Pharmaceutical decision quality is an essential journey that enables decision-makers to make strategic decisions with confidence. Although independent decision modeling may seem faster, an integrated approach provides significant transparency and value across the organization. Investing in new pharmaceutical R&D projects or portfolios is exciting, but they come with significant investment volatility that can range from blockbuster success to catastrophic failure. A well-established organizational practice for R&D investments enables organizations to grow the value of their investment portfolio, confidently progress risky innovative investments, and stabilize long-term growth projections.
To build a qualitative framework for strategic portfolio decisions, pharmaceutical organizations need to collect meaningful information and evaluate investment alternatives against each other. Key metrics and evaluation criteria must be applied consistently across portfolio investments so that they can be appropriately evaluated within a portfolio context. A maturity model can help organizations evaluate their decision-making behavior, methodologies, and outcomes of past individual decisions to adapt and mature their practice. The ability to overcome incidents and errors that lead to quality concerns and unpredicted outcomes requires an organization to broaden its capacity to account for and deal with uncertainty.
The decision maturity model comprises six levels, from gut feeling to highly analytical and structured decision-making. The aim is to ensure that the organization can confidently commit to action by aligning decision-makers and influencers in understanding the multiple dimensions of the decision.
Level 0 – Intuitive Decisions Based on Experience and Biases
At this level, the organization makes decisions without applying any decision-making framework. The decision-making process relies on the intuition and biases of the decision-makers and influencers. Although structured data inputs are considered, they are used in a way that supports the decision without any specific guidelines.
Level 1 - Ad Hoc & Independent Analysis
At this level, the decision-making process is unstructured, and inputs may be scattered throughout the organization. Decision practitioners build models from scratch and search for reliable data inputs. Ad-hoc models can be time-consuming, have quality control challenges, and scalability limitations.
Level 2 - Standardized Processes, Models, & Measurements
At this level, the organization standardizes decision-making by using common frameworks, models, and measurement criteria. Decisions are evaluated using a shared process and model, with past outcomes stored for reference. This helps manage uncertainty, adapt to change, and inform future decisions.
Level 3 - Enterprise Trusted Data & Analytics
At this level, the organization implements a standardized data management practice and platform to gather, organize, and integrate key data for decision-making. Collaborative engagement with data owners ensures trusted data sources, and managed data quality allows decision-makers to focus on the decision model's quality and insights. This maturity level enhances trust in the analytic processes and decision influencers' commitment to action.
Level 4 - Real-Time Scenario Planning & Analysis
At this level, the organization uses a collaborative platform to analyze decision options in real-time. It has standardized decision models and provides an interactive environment for decision influencers to collectively analyze scenarios. This agility allows the organization to respond confidently to risks and competition. Decision analysis, behavior, and outcomes are tracked to improve forecasting and predictions.
Level 5 - Augmented Prediction & Learning
At the highest level of decision maturity, the organization relies on machine learning and predictive insights from diverse data sources to augment human decision intelligence. This approach enhances strategic decision-making by allowing the organization to respond quickly to opportunities and effectively execute corporate strategy. While individual modeling may be faster, an integrated approach offers transparency and value across the organization, especially in industries such as pharmaceutical drug development, where informed decisions are critical.
The value of decision maturity is apparent, as both asset and portfolio decisions improve in agility and quality as the decision process matures. Ultimately, the organization becomes more aligned in supporting strategic investment decisions and can better manage overall portfolio risk.