Leveraging digital and analytics in biopharma operations: Six principles

Digital and analytics is a powerful tool for transforming operational performance. To maximize its benefits, biopharma companies can follow six principles.

As biopharma companies continue to cope with the COVID-19 pandemic and position themselves for recovery, digital and analytics can be a powerful tool in manufacturing and operations. It can drive the next wave of business optimization by transforming operational performance, shortening time to market, improving quality and yield, reducing supply chain volatility, and accelerating technology transfers.

Yet while many biopharma companies have already undertaken digital-and-analytics transformations, many have made little progress. More than 70 percent of organizations have been stuck in “pilot purgatory” as they struggle to scale their projects and deliver successful digital-and-analytics transformations. 1

I see some sites experimenting successfully with analytics, but this is primarily driven by skillful individuals. We’re encouraging the exchange of experiences but aren’t yet at the level of building common assets.

COO of global pharma company

The application of digital and analytics to biopharma operations tends to fall short against three success factors:

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  • The vision for the business transformation. Companies often fail to define a holistic vision of their transformation and its goals and priorities, resulting in scattered proof of concept and meager benefits. Senior leaders may be unaware of the transformational potential of digital and analytics, and of what it can already do.
  • The vision for the technology landscape. Companies need a clear picture of their IT and systems landscape and where it may hinder transformation efforts. For example, reducing unnecessary complexity in systems architectures and using dashboards that align IT solutions with key performance indicators (KPIs) can accelerate transformations.
  • The vision for the organization model. Companies often assign individual projects to large and unwieldy teams that make slow progress. They may lack the necessary technical resources, such as data scientists, engineers, and business translators. And they may also lack the central governance and culture of collaboration needed to manage and coordinate deployment across the business.

The following six principles can help biopharma leaders identify critical blind spots and lead successful transformations in their manufacturing operations.

1. Start with a leadership-backed, impact-driven strategy and road map

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The first step in developing an impact-driven strategy and road map is to ensure the leadership team understands how digital and analytics can improve operations. In about one in three companies, no one at the C-suite level is responsible for leading digital-manufacturing implementations. 2 Executives may recognize that artificial intelligence (AI) will change many jobs in the future, but many do not realize it is already having an impact—so they are not doing enough to build capability in their workforce. They also may not realize that AI can improve operations by detecting process deviations. To acquire this knowledge, executives may need to carve out time to learn more about digital and analytics, and its transformational potential in pharma operations.

Next, the leadership team must define the vision for the transformation, both inspirationally and practically. This includes identifying the domains of impact—a slice of the value that can be improved using a combination of digital-and-analytics use cases. Within each domain, executives can prioritize use cases for the greatest impact—for example, transforming the supply chain with AI-based demand forecasting, no-touch supply chain planning, overarching command and control, and a digital performance-management cockpit. Developing a strategy with multiple domain value areas can help leaders identify the right stage-gated funding level and the expected return on that investment.

We know where our burning platforms are and are exploring technologies that can help. The next step will be to understand how the full range of technologies can meet our needs.

Site head of manufacturing at global pharma company

To guide strategy implementation, organizations need a road map that is actionable and can adapt to evolving technology and business priorities. If you already have some digital-and-analytics projects in progress, the road map should address how to reinforce those efforts and refocus others that aren’t delivering value. It’s also essential for the road map to consider how the company will approach a longer-term view of industry questions such as, “What will pharma operations look like in the age of personalized medicine?” And “How will this affect the need for digital-and-analytics deployments?”

In terms of investment, leadership should adopt an agile, start-up type of funding structure. Instead of spending months going through a corporate process to secure funding for a massive, multiyear program, it is better to establish a design-thinking approach and fund discovery, minimum viable products (MVP), and scaling initiatives separately with stage gates.

2. Accelerate transformation with experienced leaders, skilled staff, and multifunctional teams

From a talent perspective, companies need to get the following three things right to orchestrate a rapid, successful transformation:

People and leadership. Finding people with the right combination of transformation experience, organizational leadership heft, digital-and-analytics know-how, entrepreneurial skills, and business-change knowledge is critical to success. To make cross-functional collaboration easier, leading companies have created the role of “translator,” an expert who can bridge the gap between the business and analytics teams. Creating this role recognizes that neither team can change the way the company works without the other’s support.

As part of the people and leadership strategy, it is also important for companies to identify the skills they need, including those that can be filled internally with training and development; investing in their own talent will be vital for companies to establish digital as a competitive advantage. For external sourcing, we have seen pharma and biopharma companies form successful partnerships with research and academia. These partnerships have given them firsthand knowledge of technology advancements and enabled them to bring those advancements to the shop floor. For example, working with an Ivy League university on projects to accelerate product development gave a top-ten biopharma company access to new talent while enabling it to implement new operational technologies early, thereby improving its competitiveness. These partnerships can also raise your company’s profile as an employer of choice for people in research and academic institutions who want to transition to jobs in the pharma industry.

We soon realized that digital-and-analytics experts with impressive CVs didn’t thrive at our company because we didn’t know what we wanted to build. So for getting started, it’s often better to shape younger talent.

Chief digital officer at global pharma company

Skills and capability development. A critical step in getting the right digital skills is creating a value proposition that acknowledges that digital-and-analytics talent may be different from the people you typically hire. If your company doesn’t have operations in a major technology hub, finding experienced digital-and-analytics talent will be challenging. As a result, you will need to be creative about identifying where to find the right people, how to build their capabilities, and how to retain them. Asking this question can be helpful: Why would a high-achieving 20-something with a PhD in data science want to work for us rather than for a leading tech company?

It’s not an easy question to answer, particularly for companies that use antiquated infrastructure and legacy systems. But biopharma companies have the advantage of being able to offer work with the potential to cure disease and have a positive impact on people’s well-being.

To digitize the company and change its way of working, you must do more than hire a handful of new data scientists. Achieving a full-scale transformation requires reskilling the existing workforce through training and workshops (Exhibit 1). For an organization with more than 10,000 people, for instance, this will require a clearly defined budget plan, road map, and ownership, as well as buy-in from the employees who need to transition to the new way of working. Employees must understand the need for the transformation and the value of being part of a state-of-the-art digital organization. The investment a company makes in capability building should be comparable with its investment in technology.

A full-scale transformation requires extensive reskilling of the existing workforce.
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Organization. In addition to recruiting the right talent, creating an effective organizational model is one of the biggest hurdles for companies undergoing digital-and-analytics transformations. To address this challenge, leading companies have created constellations of small, multifunctional teams organized in agile studios. These studios act as labs running projects to improve operations rapidly, find ways to embed agile more deeply in the organization, and allow functional experts in manufacturing, science, and technology to work with data-analytics experts to improve their technical skills.

3. Implement a strategy, architecture, and governance for data

Data are the lifeblood of every digital-and-analytics transformation. This is especially true in operations, where there are massive volumes and varieties of data, ranging from unstructured text in quality systems to structured data from business and IT systems. As data continue to transform manufacturing by enhancing connectivity and enabling disruptive innovations, manufacturers need to focus on the following actions:

  • Develop a comprehensive data strategy based on a vision and business case aligned with the domain areas. While developing the strategy, it’s critical to avoid falling into the trap of trying to fix your data-backbone or data-quality issues before launching digital-and-analytics use cases. It’s more efficient to build the data foundation while scaling impact cases. This approach also helps expose data-quality issues, which often cause transformation efforts to fail to meet their objectives.
  • Define and implement a data architecture to achieve the strategy, including architectural designs, vendors, and tools. Basing the architecture on readily accessible databases will ensure that you have clean and linked-up data available for use-case development and reporting.
  • Set up end-to-end data governance in parallel with data development to democratize data use while managing and improving its quality. At a minimum, data governance should include the organizational construct, roles, processes, data standards, and tools. The governance model should allow the company to go beyond using data to manage operations to improving performance.

4. Build a tried-and-tested delivery methodology for digital-and-analytics solutions

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Delivering digital-and-analytics solutions is a complex process that requires an intense commitment of time and resources. It’s not a one-time effort, but a new way of working that is essential for high-performing organizations. Success requires a delivery protocol that codifies technology-enabled best practices for delivering digital-and-analytics solutions tried and tested by practitioners. This protocol helps ensure predictability, output quality, and uniformity in solution delivery. It’s essential for scaling solutions that have been successfully piloted. Just as you wouldn’t institute a new change-over process without standard operating procedures, you should not embark on a digital-and-analytics transformation without a delivery protocol. That protocol has three major components:

  • Process. This involves the activities, deliverables, workshops, and key milestones, such as the go or no-go decision point, involved in each digital-and-analytics project. The process should be standardized to create a replicable way of delivering projects while providing modularity so that teams can pick the process components that apply to the size of their project. To create the most value, the process should be a living entity that’s continually adjusted to improve how digital-and-analytics projects are delivered. However, if you don’t already have a process in place, don’t spend months designing and building one. Instead, build a minimum-viable-product (MVP) version of the process and use an actual project to refine it in parallel with the delivery. This ensures that you are building an approach based on real needs and experience.
  • People. People bring the skills and expertise you will need for the blended, multifunctional teams that deliver projects. Whether it’s domain expertise, design, or engineering, each key skill should be mapped to the process activities and deliverables it owns or contributes to. Most companies know which skills they need but have difficultly knowing where to find them. Initially, some skills, such as data science, will need to be acquired through new hires or partners. We’ve also found that 70 percent to 80 percent of these skills can be developed internally through on-the-job and classroom training.
  • Technology enablers. It’s also vital to develop reusable technology assets for frequently executed tasks. This can be achieved by combining market-leading products and proprietary tooling to ensure that teams have the optimal workbench to complete projects quickly. The technology tools can either be domain agnostic, such as a workflow tool for ingesting data, or use-case specific, such as a reusable machine-learning code base for optimizing machine settings. When developed as assets, these technology enablers can help accelerate the scale-up of digital solutions.

5. Construct a fit-for-purpose technology stack

In operations, putting in place operational technology (OT) and information technology (IT) that enable differentiated performance is typically one of the largest and most complex investments. As a result, pharma operations executives should be deeply involved in the planning and decision making rather than delegating them to external advisers or technology specialists.

For both OT and IT, give careful consideration to the platform and how it connects to data sources (Exhibit 2). This requires a technology stack that allows for discovery and production. Discovery enables data scientists and engineers to quickly and easily access data to run analytics and discover new value sources. Production provides the capability to utilize the use cases that have proven value.

Industrial IoT (IIoT) technology stacks are developing in parallel with traditional automation stacks.
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Infrastructure is a critical element, not just because it dictates how quickly you can build and scale ideas but also because it is where you spend significant money. If you’re not careful, subscriptions can drain your budget.

To connect the platform to data sources, consider the different types of sources with varying modes of connection. It is critical to determine how to reduce the risk of disruption and rework when modifying or replacing a source system, which can happen frequently. By implementing a process-centric semantic layer, you gain the ability to abstract changes and immediately start developing analytics.

6. Drive adoption and business change by engaging with the front line

The single biggest challenge to achieving impact from a digital-and-analytics transformation is the last-mile adoption and culture change required for the new ways of working. If workers don’t use the applications and tools, your organization won’t see the benefits. How effectively companies address this challenge separates the truly transformational from those that achieve only modest success.

With continuous advancements in digitization, companies have growing populations of skilled people with knowledge no longer relevant to the new way of working. To drive adoption, it’s critical to start engaging with the front-office and shop-floor veterans who would make decisions based on new digital-and-analytics applications. This engagement should occur early and often through trusted changed agents. The rollout of new digital solutions should be accompanied by division-wide change management. Your change-management plan should include ways to get senior-leadership support, formalize new incentives, engage with employees, and empower key influencers.

A focus on the future

Following these principles will help ensure that a company’s data-and-analytics transformation is successful. To lead the initiative, executives and managers should:

  • educate themselves on the potential impact of digital and analytics on their organization
  • understand the decisions required to deliver impact at scale
  • launch bold initiatives and follow them through to results

Given the value at stake, now is the time to assemble the right team and begin accelerating your company’s digital-and-analytics transformation.





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