Uptake Acknowledged By Gartner For Having All Four Capabilities Listed In The 2019 Market Guide For Asset Performance Management Software

Gartner recently published its Market Guide for Asset Performance Management (APM) Software report. According to the report, “In asset-intensive industries, asset management is moving from simple maintenance to a business operations core competency. APM is at the core of this change. CIOs can use this guide for insights on APM offerings in order to understand market direction and support asset management strategies.”

In the report, Gartner acknowledges that Uptake has all four categories of APM capabilities: asset risk management, reliability-centered maintenance (RCM), predictive asset management and condition-based management. I firmly believe that this recognition validates Uptake’s mission and the real results our products deliver to our customers.

Let’s take a closer look at why we think this is.

Why do we think this is a big deal?

Simple: We know we’re using AI to offer a smarter approach to APM.

Our APM application, Asset IO, saves our customers time and money by helping them get more value from their industrial assets. No matter where they are on their digital journeys, Asset IO empowers them to start benefiting from AI technology. Customers can use our application to:

• Cost-optimize their maintenance programs without risking production value or safety.

• Prioritize their preventive maintenance tasks more effectively.

• Use condition-based monitoring to make repairs based on actual need instead of generic OEM guidelines.

• Run predictive maintenance to address small things before they turn into big things.

Our strategy is also fundamentally different. From day one, we chose to build our own AI platform and create industry-specific applications on top of it. Throughout our existence, we’ve remained OEM-agnostic and in pursuit of one thing: Equipping our customers with the truth about their assets regardless of manufacturer type.

Let’s dig into why we think all of this ultimately matters to our customers.

What do we think sets us apart?

1) Our platform.

There’s a reason why we built our own technology foundation from the ground up. We wanted to bring our customers an end-to-end solution that takes care of all the heavy lifting that needs to happen in order to get data AI-ready. That includes automating the complex task of ingesting, cleansing, correcting and normalizing data from all different types of sources.

Because this industrial-grade platform did not yet exist — and because generic platforms are incapable of solving industry-specific problems at scale — we decided to build it ourselves. The result: A system of intelligence for creating and deploying powerful AI models and applications that turn mountains of data into actionable insights and drive financial value for industrial businesses.

If you’re saying to yourself, “Sounds great and all, but how is that actually operationalized on the front lines?” don’t worry, we go further.

Customers can use our AI applications or we can feed our AI insights into their existing systems (e.g. Enterprise Asset Management) to increase the value that those systems are capable of delivering.

Behind the scenes, the job is never done. As industrious people who are eternally curious, we love that fact. Machine learning is an ongoing, evolving practice. Our platform establishes a feedback loop so that our models are continuously honing themselves based on how effective our insights actually are on the front lines.

Want to know more about our platform? Check out this video:

2) Our industrial AI framework.

Industrial-scale problems require AI that’s purpose-built for the industrial world. Everything we do maps to our industrial AI framework, which is specifically built for the needs of heavy industry. One of the most important steps in our framework is data integrity.

Automating data integrity:

• We know AI insights are only as good as the data inputs provided. What constitutes “good” data? Data that’s clean, normalized and ready for AI processing. Data scientists spend as much as 80 percent of their time wrangling data — cleaning and modifying data that’s incorrect, incomplete, irrelevant, duplicated or improperly formatted. This process greatly impacts industrial businesses because the data that comes off of machines is often very messy.

• Uptake’s platform ingests data from different types of sources and makes it AI-ready through a series of methodologies that normalize data. For example, our Data Integrity Engine automatically identifies missing or inaccurate data through text mining and unsupervised/supervised machine learning techniques, and makes improvements for missing values or records. The result? Standardized asset and failure mode tags for each operator action across the plant and fleet no matter what Computerized Maintenance Management System (CMMS) or Enterprise Asset Management (EAM) systems are being used. This speeds the path for businesses to start reducing their operational costs and optimizing their maintenance strategies across the enterprise.

We map that data to solve problems that impact industrial assets and operations. We fill in any gaps with curated Uptake data or with third-party data, and then we build, train and deploy models that deliver valuable insights into industrial workflows.

Building, training and deploying models:

• Our unique algorithms serve different types of industries and assets. To create new machine learning models, we use our canonical AI engines which accelerate the solving of common problems that impact industrial businesses. Then we add industry-specific content from our platform’s Digital Industrial Library, including asset data models, pre-trained models, curated data (e.g. weather and traffic) and our Asset Strategy Library (ASL).

• Our ASL is the greatest informer of cost-optimized maintenance strategies and contains valuable knowledge on 800+ asset types, 10 million components, 58,000+ universal failure modes spanning all known operating contexts, 5,000+ preventive maintenance tasks and intervals organized by operating context, and 178,000+ as-found reportable conditions. It’s the digital equivalent of 32,000+ years of professional industry experience. Our ASL enables us to quickly add value to our customers.

By using our AI engines as a starting point for our models, the time to implement an AI initiative is dramatically reduced. Here’s a look at two of our engines and the value they bring to our customers.

Machine Learning Anomaly Detection Engine:

• This engine is a versatile and highly scalable unsupervised learning engine for detecting abnormal behavior in asset data. The rules-based engine automatically learns the deviations and tolerances for related classes of assets by ingesting both historical data and data on the current conditions of the machine. By ingesting data from multiple assets in a particular class, we’re able to determine high and low tolerances for an entire fleet of assets. This enables maintenance teams to know when an asset is underperforming compared to other assets across the fleet. Additionally, the normal range expectation adjusts as operating circumstances and environments change. This means a customer won’t get a false alert when there’s a spike in engine temperature because of a normal external cause, such as an increase in payload or going up a steep hill.

Failure Prediction Engine:

• This engine ingests high-volume, live streaming data across multiple sensor readings to detect deviations. Instead of just understanding if an asset hits the high/low threshold, this engine correlates multiple sensor readings to detect and alert to deviations in order to provide advance notice that a failure is expected to occur. For example, by monitoring engine speed, engine oil temperature and engine coolant temperature of a Class 1 locomotive, we detected that the engine coolant temperature was deviating slightly from the patterns of the engine speed and oil temperature. Without us, the customer wouldn’t have been alerted to the issue because the data didn’t hit the high/low thresholds of the OEM, and the deviation in the data was too slight to be noticed just by looking at the patterns. Our customer was alerted and able to schedule maintenance in advance of the failure occurring.

Our engines are an extremely powerful tool that helps customers improve the health of their assets and operations. However, most vendors deploy models and then forget them. They don’t continuously monitor their performance, and they ultimately don’t understand the impact those models have on the business. This is why we created our Model Performance Review, which gives life to a more holistic understanding of how models can be trained and fine-tuned to deliver the best results possible. It’s the vital feedback loop that helps models grow smarter and increasingly accurate with each data point.

3) Our industry-specific applications.

Asset IO is not a one-size-fits-all APM application. Our industrial AI framework enables us to create algorithms that are tailored for specific industrial assets, forming a system of intelligence for those assets in an easy-to-use software application. Within our applications, we deliver insights that help companies optimize their operations and maintenance (O&M) spend, manage and monitor their parts inventory more effectively, and save on labor costs by eliminating redundant repairs.

Not only are our algorithms tailored by industry, but so is their functionality. For example, our Asset IO for Fleet solution uses functionality that’s needed in other industries, such as fuel management. But on top of that, it also has fleet-specific capabilities — like driver scorecards and a mobile app for ensuring compliance with Electronic Logging Device (ELD) requirements. By fitting our solutions to the specific needs of each industry, customers have tools that easily integrate into their day-to-day workflow.

Check out some of the work we’re doing for equipment dealers and see how we’re helping Carolina Cat use AI to scale its machine monitoring capacity and drive revenue.

Watch this video for a look at our industrial data science that powers our applications behind the scenes:

4) Our OEM-agnostic approach.

Most industrial companies use assets that are made by multiple OEMs. While some OEMs offer software applications to monitor the assets they make, they aren’t able to monitor assets across varying fleets without a conflict of interest, nor do they have deep failure knowledge about other OEMs’ assets.

Uptake doesn’t share that limitation. We don’t make the machines our customers use, and we’re proud to be OEM-agnostic. We also don’t make money off of maintenance. We’re all about data and the truth — using AI, we break down data silos to give customers the truth about the health and performance of their assets regardless of manufacturer. This is what enables us to monitor assets across our customers’ fleets and put entire enterprise processes into one view for them.

According to Gartner, “Uptake offers advanced analytics, including predictive and prescriptive maintenance. Additionally, it offers O&M cost analysis and the ability to enhance scheduled maintenance planning by providing ‘what-if’ scenarios of maintenance tasks with economic impact.”* This can be done across different fleets of assets, removing the bias of an OEM. By equipping our customers with these insights, we help them make better-informed decisions on the best maintenance strategies to use based on their particular needs.

Here’s more on what we believe is the value of our cross-industry network effects:

We believe that no problem is unsolvable.

Industrial challenges are unique and complex. But that doesn’t mean they’re impossible to solve. It just means they require creative solutions. I believe that Gartner’s recognition of Uptake in its Market Guide for APM Software report is a testament to our solutions and the value we deliver to our customers.

We know that our deep knowledge of machines — and all the different ways they can fail — enables us to be nimble, act quickly and solve tough challenges. Every day alongside our customers, we roll up our sleeves and do the hard work of building what comes next.

*Gartner, Market Guide for Asset Performance Management Software, Nicole Foust, Kristian Steenstrup, June 26, 2019

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