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How to Make Sure You Have the Right Tools for Your Digital Transformation Process

June 25, 2020

Digital transformation in biopharma promises to deliver exponential results and make new discoveries and solutions to complex problems a reality, but it requires companies to make big changes to get there—changes in processes as well as adoption of new technologies. For some companies and facilities, this is a bigger leap than for others. Depending on the level of digitalization and integration that currently exists within a company, the process can take from months to years.

As you consider digital transformation and what it means for your organization, you will need to take a closer look at how ready your company is and uncover areas that present obstacles. We've outlined the steps you'll need to take and present an example of a successful process below.

Predictive analytics is an important element of digital transformation in biopharma companies.

Predictive analytics is an important element of digital transformation in biopharma companies.

As companies move toward digital transformation, some important technology migrations will become essential. These include:

  • Standardization of raw material data and process data throughout the operation
  • The ability to identify, track, and control variation using multivariate data analysis
  • Remote access and management, including mobile handheld technologies to capture data and enable rapid responses
  • Using computational modeling for predictive analysis that mimics the process, equipment, identifies anything that might contribute to performance variability
  • Incorporating artificial intelligence tools for faster processing and search of large quantities of data
  • Supply chain management, including developing standard working, integration and real-time electronic data exchange standards

Standardization of Data

Standardization and digitalization of existing data can the first big hurdle in digital transformation for many companies. For some, it becomes a stumbling block. Many companies currently harbor data silos – data is being generated and stored separately in different systems, file types or non-digital storage systems. Important data may be stored in disconnected spreadsheets or on someone’s computer in a lab. Being able to streamline and access that data for manufacturing quality and process control is a game changer.

In addition, Gartner reports that as much 80% of a data scientist’s time is often spent pre-processing or organizing data. That is time that could be spent on process optimization or helping with regulatory compliance or validation. SIMCA, the multivariate data analysis software from Sartorius Data Analytics, acts as a powerful data processing tool which can save a lot of time and effort when it comes to preprocessing data.

Data scientists are hard to recruit and having them do tedious tasks such as pre-processing or sorting data is a waste of their time when software solutions such as SIMCA exist that could do this automatically.

Digital Transformation as a Process

Digital transformation is a multilevel process that involves not only adopting new technologies but also preparing internal teams for significant change.

The BioPhorum Operations Group (BPOG), which was founded in 2004, recently published its first Technology Roadmap for the Biopharmaceutical Manufacturing Industry, for which Sartorius was a contributor. The roadmap highlights all the ways using data analytics as part of digital transformation is essential.

“Computer algorithms are increasingly able to generate insights and predictions that would not otherwise be available. Application of these new capabilities to biopharmaceutical manufacturing can be transformative.” BPOG – in the DPMM

BPOG has also created a Digital Plant Maturity Model (DPMM) and a best practice guide that covers the continuum of digital maturity stages for a biopharmaceutical companies.

Process Control

Process control is an important component of digital transformation in the biopharma industry. Being able to track and manage variation, or even automatically adjust processes to prevent deviations, is a goal that is increasingly not only supported, but even mandated by regulatory agencies. Recent draft guidelines from the FDA encourages more biopharma manufacturers to shift from traditional batch/start-stop processing to continuous manufacturing and use Multivariate Statistical Process Control (MVSPC).

SIMCA-online plays a big role in tracking process variation and uncovering process faults – even predicting when a process is about to deviate before it leads to costly product loss.

Read more: Correcting the Most Common Causes of Pharma Process Deviations

Real-Time Data Analytics

Digitalization is no longer only about automating the movement of physical product, but also about fully automating the data as well. This requires moving from manual, human-generated information workflows to more real-time processes.

One of the most effective ways to ensure your processes stay within their approved critical quality attributes is with real-time process monitoring. To be effective, that means having reliable real-time data analytics tools running throughout your production runs that give your operators the information they need in time to make the right corrections.

Being able to monitor processes in real time and take corrective action immediately means more confidence in your production processes, and in your operations team, along with more consistent product quality.

With SIMCA-online, you can monitor any process on the shop floor using handheld devices, like tablets, and stay connected even from home. Operators or site managers don’t need to stay on-site to baby-sit a process but can work on other things or even be at home and get alerts if any deviations appear likely so corrective actions can be taken in time. Or, in more digitally mature settings, even be corrected automatically.

Read more: Avoid Costly and Dangerous Process Deviations by Using Real-Time Data Analytics

Predictive Maintenance

One of the stages the DPMM model addresses is predictive maintenance. Being able to predict when equipment may break down, and schedule maintenance before that happens, can save money and time, as well as prevent potential disasters and high-impact interruptions of processes.

BioPhorum’s “Smart Maintenance: Digital Evolution for BioPharma Manufacturing,” says:

“To minimize asset downtime and increase/extend overall asset life, the next evolutionary step is predictive maintenance. The goal of predictive maintenance is to identify potential issues early on, before the asset is completely out of service ….
Emerging AI and machine learning technologies, can analyze real-time asset parameter data (e.g. temperature, pressure), compare parameter trends over time with historical values, or compare with other assets in the same class, thereby providing an early-warning system for detecting potential issues before they are manifested. Predictive maintenance is one of the most touted use-cases of machine learning algorithms, with success stories across industries.”

Smart Equipment

The cutting edge of digital maturity includes next generation smart equipment that not only generates and stores data, but even has control possibilities built-in, or is connected to other equipment. This includes edge analytics, a model of data analysis that allows incoming data streams to be analyzed by a connected device or sensor, increasing speed and the ability for devices to react locally.

Similarly, Sartorius has many products that are integrated with PAT technology, in which sensors work alongside data analytics. For example, the recently launched BioPAT® Spectro is a quality-by-design tool that unlocks the full potential of Raman spectroscopy. Online Raman spectroscopy data can be automatically imported and collated with other process data to SIMCA in order to build robust Raman models – alleviating the cumbersome task of pre-processing and manually combining spectral data. Integrating spectroscopy into process steps is becoming more and more common, so an effective digital transformation strategy must include tools and data management systems that can handle this.

Supply Chain Management

Ensuring that raw materials are validated and also delivered on time are key areas where digitalization is making headway. Supply chain management is made more efficient with material tracking enabled by scanning bar codes or QR codes. Creating a standardization for these types of bar codes is a hot topic in the biopharma industry.

Deep Learning and AI

As a subset of Artificial Intelligence (AI), deep learning is another area that is having an impact on digital transformation. Applications that can read images and videos, such as live cell imaging or flow cytometry will create great advancements in biopharma research and manufacturing.

As you plan your digital transformation, it’s important that you select data analytics tools that can work with new data types and sources as well as advanced analytics that go alongside with them. At Sartorius, we have identified the fact that biologics are becoming more complex (i.e intensified bioprocessing) and innovative. Hence, there is a need for a more flexible data driven approach in order to obtain increased observability into USP and DSP processes.

Key Considerations

Some of the key considerations for digital transformation in the pharma industry include adopting tools and systems that are:

High performing. Has the ability to collect meaningful data in real-time and show trends over time. It should also do a good job of separating performance data into process frameworks for monitoring and analysis.

Uses the right language. Bioprocesses have special needs so make sure you have tools that are built for your end users, such as being able to model downstream applications.

Integrates with existing equipment. Digital transformation on the machine side is progressive. Equipment is becoming more intelligent. Some comes with software that can store data, in other cases you need to build a bridge to gather data that is useful for advanced analytics. Data repositories such as OSIsoft PI system are essential.

Supports control capabilities. Data collected from different equipment must be assembled into charts that let people quickly see system or process performance, and provide alerts about potential process deviations, and act on them.

Making the transition to a digitally mature company means looking at processes, updating equipment and training your team on new ways of working. Advanced data analytics tools, such as SIMCA and SIMCA-online, are an essential part of any digital transformation plan.

Read more: How Digital Transformation Helped Amgen Implement Real-Time Process Control

See an example

Discover how Amgen approached their digital transformation process. Download this presentation from a Umetrics User meeting.

Get Amgen Presentation

Topics: Pharmaceutical manufacturing, Continuous Process Monitoring, Digital transformation

Tiffany McLeod

Written by Tiffany McLeod

Tiffany McLeod is the (Bio)pharma Market Manager within the Sartorius Stedim Data Analytics marketing team. She has been employed by Sartorius since 2017. Within this function Tiffany acts as the teams’ subject matter expert for life science market trends and requirements. She also works to addresses and develop data analytics solutions to solve industry challenges. She is passionate about biopharma 4.0 and helping businesses pursue digital transformations. Tiffany holds a degree in Bioengineering and Bioinformatics from the University of California, San Diego.

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