5 Traps to Avoid When Selecting Asset Performance Management Software

5-traps-to-avoid-when-selecting-asset-performance-management-software

The market is saturated with software that promises to improve asset performance and reduce or eliminate downtime. Whether it brands itself as asset performance management, predictive maintenance, reliability centered maintenance or condition-based maintenance software, all seem indistinguishable from each other. But they are not. Behind the claims are deal-breaking differences that separate true operational intelligence (OI) solutions from those that come short of expectations.

The key is knowing when claims are misleading or downright untrue. Here are five “red flag” statements you may have heard.

1. The full value of OI software is in alerting you to potential asset failures. FALSE!

While alerting you to risks is certainly part of what OI software should do, it is just that: one part. True OI yields value on so many more levels. OI software should also facilitate the downstream diagnosis of root causes and prioritize issues according to how they impact your financial, safety and environmental goals. Finally, to ensure speedy and efficient issue resolution, the software should support corrective actions and provide a collaborative platform where operations and asset managers work together.

2. You have to start building math models from scratch to detect real-time asset anomalies. FALSE!

Critical to the timeliness and successful OI deployment is an automated, no-code implementation environment. A successful implementation builds custom models starting from model templates. Automation of data cleaning, feature selection, and model deployment supplement the templates to ensure high-quality custom models. For those who want to dive deeper, your OI solution should have no-code tools to simplify further custom-model building. This is typically less than 20% of any deployment, after all you are buying an OI software not consulting and software development.

3. It’s hard to prioritize issues based on financial impact. FALSE!

Your operations exist to achieve strategic business goals, including the return on investments. Therefore, true OI software must inherently quantify the risk of different options against one another and recommend actions based on their economic, environmental, or health and safety impact. Prioritization based on business impact should be an automated part of the overall workflow and process following an alert. The only way to make the right decisions for asset maintenance is to quantify how these alerts will directly affect your operations and business goals.

4. You will need a team of data scientists to implement your OI solution. FALSE!

The math required to accurately detect, diagnose, and resolve asset issues in a complex environment is extraordinarily challenging. However, a mature OI software will have libraries of proven calcs and models available to get you up and running quickly. So, don’t squander the time and talents of your data scientists by reinventing the wheel. Look for OI software that can automate the deployment of your detection environment, freeing up data scientists to work on unique problems.

5. It will take several months, or even years, to create and implement a solution for your complex operations. FALSE!

It should take weeks. Furthermore, it should require minimal involvement from your team. This is how:

1. Your OI solution connects remotely to your asset and process data sources.
2. Your data is used to build out an asset hierarchy and data structure.
3. Performance calculations are implemented to supplement your measured data with calculated performance metrics.
4. ML models are created with a single click, and a final check for accuracy of predicted values is established as a baseline for operations.

That’s it – your OI solution is live!