Integrated quality assurance: why we’re interested in systems-level solutions
We have just published a comprehensive white paper dedicated to exploring how implementing Industry 4.0 principles could help develop a systems-level solution to many of the problems felt by pharmaceutical Quality Assurance departments. But why is Binocs, as a market-leading provider of digital planning and scheduling solutions, interested in this topic? Why Quality Assurance? QA […]
We have just published a comprehensive white paper dedicated to exploring how implementing Industry 4.0 principles could help develop a systems-level solution to many of the problems felt by pharmaceutical Quality Assurance departments. But why is Binocs, as a market-leading provider of digital planning and scheduling solutions, interested in this topic?
Why Quality Assurance?
QA departments are responsible for several critical steps in the pharmaceutical manufacturing process; without their sign-off confirming that the different stages of production have been undertaken safely and to the highest standards, drugs cannot be released to market. By their very nature, QA procedures are interwoven with the various manufacturing, commercial, regulatory, operational, and management functions that make up the end-to-end pharmaceutical supply chain. As a result, problems and delays in the QA team can have immediate, cascading impacts across commercial operations.
Our client network puts us in daily contact with the biggest names in the pharmaceutical industry, which, in turn, gives us access to representatives from different organizational departments and operational functions. In March 2022, we capitalized on this access by organizing a round table of QA specialists from 4 leading biopharma companies within our network, with the goal of discussing typical QA practices and challenges.
One of the most significant outcomes of this discussion was that it highlighted a range of pain points shared across participants, regardless of the size and direction of their respective companies. After reviewing the conversation and analyzing the issues raised, we identified 3 key common areas that threaten the smooth operation of QA functions:
- Bottlenecks: congestion in upstream work that restricts QA productivity and in-department problems that limit downstream output;
- Workflow: issues that create unpredictability and variability in QA processes that hinder effective planning; and
- Visibility: a lack of transparency and clear communication channels between QA and other departments upon whom they are dependent for successful delivery.
It is clear, then, that exploring approaches to tackle some or all of these problem areas has the potential to result in significant benefits for our clients in general, not just their QA departments.
Why a systems-level approach?
The writing is on the wall: the future of the biopharma sector is rooted in digital transformation and the application of Industry 4.0 principles. By investing in advanced automation for both digital and physical systems, companies can reduce their reliance on highly-skilled staff for monotonous tasks, thus freeing them up to focus on their scientific specialisms. Done right (and with appropriate training dedicated to upskilling and/or reskilling those whose primary roles will be replaced by machines), this has the potential to improve employee wellbeing and retention, while streamlining processes and increasing output.
Life science organizations are already starting to move in this direction but it is clear that data-driven processes that incorporate tools like artificial intelligence and machine learning can be more readily associated with some commercial functions than others. For instance, using predictive planning to streamline resource allocation has clear applications in a quality control lab with a large pool of analysts and a high turnover of work tasks; likewise, robotized automation using interconnected, IoT-enabled devices makes intuitive sense for production line manufacturing. Other, more back-office functions, however, aren’t (on the surface) always so easy to incorporate in a wider Industry 4.0 strategy.
Quality Assurance teams, for example, traditionally have a less technology-focused way of working, making them less obvious candidates as digital transformation stakeholders. This can often be reflected in greater budgetary priority being dedicated to other commercial functions with clearer or more readily-justifiable technological requirements. Nevertheless, QA departments are typically plugged-into a variety of digital tools, not least LIMS, quality management systems (QMS), enterprise resource planning (ERP) systems, and others. Not only does the optimal use of these individual platforms provide opportunities to streamline processes and resolve the persistent headaches that plague QA functions, their potential for interoperability also opens the door to reducing duplication of work and the errors that stem from manual data transfers.
A crucial aspect of digital transformation is the development and application of clear, a priori data management plans that optimize both the implementation of specific software (and hardware) tools and effectively design how those tools interact. This is what we mean by taking a “systems-level” approach – looking at the application of solutions from the top-down perspective to derive the greatest impact from the gestalt.
The emergent theme of the proposals outlined in our white paper is to streamline QA functions through the development of a fully-integrated network of currently available software systems. To achieve this, we argue that the critical role of the QA departments should be reprioritized within their organizational communication and culture, and that they should be viewed (both internally and externally) as key IT stakeholders with an important role in informing digital transformation strategy.
One type of software that we propose as a key element in this network is a Resource Planning and Scheduling (RPS) system, of which Binocs is the market leading solution. We don’t make this suggestion from a purely self-promotional perspective but, rather, because we believe that the features we offer represent a critical piece in the wider jigsaw puzzle for delivering a fully-efficient QA department. This is illustrated in the below (simplified) diagram that represents one possible outcome of the data mapping exercise we recommend QA teams to undertake with their IT leads.
If you’re interested in learning more about our proposed systems-level approach to solving QA pain points, you can download our full white paper for free here.