by Oliver Yu
Manufacturing Sciences also describes the activities of supporting for-market, large-scale, GMP campaigns. The three main functions of Manufacturing Sciences are:
- Campaign monitoring
- Long-term process improvement
- Technology Transfer
- Data analysis resources - responsible for campaign monitoring
- Lab resources - responsible for process improvement
Figure 1: Flow of information within Manufacturing Sciences
The data group is responsible for monitoring the campaign and handing off hypothesis to the lab group. The lab group is responsible for studying the process under controlled conditions and handing off plant trials back to the data group.
Campaign MonitoringWhen a cGMP campaign is running, we want eyeballs watching each batch. There are automated systems in place to prevent simple excursions, but on a macro level, we still want human eyeballs. Eyeballs from the plant floor are the best. Eyeballs from the Manufacturing Sciences department are next best because they come with statistical process control (SPC) tools that help identify common and special cause.
Activities here involve:
- Defining key performance indicators (KPIs) for inoculum and production cultures.
- Control charting these KPIs
- Computing process capability.
- Running univariate and bivariate analysis
Long-term Process ImprovementThe holy-grail of manufacturing is reliability/predictability. Every time we turn the crank, we know what we're going to get: a product that meets the exact specifications that can be produced with a known quantity of inputs within a known or expected duration.
Long-term process improvement can involve figuring out how to more product with the same inputs. Or figuring out how to reduce cycle time. Or figuring out how to make the process more reliable (which means to reduce waste or variability.
This is where we transition from statistical process control to actual statistics. We graduate from uni- and bivariate analysis into multivariate analysis because biologics processes have multiple variables that impact yield and product quality. To understand where there are opportunities for process improvement, we must understand the system rather than simple relationships between the parts. To get this understanding, we need to have a good handle on:
: in order to have a shot at process improvement, you need variable data from large-scale. Meanwhile if you succeed at statistical process control, you will have eradicated variability from your system.
This is why a manufacturing sciences lab is the cornerstone of large-scale, commercial process improvement - so that you can pursue process improvement without sacrificing process variability and the results of your statistical process control initiatives.
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