Thirty years ago, the pharma industry derived most of its revenues from North America and Europe. But over the past 15 years, rising demand for innovative therapies in emerging markets has shifted the landscape. From 2005-2015 most big pharma companies saw Asia Pacific and emerging markets expand to comprise at least 25% of their total revenues我。
This has complicated forecasting efforts, since data access and data quality are often lower in emerging markets. And assumptions that are true for America and Europe just don’t resonate in many areas of the world. For example, an IQVIA report from March 2017 looked at the impact of digital communication on uptake in every market. The study found 94% of Japan’s specialist physicians felt their preferred amount of digital communication was being achieved, however, this figure was just 50% in the US and 17% in the UK.ii。
The average global-level forecast is not able to manage this type and scale of variance by market. Doing it requires global-local collaboration as well as technology platforms that explicitly adapt big picture trends for local needs.
当仅全球预测不足时
中央生成的预测为利益相关者提供了对市场格局的广泛视野。通常，这些是由全球专家生产的，这些专家可以访问大数据集，并且基于对产品的详细了解，他们生成了复杂的预测渠道。这是一个绝佳的基础，但是在当今的异质产品景观中，这只是开始。
Country-level forecasts bring more nuanced perspective from local experts. These regional affiliates are ‘on the ground’ and often have a better sense of what’s really happening in their market. But getting this regional affiliate perspective has been difficult. Global team members may find themselves making calls and sending emails, or shepherding country affiliates through a cumbersome ‘forecast submission’ process. Alternatively, local affiliates may find that their view of the market does not align well with the global standard – perhaps it is too simplistic or too complex. As a result, some build ‘shadow copies’ of commercial forecasts that have inputs and assumptions that reflect their own needs. These ‘shadow’ forecasts may never be seen by global groups, meaning valuable insights may be lost and teams are not aligned on actual goals.
但是，本地和全球团队有更好的方法可以合作。
The future: Glocal Forecasting platforms
“Glocal” forecasting platforms are an emerging trend in pharma forecasting. This new type of forecasting technology offers collaborative platforms that allow flexible frameworks and real-time access to the same information and insights.
在这些模型中，全球和本地团队共同努力建立准确的预测。在许多情况下，全球团队将提供一个预测框架，并确定应在所有市场中始终如一地跟踪的指标。但是在该框架内，分支机构可以使用自己的本地研究来调整预测预测，并为其单个市场定制漏斗的某些部分。在该模型下，团队从全球和地方一级保持真理来源中受益。
