The field of Asset Performance Management (APM) has taken off like a rocket ship in the last 3 years. It's propelled by the fact that the industrial companies want their assets to generate more revenue, but without additional expenditure on buying new assets or upgrading existing infrastructure. This is where APM comes into picture. APM software allows them to pursue this goal in an effective way. How does it do that? Where does Artificial Intelligence fit into this whole thing?
Why do I need Artificial Intelligence?
APM makes it possible by allowing them to leverage the large amounts of data generated by the industrial sensors that are monitoring critical assets. A good APM solution leverages Artificial Intelligence algorithms to achieve the business outcomes. If you are considering or have heard that Artificial Intelligence may be a way optimize your processes, then you’ve probably stumbled upon a plethora of marketing material telling you all about the spectacular benefits of such solutions. They might have also used phrases like Machine Learning, Deep Learning, Advanced Analytics, Predictive Analytics, and so on.
Every AI initiative is won or lost before it is every deployed
We love Sun Tzu here at Plutoshift. Deploying an APM solution can be quite confusing. In this series of 5 blog posts, we will talk about what we've learned about the success and failure mechanisms of these deployments, the things you should know, the benefits you can expect, and the preparation you’ll need to get the most out of your investment.
If leveraging Artificial Intelligence were easy and success was guaranteed, everybody would do it all the time. Today, it isn’t! It is a rapidly growing field. The benefits are very compelling when implemented correctly. APM can provide information and recommendations that will give you a significant competitive advantage.
How does it relate to asset performance?
When operating assets such as membranes, clarifiers, condensers, cooling systems, or clean-in-place systems, there are typically several standard practices. They are like rules-of-thumb! These static rules are used to maintain production at a reasonable level, and to ensure adequate performance and quality. They are not perfect, but the system works in general. If operators had a better understanding of the specific process and its unique response to future conditions, they would agree that the performance could be improved.
The trouble is that the number of varying conditions and large amounts of data to sift through with standard analytics is too vast to be useful, not to mention time consuming. Continuously detecting and measuring the changing relationships make it difficult to do it manually. Without continuing to do the work and getting lucky identifying correlations, any improvements that were made would fade away over time. They become no better, and probably worse, than the rules-of-thumb they replaced.
How does Artificial Intelligence solve this?
Artificial Intelligence allows us to discern correlations, find the cause to a specific process, and predict its future impact by using algorithms to analyze large volumes of data. A good APM solution uses these Artificial Intelligence algorithms to predict future business outcomes. It also continues to analyze data and optimize setting recommendations to likely future conditions on-going. The result is the actual best settings to lower costs, improve quality, and mitigate unplanned downtime.
But what if it's wrong?
Artificial Intelligence sounds like a great way to get things done. When implemented properly, instead of static or semi-static conservative settings being used, operators would receive the best settings for a specific duration. But what about the cases when the predictions are off? After all, some of these processes may affect the health of a community! It certainly will affect the health of your company if the information provided by Artificial Intelligence is wildly incorrect. This is where asset performance monitoring comes in.
In a good APM solution, advanced analytics or predictions are an important but small part of the information delivered. The rest of the information are useful metrics and key indicators that, quite frankly, are there to provide evidence of the conditions and support the recommendations derived by Artificial Intelligence. The value of these indicators is usually more important on a daily basis than the advanced analytics or predictions.
For an APM solution to be effective, it should provide a way to continuously track the impact of asset performance over future revenue metrics. This doesn't necessarily refer to predictions, but hidden patterns that are not visible to the naked eye. APM solution centered on business processes, as opposed to machines themselves, is way more likely to succeed.
In the next blog post, we will discuss the things you need to consider before implementing a Machine Learning project. We will talk about the process of figuring out when it makes sense to go with a vendor versus doing the work yourself, the factors you need to consider before choosing a vendor, and the role of subject matter expertise in the world of APM.