Increased complexity in products, global supply chains, regulatory landscapes and the rise of the Internet of Medical Things are generating big data, including exponential growth in digital sources of various types, input rates, communication channels and accuracy. Analyzing this data would help manufacturers to identify factors that affect productions costs and quality, but most medical device manufacturers do not currently have analysis capabilities even for the data they already generate. Leveraging real-time and big-data analysis will only get more difficult, yet it will be a critical competitive differentiator. The manufacturers that do it well will improve their decision making processes and their position in the market.
Here are a few key insights related to these trends:
- According to Axendia, 66 percent of all medical device companies still use paper documents during production
- A large majority (66.7 percent) of medical device executives perceive intelligent data analytics as one of the major themes for growth opportunities
- Sixty-four percent of medical device providers perceive the consumer experience is greatly improved due to operations in IoMT programs
- Axendia notes that those with an integrated and digital approach to design, engineering and production execution are four times more likely to monitor analytics and use intelligent data
To gain a competitive advantage through intelligent data analytics, MD&D manufacturers will need to focus on four specific strategies.
1. Shift from managing documents to using metrics
With big data, the manufacturing mindset needs to shift from managing documents – still on paper or digitized documents in siloed systems – to using metrics that are derived from across the value chain. Providing visibility and access to business metrics across manufacturing operations requires an integrated platform that can contextualize data from various functions and synthesize it into global performance views.
2. Standardizing data for analysis
Data across the enterprise must be standardized to create overall performance visibility and continuous quality improvement. With data in various systems and formats today, manufacturers will need to create data models and master data formats to maintain consistency in the analysis of product performance, using functional and physical attributes along with connected requirements, design and operational information.
3. Sustainable, transferable enterprise intelligence
Without a supporting digital infrastructure, medical device manufacturers have relied on the tribal knowledge of their operators and engineers for improving designs and processes. Although there is a risk those individuals will leave or retire, taking that know-how with them, the reality is the complexity and volume of data is simply too great for humans to comprehend, or interpolate from system-to-system.
Medical device manufacturers must harness their data into enterprise intelligence and retain that intelligence over time. This is critical for maintaining competitiveness and achieving long-term market leadership.
4. Creating digital models that leverage real-world performance
Regulatory agencies are increasingly looking at real-world data (RWD) as input sources for monitoring safety and adverse events of medical devices as they are used by clinicians and patients. They are using the information to make regulatory decisions. The models are also being used by: payors to support coverage decisions, providers to develop guidelines for clinical practice and generally across the healthcare community for decision support.
To benefit from these large volumes of RWD, medical device manufacturers must adopt digital solutions that capture learning from the real world, integrate it with data coming from other relevant sources (supply chain, design, engineering, manufacturing, etc.) and enhance it to create predictive insights for decision-making.
MES: Central to creating intelligent data analytics
Using MES is key to creating a seamless business intelligence infrastructure for medical device manufacturers. As the orchestration layer between engineering and manufacturing execution, MES is used to control all aspects of real-world production and interfaces with the operators, machines, automation, materials and facilities. MES enables the user to collect raw data from all these inputs and contextualizes that data with meaningful manufacturing information. In this process, data is standardized so that it may be useful to systems across the enterprise.
Using MES is central to capturing manufacturing know-how and integrating it into an enterprise-wide platform so this contextually relevant information can be transferred to design and process engineering, continuously improving quality and costs. For short-term, real-time performance indicators, MES is equipped to feed enterprise manufacturing intelligence (EMI) so users can understand key performance indicators (KPIs) such as overall equipment efficiency (OEE), production efficiency, utilization, throughput, waste, etc.
In a longer term, more strategic view, MES facilitates the use of lifecycle analytics applications for end-to-end big data intelligence across the entire product lifecycle (from design to utilization) and the supply chain (from supplier to manufacturer to consumer). Employing MES provides these applications with meaningful and relevant manufacturing data for analysis. When product-quality issues arise, it is the manufacturing data that must be analyzed to understand if the issue is a matter of design or manufacturing process. If it is a design issue, manufacturing data must be analyzed to determine the impact of a potential design change on manufacturing performance. You can do this kind of predictive analysis only if you have lifecycle analytics enriched with manufacturing data from the MES.
These connected, lifecycle-wide intelligence functions support the shift from managing documents to managing operations using meaningful metrics that can advance product quality and operational efficiency.
Read the full eBook to learn about more trends facing the Medical Device industry, and how integrated MES is addressing their challenges
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