Pharmaceutical industry

DIGITAL LEAP FORWARD ENABLES LEARNING PROCESSES

The pharmaceutical industry is in the middle of a digital leap forward. Pharma companies need to switch from print to digital process support in order to continue operating cost-effectively. But that is not the only reason to embrace Industry 4.0. Those forward-looking enough to fully integrate change at an early stage will be able to benefit from learning processes in the future.

Digitisation of the pharmaceutical industry is a complex process, requiring the integration of various types of expertise. Strict regulations add a legal dimension to each change. Long-running processes make testing expensive. Since reliability must be guaranteed, there is a need for multiple controls.

Common features of the pharmaceutical industry:

  • Batches rather than ongoing processes;
  • Appliance of the ‘first-time-right’ principle;
  • Long-running processes;
  • Detailed documentation and registration.

Safety is critical

The certainty that long-running processes are completed successfully is of vital importance. After a lead time of six weeks, the work must not be lost. By digitising the full batch from advance checklist to subsequent check, errors can be reduced and efficiency can be improved.

Looking forward through an integrated approach

A key part of digitisation is the reduction of paper. Choosing an integrated approach creates new opportunities. All aspects of the process can be tracked, and any irregularities can be identified at an early stage, allowing for early intervention. In addition, traceability can be guaranteed based on the type of raw material and batch involved.

Comparing batch results

Those who look beyond the obvious see opportunities to compare the process and results of batches. This helps to identify discrepancies between processes and provides methods for finding root causes. An integrated approach makes the production of individual batches more efficient and provides room for further process innovation.

Transition to machine learning

By using machine learning for these processes, businesses can facilitate further optimisation. Thanks to Big Data techniques, the large quantity of data which must be stored under the law can generate money. Automated quality monitoring can – for example, based on comparison of results – make suggestions to adapt processes. The greater the number of data sources that can be connected to each other and the more effective the analytical tools, the more opportunities will arise.