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In addition to the ongoing supply chain challenges, medical device manufacturers are managing increasingly strict compliance constraints from the FDA and GMP, as well as a growing but hard-to-predict demand for customized just-in-time products, product recall management and a lag in modern capabilities.  

However, with advanced analytics, medical device manufacturers can: 

  • Make smarter business decisions  
  • Gain insights for more targeted sales and marketing efforts 
  • Improve customer experience  
  • Improve quality control 
  • Enhance productivity  
  • Prevent human errors 
  • Increase efficiency  
  • Save money 

Here are five use cases for analytics for medical device manufacturers.  

Fix Supply Chain Bottlenecks 

Lack of visibility has led to vulnerability in the supply chain for medical device makers. Not being able to see end to end makes it difficult for manufacturers, who often have dozens of small distribution points, to plan appropriately for production. 

Advanced analytics, powered by AI and machine learning, enables manufacturers to gain real-time visibility, allowing for the optimization of resources, fulfillment and production planning. Manufacturers need to be able to adjust labor capacity and logistics based on cross-channel inventory levels, raw material availability and other critical requirements to meet customer expectations.  

Greater Quality Control 

Many medical device manufacturers still rely on historical data and frequent testing in a live production environment to ensure medical devices meet quality standards. But when they are reactive, manufacturers are slower and less agile – which drives up costs. 

In contrast, applying AI and machine learning to quality control helps manufacturers predict product outcomes based on data pulled from across your organization. For example, a manufacturer may identify that batches utilizing a chip from a specific supplier are more likely to have quality issues than batches without. Or production orders have a higher failure rate on Line No. 2 when operated by Joe Smith. 

Predictive quality analytics helps manufacturers to be proactive instead of reactive. Instead of reacting to quality issues such as defects as they happen, you can forecast their likelihood and act before they cost you in time, resources and money.  

Improve Employee Productivity 

With social distancing, remote work and more sick days, the pandemic put constraints on manufacturers. Now manufacturers are facing a labor shortage, which means they have to do more with less. 

Boston Consulting Group outlines key roles – demand and supply planners, line schedulers, capacity planners and procurement managers – that demand the right data and tools to respond quickly to a supply chain shock. Beyond those with a direct hand in planning, supplier management and procurement, manufacturers need the right people on the right jobs for optimum performance.  

Investing in advanced analytics allows manufacturers to access the data they need to have employees efficiently and effectively handle challenges that arise.  

Improve Data Collection and Utilization from Devices 

The Internet of Medical Things (IoMT) is a vital part of the digital transformation in the medical device industry. IoMT has revolutionized medical devices with top-notch innovations – smart devices, lightweight communication devices and smart sensors. By reducing costs, enhancing efficiency and improving patient outcomes, all through gathering analytics, this technology has had profound effects on medical device companies.  

The IoMT market size is expected to grow to $285.5 billion by 2029. By integrating IoMT with critical medical devices, manufacturers can improve data collection and its utilization to help healthcare providers make informed decisions. 

Right-Size Inventory Levels  

Efficiency that doesn’t compromise quality is key to medical device manufacturers’ ability to navigate change. They need to embrace technology that reduces downtime of business-critical assets, improves OEE and maximizes equipment lifecycle. 

They also need to right-size inventory levels based on customer demand and capacity constraints to prevent both overstocking and stockouts. With the increased demand for customization in the medical device industry, such inventory optimization is critical. In many cases, variations must be produced and stocked to meet demand, which is hard to forecast without sophisticated analysis and forecasting tools. 

What to Do Next 

Analytics can be applied in these and more spots to optimize costs while developing and maintaining a modern supply chain. Here are a few other ideas: 

  • Leverage data analytics, machine learning and forecasting tools for forecasting and planning. Make better decisions for production scheduling and purchasing, and achieve greater resilience amid supply chain fluctuations.  
  • Adopt technology that reduces fulfillment costs by optimizing order routing and transportation planning, and enabling real-time freight monitoring. 
  • Use data analytics to streamline operations, improve core functions of your business and conduct operational assessments to identify inefficient processes. For instance, you can use data to segment your supply chains, pairing products with like conditions for production, shipping expectations and process.  

Working with a trusted partner can facilitate the use of AI and machine learning for everyday use where it makes the most sense and will have the most impact for your business. At Columbus, we take our years’ worth of business and technological know-how, combine it with data strategies and help you drive growth and gain a competitive advantage in your market.  

Learn more on how Columbus can assist your organization in using data to drive forward advanced analytics.   

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