In QC, complexity is King

In a recent interview, James Morton from BCG discussed with me the varying degrees of complexity present in the world of modern biopharma QC operations and how digitalization may be the best solution for increasing visibility and value. Below I outline this topic in more detail and provide a summary of his valuable insights.

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In a rush? Here are the 3 key takeaways

1. 👉 Quality Control operations are significantly more complex than (e.g.) Manufacturing operations, which not everyone in the pharma industry fully appreciates
2. 👉 Digitalization—at both the local and network levels—is a key tool in tackling this complexity because it can enhance transparency and visibility
3. 👉 In order to tackle complexity, the digital systems themselves must become more complex—and may even make processes more complicated for site teams in the short-term—but if well implemented they ultimately drive value

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According to James Morton, Associate Director at BCG, even those working in the pharma industry tend to underestimate the complexity inherent in QC operations. It’s a multifaceted complexity that exists at every level and which will only increase as the major trends facing the industry as a whole continue to develop.

These trends range from the procedural (such as the emergence of new technologies and the commercialization of new modalities, including CGT), to the network level (such as pressure to protect supply chain continuity and the drive towards “Glocalization”), to the truly existential (including the impacts of environmental considerations and geopolitical instability).

Indeed, of the 10 trends he outlined in his presentation to the 2021 Binocs User Meeting, James considers that nine of them serve only to compound this state of ever-increasing complexity. He argues, however, that one trend in particular holds the potential to make the situation more manageable: greater digitalization.

We spoke with him again in late 2022 to discuss this topic in more detail and explore the ways in which harnessing the power of Digital could help to elevate QC operations.

Disrupting the traditional way of working

Throughout the Twentieth Century, pharmaceuticals was a highly stable industry, with established manufacturing processes undergoing gradual, iterative improvements. The development and testing cycle for new drugs represented a relatively slow process, grounded in proven technologies. With the new Millennium, however, revolutions in two major areas began to disrupt the traditional way of working:

1. drug modalities have rapidly progressed from small to large molecules and now to complex biologics, upturning established processes and significantly increasing the complexity of the final product;
2. digitally enabled equipment has increased in functionality and diversity to support the production of these new drug types; this requires companies to make sizeable investments in a range of novel and increasingly complex technologies.

With this new wave of modalities and technologies, it has been necessary to develop the procedures for pharmaceutical Quality Control in-step with manufacturing. According to James, this side of the equation has taken longer to balance:

The complexity of QC operations is far greater [than Production]. There are a lot more manual activities, many more team members, all with different levels of training maturity and availability, and lots of different products with requirements for different methods. This type of complexity is almost unique to QC in biopharma.

Digitalization: a delicate trajectory with significant value

Individual testing mechanisms and procedures are being systematically updated at the lab level. As complexity increases and budgets become ever-tighter, however, James foresees significant enhancements on the horizon that will enable the next phase of savings for QC departments. He also believes that “digital can really deliver a lot of value in managing that complexity”.

Foremost among these enhancements is the need to move more testing in-line, with analytical methods being designed into the testing processes themselves (as opposed to relying on offline testing).

This can represent a challenge for large, established players who have built considerable momentum through heavy investment in existing technologies and who will therefore need to make more changes than smaller start-ups. However, James believes that, while less mature companies can be more adaptive and agile, more able to adopt technology more quickly, this may not represent the dramatic long-term gains that some might expect:

the fact that it’s easier to implement tech if you only have one or two sites isn’t necessarily a win because it also means that there’s less value in doing it

Instead, he argues that being able to deliver significant value across a network tends to require a minimum starting network size; and that, with each additional node in the network and each added layer of complexity to be overcome, the greater the potential benefits.

That’s not to say it will be easy though: it’s a highly delicate trajectory that relies on carefully considered implementation. For instance, increased automation capability may currently demonstrate value in isolated pilot labs but companies still face considerable hurdles to wider roll-out.

Arguably, the greatest tools for helping to manage complexity are transparency and visibility, which is why one such hurdle is represented by information silos. If everyone is communicating effectively and working toward the same clearly defined goal, results can be delivered more efficiently and at less cost. If, on the other hand, information is being gate-kept or obscured, miscommunications occur and complexity increases.

Digitalizing the QC network

As James sees it, the primary challenge for QC is in scaling digital transformation across the whole network. Ideally, QC networks should aim to employ harmonized and interconnected data systems because “having uniformity and being able to manage such systems in the same way everywhere drives a lot of value”. This hasn’t always been easy to deliver at the lab level, however, because the inherent complexity of day-to-day QC operations can obscure understanding and communication between labs and Quality leadership. This not only creates difficulties in scaling potentially beneficial transformation initiatives across the organization (including the deployment of digital systems) but can also mean that the data feeding strategic decisions is compromised.

Increasingly, though, as these networks have grown and become even more complex, such siloing simply cannot be maintained. Whereas individual sites have historically operated largely autonomously, with their own QC labs dedicated to testing specific lines, modern distributed and globalized production methods have rendered this mindset unsustainable.

Leadership has seen that other areas of the pharmaceutical supply chain can be optimized using increasingly sophisticated, AI-enhanced digital twins. As a result, they have also started to push for similar optimizations across their QC network by achieving a global overview.

Sites and networks are becoming increasingly expensive to run and, as they also become more complicated, the information asymmetry is more and more of a problem. This is exactly where we need digital to step up and help.

With the increased visibility offered by new digital systems for scheduling and capacity planning, QC networks are now confronted with the fact that the status quo doesn’t work: it’s no longer possible to create a new QC lab every time a new facility or plant is built; these digital tools mean that workload can now be managed globally, with functions formerly handled by a single site instead being distributed across a network to more efficiently share the available capacity.

The case for digital scheduling and planning

Ubiquitous digital products such as LIMS, QMS or ERP systems are generally implemented for their value at the network level, even if their implementation may disrupt or increase the complexity of site-level operations; nevertheless, sites have adapted where necessary. Likewise, the implementation of scheduling and capacity planning systems can be met with some initial resistance at the lab level, where established ways of working risk being overturned. The reality is, however, that such products not only deliver value at the network level, they also represent tangible benefits for the site:

With scheduling and capacity planning systems, labs are in fact able to derive direct benefit because the master data these systems require actually drives value for the lab and helps them to manage their complexity (in a way that an ERP maybe doesn’t), while also supporting the network-wide piece.

For small labs with 5 team members, for instance, perhaps planning QC operations in Excel or on a planning board is sufficient. Once team sizes increase to 20-25 analysts working across multiple products with competing priorities, trade-offs need to be made.

Sites can significantly drive performance and productivity with automated scheduling, allowing them to run continuous improvement processes and lab production systems in a way that simply wouldn’t be possible otherwise.

By using such systems, James states, labs can be more flexible and adaptable to external and internal challenges. For instance, there’s always a trade-off in QC between cost and lead time, and therefore how much inventory is tied-up. By using a predictive algorithm to manage the complexity of this dynamic and to simulate future supply performance, labs are more able to stay on top of volatility and increase productivity, which is ultimately the biggest driver of cost savings.

Conclusion

In conclusion, then, James Morton foresees no end to the increasing complexity that faces the QC operations in the pharma sector. Historically this complexity has been seen as a barrier to making meaningful changes but it’s a problem that can no longer be ignored and it isn’t going away without digitalization to enhance visibility and transparency across the network.

As a closing point, we asked James to provide one piece of advice to QC teams embarking on their latest digital transformation journey; here’s what he told us:

The number one thing for any lab to understand is: what is their compelling business need that explains why they should transform? It’s not a budget target or a top-down directive, it’s about what they need to do to fundamentally transform their lab. This could be a question like “can we halve the QC Lead time while halving the cost per batch?”, something ambitious, understandable, compelling, and with a long enough timeline (3-5 years) to have the necessary space to do the things that really make the difference.

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