Plutoshift APM Brings Direct Financial Impact To The Process Industry Using AI

Companies in the process industry today are expected to generate more revenue using fewer resources and without buying new assets. There’s greater pressure than ever to be efficient and nimble, worsened by the potential of a global trade war. Manufacturers have questioned if artificial intelligence (AI) could be cost-effectively harnessed to transform the industry like it promises for so many others.

AI is only as good as the outcomes it supports, so our team’s biggest priority is to make it as easy as possible for our customers to leverage AI to generate the ROI that matters most to them.

Today we announced our cloud-based, AI-driven asset performance management (APM) platform designed specifically for the process industry. Using AI, Plutoshift automatically and continuously connects asset data with financial metrics, letting you easily measure performance, achieve your business outcomes, and increase profit margins.

We worked closely with our Fortune 500 customers in verticals that would push the limits, including food, beverage and chemical, to solve their critical pain points. The vast amount of industrial sensor and IIoT data manufacturers rely on to overcome challenges is often trapped in legacy systems. These aging, on-premise systems can’t correlate the impact of asset performance over future revenue metrics. Plus, these tools have not kept pace with the mobility and ease-of-use demands that today’s savvy end users expect.

That was just the beginning.

We created a platform that lets plant managers discover process inefficiencies and new opportunities to increase throughput, speed up ticket resolution, reduce resource consumption, and eliminate waste. Plutoshift’s proprietary algorithms leverage both existing historical and real-time data, extracting actionable insights between asset behavior and revenue. We work with all of your existing data systems seamlessly, providing you with immediate ROI.

Plutoshift can be accessed safely from anywhere, empowering those front-line end-users who expect an on-demand experience. While new, it’s proven – the only solution vetted by global forums of leading industrial technology evaluation committees – and is accompanied by mature features, including:

  • Deep analysis and intelligence: Easily connects to data streams including SCADA, ERP and CMMS to produce actionable insights on the costs, risks and efficiencies of plant operations.
  • Agile integration: Integrates with every process historian on the market today.
  • On-demand insights: Interactive, easy-to-use dashboards and alerts enables operators to work effectively from anywhere.
  • Pre-built asset templates: A growing library includes membranes, cooling towers, CIP systems, clarifiers, dryers, and more.

We built Plutoshift APM to help companies bridge the relationship between the data and financial performance of their assets. Contact us today for a free demo.

How to measure the success of an APM deployment

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.

What we learned from hosting our first customer event

There comes a point in every B2B SaaS startup’s life when you feel the irresistible urge to host a customer event. There are many good reasons to do it. In our case, we did it because we love spending time with our potential customers and exchanging knowledge with them. We thought Austin would be a great place to host it. Tuesday, August 21st, was a hot day down there. Just perfect for a few cool drinks at the Roosevelt Room in downtown Austin and some good conversation about cowboy boots, BBQ, and Artificial Intelligence.

Plutoshift hosted this event for the Industrial team at Carollo Engineers. Their group came from all over the United States and Plutoshift had plenty to talk about. However, the topic of water was never too far away. Plutoshift’s Northern California location led to discussing wine, but eventually found it’s way to novel water reuse solutions at California vineyards. The topic of fishing somehow led to desalination plants, and skiing led to … wait for it … après-ski drinks, which led to reverse osmosis membranes in ethanol plants. Yes, the experts at Carollo care about their work.

The event, apart from giving us a chance to get to know each other, was an opportunity for the Carollo team to learn the latest in implementing machine learning and asset performance management from Plutoshift. We shared our latest work with Carollo and discussed how to take this into future projects. We touched on the advantages of a revenue-centric APM approach and also some of the challenges industrial water and wastewater companies have with implementing machine learning solutions.

Among the challenges we discussed was the lack of open source data. One thing that has put this industry behind others is the anonymous sharing of data from processes. This collaborative sharing is the key to accelerating the adoption of machine learning. Other industries, including energy, have formal programs to facilitate this type of data sharing to the betterment of the industry as a whole.

To wrap up the night, we had a frank conversation about how data sharing might be initiated. There were some good ideas that were exchanged and better still, there was enthusiasm to pursue those ideas. Perhaps the Roosevelt Room will be remembered as the launchpad for this very important component to bring revenue-centric APM approach to industrial water and wastewater plants in the future.