Data is a double-edged sword. Unharnessed, businesses can feel swamped and bogged down by the vast volumes of data generated. They are often obligated to maintain and store it but derive little value from it. In fact, without an appropriate data strategy in place, businesses can see increasing costs with limited to no upside. It is no secret that data analytics unlock insights into an organization and can drive significant business benefits. But going from ad hoc to strategic implementation is often a challenge.
Biotech CMOs (Contract Manufacturing Organizations) have a treasure trove of data, and, as we will explore in this article, it can offer a competitive edge if leveraged to its full potential. There are three vital considerations to bear in mind when looking to implement a data analytics program:
In this article, Ross Katz, Principal and Data Science Lead at CorrDyn explores the tangible benefits for CMOs who embrace data analytics and, more importantly, how to implement data analytics in a practical way.
Streamlining processes is often the fastest way to use data to drive value. Rather than introduce disruption, it alleviates it, and the speed of the results certainly works to justify the investment. Data can provide valuable insights into inefficient processes, leading to transformational changes that simplify and accelerate work.
To optimize manufacturing processes, companies can develop a flexible data pipeline to consume machine sensor data, logs, and various pieces of metadata. The data is often already being generated, so the pipeline focuses on structuring and managing it. Once the data are properly warehoused, mining it for actionable insights is relatively straightforward. CMOs get a clearer view of how their production equipment works and where manufacturing bottlenecks occur. Analysis can also reveal production factors that hurt quality or yield of products. Teams can then concentrate R&D efforts on reducing failure rates and improving yield.
A methodology for Overall Equipment Effectiveness (OEE) could help CMOs to realize more value from their facilities. This provides 360-degree visibility into sources of waste using near real-time readings from the machines. This approach feeds into a more efficient and effective manufacturing process that minimizes avoidable costs associated with machine errors. It also frees up time and human and financial resources, allowing them to add value to other business areas.
CMOs must often grapple with the challenge of technology transfers - the handoff of pertinent knowledge and data relating to product manufacturing between stages or facilities. Properly managing tech transfer is critical in optimizing production processes. Poor management of tech transfer risks a reduction in the quality of manufacturing outputs and the efficiency of manufacturing processes. This creates a need for troubleshooting that impacts time to market. Inefficient tech transfer processes also impact the industry-wide impression of the organization, making it all the more important to perfect this process.
It is not uncommon for tech transfer processes to involve multiple manually created documents containing all relevant information. However, this can very easily introduce transcription errors and issues with data integrity. An alternative approach, and one that can be implemented as part of an overarching data strategy, would be to introduce a Manufacturing Execution System (MES) to help improve data integrity and streamline processes involved in tech transfer.
Manufacturing Execution Systems (MESs) can play a vital role in the tech transfer process. They monitor, track, document, and control the end-to-end manufacturing process. The data generated by an MES can then inform how processes are implemented. With robust data structures and pipelines, data analysis can be automated, and CMOs can see consistent results that prevent tech transfers from delaying time to market.
Every organization has blind spots that expose it to unknown issues and risks. Lack of visibility into errors or failures in processes can lead to speculation about their root causes, thus delaying relevant solutions from being deployed. Without data-driven insights, stakeholders are left to debate unfounded hypotheses about the sources of issues. As a result, bias and authority often determine the winner, rather than a precise understanding of what is actually happening in the business.
Data converts these conversations into mutual fact-finding missions where hypotheses are tested and rejected until the root cause is identified and the right solution is implemented. The “unknown, unknowns” become “known unknowns.” These blind spots can be filled iteratively through better data capture over time, leading to more complete visibility and control over manufacturing and R&D processes. Areas where errors and imperfect practices are lurking can be identified and remediated.
Data isn’t a silver bullet for driving business success, but it offers anchor points for teams when defining strategy. If you would like to hear more about how CorrDyn can help clarify how to improve processes and deliver success, get in touch.
Driving R&D efficiency is a critical strength that comes from introducing a better data strategy. The need to innovate, whether to refine processes for better quality, higher yield, or more impactful scientific advancements, is constant. Using data to support this is one of the most significant benefits for CMOs leveraging their data as it creates faster feedback loops and streamlines the testing process.
One approach is using mathematical modeling to improve simulation and give a better understanding of production constraints. Engineers can identify potential failures before real-world testing by simulating the production process using digital twins. Modeling can help teams understand outcomes and fine-tune methods before applying them to the lab or manufacturing floor.
Using digital twins, CMOs can benefit from more trial and less error. We have seen this first-hand with one manufacturer we worked with that was able to significantly reduce costs using this approach. This type of modeling does more than simply predict the outcome before it is implemented in the real world. It allows organizations to understand what is driving the results and use this to their advantage.
A major challenge for biotechs working in drug manufacturing is the increasing spotlight on data integrity. Driven by regulatory bodies including European Medicines Agency (EMA), the UK Medicines and Healthcare Products Regulatory Agency (MHRA), the U.S. Food and Drug Administration (FDA), and the World Health Organization (WHO), guidelines now require better practices for governing data integrity in manufacturing labs. There is a necessity to ensure better audit trails to avoid infractions that adversely impact reputation and result in fines.
This compliance need is only growing. Rather than address them on an ad-hoc basis, implementing a broad strategy to comply with evolving requirements for data integrity will pay dividends in the long run. Businesses can adhere to data integrity standards by developing infrastructure to manage data effectively. Compliance reporting can be automated to become more accurate and at a lower overall cost.
The FDA has significantly tightened its guidance around data integrity. It now recommends a greater volume of data to enable dynamic auditing and ensure CGMP (Clinical Good Manufacturing Practice). The example it uses is “the audit trail for a high-performance liquid chromatography (HPLC) run should include the user name, date/time of the run, the integration parameters used, and details of a reprocessing, if any.” This has increased the volume of data CMOs are required to manage along with increased penalty risks for improper handling. A consultant can put in place workflows that manage the data and generate the required deliverables for the regulatory agency.
Downtime and waste are two of the biggest opportunities to control costs in a manufacturing environment. Minimizing them is critical, and data facilitates Root Cause Analysis (RCA) to identify their underlying or fundamental causes. A detailed understanding of how machines work and why they behave as they do can help to maximize resource utilization or minimize waste from a production point of view.
Secondly, data analyzed in near real-time allows equipment failures to be identified and diagnosed immediately. Equipment can be fixed much more efficiently, with downtime minimized. An added benefit is that if a machine fails in a way that it continues to manufacture, but with product faults, the device can be shut down sooner and fixed without wasting resources on defective products.
Data can be used to guide predictive maintenance and know which problems need to be addressed immediately. By using data to support RCA, telltale signs buried in machine sensors and logs are brought to the surface. This methodology can then be used to indicate that preventative maintenance will soon be needed. Teams can plan maintenance to reduce unexpected downtime if these signals are made easier to interpret. CorrDyn worked with one biotech manufacturer to develop data pipelines and workflows to refine its internal processes. The result was a measurable reduction in machine downtime.
Listing the benefits of implementing data analytics is only part of the puzzle. In order for CMOs to be in a position to truly take advantage of their data, those benefits need to be implemented in a way that considers the unique nature of the business, does not disrupt productivity, and delivers significant ROI.
Data and its applications to R&D, process improvement, and compliance can have a transformative impact on several business areas for CMOs. The key to success is identifying opportunities with the most immediate and positive impact for a CMO. By starting there and then moving forward in order of priority, CMOs not only have a better handle on their data, but feel the positive effects of being in control over the short, medium, and long term.
If you’d like to speak to our team about unlocking opportunities within your organization through defining and implementing a data analytics strategy, get in touch by clicking here.