Quality management in life sciences faced unique challenges during COVID-19 as companies worked to oversee quality across a complex supply chain with a workforce whose ability to physically interact was severely hampered. Problems encountered by quality processes that require manual intervention were further exacerbated by the technical limitations of antiquated solutions that are currently in place at organizations. The operational hardships created by the pandemic have been an impetus to go digital, not just for continuity, but also to shift to a more intelligent quality management model.

The Digital Agenda

What does the digital agenda include? For many companies it includes replacing older technologies and outmoded platforms, process optimization and automation, next generation capabilities (AI, ML, NLP) and choosing technologies that future-proof your operations and investments.  Many legacy technologies and architectures are going away, so there is a natural migration to new platforms.  New platforms include functionality, connectivity, and access to data across the ecosystem, inclusive of supply chain and manufacturing domains. However, the most important element of the digital agenda is to rethink and reshape the way the organization uses technology and data to enable intelligent quality management, orienting operations towards predictive quality outcomes, not just corrective actions; and reskilling the workforce to be digitally enabled and data-driven.

Remove the Barriers

Quality faces a myriad of challenges to transcend compliance into a truly intelligent quality mindset.  Quality is accustomed to extended technology lifespans and the waterfall model for development, but agile principles are starting to spill over into the domain, with iterative waterfall models that mean you can fail fast, correct quickly, and deliver value in a reduced timeframe at a lower cost. The quality mindset has to break free from the tether to old ways of operating and historical limitations when utilizing new technologies and digital capabilities; meaning changing the way people think and retraining for digital. Talent management and learning development is an important piece of the puzzle as reskilling and upskilling will be crucial to shifting quality outcomes to be data-driven and moving from reactive to predictive modes of working. 

Data as the Anchor

In order to achieve an environment where compliance is the de facto outcome, not the focus, organizations must establish the data fabric and create a data-centric culture where the ultimate value of data can be realized. That means structuring and understanding your data and how you can use it to build predictable quality into the end-to-end process. Defining the attributes and identifying where the source of truth exist; and building the technology to support getting to and consuming that data based on predictive business models and KPIs; establishing your “data fabric”. Data is meaningless until it is connected, becomes informational, and is actionable. The value is unleashed when it becomes insightful, through trends and forecasted performance predictability, enabling informed, preemptive decisions to correct and adjust before a quality event occurs versus reactive measures that have to be implemented to remediate.  Automation and Artificial Intelligence technologies interwoven with core Quality Platforms can amplify the impact and outcomes as well as reduce the cycle-times to deliver a more robust intelligent framework.

At Accenture, we partner with our clients to facilitate the digital agenda for Quality transformation in several ways, through cloud capabilities, digital technology, advanced analytics, and workforce/talent management. Technological and organizational change are closely intertwined, and together they have the potential to rewrite the way that life sciences companies act with Intelligent Quality management and impacts to the patients they serve.

Neil Fausz

Senior Manager – Life Sciences, R&D Operations

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