Technology and Quality Data: Is Your Organization Keeping Up?

Industry 4.0 technologies like AI, machine learning and IIoT, have initiated a digital transformation and changed the way we access data.?AI-enabled quality management systems ?(QMS) empower Life Sciences quality teams to quickly access a tremendous volume of data from all aspects of operations and across various systems. However, without the ability to derive meaningful insights, this data is useless.

Quality teams need to have the ability to segment the data and structure it to identify and measure the critical?key performance indicators (KPIs) ?to their business.

As quality moves from conformance to performance, this capability becomes even more critical.

The Quality Shift: From Conformance to Performance

Many life sciences manufacturers have singled in on regulatory compliance, and consider quality to be a separate department that, while essential, doesn’t add business value. This view results in a missed opportunity for getting high-quality products to market faster and greatly benefiting top-line revenue.

Quality can simply not be managed in a silo. ?The quality function is intertwined with all aspects of an organization and is crucial to uncovering data throughout the product lifecycle and?using this data ?to drive continuous improvement across the enterprise. In doing so, compliance is no longer the end game. It becomes the byproduct of improved product quality and safety.

Manufacturers that use quality to effect change throughout the organization will reap the benefits of identifying issues faster, reducing costs and preventing patient harm and product recall—all while ensuring regulatory compliance.

This results in:

• Minimized Risk and Recalls:?Automated quality processes reduce the risk of human error and enable faster, more effective decision making.
• Improved Product Quality:?A high-quality product that functions as intended improved outcomes and ensures satisfied customers. Promoting a culture of quality where data is relayed to R&D and manufacturing teams ensures continuous improvement.
• Operational Excellence:?Driving quality in people, processes and products ensures operational excellence across the enterprise, which reduces cycle times, results in cost savings and performance-oriented results.
• Faster Time to Market:?Actionable quality data helps reduce errors and delays during product development. This enables Life Sciences manufacturers to deliver high-quality, safe products to market faster.
• A Strong Value Chain:?The value chain directly impacts product and process quality. Implementing quality management processes that extend to the supplier network helps to improve product quality across the value chain and?minimize supplier risk .

Data Gives You the Insights Needed to Be Proactive

To impact economic performance, quality teams need immediate access to accurate and meaningful data throughout the enterprise, and the analytics capabilities to pull and present insights that will resonate with the impacted stakeholders. They must be able to proactively address potential issues or act on opportunities upstream, rather than reacting to problems that have already snowballed downstream into costly deviations, complaints, and corrective and preventive actions (CAPA) .

One of the biggest roadblocks is lack of an effective reporting process within the QMS. Digital technologies are revolutionizing the way we collect and manage data. Organizations that embrace these technologies will enable seamless access to data, which will give them the tools needed to go from reactive to proactive quality management.

Are You Deriving Meaningful Insights from Your Data?

AI-enabled QMSs allow organizations to accelerate digital transformation by leveraging Industry 4.0 technologies.

Companies that are still using manual or outdated systems will lag behind those that have invested in the technology that drives proactive, performance-based quality management.

Now—once you have the data how will you use it??

Download our white paper ?to learn how to use the POSE Data Segmentation Model to create meaningful insights and get examples of KPIs for each of the POSE model areas.