How predictive analytics will transform your pharmaceutical manufacturing

27 July 2022

Pharma 4.0 principles are beginning to be adopted throughout the pharmaceutical manufacturing industry to improve processes and enhance data integrity. One of the key focusses within Pharma 4.0 is predictive analytics. Whilst many companies are already implementing automated systems and other aspects of Pharma 4.0, there is still a gap in the use of predictive analytics tools.

So, what is Predictive Analytics?

Predictive analytics is a system which uses artificial intelligence and machine learning to analyse large quantities of data and is able to make predictions on future outcomes from this data.

Pharmaceutical companies produce large volumes of data, which can often be too complex for humans to analyse easily. Utilising statistical analysis, artificial intelligence and machine learning, predictive analytics can analyse data, spot trends and patterns automatically and make accurate predictions about future events.

Analysis of this data can occur in real time, and trends can be predicted in processes, operations, or equipment. These predictions can then be used to improve these processes and minimise downtime and losses.

Predictive analytics allow for defined, repeating problems to be detected and therefore resolved early. However, they also provide a function to detect undefined, potentially non-repeating or non-historical problems as early as possible. This means that companies can move from having reactive controls in place to having proactive, predictive outlines and controls.

Why use Predictive Analytics in Pharmaceutical Manufacturing Companies?

With the implementation of Pharma 4.0 principles throughout the industry, we have more and more data being produced. This data is now being stored in complete and structured formats, which lends itself to the use of predictive analytics within pharma.

Highlighting trends
  • Better storage of data allows continuous monitoring of processes which can highlight drifts or trends away from the norm and allows these to be predicted and rectified before they occur, preventing issues and potential loss of product.


upwards trends

 

Figure 1 – graphical representation of upwards trends, by SmartControl EM

  • For example, Figure 1 clearly shows that the contamination rate in Cleanroom 4 has been increasing slowly but steadily for several weeks, until eventually it exceeds the threshold of 5% contamination rate. Predictive analytics tools could have detected this increasing contamination rate before the threshold was exceeded, possibly allowing preventative actions to be implemented before the excursion occurred.
  • Scheduling maintenance requirements
    • Some of these new technologies have “self-aware” components, with sensors which constantly monitor every aspect of processes. These systems can help to schedule infrequent maintenance tasks more accurately.
    • For example, if a sensor detects a decrease in the performance of the equipment, it can automatically schedule preventative maintenance before failure of the equipment. Predicting a failure of a part before the machinery malfunctions means that production downtime can be planned for maintenance rather than the breakage occurring during manufacturing processes. This can help to prevent additional repair costs and minimise production losses.
  • Predict patterns based on past data
    • Predictive analytics can use past data to predict future outcomes. This can be done by analysing past and current data and predicting patterns.
      historical patterns

Figure 2 – historical patterns which could be predicted with predictive analytics, by SmartControl EM

  • For example, Figure 2 shows the contamination rates in Cleanroom 2 following a pattern of those in Cleanroom 1. A predictive analytics tool could predict when the contamination rate may cross the threshold for Cleanroom 2 from the previous results of Cleanroom 1. Facilities could then implement preventative actions to prevent an excursion in Cleanroom 2 as well as Cleanroom 1.  

These types of predictions can help to increase the productivity of a manufacturing team, as there will be less time spent trying to fix issues or waiting for issues to be fixed before continuing manufacture. This enhanced productivity in turn saves companies money.

Predictive analytics can also help to understand problem areas or areas for improvement. This can ensure that best practices are followed at all times. It can even suggest improvements to processes, such as optimising process settings based on the analysis of the data input.

So how can I utilise predictive analytics?

Data analysis tools such as the SmartControl family are now available which are beginning to utilise the data captured within them to perform predictive analytics.

Data must be produced correctly for predictive analytics to fulfil it’s potential. Manufacturing companies often have massive amounts of data stored, but this data is often either incomplete, stored incorrectly, or just not useful data.

Take environmental monitoring as an example: samples are taken and booked in on one system; results can be produced and stored on an alternative system; and then identification and additional information can be stored in a separate system.

This makes it extremely difficult for any automated system, let alone operators, to view all of this data holistically and make informed decisions based on this data. Operators and analysts are expected to manage these high volumes of data but often will miss issues, or only find them long after they have occurred.

How can SmartControl EM help?

Systems such as SmartControl EM help to gather all data into one place in a format which allows users to holistically review their data. Not only that, but it also performs many predictive analytical functions for you. The system continuously analyses all data and creates a picture which includes relationships between the data.

The implementation of systems utilising predictive analytics can change the way pharmaceutical manufacturing companies manage and use their data. Systems such as SmartControl can help to move companies away from voluminous, meaningless data towards concise and meaningful results.

Part 2 of this blog will be coming soon, which talks in more depth about how predictive analytics and machine learning could be used in the future, with SmartControl EM and the SmartControl Colony Counter.