Three Steps to Gain the Full Advantage of Pharmaceutical Business Forecasting

By Sanobar Syed
April 11, 2023


• Categories: 02-MedTech Economics, 04-Product Design & Development, Health Data, Market Access

Business strategy and forecasting influence several functional areas within an organization. These relationships may be in one direction (where forecasts feed into decisions made by the other functional areas) or in many directions (where the forecast is used to generate changes proposed by other cross functional areas). These relationships reflect the various uses to which a forecast can be applied, including R&D forecasting, pipeline planning, revenue planning, sample and free goods, inventory, supply and production planning, resource and budget allocation, project prioritization, partnering decisions, compensation plans, and market access efforts. These varied uses reflect the first major challenge of forecasting: meeting the needs of varied and diverse stakeholders.

The relationship between sales revenue and unit volume in manufacturing is obvious, whereas forecasting outputs are used cross-functionally by various departments to derive the basis of their deliverables. For example, a market access team will use the annual forecasting numbers to prepare its submission to the health authority showing the unmet need and value the drug or device will bring to the market. In this case, multiple pricing scenarios are created to arrive at a value proposition, which creates an impactful dossier for submission.

A sales team will use the forecasting numbers to create segmentation and targeting exercises based on the territories which are high performers and hence create more revenue for the company. In many organizations, the role of business development (identifying and evaluating licensing, co-marketing, and co-promotion opportunities) relies upon the ability to quantify these opportunities—a forecasting function.

From these key real-life examples, we see that a forecast will have different usages depending upon the end user. Other examples include, a volume forecast at the supply level, scenarios to evaluate different policy options, or a forecast that explicitly evaluates the contributions of two companies to a product’s revenue potential. These differing requirements add to the complexity of what is required of a forecast.

Coordinating forecasting capabilities across forecasting functions to ensure consistency in analysis, reporting and operationalized analytics is the key to successful long-range planning, insights, strategy, and action. Following are three steps to follow.

Step 1: Determine Your Audience

In each phase of strategic forecasting, it is important to determine what the stakeholder wants. Are they awaiting the forecasting outputs to make a decision, or is it a routine demand activity? Often the company is looking to apply technically heavy advanced predictive analytics such as AI/ML models for forecasting, but they first need to determine whether they have the resources, capacity and investment to get this to fruition. Sometimes teams realize later that this is not a simple process or that there may be a ‘bias’ attached with complex machine learning models. Moreover, they realize that it is difficult for end users to make simple changes to such a model to adjust the forecast frequently.

With technological advancement, there are now models built over machine learning models that allow companies to tweak projections based on different scenarios. Knowing and anticipating the stakeholder needs helps prevent last minute alterations.

Step 2: Immerse Yourself in the Disease Area and Brand

It is imperative to understand how the brand to be strategized is or will be placed in the market (e.g., is it a new launch or a mature one?). If it is a new launch, predictive modeling might not be feasible due to the incompleteness of data. In this case, market research and competitive intelligence will be preferred (if it is feasible to find an analogue that can be modelled). If the drug is a mature drug, there may be enough historical data to proceed with predictive modeling.

Chronic diseases, such as cardiovascular disease, cancer, and diabetes, progress slowly throughout a patient’s lifetime. This progression can be segmented into “stages” that manifest through clinical observations. A growing area in oncology is the forecasting of niche disease. It is important to immerse yourself in the disease details including incidence, prevalence, diagnostic rate, treatment rate, comorbidities, affordability, market access, etc. to be able to drive an all-round strategy.

Step 3: Choosing the “Right” Method

Before launching a new product—typically in the R&D phase—companies will create forecasts about the brands. The typical method used is the “Epidemiology method.” With this method, forecasters use data and assumptions around prevalence, persistence, compliance, and market share to determine how many patients are taking a drug, and use this to forecast future revenue. This model is most often used when a product is new to the market, or where patterns of usage are complex (e.g., rare diseases, cell and gene therapy, or oncology).

Pharma companies also seek demand or sales forecasts about their drugs that are already established in the market. This is usually done with the help of historical sales and patient trends. The typical method used is the “Demand forecasting method.” When commercial sales data or real-world evidence are available, forecasters often use a demand-based model fueled by historical sales data (volume or revenue) to predict short- and long-term future sales. This approach “trends” past performance into the future and is particularly valuable when a drug’s sales have reached steady state, where the past is a good predictor of future performance.

Demand forecasting is one of the main inputs when developing long-term strategic plans. It is a method of analyzing the past and current historical data to determine future values. Hence, forecasting is the making of predictions about future performance based on past and current data.

A forecaster should be equipped in understanding how to identify and validate different data sources to arrive at a meaningful number. Traditionally, forecasters pick a model based on the rationale explained above. It is very difficult to build both models and, if you choose to do so, the two projections rarely line up, which creates an entirely new set of work in attempting to reconcile the two.

The integration of forecasting creates tremendous value as well as competitive advantage for an organization. The future of forecasting is its transformation from simple number reporting to offering these holistic frameworks in which to evaluate potential futures. Integrating forecasting can help your company stay ahead of the curve and steer both the organization and investors in a win- win direction.