Few industries have as much to gain from data-driven decision making as pharmaceutical manufacturing. The disruptive forces impacting the industry — rising cost pressure, greater use of contract manufacturing, the introduction of personalized medicine, increasing packaging complexity, unrelenting quality control demands — cry out for the insights achievable through big data techniques.

Steering the industry through these disruptions will require not only rapid technical advancement, but also organizational and cultural adjustment. The industry’s culture is marked by a widespread, if largely unstated, belief that more information can be dangerous, as it invites more regulatory scrutiny and increased cost of compliance. This cultural legacy has become counterproductive and must be shed by companies seeking to thrive in the era of big data.

Pharmaceutical and life science manufacturers have traditionally been more reactive than proactive in technology adoption largely due to regulations and domain complexities. Success in life sciences has historically depended far more heavily on research and product innovation than on manufacturing prowess. But challenging market conditions are now pushing companies to innovate in using data visibility and analytics to improve manufacturing.

Most large pharmaceutical and life sciences companies have undertaken digital manufacturing initiatives. The majority are in early stages, doing proof of concept projects, but a growing minority — after demonstrating initial results — have begun rolling out those initiatives at scale.

WHAT IT TAKES TO SUCCEED IN A DIGITAL MANUFACTURING PROJECT
Pharma companies face similar challenges in digital transformation as their peers in other industries, and similar combinations of technical and organizational skills are needed for projects to succeed. Knowing what to look for in both sets of skills is important for selecting appropriate first-mover projects to gain proof points and momentum.

On the technical side, making the data available is the first step. At a foundational level, the key machines must be networked, and the data must be captured. Remote visualization of production data requires appropriate cloud and network technology, and companies need to be mindful of security while embracing new technology adoption. This provides a foundation of raw data to feed data models and sophisticated algorithms.

Companies also must navigate organizational challenges. If the top of a company is committed to digital transformation but plant leadership sees it as a threat, the project is unlikely to succeed. Similarly, an initiative at the plant level may struggle to reach full potential without the buy-in of corporate IT leadership. Also, an organization’s ability to realize the benefits of new technology adoption relies on change management, so there must be committed and engaged internal champions.

Sight Machine has made freely available a methodology for evaluating a manufacturer’s readiness for digital transformation. The methodology, called the Digital Readiness Index, is available at sightmachine.com/digital-readiness.

BUILD A PROPER DATA MODEL
Scaling digital manufacturing initiatives relies on a few critical areas of focus. From an IT perspective, handling the storage (Volume) and processing power (Velocity) needed for large amounts of information is typically front of mind. Of the three V’s of big data, the Variety of that information is typically a distant afterthought, if considered at all during the planning phase.

Forming a semantic model of a complex process such as a manufacturing line requires deep collaborative effort between those familiar with the technical tools for handling large amounts of data, as well as those well versed in the art and science of creating drug products. These are very different cross-functional skillsets, and many projects have failed due to the lack of alignment between Operations Technology and Information Technology.

Assuming OT/IT are aligned, enablement technologies can save appreciable cost and development time, as well as increase the likelihood of success in digital transformation. For instance, when looking at a number of manufacturing processes, it is challenging to ensure that a standard data model will apply to lines from dissimilar vendors, or those that produce dissimilar products. Interoperability of modeled data is critical for systems-level thinking. It is important not to fall into the project-based trap of doing one-off data projects, as this will curtail the upside from using digital technologies across the enterprise.

PHARMA/BIOTECH USE CASES
Data-driven decision-making is a powerful tool to address a wide variety of use cases in pharma and life sciences, including:

Cost Control
While there are still promising new R&D developments and the future of drug discovery is bright, there is a marked shift to cost control as the frequency of blockbuster drugs, and the associated margins, are decreasing.
Other industries, such as automotive, have been under significant cost pressure for some time. Lean thinking at each stage of the process is important for productivity gains, such as reduced scrap and increased throughput. Modern big-data techniques to model manufacturing facilities and apply statistical or machine learning methods can deliver profound increases to bottom line.