Uptake Named a Representative Vendor in Asset Performance Management Software

Gartner recently released its annual Market Guide for Asset Performance Management (APM) Software and recognized Uptake as a representative vendor.

APM helps operators in industries like manufacturing, transportation, mining, rail, chemical processing, and oil and gas to move beyond time-based preventive maintenance strategies to condition-based approaches tied to financial outcomes. The research notes, “Business processes supported by APM software are becoming an important core business capability for asset intensive organizations. CIOs are increasingly realizing benefits which aid the market transition beyond the use of APM focused on equipment reliability to increasingly leveraging APM to also help improve overall business operations.”

Uptake’s Industrial Intelligence AI software supports and enhances many APM work processes and methodologies used to prevent asset failures, reduce risk, maximize performance and move organizations toward greater operational excellence.

Among the key benefits asset-intensive enterprises are realizing from APM include:

• reduced unplanned repair work
• improved asset availability and safety
• minimized maintenance costs
• reduced risk of failure for critical assets

Gartner uses four key product capabilities to define APM: asset risk management, reliability-centered maintenance (RCM), predictive asset management, and condition-based maintenance (CbM).

Uptake confronts the challenges that maintenance teams experience, and empowers them to quickly effect change. Those challenges are often costly, complicated, and central to operations. We’ve leveraged the power of data and AI through data cleansing, integration, analytics, and a simple user interface to optimize maintenance, mitigate risk, and ultimately support heavy industry operators through actionable analytics to address these challenges.

And yet many of these challenges slow and complicate the adoption of APM. Gartner predicts, “by year-end 2022, only 35 percent of asset-intensive organizations will have asset maintenance activities across their asset base, beyond condition-based maintenance (CbM).”

CbM is one of the more foundational APM methods. Those operators who leverage the varying degrees of APM will proportionately advantage their business by unlocking efficiencies in reliability, sustainability, safety, and productivity hidden in their own data.

We’ll consider some challenges to the adoption and use of APM that we’ve encountered, and how Uptake is solving each of them to quickly realize results for our customers.

Data collection is necessary but sometimes lacking: One of the primary obstacles to APM is adequate data collection, including from a range of industrial sources like signal readings, fault codes, fluid analysis, and work orders and also from contextual places like weather, geospatial data, and market prices.

• Meeting maintenance and reliability (M&R) teams where they are: We know the effectiveness of APM hangs on the quality and range of data provided. It’s why Uptake has worked to meet M&R personnel where their current data management practices are with Uptake Compass, which ingests, cleans, and organizes industrial data. But Uptake Compass isn’t tethered to these legacy systems, without which cleaning and structuring data becomes time-consuming and costly. From work-order data and fluid analysis, Uptake Compass also collects and organizes the industrial information necessary for cost-effective maintenance optimization. In either case, a hardware plug-in isn’t necessary.

Unstandardized data makes analytics expensive or infeasible: The range of available data is often inconsistent, incomplete, redundant, inaccurate, or imprecise. Within single sources, metadata variations and quality issues make standardization difficult. These problems with data integrity explain why the typical data scientist today spends around 80 percent of her time cleaning, structuring, and enriching data. Dirty data often also puts more advanced maintenance strategies out of reach.

• Automated ingestion, cleaning, and organization of data: Uptake normalizes raw data from industrial sources to prepare it for more advanced analytics. For example, the Data Integrity Engine incorporated into Uptake Compass uses AI to automatically identify missing or inaccurate data through text mining and unsupervised/supervised machine learning techniques to make improvements for missing values or records. Automated data cleaning removes busywork from internal engineering teams and re-focuses attention on value-impact areas.

Difficulties of ERP, EAM, CMMS Integration: Depending on the state of data use in-house, some enterprises are already leveraging their ERP, EAM, and CMMS to monitor, schedule, and measure asset maintenance. While such cleaning and standardizing features of these systems as data historians and programmable logic controllers give operators visibility over general asset health, this condition-based monitoring (CbM) reveals the need for maintenance but often only after a failure has occurred. CbM stops short of the proactive maintenance possible through the three other key areas of APM: asset risk management, reliability-centered maintenance (RCM), and predictive asset management.

• Optimized maintenance and asset reliability are the bases for advanced analytics: One of those areas of operational excellence unlocked through data prepared for advanced analytics is reliability-centered maintenance. Maintenance focused on reliability enables teams to make repairs based on actual needs rather than on service provider or OEM guidelines. With this need for proactive maintenance in mind, Uptake Compass empowers users to prioritize preventive maintenance tasks to optimize service without the significant cost and time required to conduct thorough RCM studies.

• Backed by the largest library of cost-optimized maintenance strategies: The Asset Strategy Library (ASL), which forms a central part of Uptake Compass, provides asset and component- level survival cost analysis so that operators can prioritize and perform maintenance before failure occurs. As a result, operators are well-positioned to move their maintenance strategy from reactive to preventive maintenance to CbM before taking additional steps toward more dynamic maintenance strategies like predictive maintenance.

Check out this video overview on Uptake's data science:

Market Confusion: Gartner’s Guide notes, “Conflicting vendor claims overlap with complementary products such as with industrial Internet of Things (IIoT) platforms that can support predictive analysis, growing the number of EAM systems that can now also provide CbM and beyond, APM included as part of digital twins and, OEMs including predictive analysis support.”

In addition, with many asset-intensive industries lacking agreed-upon maintenance standards (though they have been moving toward shared conventions like ISO55000), the compatibility of vendor-operator relations is determining the rate and effectiveness of digital transformation initiatives.

As remote work, mobile solutions, cloud tools, geospatial capabilities, and asset investment planning (AIP) capabilities figure into considerations around the selection of APM software, many operators return to a basic question: What digital tools complement my EAM system, or does my business have the foundational EAM system in place in order to implement and act on APM?

• Configurable by Design: To that effect, Uptake has industry-specific integrations to ensure seamless integration and use with enterprise resource planning (ERP), enterprise asset management (EAM), computerized maintenance management system (CMMS), and telematic solutions. In transportation and logistics, for example, Uptake is available to Geotab Marketplace users within their myGeotab dashboard.
• Codifying Expert Knowledge & Decision-Making: Uptake also makes expert knowledge accessible by pairing the hard-earned knowledge of industrial experts with precision AI. In Uptake Scout, for example, users unlock AI-enabled insights into their own asset condition data with custom rules based on preferences. That way, internal teams wield control over critical assets the way they know how through easy navigation of their organized asset data.
  
• Supporting Expert Knowledge & Decision-Making: Converged around this point of AI-enabled subject matter expertise, Uptake specifies internal best practices that then automates maintenance orders throughout the organization. At the same time, Uptake provides a pathway for transmission of expert knowledge to less experienced members on reliability and engineering teams. With diesel truck technicians, for instance, more experienced and younger technicians alike have benefited from more precise diagnostics and improved wrench time.

Expert Knowledge amid Talent Shortages & Retirement: Many asset-intensive industries are facing a shortage of available talent and a wave of retirement of maintenance operations leaders. Executives in chemical processing to utilities to manufacturing and transportation report that this personnel transition, alongside digital transformation, challenges organizational measures to prepare their future workforces for operational excellence.

• Collaborating on New Expert Knowledge & Decision-Making: Uptake leverages the aggregated scale and scope of industrial information to solve the problems that impact industrial assets and operations. And as enterprises use new makes or models, Uptake keeps pace through the deployment of pre-trained models built on the same Digital Industrial Library. To create new machine learning models, we use our canonical AI engines which accelerate the solving of common problems that impact industrial businesses. We then fill in any gaps with curated or third-party data, and then we build, train, and deploy models that deliver valuable insights into industrial workflows. Our collaboration with General Motors iterated our data science framework to prevent failures on automotive stamping presses — an asset module now available for Uptake Radar.
• Acting on New Expert Knowledge & Decision-Making: One example of Uptake’s development of actionable industrial expertise is our Machine Learning Anomaly Detection (MLAD) Engine, which is a core part of Uptake Radar. The MLAD 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, the MLAD Engine determines 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.

A Toolbox Approach: The Gartner report finds, “Organizations realize the need for a combination of asset maintenance strategies suited to a variety of asset types and situations across the business through a toolbox approach. However, vendor products are not created equal and may require organizations to choose more than one APM product, creating more customized functionality or accept fewer capabilities.” Many enterprises are wanting assets or maintenance strategies covered that a single provider cannot offer.

• The Industrial AI Platform: We recognize that enterprises have their unique challenges towards enterprise-wide APM solutions. Our industrial AI framework enables us to create models that are tailored for specific industrial assets and maintenance strategies, forming a system of intelligence for those assets in a simple software application. Within our applications, we deliver industry-specific insights tied to industry-standard benchmarks that help companies optimize their operations and maintenance spend, enhance the reliability and performance of their critical assets, manage and monitor their parts more effectively, and save on labor costs by eliminating redundant repairs while investing in their people by developing best practices — all to drive operational excellence.

For more on how our Industrial AI platform yields insights into specific industries, take a look at this video:

ROI from APM: Many asset-intensive companies have struggled to demonstrate a hard ROI from APM software in the past. Forecasted asset performance and operating conditions often aren’t comparable, and limited data availability or quality also complicates comparisons. With rising operational costs, heavy industry operators are under great pressure to substantiate the dollar value of the benefits of APM.

• Competitive Operations & Maintenance: The imperative to drive financial outcomes company-wide demands that APM follow suit. Because of this challenge, a validated dollar value is a consistent metric across the Platform. In Uptake Compass, that clarity around financial outcomes takes the form of asset criticality measured in mean time between failure (MTBF), runtime to failure, and also the estimated value of repair. In Uptake Radar, a Value Calculator empowers users to gain insight into the value of performing maintenance on pending failures. This functionality enables maintenance managers to make decisions that reinforce and demonstrate productivity objectives throughout the company. The Calculator enabled one of the world’s largest copper producers to identify $28 million in cost savings and eliminate unnecessary maintenance through insights that, when acted upon, prevented failure on critical assets.

Uptake is in the business of building whatever comes next for heavy industry. Gartner’s Market Guide recommends, “The ability of the solution to support collaboration across the organization, as well as with external business partners such as OEMs.”

We’re saving our customers time and money by helping them tap into the fuller value of their critical assets. Operators, service providers, and OEMs use Uptake to:

• cost-optimize their maintenance programs without risking production value, quality, 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.

We also know that our compatibility with industry standards, challenges, and integrations empower our customers to move quickly, prudently, and decisively on AI-powered operational insights to optimize maintenance, enhance reliability, promote sustainability, and mitigate risk. And while that result shows up on the bottom line, our commitment to precise and actionable APM begins with understanding the value we deliver to our customers and, foremost, to our everyday users.