Three ways to use mobile predictive analytics to increase operational efficiency
Industrial companies are integrating mobile apps into their workflows so they can leverage predictive analytics. The following are three types of solutions that companies can use to integrate mobile predictive analytics into their operations to improve efficiency and reduce downtime:
1. Predictive analytics helps organizations respond to risks
Consider the large, heavy equipment used in most manufacturing applications. These assets have thousands, if not millions, of pieces, and as these machines are refreshed and repaired, they are increasingly being outfitted with sensors that send off status and health information. These status reports can be aggregated, and over time, data scientists can build statistically significant models that allow companies to predict when certain components or even entire machines will need to be repaired or replaced.
This modeling and predictive analytics can significantly increase efficiency, as failures are no longer a dark horse lurking in the background. As part of a company’s overall mobile strategy, team leaders and factory supervisors can use mobile predictive analytics within apps using this modeling algorithm, and the resulting output can be used to redirect the flow of work and schedule parts and repairs in advance, minimizing disruption time.
2. Analytics assists companies in reporting incidents
The power of predictive analytics models and algorithms lies not just in their ability to detect problems and issues faster, but also their ability to analyze patterns and enormous amounts of data to predict incidents companies would have never seen coming if they relied on manual processes.
Additionally, the data that is reported from an incident is better informed and more complete when analytics are used, compared to relying solely on human-generated reports. In the past, users had to entirely report an issue, including all underlying data, and most users do not offer a complete picture of their system or actions. Incident reporting with complete, granular data offers a number of pathways to reducing future interruptions.
3. Predictive analytics alerts nearby employees of pending issues
The available data from sensors and their constant stream of telematics is not just useful for predictive maintenance — it can also be used for real-time analysis of temperature, vibrations, revolutions, friction, time to complete, time to production, number of rejects and more. When events happen that exceed or underperform a threshold for normal operation, the team can use mobile apps with event-driven alerts and push notifications to learn about these issues as they are occurring, mitigate them and prevent minor issues from building up into catastrophic problems.
Workers and repair personnel that are nearest to the equipment with issues can automatically be alerted based on location sensors in their mobile apps, and the root causes and fixes can be recorded in the app for further analysis and integration into predictive failure algorithms.