Digitizing Bioprocessing: Practical Lessons and Pitfalls for 2026
Written by Tamlyn Oliver for Biocompare
As published in Biocompare
I recently spoke to Greg Walker, Managing Principal at Life Cycle Engineering and a former Global Reliability Program Leader at Pfizer, about the digital transformation of bioprocessing. With his deep expertise in both reliability and life science operations, his astute observations and insights highlight the importance of value-driven implementation rather than just theoretical excellence.
Biocompare: Can you explain what it means, in practice, to digitally transform bioprocessing?
Greg: “In practice, digital transformation is not just about trying to apply new software tools and applications in your operations; it’s about adopting technology to solve tangible operational problems. It requires a planned approach involving requirements development, data preparation, IT/OT integration, and organizational change management to successfully transition from paper and analog systems to those that enable you to truly understand the real-time health and criticality of your assets.
“For example, instead of just running a bioreactor until it fails, digitalization involves using sensors and visual management systems to track if all operating parameters are within expected ranges (not just quality critical parameters) and to provide advanced warning when a failure may be developing—with time to react and correct issues and save the batch.
“By 2026, this has evolved into smart pipelines where data flows seamlessly from sensors to analytics. Practically, this looks like a proactive pre-run checklist for equipment: ensuring it is calibrated, clean, and ready for operation before a high-value batch is started. It’s the difference between hoping equipment works and knowing it will based on its current condition and recent performance data. Ultimately, it is about creating a culture of ownership where technicians use data to prevent the firefighting and costly consequences associated with equipment failure during their shift.”
Biocompare: How do you decide which parts of the bioprocess to digitize first?
Greg: “Deciding where to start requires a risk-based approach and focusing on the areas that directly impact product quality, throughput, and waste. A highly effective starting point is the filling lines or late-stage processes. These areas are often the final gateway to the finished product, having absorbed all the cost of upstream processing, and even microstops of three-second pauses that occur every few minutes can accumulate into hours of lost production over a single shift.
“Another strategic method is identifying bottlenecks and high-value opportunities through value stream mapping. If your facility can fill twice as many vials or syringes than your inspection lines can handle, your digital investment should be focused on improving inspection throughput. Rather than digitizing a one-of-a-kind, complex piece of equipment, it is often better to pilot digital tools on workhorse assets that are common across multiple lines. By focusing on these common assets, more operational data will be generated, making it easier to establish a baseline for what “good” performance looks like and to accurately train performance monitoring and predictive models. Once the digital tools being applied can be proven, they can then be focused on more complex and unique assets.”
Biocompare: Can you describe a successful digital transformation you were involved in and how its success was measured?
Greg: “A successful transformation often involves moving from isolated silos to a fleet management model. In one instance, I helped a company track the performance of similar assets, in this case lyophilizers, across multiple global sites. By using sensors to bring data into a centralized data lake, the team could see which site had the highest operating efficiency. This allowed them to identify that the top-performing site wasn’t just using better tech; they had better operator training and loading procedures.
“Success in these projects was measured through three core KPIs: Availability, Performance, and Quality (APQ).
- Availability: Was the equipment ready when the batch was scheduled?
- Performance: Did the operating cycle take the standard one hour, or did it drag to 75 minutes due to a failing vacuum pump?
- Quality: Did the run produce the expected yield, or were there anomalies suggesting equipment fouling and potential product quality impact?
“Improving these metrics leads to lower-stress shifts and significantly lower operational costs.”
What are some of the challenges in digitalizing processes?
Greg: “The primary challenge in digitalizing bioprocessing is the “garbage in, garbage out” trap regarding data integrity. Many organizations rush toward advanced analytics without realizing their legacy data is neither standardized nor clean, often requiring significant efforts in data scrubbing before algorithms can yield actionable insights. Furthermore, there is a significant hurdle in navigating the siloed nature of global sites. Even when companies try to replicate successful digital lines across regions, local variations in service support and infrastructure can undermine standardization efforts.
“Another critical challenge is the human element and cultural resistance. Technicians often view digital tools as an additional administrative burden rather than a facilitator of a hassle-free shift. This is compounded by strategic overreach, where companies chase moonshot solutions like lights-out manufacturing before they have mastered the basics of asset health. Success requires bridging the gap between high-level corporate digital mandates and the practical, local expertise of the operators who actually interact with the equipment every day.”
Where have you seen the most value so far from digitization and what still feels more like hype than reality?
Greg: “The most tangible value in 2026 stems from using instrumentation data to address the hidden secrets of bioprocessing—specifically, the batches that are discarded due to quality failures. Real value is found in using basic sensors to identify microstops on filling lines, which can snowball into hours of lost throughput over the course of a shift. Machine learning has also proven its worth in predictive maintenance; for instance, identifying when a sensor is becoming fouled by fine dust and triggering a pit stop intervention before the reject rate spikes.
“In contrast, much of the flashy technology still feels like novelty or hype. Tools like AR Glasses are often showcased as revolutionary, but in many practical field applications, a tablet or laptop PC and a simple 2D barcode system achieves the same result with less complexity and lower cost. The industry is learning that the most effective digital transformation isn’t always the most high-tech; it is the one that provides the right data sets to enable proactive fleet management across sites. Value comes from solving specific problems, not buying expensive solutions in search of a problem.”
Biocompare: If you were advising a company just starting its digital transformation journey, what are the one or two lessons you would tell them to watch out for?
Greg: “The first and most vital lesson is to start with your capital plan and look forward, not backward. It is a common pitfall to spend millions trying to retrofit legacy equipment with modern sensors, which is often cost-prohibitive. Instead, ensure that every new asset purchased today is equipped with the specific sensors and data standards you will need three years from now. By setting these standards on day one for new equipment, you build a foundation of clean data that allows for advanced modeling and analytics as the facility scales and advances in digital maturity.
“The second lesson is to prioritize the workhorse assets over unique systems. Companies often make the mistake of piloting digital tools on their most complex, one-of-a-kind asset. Instead, focus on high-volume assets like lyophilizers or filling lines where you have multiple similar units. This allows you to use fleet management to compare performance across the operation, identifying which assets are ‘bad actors’ based on operator feedback and actual process data. This approach builds a culture of ownership and reliability that is far more sustainable than a site-wide digital overhaul.”
Take the Next Step
Let’s drive your success together. Reach out today to see how our expertise can transform your operations.