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製品ライフサイクルに応じて変化する製薬業界の売上予測アプローチ(所要時間3分)

Andrew Ward

J+D Director of Services

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製品ライフサイクルに応じて変化する製薬業界の売上予測アプローチ(所要時間3分)

Andrew Ward

J+D Director of Services

Running time

Published

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製薬業界における売上予測は、「一度やって終わり」の活動ではありません。

ひとつの医薬品のライフサイクルは、臨床医の着想という初期段階から、確立された治療法として特許満了(LOE)に近づくまで、非常に長く複雑です。そして、各ステージにおいて求められる売上予測のアプローチは大きく異なります。

来ないご紹介するショート動画では、「医薬品ライフサイクルの各段階に応じて、売上予測の手法をどのように変えていくべきか」を解説しています。
初期の臨床試験フェーズでは比較的ハイレベルな視点から、上市前後や上市後においては、国別・地域別に落とし込んだ詳細な売上予測へと進化していくことが求められています。

So as you move through the product lifecycle, there’s a definite need for your forecasting to adapt. And that’s because the product and its nature, the investment, the resources behind it changes as it moves through the product lifecycle. So at the beginning, there’s lots of uncertainty, there’s lots of different products in the mix. Each product individually is having a relatively low resource. As it progresses through the phases, phase one, two, and three, and gets closer to launch, the resource and focus and attention from the company being placed in that product is increasing. So your forecasting needs to change. It needs to become more robust, more resources being put into it, more stakeholders get involved. The attention on it becomes more prominent, as ultimately that product is going to contribute to the company bottom line. So therefore, understanding what that could look like is really critical.

And as you move through to the end of the product cycle, understanding the loss of revenue and the impact on that is also really important. So that means you become more detailed, more focused on the individual product and resource increases. Your forecasting methodology and how you actually forecast will change as well. At the beginning, it’ll be quite simple. There won’t get that much data. It’ll be a lot of unknowns. And there’ll be about a range where forecast can land. Is it a $50 million, a $500 million or $5 billion product? So you’re dealing with quite different situations there based on limited data. But as you get towards launch, you need to be thinking, “how many sales reps do I need for this product?” So it becomes, it’s not $50 million or $500 million, it’s $50 million or $60 million. So you need to be more specific. So that means your methodology underpinning that needs to be more sophisticated, more comprehensive in order to deliver robust forecasts, which are expected when you come to launch.

The people involved as well…so when you forecast the early stage, there’s a few of you a global function. When you’re about to launch across 40 countries, you’ve potentially got hundreds of people in some way contributing to the forecast at different points with different agendas, different needs. So therefore, the complexity around managing the forecast, it’s not just about putting the numbers in and seeing what comes out. It’s about coordinating people, understanding different data available, that becomes really important as well.

And the role of a forecast might change if you think about a forecast methodology, which is more the consensus type forecast, et cetera. At the early stage, that might be a founding point as a reference point. But as you move through, companies will develop their own internal forecast and then they’ll use consensus to validate that or cross-reference that. So the nature of forecast and how it’s used throughout the product lifecycle will also change. And that means that as a forecaster, there’s lots of variety in your role, but then you also need to have several hats on and flex in different points. And often you will be focusing on a certain area or range in that product lifecycle. You might be focused on the pre-launch preparation and whether you are global or regional or in an individual affiliate country, the nature of your forecasting will change as well because you’ll be sitting at a different point in the product life cycle.

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よくある質問

売上予測は、製品がライフサイクルの各段階を進むにつれて、前提となる不確実性、利用可能なデータ量、そしてビジネスへの影響度が変化するため、その手法も進化していきます。
開発初期段階ではデータが限られているため、売上予測は比較的ハイレベルで、幅を持たせたものになります。一方、上市が近づき、売上への貢献度が高まるにつれて、予測はより詳細で、リソースを要し、かつ高い精度が求められるようになります。
開発初期段階の売上予測は、利用可能なデータが限られており、不確実性が非常に高いため、精度が低くなります。この段階では、正確な数値を算出するというよりも、幅を持たせた売上レンジで将来性を捉えることに重点が置かれます。
その後、臨床データが蓄積され、製品への投資が拡大するにつれて、不確実性は徐々に低下し、売上予測の精度も高まっていきます。
上市が近づくにつれて、売上予測の手法は、オペレーションやコマーシャル上の意思決定を支えるため、より高度なものへと進化します。
幅広い売上シナリオを描く段階から、より具体的で現実的なレンジに絞り込まれ、営業体制の規模や国別計画といった判断に直接活用されるようになります。
その結果、より多くのデータ、より多様なステークホルダーの関与、そしてより複雑な予測モデルが必要になります。