearly articulates and exposes the assumptions upon which the forecasts are built, enabling stakeholders to understand the basis on which the forecasts have been generated.
Pharma organisations also integrate different sources of information, such as historical sales, epidemiology, market dynamics, and competitor intelligence. By using different sources of information, organisations are able to generate more accurate forecasts and predict the adoption curve, market dynamics, and competitor activity.
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Scenario planning is also a new feature in forecasting. This enables organisations to plan and assess different assumptions and the impact of different risks on the forecasts. By having forecasts that can be defended in different scenarios, organisations can build confidence in the forecasts and enable leadership to take more informed decisions.
Updating the forecasts continuously also ensures the forecasts’ relevance. By using new information on the market, sales, and regulatory environment, forecasts can be updated to reflect the latest conditions.
Several forecasting models are used in the pharmaceutical industry. These models vary in the context in which they are used. Time series forecasting is a common technique used in the pharmaceutical industry for products where historical sales data is available. This technique forecasts the future demand based on the trends and seasonality in the historical data. However, time series forecasting might not be the best technique when forecasting new products.
Analogue-based forecasting uses the information derived from similar products or markets to estimate the demand. This technique has been found to be effective in pre-launch forecasting. However, the accuracy of the forecasts depends on the quality of the analogue and the market data.
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Patient-based forecasting models use the information derived from the epidemiology and treatment pathway to estimate the demand. These models have been found to be effective in forecasting the demand for products in the pharmaceutical industry, especially those targeting special diseases.
Hybrid or driver-based forecasting models involve the use of a number of factors in a structured framework. These models allow the forecasting team to link the key commercial drivers to the forecasts. These models have been found to be effective in forecasting the demand in complex markets where several factors influence the demand.
How to Improve Demand Forecasting in Pharma
The first step in improving forecasting performance is setting the objectives of the forecast. This includes setting the purpose of the forecast, the degree of accuracy needed, and the time horizon.
Stakeholder alignment is also crucial in improving forecasting performance. It is important for commercial, finance, supply chain, and market access teams to align themselves at the beginning of the forecasting process. This will help in avoiding conflicting forecasts.
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The move towards driver-based forecasting will help in making assumptions explicit and in linking inputs directly to outputs. This will improve forecasting performance, and the inclusion of a wide variety of data sources will help improve the reliability of the forecast. This will include internal sales data, external market intelligence, epidemiology, and competitor analysis. Scenario planning will help in evaluating different possibilities for the organisation’s future, helping to evaluate the impact of different strategic, market, and supply chain changes.
Common Pitfalls in Pharmaceutical Sales Forecasting
The forecasting processes are often unsuccessful because of excessive dependency on manual spreadsheets, which are often difficult to control, lack transparency, and are hard to audit. Unstated assumptions in spreadsheets can lead to a lack of trust, especially in situations where the forecasting has to be presented to the finance team or the company’s top management.
Static forecasting models that are not frequently updated will not be able to cope with changing market conditions, like unexpected launches from competitors, changes in regulatory requirements, or changing patient demand.
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Lastly, forecasting models that are too complicated in their approach, in an attempt to cover all eventualities, can sometimes become unmanageable. It is important to steer clear of these pitfalls by adopting a transparent and collaborative approach in forecasting, where multiple sources of information are used, and scenario planning is done to accommodate changing market conditions.
Addressing Common Concerns
Some organisations believe their current forecasting processes are sufficient. However, even functional forecasts often lack the transparency, adaptability, and defensibility required in complex commercial environments. In practice, teams frequently encounter pushback when forecasts are challenged by leadership or finance because the underlying assumptions are not clearly documented.
There is an inherent level of uncertainty with pharmaceutical forecasting, especially with new product launches and markets that are subject to high rates of change. New methods do not remove this uncertainty but allow an organisation to effectively manage it through a series of models and data integration techniques.
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The concerns regarding complexity are legitimate. New forecasting solutions do take time and effort to implement. However, modern forecasting tools have removed much complexity by streamlining data inputs and making calculations much easier. Explainability is no longer a ‘nice to have’ but a ‘must have.’ Stakeholders expect not only to understand the results of a forecast but to be able to understand the reasoning and logic behind the assumptions, drivers, and changes. These concerns ensure that forecasting is not a debating point but an accepted tool.
Choosing the Right Forecasting Approach
Selecting the right forecasting approach or partner requires careful evaluation against multiple criteria. Transparency is critical, as teams need to understand exactly how forecasts are generated and how assumptions affect outcomes. Models that obscure the logic behind predictions reduce confidence and limit the ability to make defensible decisions.
Flexibility is another key consideration. Forecasting approaches must accommodate different products, geographies, and commercial scenarios. This is especially important in global organisations where variations in payer access, regulatory environments, and market dynamics are significant.
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Scenario capability allows teams to test and compare multiple potential outcomes. By simulating different market conditions, product uptake rates, or competitive actions, teams can better prepare for uncertainty and make proactive decisions. Integration with existing data systems ensures that forecasts are based on the most complete and current information, reducing errors caused by fragmented or outdated data. Ultimately, forecasts must be defensible, providing confidence to both internal and external stakeholders.
How RLS Can Help
RLS Consultants works with pharma organisations to transform forecasting from a routine operational task into a strategic advantage. Our approach focuses on building transparent, driver-based forecasting models that make assumptions and inputs explicit, providing clarity for all stakeholders.
We help integrate commercial, epidemiological, and market data to create forecasts that are both reliable and defensible. Our scenario planning frameworks allow teams to explore different commercial and market conditions, preparing organisations to respond effectively to change.
In addition to improving the accuracy of forecasts, we focus on governance, explainability, and continuous improvement. Teams gain the ability to track forecast performance, validate assumptions against outcomes, and refine models over time. This ensures that forecasting supports more confident decision-making across commercial, supply chain, and market access functions.
