6 Ways to Create Operational Efficiencies with Industrial Asset Management

6 Ways to Create Operational Efficiencies with Industrial Asset Management

April 10, 2017 0 Comments
6 Ways to Create Operational Efficiencies with Industrial Asset Management_870x450

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, owing to 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 #PdM (tweet this)

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 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 err

2. 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.

3. 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.

4. 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.

5. 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

6. 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 (tweet this)

Start spending less time analyzing issues and more time implementing solutions with smart APM techniques. Let’s get started now, shall we?

Anita Rajasekaran

Anita Rajasekaran

Anita Raj is a Product Marketing and Growth Hacking Strategist on the Progress DataRPM team, with over 10 years of experience working in the field of big data, cloud and machine learning. She brings a deep expertise in running growth marketing leadership experience at multi-billion-dollar enterprise companies and high growth start-ups. 

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