One of the most important is the clean in place (CIP) schedule. Cleaning between batches eliminates impurities in the product and helps to ensure compliance and quality.
However, it is important to optimise this process as much as possible. Cycle times need to be minimised to avoid wasted resources and time. The aim is to reduce time taken without compromising compliance or standards, but this can be complex.
Using conventional existing techniques companies find it difficult to identify CIP cycle time inefficiencies. The safe approach will inevitably include some degree of overcleaning and accept inefficiencies.
Data can improve CIP cycle efficiencies and reduce the time needed for each run. In addition, it can enhance the cleaning process and reduce the risks of error.
A case study on Pharma Online demonstrates how a major drug company did exactly that. To shed more light on the CIP the company had to see where it was spending time on CIP and use the data to develop process models across different circuits using all the data available to them. This helped them establish a clear view of the existing model and identify improvements.
Using data analytics, the team developed models for each of its CIP units. This enabled them to identify processes which were excessively long and involved over cleaning.
The team relied on metrics including percentage of time per mode, total water use and peak conductivity in each CIP cycle. Using this, they compiled a report to quantify potential savings and propose improvements to cycle times.
Using this data they succeeded in reducing CIP dryer cycle time, with administrative control rather than a feedback closed-loop control, increase the efficiency of the CIP operation, enable visualisations of durations and troubleshoot delays, and identify short term opportunities for process improvement.