Data digitalization and integration for technical operations within life sciences

Andrew Nassau and Suyash Mate

In order to stay competitive, optimize production and increase efficiency, life sciences companies need to improve data digitalization and integration within their technical operations functions, specifically within supply chain and manufacturing. These functions tend to rely on siloed data that cannot be effectively leveraged. We recently interviewed industry leaders as part of a study, and these conversations made it clear that data digitalization and integration within supply chain and manufacturing of life sciences are lagging, both when compared against other internal functions as well as against similar functions in other industries more broadly. Even leaders in the area are grappling with issues such as end-to-end data access and data visibility, and the COVID-19 pandemic has further highlighted the need for data integration that can allow companies to better track and prepare for disruptions across functions such as procurement, production and supply chain.

Benefits of data digitalization and integration

Two clear benefits to improving data digitalization and integration are increased visibility and enhanced efficiency. First, digitalization will allow for end-to-end visibility. Currently, visibility between production and demand can be limited, leading to sub-optimal planning. The more that data is accessible, both internally and externally, the more accurate planning becomes, not only improving the robustness of supply chains but also reducing inventory and waste.

Second, teams will become more efficient as they spend less time coordinating and sharing data and more time investigating and analyzing that data instead. Substantial time is often wasted moving data between platforms and processing it manually. Data digitalization and integration allows teams to use that time on more substantive, positive-value projects.

Evolution of data digitalization and integration

There is a natural progression of data integration within technical operations functions at life sciences companies. As companies bring their first product to market, data integration is often limited; there are many different sources of data, formats are not standardized, and non-digital paper batch records are frequently used. Due to the uncertainty of clinical trial success, upfront investment in standardizing and digitalizing these data sources is not necessarily warranted. But as companies succeed, grow and mature, these vestigial ways of tracking production and shipments often are left unintegrated, even though upgrading would provide a positive return. It often takes an external catalyst, such as an FDA review or a disruption to the supply chain, for companies to assess what can and should be done to digitalize and integrate their internal data. The changes come too late, when valuable time and resources have already been lost.

Counterintuitively, data digitalization and integration can often lag in manufacturing when compared against supply chain, even though manufacturing generally collects and stores more data. This is the case for two reasons. First, data storage in manufacturing can be more siloed, since manufacturing data is sometimes stored locally or on paper. Second, analytics in manufacturing functions tend to be more data-intensive than in supply chain.

There are also nuances based on product type. For instance, biotech products may require more coordination between sites, suppliers and specialty pharmacies. Likewise, cell and gene therapies are tracked for each patient and require logistics to and from the site of care.

Figure 1: Nuances of Data Digitalization and Integration by Drug Type

Eventually, most large life sciences companies recognize the benefits of digitalizing and integrating their data. Once data for their products and production sites is integrated onto one or two platforms, these companies realize substantial short-term improvements. With support from IT, data integration can be further refined over time and simple tasks can be automated.

Looking forward, the next stage of this evolution will be incorporating external data to increase the speed at which companies can respond to external events and to enable further process optimization. This data can come from a range of partners, from clinical trial sites to contract manufacturing organizations (CMOs) or logistic partners.

Figure 2: Maturity Assessment Framework for Data Integration

Though the progression toward integrated data is rational for many companies in the life sciences industry, not all companies need to immediately integrate their internal and external data. This type of integration becomes more valuable as production matures and processes are refined. Pre-commercial companies whose technical functions operate at smaller scale and that face larger uncertainties may have less need to invest in integrating their data sources. For larger and more mature life sciences companies, data integration improves planning and increases the effectiveness of the supply chain and manufacturing teams. However, the longer a company delays investing in data integration and digitalization, the harder change management will be.

Next steps for organizations looking to improve data digitalization and integration

Technical operations divisions that have not already performed a self-assessment to understand the current state of their data integration should consider doing so promptly. This exercise will generally proceed in a top-down manner and span across internal functions. The process can be kept at a relatively high level, capturing the current technology platforms and their purposes. Internal interviews will quickly provide this information and should also highlight redundancies and pain points.

Once an organization understands the current state of its data systems, it can then decide whether it makes sense to invest in improving its data integration. This decision can be approached with a cost-benefit analysis, comparing the value of the man-hours lost navigating the current systems against the cost of integration. However, a strategic approach to this question rather than a purely quantitative one is often warranted. Companies should consider the number of products they offer, whether robust demand and supply planning is critical to their operation and if the time is right for change management. Ultimately, companies should create a long-term data plan or data vision that lays out which improvements will be made and when, linking them to return on investment and strategy as befits the company’s particular situation. It is important to consider if platforms or technologies can be leapfrogged and to aim to shift to a mindset based on creating accelerators and assets instead of quick fixes. Decision makers should keep in mind that while improving data integration and digitalization provides greater benefits to larger organizations, the longer an organization waits to upgrade, the harder and more expensive it becomes.

Once the decision to move forward with data integration and digitalization has been made, the company should start with a pilot. A pilot typically works best if it is focused on a single type of data, such as manufacturing execution systems (MES) or laboratory information management systems (LIMS), across all sites. A good place for the company to begin is with a data lake or a data mart that provides a single repository of the chosen data, allowing for current platforms to query data from the same source. A second step would be to retire technology platforms which are no longer best in class, and to roll out a new, integrated platform. Buy-in and change management are critical for a successful pilot.

As life sciences companies look to continuously improve their manufacturing and supply chains, they should examine integrating data as an impactful option. Beyond quantitative and strategic evaluations, each company needs to also keep their patients in mind. Digitalization is key for developing the patient-centric approach to manufacturing and supply chain made necessary by more complex and patient-centric therapies. Companies need to make sure that changes to their data systems will provide tangible improvements in the patient experience, in addition to reducing costs and increasing efficiency. Following this road map of the considerations, benefits and next steps for data integration, companies can realize efficiencies and stay current in their data management.

ZS Associates is a global professional services firm that leverages industry expertise, leading-edge analytics, technology and strategy to create solutions for clients that work in the real world. ZS has deep expertise in life sciences, helping these companies with issues ranging from R&D, manufacturing and supply chain through sales and marketing.