In the next five to ten years, new technologies that characterize Industry 4.0—from connectivity to advanced analytics, robotics, and automation—have the potential to revolutionize every element of pharmaceutical quality control labs. The smart quality approach allows pharma companies to deploy these technologies and to integrate their quality controls in development and manufacturing.
(see sidebar, “Smart quality at a glance”). Well-performing manufacturing facilities have started to create paperless labs, optimize testing, automate processes, and shift testing to the shop floor. These moves have enabled well-performing labs to substantially improve speed. These technologies typically boost productivity by between 50 to 100 percent.
Average-performing labs could achieve an even larger productivity improvement of 150 to 200 percent of their current rates.
Beyond these effects on efficiency, digitization and automation specifically can also ensure better quality and compliance by reducing manual errors and variability. They enable faster and more effective problem resolution and a risk-based approach to optimizing testing volume, tools, and methods. In some cases digitization and automation have resulted in a more than 65 percent reduction in overall deviations and over 90 percent faster closure times. They can also prevent major compliance issues, which can in themselves be worth millions in cost savings. Furthermore, improved agility and shorter testing time can reduce lead times for quality control labs by 60 to 70 percent and eventually lead to real-time product releases.
However, few pharmaceutical companies have seen such significant benefits yet. This is usually due to the sometimes-significant upfront investments required, and the fact that some labs are simply not large enough to justify such investments. Many lack the granular performance and costing data necessary to build adequately sized digitization and automation business cases, and their efforts do not meet expectations for creating business value. Organizational silos and misaligned objectives between analytical method development and quality control labs often slow down innovation in both the mid- and long-term. In addition, since pharma product portfolios—and therefore pharma companies’ technological needs—evolve over time, it is sometimes hard to ascertain the right, clear, long-term lab-evolution strategy and blueprint needed for a clear long-term business case.
Before implementing and capturing benefits from new technologies, companies must first set clear goals, define robust business cases for any level of investment, and create rapid pilots of emerging technological solutions. Then, they must quickly scale-up the pilots that deliver promising results. To succeed, pharma companies need both the foresight to make long-term strategic investments, including those in R&D for developing and filing new test methods, and the agility to adapt those plans as technologies rapidly evolve.
Three horizons of lab evolution
Multiple digital and automation technologies have created opportunities for change in pharmaceutical laboratories, and this transformation typically evolves over three horizons (Exhibit 1). Most pharma labs have not yet achieved full technological transformation, but labs can start by aiming for one of the three future horizons of technological evolution. However, these horizons are not mutually exclusive and may not follow a linear path. In fact, pharma companies can create a compounding effect when they implement an element from another horizon at a different stage: for example, testing automation can be implemented in paper-based labs.
At the same time, some elements of one horizon may be a prerequisite for elements of another. For example, without having digitally enabled labs in place, a company would not be able to fully capture the benefits of automated labs.
Horizon 1: Digitally enabled labs
This horizon is comprised of the transition from manual data transcription and second-person verification to automatic data transcription between equipment and the laboratory information-management system (LIMS). Integrating quality control systems and sharing data with internal and external suppliers by automating data transcription creates better visibility and helps reduce risk. This integration also allow for targeted investments that improve the quality of inputs upstream, minimizing the need for often redundant raw-material testing, and accelerating the release of incoming materials.
Digitally enabled labs use advanced real-time data analytics for ongoing process verification to track trends and prevent deviations or out-of-specifications, and for optimizing scheduling and capacity management. These labs employ digital tools such as smart glasses to explain standard operating procedures with step-by-step visual guidance on how to execute a process. A digital twin can help predict impact before making physical changes to a lab. All these technologies have already been available for at least a few years, and the time to impact for each case can be as short as three months.
By becoming digitally enabled, an average chemical quality control lab could reduce costs by 25 to 45 percent, and an average microbiology quality control lab 15 to 35 percent. Productivity improvements come from two main sources:
- the elimination of up to 80 percent of manual documentation work and the requirement that two people must review everything (the four-eye principle);
- the automation, and especially optimization, of planning and scheduling to improve personnel, equipment, and materials utilization.
After the lighthouse plant of one large global pharma company transitioned to a digitally enabled lab, for example, their lab productivity jumped by more than 30 percent.
This increase was largely due to dynamic schedule optimization achieved with a modular and scalable digital-twin platform. The site also used advanced analytics to reduce deviations by 80 percent, eliminating reoccurring deviations altogether and accelerating deviation closure by 90 percent.
Horizon 2: Automated labs
In this horizon, pharma companies use robots, such as collaborative robots or other advanced automation technologies, to perform all repeatable tasks such as sample delivery and preparation and other lab-specific automation techniques. At the automated-lab stage, some high-volume testing (for example, instantaneous microbial detection in water for injection and air) can be performed online instead of physically in labs. Automated labs can also use predictive-maintenance technologies to plan for infrequent tasks such as large-equipment maintenance, which can be performed by lab analysts with remote expert support.
Automated labs can build upon a horizon 1 level of digitization to deliver greater value and higher cost savings. Automated microbiology labs can reduce costs beyond that achieved by digital enablement—10 to 25 percent savings inside the lab, while also capturing a similar amount of savings outside the lab. Likewise, in chemical labs automation can produce 10 to 20 percent incremental savings inside the lab.
The productivity improvements come from automating up to 80 percent of sample-taking and sample-delivery tasks and up to 50 percent of sample-preparation tasks. Improvements also come from reducing equipment-maintenance costs through remote monitoring and failure prevention. Outside the lab, automation reduces the number of sampling and related logistics tasks performed by operations, which saves the equivalent of up to 25 percent of lab costs for microbiology labs and up to 8 percent for chemical labs.
There are additional benefits: remote-monitoring and predictive-maintenance capabilities built into the equipment will decrease downtime and ultimately enable companies to reduce their use of expensive devices, such as chromatographs, near-infrared spectrometers, and isolators. By shifting to instantaneous microbial detection for environmental monitoring, companies may also reduce their overall lab lead time by 40 to 75 percent.
Technologies already exist—in healthcare and research labs and in manufacturing operations—that can be adapted to pharma quality control labs in a relatively straightforward way to reach the automated-lab horizon. Vendors offer solutions such as sample-distribution systems, online and instantaneous microbial-testing systems, automated sample-preparation stations, workflow optimization with visual guidance, and remote equipment monitoring and assisted maintenance. Next-generation analytical testing technology such as Ultra Performance Liquid Chromatography (UPLC) and Raman spectroscopy, often offer a more industrialized design with additional automation features or design for more and faster throughput.
Since 2018, additional technologies have emerged, such as remote equipment monitoring to reduce downtime and failures, advanced sterility testing techniques, and advanced analytics for faster and more effective lab investigations. Moreover, parallel to the evolution in the general robotics and collaborative robot space, lab-automation technology has become more cost effective.
Horizon 3: Distributed quality control
The third horizon represents a true disruption to traditional quality control, where nearly all routine product testing takes place on the production line, enabling real-time release testing (RTRT). To date, pharmaceutical manufacturing facilities have been slow to adopt process analytical technology and RTRT—both essential to Horizon 3—due to complex regulatory requirements. To be able to make a smooth shift to online testing in the future, operations need to start collaborating with R&D in earlier development stages to define an optimal quality control and filing strategy, especially for new products and manufacturing sites. At distributed quality control facilities, equipment and robots have artificial-intelligence capabilities, and labs continue to perform specialty and stability testing. This testing can take place on- or off-site, such as in a centralized and highly automated location.
Distributed quality control facilities add value by significantly reducing the physical footprint and costs of a traditional lab and accelerating product release. Because of the significant R&D-investment requirements, as well as the need for equipment and operational changes, existing sites with stable or declining volumes are unlikely to make a compelling business case for distributed quality control in the short and even medium term. However, sites that have been rapidly growing or under construction may be able to capture significant value from reducing capital-expenditure investment for building or expanding traditional quality control labs if they can move a significant share of routine testing online. Distributed quality control and real-time release, as well as supplier-produced certificates of analysis as a form of distributed quality control, would also enable true continuous-manufacturing processes.
As pharma companies start exploring ways to build distributed quality control facilities, they may be able to pull in relevant technologies from adjacent spaces. For example, platforms that can provide the advanced process control necessary to enable parametric release are now available. Meanwhile, artificial-intelligence systems could allow pharma companies to automate tasks that historically have been performed by highly trained expert employees.
The COVID-19 pandemic has levied new constraints on quality control operations—such as social-distancing requirements in often-crowded laboratories, restrictions on having analysts present on-site, and requirements to document all close, interpersonal contact for contact-tracing purposes. A “smart” approach to quality controls offers practical solutions to manage testing with fewer analysts present in the labs, as more work can be completed remotely (Exhibit 2).
Key success factors in implementing a smart quality control approach
While pharma companies continue deploying traditional operational-excellence levers, they have an opportunity to do so in tandem with their journey toward smart quality. As they progress in their digital quality control transformations, most pharma companies face five challenges: clearly articulating a vision, defining a business case, scaling up with an agile approach, formulating a technology plan, and ensuring they have the change-management capabilities they’ll need to transform.
Developing a cross-functional vision
Quality control leaders often struggle to envision a comprehensive future state, such as what we describe in our horizon model, that combines a number of technologies and use cases to create the most impact. There are a few possible explanations for this phenomenon. First, in many organizations, analytical method development and operational quality control are separated so they often have different objectives, and even report into different functions. Second, quality control innovation projects are chronically deprioritized in favor of individual innovation projects in other areas, such as in production, or in favor of lab-digitization programs triggered by regulatory requirements that usually focus on compliance and data integrity. Third, most innovation focuses on the narrow parts within quality control (such as reducing incoming inspection and automating in-line testing) and therefore misses an opportunity to create end-to-end impact by redesigning the entire quality control approach.
To break existing silos and define a shared, cross-functional vision for delivering quality controls in a new way and that span the entire value chain, quality control leaders can start by sharing perspectives on possibilities offered by new technologies with their peers outside as well as within the quality control organization. Setting aspirational business-performance targets helps send the signal to the organization that change is imminent. Visiting other lighthouse quality control labs can showcase the potential benefits of amalgamating these innovative technologies and create further excitement within the organization.
Defining a compelling business case for the transformation
To build a compelling business case, companies must define the right set of use cases for each lab—and the cases work best when they are integrated. Note that the baseline cost and the impact of improvements may differ significantly for chemical labs versus microbiology labs, and therefore different sets of levers may need to be deployed. Also, organizations often define their use-case scope too narrowly. For example, scheduling automation can deliver 2 to 3 percent of the quality control cost savings, but automation plus dynamic scheduling optimization can yield a more than
20 percent efficiency improvement.
While most labs can make a solid business case for working toward the digitally enabled horizon 1, not all labs have sufficient volumes and operational setups to justify reaching horizons 2 (automation) and 3 (distributed quality control). For example, it could be hard to justify an investment in automating a smaller lab where the potential cost savings might be less than $200,000 a year, whereas the same investment at a large sterile-manufacturing facility with significant environmental-monitoring volumes would quickly generate positive ROI.
In our experience, a good early place to start is establishing both a clear, holistic performance baseline and a clear target state with the tailored use cases for each lab, and then launching use cases in waves. By deploying use cases in waves, companies can track how much value is captured along the way, and reinvest the savings toward the next set of use cases. This approach helps capture value faster and with lower initial investment.
Using an agile approach to design and scale up pilots
One common misstep is targeting a fully tested, potentially overly complex end-to-end future state that takes a long time to design and even longer to test and implement. An alternative approach that has been proven successful is based on the lighthouses established by the World Economic Forum to advance progress in scaling Fourth Industrial Revolution technologies: rapidly testing possible solutions, identifying high-value ones, then rapidly scaling them up to capture benefits faster. When companies take this approach, they can, for example, implement schedule automation and optimization quickly and start generating significant value even if a lab is not fully paperless nor fully digitized.
Companies who are most successful in their smart quality control journey focus on identifying the innovative tools that can have the greatest immediate impact, and then rolling them out quickly across multiple sites.
Creating well-considered plans and structures for rolling out new systems and technologies
While some use cases can be built using existing IT systems, many require additional investment into IT infrastructure. In extreme cases, it can take pharma companies several years and more than $100 million to implement a LIMS. Given this lengthy time frame and the fast pace of technological change, some of the LIMS capabilities are liable to become obsolete before they’re even rolled out across an entire network. A poor rollout can cost five to ten times more and take three to five times longer than a properly planned and executed investment.
To avoid these issues, pharma companies need skilled resources, in the form of a project management office (PMO) and formal change-management programs for helping workers acquire new skills. A PMO will help accelerate technology rollout by eliminating the temptation to excessively customize technology at each site.
Investing in robust change-management capabilities
Digital transformation requires radical changes in mindset. This has major implications for the organization and for individual employees who must develop new skills and competencies. To succeed, companies must invest up-front effort in creating buy-in across the organization, defining and launching up-skilling and re-skilling programs, and forging strong links between business and IT functions.
For example, a typical pharma lab does not have the advanced analytical capabilities needed to get the maximum value from its data sources. As a result, the labs collect a lot of useful data, but fail to generate the insights that could prevent problems, improve test methods, or optimize testing volumes. It is critical to define the right operating model for impact. For example, when labs build internal skills for defining advanced analytics use cases, they can work with specialized advanced-analytics specialists, often outside of labs, who can execute the complex analyses and help gain deep insight into specific cases. Clearly understanding future capability needs, investing in training high-potential employees, and hiring employees with the new required skill sets (for instance, advanced data analytics) during early stages will enable faster scale-up. And where labs are unable to develop the right skills in house, they will need to effectively recruit talent from outside their organizations.
The latest technologies and digital solutions can make quality control faster, more agile, more reliable, more compliant, and more efficient. By setting appropriate goals, choosing the right technologies, and developing the right capabilities, pharma companies can transform the way they do quality control to deliver safer and more efficacious products in a cost-efficient way.