Friday, July 27, 2012

Manufacturing Sciences: Campaign monitoring v. Process Improvement

Manufacturing Sciences is the name of the department responsible for stable, predictable performance of the large-scale biologics process.

Manufacturing Sciences also describes the activities of supporting for-market, large-scale, GMP campaigns. The three main functions of Manufacturing Sciences are:
  1. Campaign monitoring
  2. Long-term process improvement
  3. Technology Transfer
Within the department are:
  1. Data analysis resources - responsible for campaign monitoring
  2. Lab resources - responsible for process improvement
manufacturing sciences flow
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 Monitoring

When 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:
Ultimately, all this statistical process control enables data-based, defensible decisions for the plant floor and to production management, much of which will involve the right decisions for decreasing process variability, increasing process capability and reliability.

Long-term Process Improvement

The 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:
Note: 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.

Outsource Manufacturing Sciences

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