Closing data gap in medical device manufacturing key to reducing inefficiency

In an increasingly data-driven manufacturing environment, medical technology manufacturers must be able to make data-driven, informed decisions about the state of their business.

Yet paper and disconnected systems are creating an offline data gap that makes it difficult to track production and nearly impossible to make informed decisions.

Three issues are driving a data gap in manufacturing. The first is the continued reliance on paper processes. When manufacturers rely on paper-based processes, cumulative manual errors turn into poor data that advances through the production process and slows everything down. That means more deviations, production bottlenecks, downtime, wasted resources, and delays in quality review and product release.

The second is data trapped in disconnected systems. When manufacturers work in disconnected systems, they’re relying either on paper or manually tracking and transcribing data from one system to another. Disconnected systems create a data gap that reduces visibility into production processes and raises the risk of data integrity issues, increasing inefficiency and waste.

The third is organizations’ approach to digitization. Even when manufacturers have digitized and are collecting data, many aren’t at a level of data maturity to leverage advanced analytics to connect the dots between data points and improve decision-making.

So much data is captured on paper or in a way that makes it hard for companies to analyze, contextualize and make business decisions that improve efficiency.

Unlock manufacturing data with digital connectivity
Digitizing manufacturing with a data-first mindset is the key to improving decision-making and efficiency in production.

Embracing a modern manufacturing execution system (MES) solution with fully electronic device history records (eDHRs) makes manufacturers’ data accessible. Digitally connecting the MES solution and eDHRs with core systems, such as an enterprise resource planning (ERP) system, connects data sources, processes, and people for a holistic view of data. This way, manufacturers can capture and share real-time production data across systems and departments seamlessly and eliminate data integrity issues before they spread.

One factor that manufacturers often overlook when digitizing is considering the intelligence they wish to gain. For example, if understanding on-time release metrics is a goal but the manufacturer isn’t tracking the target release date, how will they adequately track this measure? For this reason, in the process of digitizing, manufacturers must keep a data-first mindset in their MES configurations.

Keeping a data-first mindset may entail not only ensuring a single source of truth for lists of values and ensuring best practices and standards for configuration, but also understanding the data questions the manufacturer wants to answer. Ensuring that data is captured in a consistent, unambiguous manner allows data to be more readily comparable and used. Common lists of values and consistent best configuration practices ensure users who build their own dashboards do so in a concise manner that will reduce the risk of multiple people creating the same report/dashboard and getting different results. Ensuring the right data captures are built will ensure the most pressing questions are answered.

Put digitally connected manufacturing data to work
Implementing a modern MES solution with a data-first mindset will bring offline data online by collecting production data digitally and directly at the source. This automates time-consuming and error-prone manual processes such as DHR data entry, signature gathering, and tracking standard operating procedures (SOPs), training records, and quality events, ensuring high data integrity throughout the entire data life cycle.

Data now provides value because manufacturers can connect the dots in new ways to unlock what the data means for operations and react to reduce inefficiency and waste. Real-time data tracking enables work-in-progress (WIP) visibility and traceability into the status of lines, lots, and operator performance. If something doesn’t look right, the manufacturing supervisor can drill deeper into specific production runs to determine which lots are impacted, which unit procedures or operations are impacted, and more.

The next step is using the data to forecast and process trends. This allows manufacturing to become more proactive, entering the predictive phase of analytics. No longer is it just the manufacturing supervisor looking at the day-to-day production data;, Leadership can analyze trends and make data-driven, evidence-based business decisions.

From there, organizations can grow with advanced analytics to further evolve into the prescriptive phase of analytics, where data can recommend actions to prevent inefficiencies. Finally, the data becomes truly intelligent by proactively recommending ways to improve efficiencies.

Immediate value in digitally connected manufacturing data
While manufacturers can expect long-term benefits from digital transformation efforts, just starting to digitize unlocks value for manufacturers, including:

• Risk reduction – De-risk processes with visibility of fully connected data across systems;
• Efficiency gains – Use current data as the foundation for future process improvements;
• And cost avoidance – Understand where material loss and rework are occurring.

Manufacturers now generate more data than ever. But in-process visibility into all that data is severely limited when so much of the data is trapped in paper and disparate systems.

To begin making the data work for them and close the offline data gap, manufacturers must remove paper from manufacturing and shift their digitization approach to focus on the data they want to analyze.

Once these foundations are in place, medtech manufacturers must start analyzing by looking at current processes to see how to de-risk, build efficiencies, and reduce waste. Medical Design & Outsourcing