Predictive Manufacturing analytics use technology and data from operations and events for quality assurance, performance and yield enhancement, cost reduction, and supply chain optimization. This type of analytics is being used to transform factories into fully automated facilities with Internet of Things or IoT devices and cloud technology. Here are some examples of predictive manufacturing analytics.
Predicting machine failure with sensor values.
Factories require their machines to be in top shape so they can get the job done. If they’re not working to their full capacity, the product quality may lower and the clients will not be happy with the results. You can set up alerts or alarms that will get triggered when sensors detect equipment failure or unplanned downtime. This real-time data can be viewed on a dashboard with predictive models and insight on what the issue is at the moment.
Predicting maintenance for machinery.
Moreover, there can be the case when a machine may not be working at its full capacity but it’s still producing material or parts. This can happen when machines need maintenance. To help predict when your machines need maintenance and reduce maintenance costs in the process, you can implement a predictive analytics algorithm. It will alert you when you need to replace any parts in your machine or clean it. This preventive maintenance measure will ensure your machines are always in their best condition and prevent any unplanned downtime.
Predicting quality analytics.
To make better predictions, you need to have consistent data. Noticing discrepancies in your data can help you increase your product quality. With predictive analytics tools, you can predict when this data will lower from its optimal state, and therefore predict when your product quality may begin to lower.
Enhancing manufacturing operations.
Additionally, with the application of predictive analytics, you can improve your manufacturing operations. It can do this by preventing unnecessary downtime, future trends that can help improve your processes and even decrease your production time. Applying analytics in your manufacturing organization will help you see where you can make adjustments in the manufacturing line for example to improve the workflow.
Improving workforce management.
Your workers play a key role in the manufacturing process. By implementing an accurate predictive analytics model, you can detect which areas of improvement are available for your product managers. Knowing where they are more prone to fail can help you implement process changes and train your operations teams so they can have a better delivery overall and improve the production environment. This can help improve their machine utilization skills, reduce material waste, and even improve the product demand.
Predicting risks and insurance.
Lastly, you can help your manufacturing organization reduce accidents by predicting the risks within the supply chain management. For instance, you can predict when a machine will potentially cause harm from poor maintenance. Additionally, you can help you pick the right features for your machines so they’re covered by insurance. This can help you establish substantial cost savings on insurance as well as change on demand depending on your business needs and insurance wants.
Likewise, the implementation of predictive analytics can help you disprove operational errors in your insurance claims as well as explain any equipment errors you may encounter. Since math is the best way to explain and understand how things happen, predictive analytics solutions can be just what you need in your manufacturing organization.
As you can see, predictive manufacturing analytics solutions can help improve the potential outcomes of your supply chain as well as the global competition in the manufacturing industry. It can also help save lives from possible risks as well as save on manufacturing costs and maintenance.
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