What Does “One-Source of Truth” Imply in Business Strategy & Forecasting and Why Does it Matter?

In the pharmaceutical world, market research & forecasting holds an important role and function in the central decisionmaking process. Typically, this function resides in the commercial excellence or business excellence function. There might be a global function and a local function which is responsible for the forecasting role. Market research & Forecasting is a critical function and influences many other functional areas within an organization. The cross functional relationships reflect the various uses to which a forecast outputs can be applied such as R& D forecasting, pipeline planning, revenue planning, sample and free goods, safety stock, supply & production planning, resource and budget allocation, project prioritization, partnering decisions, compensation plans, market access efforts etc. These varied uses, and the effect of forecasting on many functional areas in an organization, reflect the first major challenge of forecasting – meeting the needs of varied and diverse stakeholders. One shouldn’t create a single number forecast: if there’s variability due to lack of data then this needs to be shown either through sensitivity analysis or by creating a probability-based forecast. Any forecast, no matter what the market, is an estimate that will inevitably include inaccuracy. It’s a means of understanding both the risk and the potential of an investment. Therefore, it is essential that those who are basing their decision on the forecast understand both the range and the underlying drivers [1].

One of the emerging approaches in pharmaceutical research and forecasting is to blend and make forecasting as a bridge to narrow the outputs and approaches into “one single source of truth”. When all the key stakeholders are aligned it creates a unique forecasting blend and defines outcomes with better certainty [2]. A few years ago, multinational pharma companies seeking growth and respite from market uncertainty in Europe and the United States found a haven in emerging markets. Their rapid economic growth triggered an expansion in healthcare coverage and the emergence of a new cohort of consumers able to afford larger out-of-pocket spending on drugs. But early euphoria was soon replaced by a more somber outlook. Some emerging markets either suffered downturns or showed weaker growth forecasts as commodity prices fell; some healthcare systems struggled to scale up adequately their provision of care; and local companies became increasingly effective competitors in the pharma market. Furthermore, multinationals themselves often found it difficult to scale up in emerging markets, with challenges in talent recruitment, compliance, and organizational setup [3]. By contrast, developed markets like the United States were doing rather well. As launches become more complex, companies are looking towards strategic questions and specific launch and market insight requirements to build sustainable data leads in product launches. In addition to the need for customization, and thus the need to first understand each market and its characteristics fully, there are multiple other obstacles to tackling emerging markets. These include things like poor healthcare infrastructure, limited public funding, minimal or no intellectual property protection and regulations, complex market access regulations, and local competition, often in the form of generics.

The key challenge to forecasting is to create a process where the needs of function can be met without compromising the integrity of the forecast approach. The Key challenge is how will we create the “one source of truth” across the organization, especially when we have monthly, quarterly, annual, and other time intervals of forecasts. It also depends upon the frequency of inputs or data arriving in to churn out as forecast deliverables. The backbone of accurate forecasts are strong, rigorous models that can be deployed across various market scenarios. This provides a structured and articulated view of resource needs for operations, a key competitive advantage that reduces risk. For pharma organizations, factoring the impact of market access into product forecasts is becoming increasingly complicated. An analytical approach can help refine the forecast for treatment opportunity, brand selection and patient fulfillment. 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 [4].

“One source of truth” becomes more imperative for organizations especially in an international and expansion mode.

A pharmaceutical forecast needs to model both a disease and a healthcare system, and the key to doing this well is using the available data to reflect this complexity in a way that supports effective decision-making. Generally speaking, a pharmaceutical market research and forecast needs to model both a disease and a healthcare system, and the key to doing this well is using the available data to reflect this complexity in a way that supports effective decision-making. There is a lot of data involved, but for that data to be valuable, it is important to consider how it is used, integrated, and delivered. Making the right decisions with the right information and insights will always be key for any business, and the challenge is to bring all these different elements together.

There is a lot of data involved, but for that data to be valuable, it is important to consider how it is used, integrated, and delivered. Making the right decisions with the right information and insights will always be key for any business, and the challenge is to bring all these different elements together. The “one source of truth” approach always triumphs in the organization.

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No conflict of interest.