Complex manufacturing processes with particularly high quality requirements can benefit enormously from a detailed knowledge of all existing data and from the resulting insights into influencing factors and variables. Analyses of large data volumes can serve to convert information into predictive models, which then allow the configuration of processes within a range of optimum parameters. These ranges are customarily defined to lie safely within the specification limits while, at the same time, allowing high yields.
Biopharmaceutical production processes provide a particularly suitable test environment for the technological potentials of big data analytics. One example is the technique of producing antibodies. Antibodies are first generated in botanical plants before they are converted into a biopharmaceutical product through a cleaning process. Since the growth of plants is determined by many factors including light and temperature and since the growth rate in turn determines key pharmaceutical parameters such as stability and effectiveness, a profound understandíng of the underlying processes is vital. Such an understanding can be gained from data-based models that allow a batch-specific adjustment of the conditions of the plant cultivation process.
The highly flexible and adjustable analytic tools and models that are developed by the ICNAP can be used in a wide range of fields where production conditions must be frequently adjusted, for example across the entire process industry including pharmaceuticals, cosmetics and agriculture. Intelligent analytical methods can also be highly useful in classical discrete manufacturing environments where large and highly heterogeneous data volumes must be processed. Under such circumstances, predictive models can serve to establish and to eliminate potential error sources at an early stage.