Umetrics Suite Blog

What Does Digital Transformation in Biopharma Look Like?

June 18, 2020

Digital transformation in biopharma, like other industries, is accelerating as Pharma 4.0 and Industry 4.0 begin to take shape in companies of all sizes. But knowing that digital transformation is inevitable is one thing, successfully managing the transition process is another. What are the steps biopharma companies should be taking to ensure a smooth digital transformation?

digital transformation lets you used data created across departments to improve processes

Digital transformation lets you use data created across departments from R&D to operations to supply chain to improve your processes.

Before starting, it’s important to ask the question of whether your company is ready for digital transformation. Digital transformation means looking at everything from your supply chain to equipment to your processes to consider how it will need to change, adapt or integrate to allow data to flow between systems.

Keep in mind that digital transformation is a multilevel process that involves not only adopting new technologies but also preparing internal teams for significant change. It means migrating legacy processes and equipment, reaching across departments and teams, and being prepared to analyze data in meaningful ways in order to see a positive impact on your overall operations.

According to a 2018 survey of biopharma companies by Deloitte and MIT Sloan Management Review, biopharma ranks somewhere in the middle of industries in terms of digital maturity and adoption of digital working models.

“While many biopharma companies are experimenting with digital, most have yet to make consistent, sustained, and bold moves to take advantage of the new capabilities,” the report stated.

digital transformation in biopharma companies

Digital transformation isn’t just a bandwagon effort but something that requires buy-in and active support from your entire team.

What’s the First Step?

The first step toward digital transformation is having the right mindset of change within your company. Being able to convince the main stakeholders that digital transformation is important and essential for the future is a key element of success. Often, the push toward digital transformation is an idea that comes from the top down, but the key participants from R&D to the manufacturing floor must understand and accept this process as well. Methods and ideas such as predictive analytics only work if the people using them accept them.

However, when it comes to leadership, many companies are lacking support (or know-how) to make digital transformation happen at the top, as well. This was pointed out by the same Deloitte/MIT survey:

“ More than three-quarters of biopharma respondents (78 percent) say their organization needs to find new leaders to succeed in the digital age, and only 20 percent think their companies are effectively developing the type of leaders who have the capabilities necessary to lead the organization in a digital environment.”

blog 12-deloitte center for health solutions graphic

Image source: Deloitte Insights: Survey finds biopharma companies lag in digital transformation

A 2018 Dell technologies survey on digital transformation found lack of senior leadership support and sponsorship to be a barrier to digital transformation, as well. The report also showed that one of the top barriers to digital transformation across many companies was data privacy and security concerns. In biopharma companies, this can be an especially prevalent concern. Other key barriers include:

  • Lack of budget and resources
  • Regulation and legislative changes
  • Lack of the right tech to work at the speed of business

And key, one of the barriers often mentioned was:

  • Unable to extract valuable insights from data

This points to a greater need in companies to not only collect and store digital information, but also to make sure they are able to analyze the data properly in order to put it to good use both for strategic planning as well as for process improvements, for example using predictive analytics and process control.

Commit to a Digitalization Process

A key reason for implementing digital transformation is to allow process management to happen based on data rather than just “gut feelings.” Enabling data-driven decisions also requires a change in mentality, in which the organization and all members of the team value the insights provided by quantifiable data points. This includes creating a more structured approach to Design of Experiments (DOE), processes and production, based on actual data rather than intuition and developing standardized, reproducible processes based on historical data.

Collect and Standardize Data

A key step in digital transformation is collecting all your data into an accessible, yet secure, digital repository or data storage system. In order to be useful, your data needs to be accessible. And it needs to be collected and stored in a format that is readable by other systems. Consider what your master governance plan looks like and how data exchanges occur. Does the system need a two-way integration or is it one way? Which data is master if two elements conflict, and do you have a way to reconcile data points if you store both?

blog12-digital transformation diagraion process change color-1

(Image source: Amgen User Meeting Presentation)

Typically, the first hurdle companies face in digital transformation is connecting the data between departments or sites. The data could be in different formats, reside on different systems, and not be easily accessible. For many companies, getting all the data, from testing to production to distribution, gathered centrally into a compatible format is a daunting task that can take months or years. Gaining global access or remote access through the cloud may come next. But accessibility introduces additional security concerns.

One way some companies are solving this issue of storage and security, along with accessibility, is by using hybrid clouds. These are a combination of a public and private cloud systems that store some information behind firewalls – keeping sensitive data out of the cloud – but allowing specific information to be accessible for the next step in the process. Information that needs a higher level of security, such as patient data or clinical data, can be stored locally while information such as anonymous summary data or supply chain data can be in the cloud.

Select the Right Tools

From smart equipment that collects and stores its own usage data to operational storage systems that can feed both control systems and analytics, part of your digital transformation journey could be new hardware and software. The essential tools are:

• Time series data storage. To start your digital transformation journey, you may need a collection database that can support time series data from multiple sources and systems across all operations. OSIsoft PI system, which can store process and time series data along with results or usage – is the gold standard. OSI-PI is compatible with many control systems and analytics software solutions such as the Umetrics Suite, and provides a robust and scalable repository for your operational data.

Alternatively, some companies develop in-house systems using SQL that are compatible with time series data, or use one of the systems offered by smaller companies for specific needs.

• Equipment data. Whether you use “smart devices” that store and regulate their own usage or collect the data externally (either automatically or manually), getting all of your process and functional data into a digital format is essential. Depending on your operation, you may need to budget for replacing equipment with digitally compatible versions or append equipment with systems that can log the usage and results data.

• Control systems. Control systems bring data collection and storage to a new level by allowing real-time adjustments to processes based on data analytics. Automatic feedback and control become possible when data analytics is integrated. Some advanced control systems, like Siemens SIMATIC PCS7 or Emerson’s DeltaV, can adjust processes automatically. For example, the control system can adjust the stirring speed in the bioreactor based on data analytics feedback.

• Data analytics. One of the goals of digital transformation is to be able to optimize your processes and achieve better control. This step requires a powerful data analytics solution. Just collecting the data and storing it isn’t enough to make it useful. You need to be able to gain important insights from your data. And for that, a data analytics solution, such as the Umetrics Suite, that is compatible with your control system and data storage system, is essential.

Get Your IT Team Involved

IT is a critical stakeholder in the digital transformation process. Work closely with your IT team to ensure that any of the technology you invest in is not only compatible with what you have already, but that it is secure and will be maintained. You also want high performing systems that can deliver metrics by the second or faster.

Hire the Right People

Increasingly, companies are finding that bringing a data science department in-house is advantageous, as it means you have people who can create the models, translate the results for the operators, and support the scientists who are making decisions based on the analytics and process results. You need a close collaboration between the people on your data science team, the process development team and the manufacturing floor in order to achieve digital transformation.

New ways of working

Don’t overlook the fact that digital transformation takes time. It’s a gradual process that requires building databases, creating data lakes, connecting equipment and legacy systems, and managing data integration across multiple departments, sites and systems that may have different data structures.

People often underestimate how much work digital transformation is and don’t anticipate the timelines needed to implement. Be practical from the outset about what’s involved so your team doesn’t feel discouraged or think nothing is happening as your digital transformation journey continues. Offer your team time and training to get used to new ways of working.

Create and present a system that is scalable over time. Set milestones, and make sure to recognize when each are reached. Plan for digital transformation to be a multi-year journey. Consider Amgen, for example. The company implemented a company-wide digital transformation that was a 5-year process.

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

Key Steps for Digital Transformation

To summarize, the key steps for digital transformation include:

  • Get buy-in from all departments, especially senior leaders
  • Commit to processes (make sure everyone understands the value)
  • Make data available (robust data planning from a master-data governance plan to real-time data exchanges)
  • Engage with IT
  • Select the right digital tools
  • Hire the right people (data scientists and data specialists)
  • Prepare for a new way of working

Getting digital transformation right means gathering and using data that is meaningful, accurate, and provides insights that support your process optimizations and regulatory compliance.

Looking for more information?

Watch this webinar on Advancing real-time monitoring of biopharmaceutical manufacturing operations: Predicting in-process control parameters in column chromatography.

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Topics: Real Time Process Monitoring, Pharmaceutical manufacturing, 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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