On several occasions, Acting Commissioner of the U.S. Food and Drug Administration (FDA) Janet Woodcock has called attention to the importance of quality in product manufacturing. Given the high rate of drug shortages, she stresses that quality goes beyond simply meeting the FDA’s current good manufacturing practice (CGMP) requirements. Quality in manufacturing is the ability to reliably produce products in sufficient quantities to ensure that supply meets demand.
“Analysis of recent drug shortages indicates the need for rewarding more investment in quality. A team of FDA economists examined a sample of 163 drugs that first went into shortage between calendar years 2013 and 2017. They found that of the 163 drugs in shortage, 62% went into shortage after supply disruptions that were associated with manufacturing or product quality problems,” said Woodcock.1
Life sciences companies have long generated vast amounts of data as a byproduct of their processes for developing and manufacturing products. Quality is one area of the business that is particularly dependent on data. Comparatively, quality gathers and uses more data than other business units in an organization.
As long as companies follow the regulatory guidelines for data management and have the required documentation in place, they can achieve regulatory compliance. However, quality processes that involve compiling and storing data on paper documents require more time and effort than is necessary. In fact, it fosters more of a reactive rather than proactive approach to quality. Quality managers often need to spend more time double checking documents and following up with stakeholders to ensure records are complete and accurate. The challenges associated with this approach include:
Your quality efforts and objectives should be driven by the needs of your customers. Based on these needs, you will typically create a product specification that indicates a precise target for each of the product’s critical characteristics — with minimal variation around the target for quality. But how can you ensure you maintain that level of quality throughout the product’s life cycle?
Advanced technologies are making data more abundant and more focus-area specific. Incorporating the following technologies into your quality processes gives your company a tactical advantage:
According to a Garter survey on internet of things (IoT) technology, life sciences companies are implementing digitized technologies specifically to gather more data and gain more value from it. For example, using predictive analytics, life sciences companies can quickly identify trends, spot unforeseen or overlooked risks and mitigate deviations before they cause delays or result in a product recall.2
Employing a data-driven quality model helps you apply quality processes more holistically throughout the organization, as quality needs to be more of a collective effort, not just the responsibility of one department. This approach to quality will become a key business strategy as you strive to boost production capabilities, reduce throughput times and pursue continuous improvement. Other benefits include:
A survey report from Accenture Life Sciences explains how the life sciences industry is at an inflection point. Companies throughout the industry now recognize that to remain competitive they need to make better use of all their data assets. It further cites that to be a leader in this new data-driven world, life sciences companies must fundamentally transform how they create, manage and effectively use all their data.3
Organizations that adopt a data-driven, platform-enabled quality model will augment their capacity to yield real-time quality intelligence and predictive insights. When equipped with the ability to collect and share data within a common platform, every function within an organization can have an appreciable impact on product quality.