Measuring, tracking, and interpreting your reliability score doesn’t need to be time-consuming or complex. Here are three simple ways that the food manufacturing industry can use IIoT to improve reliability.
1. Automate your condition monitoring
Are your machines running as expected? Are you on track to hit your output goals? If you’re not sure what’s happening on the shop floor, it’s hard to guarantee that you will hit your KPIs.
Shop floor visibility is key for understanding and improving reliability. However, it can be difficult to compile data from different machines or processes using traditional enterprise resource planning tools (ERPs).
Why? IIoT platforms are more agile than the ERP systems typically used by food manufacturers. IIoT platforms bring together data from shop floor hardware (e.g., edge/IoT devices, sensors) into specific software components. In most cases, IIoT converges IT/OT together much more effectively than ERPs.
In the case of condition monitoring, there are automated IIoT tools that make it easy to gather and visualize data coming in from the shop floor. For example, the Dashboard App from Braincube gives you a 360-view of your current production. It covers a wide variety of use cases for everyone on your team: tracking KPIs, reporting on compliance with production standards, and logging key events for easy troubleshooting.
By automating this visibility, teams can monitor and react to live conditions from within the plant or via remote access. Engineering and operations teams can expedite troubleshooting. Plant managers can clearly and concisely visualize key KPIs to access process performance.
All of these employee efficiencies have an impact on your reliability. When teams can address issues quickly, improved uptime and output are close behind.
2. Optimize maintenance planning
Without the right data, it can be difficult to figure out exactly how much planned downtime you need for maintenance.
Too much planned downtime can result in unnecessary production losses and decreased run times. Yes, it’s reassuring to know you’re minimizing unplanned downtime by keeping your machines running smoothly. However, you may jeopardize output, which directly impacts your reliability.
Pushing your machines too hard for too long might seem like a good way to increase output. In reality, though, you could overextend their capacity. This could backfire and result in excessive unplanned downtime.
Deploying predictive maintenance software tools, like those bundled in Braincube’s IIoT Platform, can help you avoid equipment failures and unplanned downtime events: two extremely costly events for food manufacturing.
This is because IIoT can give teams a better understanding of operations. These tools can detect—or, with AI, predict—things that teams can’t easily see.
Consider Braincube’s FFT node. This tool can be used to detect and alert on abnormalities in machine vibrations that maintenance engineers might not have time to constantly monitor.
For example, let’s say an ingredient tumbler starts vibrating at an abnormal cadence. This vibration variance is detected by Braincube and an alert is sent to your maintenance teams. They can investigate the issue quickly, making it more likely that they can prevent a significant downtime event by resolving any issues quickly—before downstream production is impacted by a major event.
These asset-level insights into asset health can help maintenance coordinators more accurately plan scheduled outages and machine maintenance. Automated IIoT functionalities make it possible to monitor everything taking place in your facility at one time. Teams only intervene when necessary. This provides a solid foundation for setting maintenance schedules that strike the right balance between uptime and planned downtime, enabling you to optimize your reliability.
Many teams use these advancements to improve their predictive maintenance strategies, too. As teams spend less time fighting unforeseen fires, they can start spending time with the data and learn how to better predict when outages or breakdowns will occur. This gives them more time to plan for repairs, source the correct parts, and minimize overall downtime.
3. Improve changeover between products
For food manufacturers producing multiple products on the same equipment, reducing the changeover time between products is a vital way to improve overall output and meet customization demands. It’s also an opportunity to improve reliability.
IIoT can help make changeover easier and quicker. Tools like Braincube’s Live app make it possible to display production standards on the shopfloor with just a few clicks. Operators can quickly put the new standards into place with reassurance that they are using the correct settings.
The Live App can also display any setting changes that may occur during production. For example, Braincube’s proprietary CrossRank AI provides recommended settings based on the specific outcome(s) you want to achieve. These continuously-updated, AI-recommended settings can be pushed directly to the shop floor for immediate implementation, improving your overall production.
Many manufacturers measure repeatability when trying to gauge the success of their changeovers. Repeatability is a metric that measures how well a successful machine task is replicated on a subsequent run. Were you able to spend less time entering the settings? Did an operator need to keep fine-tuning the machine after a changeover?
Automating these processes can help improve changeover repeatability, which also improves reliability. Automation can be achieved in multiple ways, whether you are displaying the right settings to operators using something like the Live App or programming machines to recall previous settings for each product run. Regardless of how you achieve it, improved repeatability via automation gives your teams valuable time back, increases line efficiency, and can improve your ROI.
It can be difficult to move the needle on reliability, especially if you don’t have the right data to make process and production improvements. IIoT technologies can simplify the data collection and analytical legwork that food manufacturing companies need to improve reliability.
Automated IIoT technology can empower employees across multiple teams: operations, engineering, maintenance, and more. Together, these collaborative tools make it easier for teams to work together and drive productivity forward.
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