Medicine

Strategic Insight and Forecasting: From Pipeline to Pharmacy

How pharma companies can be successful in financial forecasting, including why these forecasts should be powered by a patient-based approach.

In order for pharmaceutical companies to grow in a competitive marketplace, they need to be able to look ahead, anticipating the trends and developments that will shape their business strategy going forward. Some may say that it’s a tricky proposition for the pharma industry, as pharmaceutical insight generation and forecasting can be more tedious and more complex than in other sectors. When it comes to pharma, forecasting entails modeling a disease, patient journey, and complex healthcare systems that vary by geographies. No two patients, diseases, or countries behave the same. Often, treatments within the same disease need to be forecasted uniquely to depict the true market and its challenges.

In the past, pharmaceutical companies have used a mix of bottom-up and top-down processes to budget for the next fiscal year, and then made quarterly revisions during the current year. Today, volatility is more the rule than the exception, on both the revenue and expense sides. In terms of demand, brands in different diseases are impacted differently in the current, post-COVID-19 environment. That demand means figures spike again once hospitals are able to take on less urgent cases. Additionally, the timing of the impact in this new era varies by country and is difficult to predict. These realities only underscore the importance of strategy and forecasting in the pharmaceutical industry.

Letting data lead the way

How can pharmaceutical companies get financial forecasting right? “Using the data at your fingertips is a key piece of the puzzle,” said David James, CEO of J&D Forecasting, a consultancy that specializes in pharmaceutical forecasting. James notes the need to model a disease when forecasting in the pharma industry, which, along with a complex supply chain, adds degrees of difficulty to the process. “No two diseases or countries behave the same,” he said, “and often treatments within the same disease will need to be modeled differently.”

While pharmaceutical forecasting might appear on the surface to be very specific and niche-based, it is a business-critical activity that requires a broad range of stakeholders to support a range of business decisions. As such, each choice the organization makes with regard to forecasting requires a very specific approach geared toward getting the right information at the right time, James noted, adding that the way this information is presented is equally important.

It’s vital that pharmaceutical forecasts model a healthcare system as well as a disease. Many pharmaceutical forecasts will rely on some type of patient-based approach, as this method offers a better understanding of the causality of market changes, according to James. This is a complex model that requires a great deal of data, he adds, which leadership must carefully consider how to properly use and integrate.

Technology and tools

Forecasting in the industry is a complex task and one that is always evolving. The way pharmaceutical companies must approach insight generation and forecasting has changed significantly in recent years, with the arrival of the coronavirus pandemic. COVID-19 has “turbo-charged digital acceptance across the world and within most industries,” says James. “We have seen a big shift in the digitalization of forecasting, with a demand for solutions that allow forecasting teams to work from remote locations in a more effective way.”

In the past, stakeholders typically received forecasts (using Excel-based models) via email, he says. This delivery method “made for a disjointed process that was time-consuming and error-prone, with expensive resources needed,” continues James. “Since the pandemic, the market has shifted toward digitally driven, cloud-based platforms, providing better collaboration and more rapid delivery of forecasts.”

Historically, forecasting lacked the same level of investment that other business-critical functions enjoyed, he notes, adding that, as the pharmaceutical sector has matured, the focus has shifted toward more niche and rare diseases. This development, however, adds complexity to forecasting. At the same time, as pharmaceutical companies’ reliance on technology in other business areas increases, the speed of drug development will increase as well, says James, adding that “identifying the right compounds with the right profiles for diseases will improve, because companies are now using innovative technologies like artificial intelligence (AI) in clinical trials and data analysis.”

Looking ahead, James sees several potential applications for the use of AI in the development of forecasts. Machine learning, for example, could be used to model the evolution of diseases to create more accurate market sizing and to estimate the peak share of drugs coming to the market, as natural language processing (NLP) may also be useful in automatically generating insights.

As James points out, AI may have some utility in helping to predict large-scale events such as a pandemic. For example, a team including researchers from the Universitat Oberta de Catalunya and the University of the Balearic Islands are using AI to investigate new models of predictability and to assess the duration of consequences of epidemics.1 Applying the same approach to other disease areas can enable more accurate predictions of how some diseases will evolve, considering demographic, causal, environmental, and socioeconomic factors, James notes.

Fine-tuning the forecast

Technological advances are just one factor, albeit one very significant factor, shaping pharmaceutical companies’ forecasts and business strategies for the future. As a pharma company looks ahead and attempts to map out its future strategy, it needs a solid grasp of the environment in which it will be operating.

In an interview with Outlook India, Sanobar Syed, associate director of market insights and strategy at BeiGene, urges organizations in the industry—especially large, multinational pharmaceutical companies—to avoid taking a one-size-fits-all approach.2 “It needs to be kept in mind that the needs and regulations of emerging markets differ not only between countries, but also between regions,” says Syed, who has more than a decade of experience in the field of pharmaceutical business strategy and forecasting. “A strategy that works in rural Brazil will not necessarily work in urban cities in the same country and can likely also not be directly translated to most rural areas in India or Mexico.”

Syed also pointed to gathering insights on both current and predicted future market trends as critical to pharmaceutical forecasting success in any market, noting that this effort must include considering factors such as local healthcare access, reimbursement for original and generic products, and the competitive environment, for example.

“This can be done on a large scale by analyzing quantitative data from similar markets and/or by collecting qualitative insights directly from local healthcare stakeholders such as physicians, payers and patients,” she told the publication. “For the latter approach, digital technologies such as online advisory boards and virtual collaboration tools can be leveraged to streamline the [process].”

Comprehending key business needs and developing a forecast based on those needs is indeed critical to any pharmaceutical forecasting project, regardless of market-specific variables. “Just as important is understanding where that forecast is going and what is required from it—which other elements of the business will need it?” says James. “Understanding this will help to fine-tune the forecast and to understand the right methodology to be used. Quite often, forecasters overcomplicate it and create elements of the model which are not required, resulting in more time, money, and effort spent in the process.”

Determining best practices for forecasting is fundamental to creating a forecasting model for an organization, James concludes, adding that a model that delivers transparency, flexibility, and accuracy is the first step toward a forecasting project’s success. “Then, consider the usability of the model,” he continues. “Can it adapt to different stakeholders and their needs, and remain consistent across different diseases?”

Ultimately, forecasting a pharmaceutical market requires a high level of expertise. It is vital for an organization’s leadership team to avoid an overly complex model that can ultimately have the opposite effect of what forecasting is intended for, instead becoming a barrier to good decision-making.

References

1. Can artificial intelligence predict future pandemics? Labiotech. April 21, 2023. Accessed January 3, 2024. https://www.labiotech.eu/trends-news/can-artificial-intelligence-predict-pandemics/

2. Global Business Strategy & Forecasting Expert Sanobar Syed On How Do Pharmaceutical Companies Build Infallible Business Strategy And Forecasting To Conquer Markets? Outlook India. March 10, 2023. Accessed January 3, 2024. https://www.outlookindia.com/business-spotlight/global-business-strategy-forecasting-expert-sanobar-syed-on-how-do-pharmaceutical-companies-build-infallible-business-strategy-and-forecasting-to-conquer-markets–news-268938

About the Author

Mark McGraw is a writer and reporter with more than 20 years of experience in journalism, covering workplace and HR issues, healthcare, and a number of other business sectors.

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