Over the past two years, the biopharmaceuticals industry has disrupted its established ways of working to deliver on humanitarian needs. New drugs have been approved in months rather than years. Manufacturing capacities have been added in a quarter of the usual time. Companies have revamped their collaborations with suppliers and contract development and manufacturing organizations (CDMOs) to accelerate drug delivery. And hybrid working models have been instituted to manage plant operations despite staffing shortages.
The question now is whether biopharma can sustain and potentially build on this extraordinary metamorphosis. The answer is a definitive yes, but making it happen will require a bold reimagination of the role of digital and analytics across manufacturing operations.
Digital and analytics can enable companies to bring the plant of the future to life today, yielding leaps in quality, speed, agility, and resilience. Leading biopharmaceutical players have already used these tools to achieve significant spikes in performance, including an increase of up to 40 percent in plant capacity, a 20 percent reduction in lead time, and a 15 percent reduction in conversion costs. This article will look at how biopharma manufacturing can unlock value through digital, how leaders can set the right strategic ambitions for their organizations, and which approaches are most likely to lead to success.
A vision for digital in biopharma manufacturing
What might manufacturing operations look like if biopharma companies adopt digital and analytics at scale? Consider the way digital technology has reshaped the auto industry, transforming the design, manufacturing, and environmental impact of cars in little more than a decade while enabling autonomous vehicles and other new driver experiences. In much the same way, digital could open up an array of innovative possibilities in biopharma manufacturing. The biopharma plant of the future will deliver a variety of benefits from the following features (Exhibit 1):
- No-touch planning. The adoption of digital twins—real-time, virtual versions of physical objects and processes—can enable biopharma companies to optimally balance short- and long-term demand with supply of raw materials, equipment capacity, and human assets (staffing), and allow companies to overhaul the scheduling of production and lab activities.
- Reimagined processes. Automation and advanced production technologies such as line sensing, augmented- and assisted-reality tools, and parametric product releases can enable human operators to make remarkable gains in productivity.
- Zero deviations. Advanced analytics models can help plant leaders predict and mitigate quality risks, ensure compliance, and maximize product robustness.
- Paper-free factory operations. Electronic documentation can create a seamless information flow from raw-material supply to planning to production to quality to warehouse, spanning internal and third-party manufacturing networks.
- Proactive risk management. Predictive analytics can enable real-time monitoring and management of maintenance, environmental, quality, and other supply-chain risks to maximize throughput, cost, compliance, and sustainability.
- Remote performance monitoring. Interconnected systems can allow real-time tracking of site performance, product status, and issue detection, resulting in more effective decision making by management.
Together, these changes could usher in a new era for biopharma manufacturing. Asset utilization and labor productivity could be doubled, product transfer times could be cut from months to days, the costs of poor quality could be all but eliminated, and conversion costs could be reduced by as much as 80 percent—on top of all the other advantages of more agile, more resilient operations. Far-fetched as it may sound, this vision is within reach, as demonstrated by McKinsey’s research on industry 4.0, conducted in collaboration with the World Economic Forum, and its experience of supporting companies’ digital transformations. It is clear that digital manufacturing can help companies develop new business models, expand capacity, boost productivity, and build lasting business value.1
Leading biopharma companies have already begun to capture some of these benefits, although not yet at full scale. An array of digital and advanced-analytics use cases have brought radical improvements in the most critical performance metrics: a 30 to 50 percent reduction in deviations (with an 80 percent reduction in recurring deviations), a 25 to 40 percent increase in plant capacity, a 15 to 20 percent reduction in lead times, and a 30 to 50 percent increase in direct and indirect plant operation productivity.2In addition, the greater transparency inherent in digital can make decision making more nimble and effective, freeing up as much as 12 to 15 percent of plant leaders’ capacity.3 Perhaps most important, a digitally enabled workplace led by digital natives will not only make these dramatic improvements in performance but also sustain their impact over time (Exhibit 2).
Setting ambitions for digital
To capture full value from digital manufacturing, companies need to set the right ambitions—ones that are bold and in line with their broader strategic aspirations. This can help unite the organization behind a common purpose and ensure that resources are directed to the areas that need them most. When setting their ambitions, companies should consider six elements:
- Impact. What level of improvement do we expect our digital transformation to deliver? Are we working to change any one aspect of our performance or to optimize the full range of metrics across quality, throughput, productivity, cost, timelines, and talent?
- Scale. How far will our transformation extend? Should we focus on a specific area, such as a quality lab; address all manufacturing operations across an entire site; or the entire manufacturing network?
- Leadership and governance. How should we manage our transformation? Should we take a top-down approach led by central resources with local site teams overseeing implementation? Or should we opt for a bottom-up approach led by site teams with support from a digital center of excellence?
- Technology and data. What investments must we make in IT (for example, manufacturing execution systems, laboratory information management systems), operational technology (for example, production-line sensors), and data infrastructure, including data processing and storage? Should we rely on readily available data sources and minimize changes to our existing infrastructure, or would it be better to build a future-proof tech infrastructure that allows us to derive novel insights from new sources of data?
- Production systems. How far should we digitize our production and management systems? Should we adopt specific digital and analytical solutions to improve current processes or redesign our entire production system to make the most of digital tools, technological advances, and new ways of working?
- Workforce capability and culture. Should we use change agents and experts to build digital capabilities in specific areas? Or should we instead reorient our whole organizational culture around digital working, committing to a major upskilling and reskilling effort?
Thoughtful choices in these six dimensions will help an organization consider how it can achieve its digital aspirations, what resources it will need, and what return it can expect on its investment. Any of these choices—or a combination of them—could be optimal under the right circumstances. Many solutions can help companies deliver on their desired outcomes; however, these decisions will profoundly affect the approach to the digital transformation.
Charting the digital journey
Although organizations may have different ambitions, companies that succeed in making digital headway typically follow one of three paths: implementing use cases, developing “lighthouse” sites, and transforming the entire network.
Implementing use cases
The path of implementing use cases targets a series of digital projects rather than implementing a broader suite of digital offerings across a value chain. Companies typically address specific pain points that recur at multiple sites in a network, such as high deviation rates, low yields, subpar lab productivity, or slow batch changeovers.
One global biopharma manufacturer implemented digital use cases to improve yield in fermentation and primary recovery processes in a specific manufacturing line. The company built an in silico model that optimized batch, asset, and human parameters in real time while requiring minimal disturbance to existing production systems and governance models. The effort was led by a cross-functional site team with advanced-analytics capabilities developed through a tailor-made learning program. Not only did the company improve its yield in the targeted processes by 5 to 10 percent, it also built a backbone of data pipelines and machine-learning models it could scale up to other cell lines and sites across its network. In addition, it built targeted capabilities across the organization that caused similar initiatives to mushroom from the bottom up.
Developing lighthouse sites
Companies that develop lighthouse sites implement a holistic digital transformation across end-to-end operations at a few sites. This maximizes each site’s potential in line with its business priorities while enabling the company to settle on an approach to the transformation that can be codified and rolled out to other sites across the network.
A global biopharma manufacturer sought to increase throughput at one of its plants while tackling planning, production, and quality challenges arising from workforce attrition and a complex product portfolio. The company decided to develop the site as a beacon illuminating a new way of managing manufacturing operations. Leaders invested in technology upgrades rallied the plant team around a vision of future growth and set up a capability-building academy to turn the vision into reality. They implemented ten use cases along the site’s value chain and built a pipeline of several others to be implemented over the next 12 to 18 months. In less than a year, the company increased plant throughput by 40 percent and the capacity of its quality lab by 50 percent. Deviations were reduced by 80 percent, repeat deviations were eliminated, and manufacturing conversion costs fell by 20 percent. The site was recognized by the WEF as one of the first digital lighthouses in biopharma globally, and the company is now scaling up its effort to other locations in its network. Several other pharma players—innovators, generics, CDMOs, and more—have had success with a similar approach.
Transforming the entire network
The objective of transforming the entire network is to reengineer site operations throughout the manufacturing network and across all technology platforms and locations. The approach relies on a bold vision to create a culture that embraces digital as central to all ways of working, supported by a capability-building effort that spans every level of the organization (see sidebar, “Stages of a typical digital manufacturing transformation”). Compared with the other approaches, successful network transformation requires significantly more management focus, investments, and other resources, and the impact on the overall company’s performance also is much greater.
Although a few biopharma companies have recently embarked on a network-wide digital transformation, examples from other industries show that this approach can be achieved. For instance, one global electronics company set itself the ambitious goal of using digital to achieve autonomous operations across a network of more than 30 production sites. The chairman and executive team began by developing an inspirational change story and cascading it across the organization, going on to deliver a package of more than 4,000 initiatives across the first set of sites in three years. The effort was governed by a new digital center of excellence and supported by a digital academy that trained more than 1,000 initiative owners globally. Through a combination of digital and AI use cases, the company automated up to 70 percent of its manually intensive activities in key processes, with impressive results: down time, lead times, and quality losses were halved, while labor productivity doubled.
Getting started
Success stories like the previous example of digital transformation should convince biopharma organizations they can unlock value from digital manufacturing by setting a bold digital ambition and deploying any of these approaches. As the initial efforts start to bear fruit and the impact from digital expands, the company may progress from one approach to another. For example, it could begin with use cases and move to a broader transformation over time. In our experience, however, the best way to achieve critical velocity for change in biopharma manufacturing is to start with a lighthouse approach that improves performance holistically across a plant, using the experience to create a recipe to scale up across the network.
Leaders should begin their manufacturing transformation by taking stock of their digital maturity. This involves conducting a comprehensive assessment of the organization’s operational performance, ongoing digital pilots, technology infrastructure, and capabilities. Before developing the business case in their own context, they should draw inspiration and gain insights from digital leaders across industries. Lessons from other industries can inform the digital ambition around which they will align and mobilize the organization, draw up an execution road map, and forge capabilities to drive the transformation.4
To keep pace with innovation, overcome the headwinds buffeting the industry, and meet customer needs in a timely way, biopharma manufacturing must become more agile, efficient, and value-conscious—a need best met by embracing digital in manufacturing. The prize for companies that succeed will be greater resilience to future shocks and new ways of working that should serve them well for decades to come.
