Gartner quotes “By 2018, 50% of asset-intensive organizations will rely on asset performance management to optimize performance of mission-critical assets”.
Industrial asset performance management (APM) can be tricky, going by the fact that less than 20% of the available data is explored currently! While APM has everything to do with connectivity, data capture, visualization and analytics to automate the entire asset lifecycle it also involves getting information at the right time to the right people. After all, your assets take charge of all the heavy lifting of industrial operations and are directly related to production. Which means Healthy #asset base = towering efficiencies #IIoT #IoT @anitarajsekaran #PdM Click To Tweet
The future of APM in a connected factory lies in predictive maintenance, which will become an integral component of the next-generation smart factory mechanism based on real-time data. But predictive maintenance based APM will only reach its full potential if there is an effective platform that can deliver end-to-end visibility across all stages of a connected factory digitization journey – including the factory’s design, construction, operation and performance assurance. By analyzing both historical patterns and real-time data, its overall performance, efficiency, response time and security can be tracked easily.
In the previous posts, we have seen how manufacturing companies can leverage APM to derive greater value, we will now talk about how you can get started with the perfect APM plan to begin sowing the seeds of operational efficiencies.
1. Start with the basics – Getting your data right
The industrial environment is chaotic, bursting with data spurts from all corners. The ideal way to begin would be to get a consolidated view of all your data stacks, sensors, machine systems etc by using a unified platform meant for further machine data analytics. A complete assessment of your data flow will go a long way in detecting flaws and errors.
- Separate Signal Vs Noise – Pick what’s important
The next step is to refine this noisy data to pick out key signals from the incoherent machine data. Using capabilities driven by advanced cognitive predictive analytics and machine learning, individual asset sensor data is studied and analyzed to zero in on mission critical issues.
- Understand how assets work- In tandem and isolation
It’s important to get a consolidated view of each equipment to understand the behavior of every asset and map out interdependencies. When multiple industrial operations occur simultaneously, there might be a ripple effect. One minor issue with a single asset could create a significant impact on another.
- Making every asset count- Be prepared, be proactive
Make sure you know your asset behavior so well that you can predict failures even before they occur. Unplanned downtime can be seriously damaging to your operational lifeline. By using automated productive maintenance techniques, proactive action can be taken to avoid component failures thus providing a larger scope of continuous operations.
- Staying Safe -Taming Risks
An unplanned equipment failure can create potential risk hazards not only to your industrial environment but also to the lives of factory workers who are an open target. Several safety risks need to be considered and carefully monitored to create a safe working environment by adopting an organized and scheduled maintenance approach
- Automate, automate, automate
The final and most critical step is to completely automate your asset lifecycle using the cognitive route. Lesser dependency on manual intervention by using a machine first approach can create a structured and systematic approach in dealing with erratic asset behavior.
Get the maximum dollar for every asset using #Cognitive approaches to #Predictive #Maintenance #IIoT Click To Tweet Start spending lesser time on analyzing issues and more time on implementing solutions with smart APM techniques. Let’s get started now, shall we?