Beyond these data limitations, many biopharma companies lack the skills and tools necessary to develop actionable insights from the data they have. Even if some statistical analysis of processes and tentative correlation of parameters are occurring, the tools used are typically not up to the task at hand. ANOVA, single-variable correlation or other Six Sigma tools are not sophisticated enough to handle the multidimensional and highly complex manufacturing processes of biopharma.
With gaps in the data, skills and tools required for advanced analytics, biopharma companies often have an incomplete understanding of their performance. Furthermore, they may not be able to fully seize the opportunities that they do identify.
Biopharma companies that do build proper advanced analytics capabilities could forge an advantage in manufacturing that will differentiate them from their competitors. To help companies understand that analytics journey, we outline a standard and granular approach here.
1. Create the conditions for analysis.
Gauge the potential for improvement by using historical data to estimate the size of the gaps between average and best-case performance. To start, aggregate data from every available source across the organization into a single, exhaustive database. Map the organization’s operations from end to end to account for every aspect of each production process. It is critical to ensure that the data is high quality and that enough data is collected to generate relevant insights. Then segment the data into clusters (e.g., fermentation) of closely related activities that can be analyzed as coherent units. For each cluster, list every process parameter and material characteristic.
2. Analyze data and develop insights.
Once a threshold of data is gathered and segmented, use a variety of advanced statistical tools to identify improvement opportunities and spot trends (see Figure 2).
Correlation analysis can be used to identify relationships and linkages among process parameters. Standard statistical analyses — including moving averages, distribution histograms, standard deviation, and clustering analyses — can be used to identify patterns and prioritize data that has the most predictive power. Statistical significance analysis can be used to test initial hypotheses about the root cause of titer and yield variability and identify relationships among parameters that were not surfaced by correlation analysis. And artificial neural network analysis, which seeks to emulate the structure and functional aspects of biological neural networks, can be used to model complex processes and determine with greater precision how particular parameters affect productivity.
Accelerate progress by conducting workshops with biopharma experts to investigate the trends, correlations, and other phenomena identified. This will foster a deeper understanding of the underlying biopharma parameters along the value stream.
3. Build an action plan
In many cases, opportunities will be identified that are worth pursuing. Rank these opportunities based on potential impact and the effort required to implement. Then prioritize opportunities with the highest impact that are easy to implement. Develop clear initiatives to capture these priority opportunities. These initiatives should include provisions for training and coaching employees at all levels in the organization to ensure they develop the necessary capabilities and mindsets. Also include provisions for monitoring performance to ensure that the initiatives are implemented properly and on time, and provide mechanisms to help teams correct course when they encounter challenges. It is critical to assign clear lines of accountability for every aspect of each initiative.
Executing in waves is recommended. Consider this approach to allow teams to learn on the fly and refine their approaches as they roll initiatives out in full.
Here is a second case in point. A biopharma firm that used this approach increased its yield by 30 percent by stabilizing cell growth. This improvement, triggered by insights gained from analytics, derived largely from improvements in cell storage and adopting more effective standards for discarding expired media used to grow cells.
Manufacturers that use advanced analytics to improve their operations have the potential to transform the industry, establishing new standards for efficiency while reducing costs and increasing sales. The resources they free up can then be poured back into research/product development, fueling their growth far into the future.
ABOUT THE AUTHORS
Eric Auschitzky (eric_auschitzky @mckinsey.com), Alberto Santagostino (alberto_santagostino @mckinsey.com) and Ralf Otto (email@example.com) are leaders in McKinsey & Company’s Pharmaceutical Operations practice.