Why medical manufacturers need next-generation production monitoring.
Precision manufacturers want to understand more about their operations so they can make data-driven decisions to help increase productivity, improve quality, and ultimately, raise profits. This is especially true for the manufacturers who are supporting the medical industry, turning out high-quality parts that save lives and positively affect patient outcomes.
Unfortunately, collecting and analyzing production data has historically been a difficult proposition. Factory owners use handwritten logs, enterprise resource planning (ERP) systems, or first-generation production monitoring systems to collect and analyze details about daily operations. And even when using first-gen production monitoring software, operator input is fraught with challenges, including errors – accidental and intentional. At the end of the day, bad data delivers bad insights.
Existing methods of collecting, analyzing, and visualizing operational data from CNC machines are limited and backward facing, meaning you don’t see the results from today until tomorrow or later. This eliminates real-time decision-making when it comes to day-to-day performance monitoring, causing frustration and loss of enthusiasm for data and analytics, and reducing the manufacturers’ ability to affect change based on real, actionable data.
Manufacturers that want to understand production as it’s happening need to consider advanced monitoring, such as Automated Production Intelligence, a next generation of production monitoring. Automated production intelligence addresses the shortcomings of first-gen systems and delivers deeper insights into production, both in real time and over time.
Here’s how next-gen production monitoring from Datanomix helps during the manufacture of medical devices or components of those medical devices.
To make improvements in any process or system, you must first understand where you are right now. Datanomix makes it easy by benchmarking the performance of every job, monitoring the performance as parts are run, without any operator input. Cycle times, Takt parts, machine utilization, machine downtime, alarms, and other important metrics are tracked in real time. From this machine data, the system assigns a production score from A+ to C- for each job. The score is displayed on large-screen TVs around the shop floor, showing everyone an easy-to-understand status for all their jobs. And for poorly performing jobs, experienced operators naturally flow to the jobs with challenges to get production back on track and profitable.
Elos MedTech, a manufacturer based in Memphis, Tennessee, can easily interpret the daily operations of the machines with information and insights purpose-built to address the data needs of manufacturers. According to Tim Martin, engineering manager at Elos, “It’s easy to make sure our machines are being used the way they should. With Datanomix, we see if we’re getting the part counts we need, and if the cycle times are where they should be. We identify issues quickly, and communicate with the operators to uncover the issue, such as an extra brush detail, an issue with the machine, or if there’s a need for additional training.”
Understanding historical trends delivers additional context into how long-running jobs are performing in relation to the benchmark. Tracking performance across the entire job – and across multiple machines, shifts, and days – delivers important insights into how well the shift is performing in relation to the benchmark ideal. By digging into the data, you can see if a job’s performance issues can be attributed to personnel, equipment, or the process. Many customers have found bottlenecks in long-running jobs that they’ve been able to improve based on understanding production delays and stoppages.
By identifying bottlenecks in production workflows, M&H Engineering in Danvers, Massachusetts can focus on the jobs and processes that need attention, especially from an engineering perspective.
“Some of our jobs have been running for 10 to 15 years, and the processes can be a bit outdated,” said Chris Burns, vice president of business development. “Datanomix helps us quickly and efficiently identify jobs that engineering needs to retool so we can improve the machining process. This has been a huge differentiator because efficiencies are everything, especially as it’s become harder to hire and we look to use automation to take up the slack.”
For medical manufacturers, regulations are in place to ensure the safety of the patients who are treated with the finished devices and parts. With Datanomix, all data generated by the CNC machine used to produce the part, including all details of which day, shift, and machine, are stored in the system. With very little effort, factory management can access all the information about how the part was manufactured, and as more data is collected, overall factory trends are exposed. When management understands how jobs and people perform throughout the long term, it’s easier to make decisions around capital, job costing, and personnel.
Nikel Precision, based in Saco, Maine, is excited about the opportunities to turn more of the data from their systems into actionable insights, especially around the convergence of people and machines.
“Datanomix helps us focus on the things that need focus,” says Jamie Bell, vice president of operations at Nikel Precision. “Today, I can look at the dashboard, and see that machine A is running at 68% and machine B is running at 98%, and they’re both running the same part. What quickly becomes obvious is machine utilization and efficiency has a lot to do with the guy running the machine. We can identify these gaps and work out a plan to address them, including training on best practices.”
Accurate quoting can be notoriously difficult, causing issues with profitability or reducing the competitiveness of quotes by adding too much fudge factor. Because the Datanomix platform calculates true machine costs for every job, there’s an established basis for improving job costing for medical manufacturers. Using the shop rate or the rate for each machine, the Datanomix Quote Calibration report shows the actual cost of producing the part, the potential cost if production met the benchmark, and the overall opportunity for increased profitability for every job. Now, manufacturers know exactly where to focus their efforts.
“When we price our jobs, we always use data from past jobs to make sure we get the margins we need to be successful,” says Craig Michaud, director of engineering at ARCH Medical in Seabrook, New Hampshire. “When the job is not as profitable as we like, we go back and analyze whether it was a machine issue, the process was bad, or we just made a mistake on the quote. Having all the operational data from Datanomix – cycle times, utilization, alarm codes, and more – gives us deeper insights into every job so we can be more efficient when we quote.”
Modern medical manufacturing needs modern solutions for collecting, storing, and analyzing data to make the best decisions when it comes to improving operations. Even though data and analytics have been historically difficult, next-gen solutions deliver deep insights and real-time operational observations without the need for extracting data and using business intelligence (BI) tools or spreadsheets – it’s done for you. Instead of data wrangling, medical manufacturers can focus on their own workflows and getting life-saving medical parts out the door.
About the author: Chuck Smith is director of product marketing at Datanomix. Learn more or request a demo at http://datanomix.io.