5 key management metrics for medical manufacturers

Features - Management

Overall equipment effectiveness analysis and data intelligence let medical device manufacturers improve quality management.


Nearly all manufacturers (92%) say product quality defines success in the eyes of their customers, and half of them define success by their ability to meet on-time deliveries, according to a recent IQMS survey.

Savvy medical device manufacturers rely on key metrics to define unique, individualized roadmaps for enterprise-wide compliance and quality goals. The highest performing companies realize that nearly all of their systems constantly contribute data regarding quality and compliance. However, that wealth of information must meet a complex web of demands.

Compliance tracking and reporting for the U.S. Food and Drug Administration (FDA), Code of Federal Regulations (CFR), and International Standards Organization (ISO) 9001 standards often require several different systems.

FDA 21 CFR Part 820 regulations require the maintenance of a complete device history record based on tracking each batch, lot, or unit to demonstrate that every device has been produced in accordance with the Device Master Record (DMR). Staying in compliance with 21 CFR Part 820 requirements makes it possible for manufacturers to attain Current Good Manufacturing Practice (CGMP) compliance as well.

Meanwhile, supply chains are increasingly the life blood of every medical device manufacturer, accentuated by these companies’ need to integrate technologies outside their areas of expertise. This further complicates the ability to manage high quality and compliance standards, as companies need insights not only throughout their own facilities but across their ecosystems.

To ensure quality management and compliance systems stay synchronized with supply chain, production, fulfillment, and service systems, it’s important to understand:

  • How medical device manufacturers are aggregating key performance indicators (KPIs) for greater insights
  • The role of these insights in how shop floor decisions drive top floor results
  • The use of overall equipment effectiveness (OEE) to track use and performance of production machines and other assets
  • Metrics, KPIs

    A common challenge is taking a strategic view of the world of data and identifying which KPIs are suitable. KPIs should determine if a business’ performance measures are red, yellow, or green.

    Comprehensive dashboards can aggregate metrics across an entire product life cycle, from pre-market to post-market, communicating the status of suppliers, quality, plant output, shipments, and customer feedback. The hidden value of this data is in understanding the correlations that exist between the data sets to help identify something previously unknown.

    In a 2018 study by Accenture, 79% of executives agreed that organizations are basing their most critical systems and strategies on data, yet many have not invested in the capabilities to verify its validity. By developing data intelligence to ensure veracity, integrity, and quality, companies can establish, implement, and enforce standards for data provenance, context, and integrity.

    A quality auditor checks the NC/CA levels for an X-ray manufacturing center.

    Shop floor/top floor

    Shop floor decisions are based on data and information presented in real-time through human-machine interfaces (HMIs), overhead process screens, and mobile devices. To strike a balance between product and customer requirements, shop floor decisions need to be based on accurate, verified data.

    The key is to avoid extremes to sustain in-control processes. Low-yield/high-cost products that exceed all requirements and high-yield/low-cost products that miss requirements both fail to meet top floor desired results. Displaying accurate information provides the correct action at the correct time based on factors such as environmental conditions, customer demand, raw material lot variances, time of day, and continuous runtime hours.

    Variables can be evaluated to determine what information should be included when making shop floor decisions to optimize process controls. Insights from aggregating data may appear to be complex because much of this kind of data analysis has never been done on an on-going basis since most companies have lacked the necessary tools or resources. The correlations help to shape changes to shop floor decisions and set internal expectations based on customer expectations.

    Using OEE

    Because quality and on-time delivery are critical to medical device manufacturers’ success, OEE is important in evaluating production machines and other assets. OEE is a calculation comprised of several different variables multiplied together to get an overall value in an easy-to-use number. It’s calculated by the formula of availability x performance x quality, three key strategic, operational process metrics. OEE values can quickly be compared to communicate an overall plant utilization rate and then be split into production areas, production lines, and individual metrics. When OEE data is analyzed across different production areas, it offers new insights and correlations.

    Another potential source of insight can come from comparing OEE values to production plans of scheduled products. Specific products consistently have a better OEE value than others, so the planning department can better optimize scheduling to maximize resources and results. That new knowledge can be used by quality and process engineers, as well as research and development professionals, to determine what is driving the OEE to improve effectiveness on other lines.

    5 key metrics

    1. Corrective action – Automated corrective and preventive action (CAPA) management streamlines traditional manual and paper-based processes, such as approvals and escalation. Quality management system (QMS) software facilitates identification of root causes faster and communicates them to the organization – a critical component for realizing continuous improvements in processes, as well as optimizing OEE throughout time.

    2. Complaints – Are complaints being addressed quickly? Data should be turned around efficiently to improve product quality. Metrics to monitor include the number of open complaints, the average time they spend open, and the number of overdue complaints.

    3. Audit findings – Were external and internal audits effective? Leading indicators can be developed from audits – on-time completion rates for scheduled audits, number of major noncompliances, the percentage of high-risk noncompliance instances, recurring problems, and time to closure for corrective action requests.

    4. Medical device reporting (MDR) – What percentage of MDRs are being submitted to the FDA on time, and what is the total number of MDRs submitted? Automating MDR reporting and tracking product quality are essential for complying with 21 CFR Part 11.

    5. Nonconformance – Metrics should include the number and percentage of nonconformance instances closed within 60 days to identify patterns in recurring nonconformance instances by type. QMS can be used to ensure that out-of-specification conditions are automatically detected and communicated, providing real-time visibility into what is happening. One of the key metrics commonly used for tracking this aspect of quality is nonconformance/correction action (NC/CA). During a quality audit, auditors will check NC/CA levels and closed case rates.

    A technician completing OEE calculations on production machinery.

    Proper reporting

    OEE can explain why one or all five metrics are out of compliance, since all of these variables are closely interrelated and, to some degree, dependent on each other. Operational excellence requires balancing quality, asset performance, operations, and environmental health and safety (EHS) management. A poorly performing compliance indicator will impact quality and productivity, which can become a cascading problem that leads to disastrous results. Production assets operating outside normal parameters due to malfunctions, misalignment, or outright failure cannot be expected to produce goods that meet quality specifications.

    With the systems in place analyzing OEE, medical device manufacturers can take advantage of the technology to track other metrics. These include perfect order performance, percentage of products in compliance, and cost of quality, providing manufacturers further insights into how to ensure that their product quality excels.


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    About the authors Anthony Parise is a medical device product strategist at EtQ, a provider of quality and compliance management solutions and can be reached at aparise@etq.com.Louis Columbus is a principal at manufacturing enterprise resource planning company IQMS and can be reached at lcolumbus@iqms.com.