The first step in this process of governance is to determine the level of asset risk the organization is exposed to.  If assets are over-maintained, excessive materials, labor, and energy could be needlessly consumed and unnecessary waste is produced.  If assets are under-maintained, failures can occur, put people in harm’s way, exceed the organization’s budgeted impact on the environment and erode financial performance.  Asset Performance Management (APM) is the set of defined processes that produce a lucid and objective view of asset risk.  As the demand for climate-related financial disclosure increases, organizations will become increasingly dependent on APM to enable this critical part of their asset management system.

The post Solution Spotlight: Assess Asset & Failure Mode Risk appeared first on Itus Digital.

A key challenge for AI technology in asset management is the complexity of using it unless you are a data scientist or programmer.  How do organizations take advanced technology, like AI, and make it available and easy to use for those who could drive the most benefit with it?  The front-line team, operating and maintaining the plant, have the knowledge and expertise to make a risk vs. cost tradeoff decision to deliver operational results.  AI is a supporting technology that should be used primarily to enable teams to be more productive and efficient in their daily roles instead of being overwhelmingly complex and hard to use. 

Would you really trust the Pit Crew to control the steering wheel of a Formula One Car?  Of course not.  Their role is to support and enable the drivers to be as efficient and productive as possible. The Itus Digital solution provides a way to visually model patterns or anomalies in machine data.  The key word is visually.   This approach allows an engineer, who “knows” the asset operating conditions and failure risks to build models based on the variances that occur from those conditions. 

Using the comparison to Formula One, the F1 driver, from their cockpit, sees a condition they want to use as a data point.  The driver can “mark” the track by pressing a button on their steering wheel.  For example, the driver might want to associate this data point (or section of the racetrack) with performance data involving braking power, or cornering.  The F1 team uses the data that’s collected to adjust their race strategy to achieve incremental performance improvements. 

Our visual approach uses the engineer’s expertise and enables them to observe, and “mark” data variances which indicate emerging risk of failure, deviations which could affect quality or even expected operating conditions. 

The process starts with the engineer evaluating the historical data for the machine to supply context for modeling the asset.  Information such as work performed, identified failures, process and machine condition are brought together in a timeline view.  With one complete view of the machine’s behaviors and activities, engineers can identify “markers on the track” so that specific areas of improvement can be found and encapsulated in a machine learning model.   

But what if you only know what ‘good’ conditions look like for a machine and aren’t entirely sure how to pinpoint ‘bad’ anomalies or conditions?  Many AI techniques focus on historical behaviors which are precursors to ‘bad’ conditions where the machine is likely to fail. These ‘bad’ conditions can be highly erratic and may not look the same every time.  So how do you help the reliability engineer that only knows what good operational conditions look like and simply wants to be notified if there are deviations from that?

The Itus Digital approach allows its users to visually model ‘good’ conditions where equipment is running as expected and send notifications when deviations from those conditions are detected.  This solution is a great starting point for organizations which may not have exact tracking of historical failures but look to know when key operating parameters show potential machine issues. 

There are still many mysteries surrounding AI, but we are simplifying the on-track experience for our drivers, the engineers, so they can spend their time solving problems, improve the quality of their decisions and accelerate their asset management maturity.

The post Accelerating Artificial Intelligence in Asset Management – Part II appeared first on Itus Digital.

Back in the mid-1990’s members of the Itus Digital team were involved in a project to consolidate service intervals of a heavy haul fleet in an open pit coal mine.  Rather than spend ten or twelve million dollars to increase the size of the haul truck fleet, we wanted to see if we could achieve an increase in production capacity from the existing fleet. One of the requirements of the project was to double the engine oil drain interval.  This would allow the service intervals to be consolidated sufficiently to keep the existing haul trucks working for longer and unlock un-utilized production capacity.  However, we needed to have confidence that we could increase these oil drain intervals without increasing the risk of engine failure.  The things we needed twenty-five years ago are the same things we need today:

• Understanding of failure modes
• The protections that needed to be in place to sufficiently reduce the risk of failure
  • Doing the right things
  • Monitoring the right things
• Historical data about:
  • How the lubricant breaks down
  • How the engine can fail due to lubrication-related problems
• Analysis of used oil and filters to justify a PM extension

If we’d been able to apply deep neural networks back then, to watch one or multiple conditions, and detect anomalies that indicated potential failure risk, we might have reduced the duration of the project by several months and achieved a lower-cost way to monitor certain failure modes with higher confidence.

Unfortunately, we experienced two engine failures during the project.  These failures were directly attributed to maintenance activities that were not done when they needed to be done.  One of the failures occurred because the lubricant hadn’t been topped up.  The second failure occurred because the engine lubricant filter hadn’t been properly serviced.  In this scenario, anomaly detection would not have detected these failures and highlights the need to treat Artificial Intelligence (AI) and Machine Learning (ML) as a specialized technique to complement foundational asset management practices such as preventative maintenance compliance.

With ChatGPT, AI/ML has reached a new level of hype, promise, and even a dose of fear.  Have we reached a point, like a Star Trek episode, when we can simply ask the ship’s computer to answer questions, provide options, or execute certain tasks?  While ChatGPT is a powerful tool that can quickly generate an answer to your question, it has no mechanism for telling you when the right time is to apply the solution.  An effective asset strategy requires more than just a simple answer to a question.  It must be operationalized, dynamic, and the enabling capability of your work process.

The anomaly detection concept involves picking up an incipient failure signal, further leftward on the IPF curve.  This simple concept implies that the earlier we detect the deviation of the expected signal, the more time we’ll have to do something about it thus, minimizing potential impact to operation.

This isn’t a new technique in the industrial space.  Mathematics and models of failure have been around for decades.  There is a vast amount of data available to feed these models and produce the decision support we need.  The role of AI/ML is to bring down the cost of and increase the accuracy of prediction by efficiently analyzing large volumes of data and advising equipment experts when failure risks emerge.  It should also bring about a more optimal division of labor between humans and machines.

Industrial equipment can have significant variation in risk. Failure modes exist which are not optimally detected with AI/ML, and every organization must work within budget and cost constraints.  Furthermore, when assessed appropriately a run-to-failure strategy could be the optimal answer.  Consideration of all these factors, the classic constructs of asset management, is more important than ever given the level of hype given to AI, as the singular answer to optimizing asset performance.

In summary, Asset Performance Management requires more than just anomaly detection or understanding of when maintenance activities occur.  Asset Performance Management helps organizations balance the risk, cost, and performance of assets.  As such, anomaly detection may be an important part of an operationalized strategy, if it is in service to that outcome.

The post AI, Anomaly Detection & Asset Management – Keeping it Real appeared first on Itus Digital.

Along with all this cutting-edge technology, Hexagon demonstrates how paying attention to fundamentals is also vitally important to customer success.  We had numerous Hexagon Enterprise Asset Management (EAM) customers come by the Itus Digital booth to talk about the next natural steps in their asset management journey.  We learned how these organizations manage maintenance activities and react efficiently when things require attention. With Hexagon EAM in place, they have a solid foundation to drive the next steps of achieving an optimal balance between risk, cost, and performance of assets.  Now Hexagon EAM customers are asking themselves, what’s next?  How do we eliminate failures altogether and optimize maintenance activities based upon the current risk in the business?  Asset Performance Management (APM) is the additive business process to help achieve these objectives.

As a Hexagon partner, Itus Digital is ready to take these organizations to the next level in their asset management journey.  Itus brings the power of APM to mitigate risk to an acceptable level by operationalizing asset strategies.  With operationalized asset strategies, preventative maintenance and inspection activities are driven based upon current failure risk and early warning is provided when potential failure is detected.  Armed with asset strategies, industrial organizations can optimize the use of maintenance resources and minimize equipment downtime.

Itus Digital collectively has a generation worth of asset performance management experience and has strong ties to Hexagon from a technology perspective.  If you would like to learn more about how to drive APM processes from solutions such as EAM, SDx, and J5, connect with us here.  We look forward to the productive dialog we started at Hexagon Live and to next year’s conference where we will present the Asset Performance Management stories that began at #HxGNLiveGlobal 2023.

The post Hexagon LIVE 2023 brings together industrial experts appeared first on Itus Digital.

The primary mechanical failure risk being investigated in the accident is the overheating of a wheel bearing. Overheated bearings are not the only problem that can cause a train to derail, but they are essential to a train’s safe and efficient operation. Inside each bearing is a series of rollers that are a critical component in turning the rail car axle. When lubricated, the bearings limit friction while supporting the railcar’s weight. If a bearing gets too hot, usually from a loss of lubricant, it can melt, causing it to seize up or come off the axle. The resulting damage can throw a railcar out of alignment and cause it to jump the tracks.

“Roller bearings fail. But it’s absolutely critical for problems to be identified and addressed early so these aren’t run until failure”  NTSB Chair Jennifer Homendy

Norfolk Southern has implemented protections to mitigate bearing failures on its railcars using a Hot Bearing Detector (HBD). The HBD is placed trackside at fixed points and automatically measures the axle temperatures as the train passes by. Its function is to detect overheated bearings and provide real-time warnings to train crews so they can take appropriate action. A more detailed view of the protection scheme, evaluation thresholds, and prescribed actions is provided below.

The protection definition follows very similar constructs to an Asset Twin in the Itus Solution. Key components which are defined include a Failure Risk, which is the elevated bearing temperature due to lack of lubrication. Also defined is the specific Protection, which monitors the bearing temperature over time. The Advisories (prescriptive actions) are also defined, which detail what should be done when certain conditions identify emerging threats.

The report found that the temperature of the bearing in question had been increasing for 30 miles before reaching East Palestine. However, only the third reading reached Norfolk Southern’s threshold to stop and inspect the train via a real-time audible alarm. Unfortunately, the alarm was triggered too late as the train derailment was already in process.

The bearing temperatures were evaluated at three data points before the train derailment. The 23rd car’s axle had a recorded temperature of 38 degrees above ambient temperature at Milepost 79.9 on the Fort Wayne Line. At the next detector at 69.01, it increased to 103 degrees, and at Milepost 49.81 on the east side of East Palestine, the recorded temperature was 253 degrees above ambient.

The accident highlights key constructs to consider when designing failure risk protection models.

Data polling rates should consider condition escalation rates to allow for enough time to detect and respond to mitigate the identified failure risk. In the case of the NS derailment, the distance between the last two temperature measurements on the wheel bearing was 19.2 miles, and the temperature difference over that time was 150 degrees. Somewhere over that time, the bearing temperature passed through a non-critical threshold that would have advised the train engineer to stop and inspect the rail car but significantly pushed past the critical threshold of 200 degrees. To mitigate this potential data gap in the future, the Association of American Railroads announced that all seven Class 1 railroads in the country have committed to adding approximately 1,000 detectors to close the gaps between detectors and achieve an average spacing of 15 miles.

Design thresholds, analytics, and actions from actual experience. When designing a Failure Risk Protection, reliability engineers must make key decisions, including how many thresholds to implement, how much tolerance should be given for each threshold, and what to prescribe for risk mitigation at each level. Design thresholds, analytics, and actions from actual experience, using historical data to simulate a model and define analytics for a specific operating context. Sometimes this information is available through the collective knowledge of industry experts via standards such as ISO10816 (mechanical vibration).  In other situations, this information may be available from OEMs in their operation, maintenance, and troubleshooting manuals defined from their specific failure testing.  Many times, these models are developed from a ‘really bad experience’ or consequential failure.  As a result of this accident, Norfolk Southern is working with manufacturers to develop more sensors, reevaluate triggering thresholds, and analyze data for patterns that could provide earlier warnings. A more robust method to design Protections is from actual operating experience.  Solutions like Itus provide an ability to simulate a model from historical data which is an ideal method to define analytics for a specific operating context.  The capability allows engineers to understand how often the system will advise at various threshold levels, ensuring there is appropriate time to respond with the most appropriate prescriptive action to mitigate the risk.

Accounting for the worst-case consequence is essential in evaluating risk and advising on mitigations with critical context to drive appropriate action. Unfortunately, we often see analytical models which are not designed from a complete risk context.  By injecting risk into a Failure Protection scheme we can assess the situation more accurately (historical probability of failure) but more importantly we can advise on mitigations with critical context to drive appropriate action.  According to the Federal Railroad Administration, about 1,000 derailments occur each year but on average only 17 of those accidents involved rail cars with hazardous cargo that could present significant safety or environmental risks.  Ideally, risk should be evaluated within the analytical model to provide more advanced warning, increase time to respond, and offer more specific context on what is happening and what to do. As we are learning from the East Palestine accident, there is a very different consequence when a rail car carrying freight has a failing bearing vs. a rail car carrying highly toxic vinyl chloride.

The East Palestine accident impacted 2,000 residents with long-term environmental and health concerns. The rail industry has a keen understanding of the wheel bearing – lack of lubrication failure mode for rail cars and is making progress on the protections that can be put in place to mitigate the risks. The preliminary report has provided great insights for anyone building analytical or failure risk monitoring models that are great reminders for design moving forward.

If you are interested in learning more about Asset Twins and how the Itus solution can rapidly implement failure risk protections to predict and minimize unplanned events, feel free to reach out to us, we would love to chat!

The post Failure findings from Norfolk Southern train derailment in Ohio appeared first on Itus Digital.

Asset performance management started as early as the mid-1960s with very early versions of systems known as CMMS (computerized maintenance management systems).  Developers originally built these solutions to help with the complicated task of coordinating people and performing industrial equipment work.  As computing technology became more prevalent within industrial organizations, ERP systems (enterprise resource planning) emerged as another category of business systems.  ERP solutions focused on bringing data and work processes together across all the major functions of an organization, such as finance, operations, and sales and provided significant productivity gains for industrial organizations.  The advent of ERP systems provided the ability to manage an assets lifecycle with integrated processes to procure, maintain, and dispose of assets within one system which established the next generation of CMMS systems called EAM (enterprise asset management).

With the deployment of CMMS and EAM solutions, organizations were able to address many foundational aspects of asset management. Having a common equipment database and a systematic approach to managing work made them much more efficient. But these solutions did not address how an organization could be more effective with their resources or ensure that maintenance activities addressed a critical organizational objective, preventing unplanned equipment failures. This functional gap led to the creation of APM (asset performance management) solutions in the mid-’90s.

The state of APM solutions today

Today, industry analysts estimate the Asset Performance Management solutions market to be over $4 Billion in annual revenue with yearly double-digit growth rates. The market’s size and projected growth rate directly reflect an APM solution’s impact on key business objectives such as increased equipment availability, optimization of maintenance spend and lowering risk to people and planet.

Currently, many organizations have the fundamentals in place in the overall context of asset performance management. For example, many catalog asset plans and schedule asset maintenance. In addition, many organizations have implemented some form of monitoring and inspection programs to proactively identify potential equipment problems. Over the last few years, the scope of APM solutions has grown with technology advancements such as expansion of sensing technology, management of large volumes of data, and advanced analytical modeling.  As a result, the asset performance solution category has a broad functional range. Its management scope spans from classic approaches of developing asset strategies (RCM, FMEA), historical analysis of maintenance and repair history (Pareto and statistical analysis) to advanced failure prediction utilizing operational data and machine learning.

A recent study by Verdantix identified 27 prominent APM solutions in the market. Given the number of providers and variations in approach, how can an organization find the one right for them with so many solutions to choose from?  Utilizing a structured evaluation approach and staying focused on your requirements can ensure the selection of the most ideal APM vendor for your specific objectives.

To help with this process, here are five practical elements to consider as you evaluate APM solutions.

Clearly define your business objectives

Asset Performance Management programs have the ability to drive variable business outcomes which are often determined by factors such as what industry you are in, the current business climate, and the size and maturity of your asset management team.

Typical business objectives consist of:

• Enhanced availability or production throughput
• Lower operating costs
• Lower risk to the people and environment that co-exists with industrial assets
• Increased productivity & collaboration between maintenance, reliability, and engineering resources

In some cases, organizations may be looking for improvements across all the objectives mentioned, but it is also common for an organization to be targeting just a specific improvement area. The classic example here is an industrial company working in a down-demand cycle that may have no problem meeting production targets at a lower equipment availability. Their primary objective could be lower operating costs. Supporting this goal means reducing costs in areas like maintenance instead of enhancing production throughput. Starting with a simple understanding of the core business objective is a great way to define your endpoint. Then you can establish the core APM use cases and requirements.

Key Takeaway:  Before jumping into APM vendor evaluation, take some time to:

• Establish the core business objectives for your program,
• Map them to your corporate improvement initiatives,
• Create simple but measurable metrics and goals that allow you to track your program ROI.

Align your APM use cases to your business objectives

With the varied functional capabilities of APM solutions and different techniques to improve the performance of industrial assets, a key step in the process is to determine which APM use cases are the best fit for your core business objectives. Ideally, you want to leverage these use cases against what capabilities your organizations can successfully “absorb.” Today’s market offers many large, monolithic APM solutions which attempt to offer and sell you a plethora of APM features which may be beyond your needs. Historically, these solutions were designed to support the largest and best-funded APM transformations, so they may not align with your business objectives, requirements, or budget.

A few of the common APM use cases for consideration include:

• Optimization of maintenance/asset strategies
• Failure prediction through anomaly detection
• Equipment health monitoring
• Condition-based/predictive maintenance
• Root-cause failure analysis

This list presents a significant variation in the potential impact for an organization. One example to highlight is the amount of buzz about using Machine Learning to help identify operating anomalies before failures occur. These techniques work for highly critical equipment where maximizing availability is the primary goal. They can provide early warning of a potential failure, thereby reducing downtime. However, these approaches can be expensive to deploy, are usually specific to certain equipment and operating contexts, and are ineffective for identifying areas where the organization can materially reduce maintenance spending. Anomaly detection requires high-quality data, the ability to get at it, feedback mechanisms to train the model, and most importantly, the organization must be ready to receive, accept, and manage the identified anomaly further.  Some organizations are mature enough for this level of application capability, while others may never need this sophistication to meet their business needs.

It is essential to understand your specific requirements and internally align with them before getting distracted by all the potential “shiny new objects” that APM vendors put in front of your evaluation team. Keep in mind that there is no magical solution. Microsoft’s Word product is a great example of this scenario. Are your requirements to write a short report or publish a novel? Word has thousands of features available, but most of us only need a fraction of them to meet our needs.

Key Takeaway: Avoid the temptation to just adopt a APM vendors use cases and assume they will work for you, APM is not a ‘one size fits all’ business process:

• Consider your APM use cases and ensure they align with your business objectives
• Avoid the trap purchasing massive feature sets which will never get utilized
  • Do you need all of the capabilities being pitched to you?
  • Is your organization mature enough to leverage all the capabilities?
• Focus on your requirements to avoid unnecessary spend on software that never gets adopted

Assess your need for a solution or platform

Companies with successful APM programs design them considering all aspects of people, processes, and technology. Today vendors offer APM solutions in one of two forms; a defined software application or a technology platform that customers can leverage to build a specific use case. Software solutions typically deploy faster and have higher user adoption rates as they provide “ready to go” use cases to solve everyday problems. Platforms provide enabling technology that requires more effort and cost to develop and integrate with your internal systems. They have the additional benefit of offering significant use case flexibility and control.

To illustrate this scenario, let’s look at the earlier use case, anomaly detection. Anomaly detection software provides analytical capabilities to detect changes in equipment operating state and a work process to manage what has been discovered. This process includes prescribing actions to mitigate risk, a method to drive these actions to completion, and an optimization step to ensure that the asset strategy is continually improved.

Conversely, some organizations choose to address anomaly detection using platform technology such as “R” or “Python.” Although this avenue offers significant capabilities and flexibility to develop models and algorithms that detect state changes or deviations in operating data, it also requires organizations to integrate those outputs with other tools and systems. Why? Because this technology lacks a mechanism that ensures the complete management of the threat and feedback to the original strategy.  To complete the solution, the analytic needs to be incorporated into a broader solution or work process.  A platform approach to this problem might offer flexibility, but it will require more integration and users to self-manage the process. This situation presents a challenge, especially with today’s remote and transient workforce.

Key Takeaway:  Determine your need, platform or solution:

• Platforms offer flexibility to address requirements but often lack a holistic process and require dedicated technical resources to implement
• Solutions offer prescribed work processes and quicker implementation but may have limitations in customizations
• Assess the pros and cons of each approach as it relates to your organization, resource availability and use cases

Evaluate functional design, it impacts value creation

A key factor in ensuring user adoption and broad scaling of an APM program is a unified work process and user experience that fosters value creation. A common mistake in APM solution selection is not fully considering the “functional design” and assuming your team will figure it out with enough training and time.  A good functional design drives a defined process which leads to value creation and has considered the system user in every step of the process.  A great example of this is Apple’s iPhone which effectively eliminated the Blackberry with great functional design because it was easy to use and rapidly created value by integrating the camera and phone.

When evaluating APM solutions it is important to evaluate functional design as many have been ‘stitched together’ through acquisitions of multiple products and technologies.  While these applications may provide a broad feature set that checks the functional boxes, they likely have limited integration between individual products or modules and will be difficult to upgrade or maintain. To top it all off, this lack of integration results in a jagged user experience that hinders productivity and adoption which ultimately limits value creation.  To ensure you are evaluating functional design of your APM solution consider the following:

• Does the application offer a integrated work process that drives continuous improvement?
• Is the user experience intuitive and available on all devices (computers, tablets, phones, etc.)?
• Are there in application tutorials available to keep users effective after go live?
• Are there multiple products which need to be purchased or navigated to complete a process?

Key Takeaway:  As you assess APM solutions, formally evaluate the functional design:

• Look for a seamless user experience across a defined and guided work process in the actual solution, get beyond the marketing view which always shows an integrated process
• Evaluate ease of use and determine if the solution has in-app tutorials so users stay effective over time
• Select the iPhone over the Blackberry to ensure adoption, scaling and ROI

Assess your pricing risk

The industrial software market has seen significant business model changes over the last ten years as the adoption of hosted or cloud-based architectures have become more prevalent. The most significant change is the movement away from the actual “purchase” of a software license to a model that looks more like a “rental” program. Here is a summary of the most common software licensing constructs available today:

• Perpetual – Perpetual software licensing is an actual purchase of the software with ownership in “perpetuity.” You purchase the software and own it forever, even if the provider goes out of business.  Perpetual licensing typically presents the greatest risk to an organization purchasing software. Customers pay most fees upfront, and they are not refundable if the project is not successful or if the organization ends up not fully utilizing the solution.
• Subscription – Subscription licensing follows a rental model, where vendors license access to a software solution for a defined period (usually an annual term in enterprise software). Subscriptions offer lower up-front costs to purchasers but tend to have a higher total cost of ownership when licensed for more than four years.  Another benefit of this model is the cost is usually treated as an operating expense and can be cancelled if value is not generated in the subscription term.
• Pay as you go – Licensing software in a “pay as you go model” is like subscription licensing with an additional benefit. You only pay for your actual usage. The solution automatically tracks your system usage (such as the specific number of assets your APM program manages), and you pay a predefined fee for that number of assets under care. This licensing model presents the smallest amount of risk to the APM solution buyer and places it firmly back on the solution provider, where it should be.  In this model, the APM vendor and purchaser have aligned motivations to ensure program success.

The evolution from perpetual to “pay as you go ” licensing models dramatically changes who can consume APM products. It lowers up-front costs, shifts licensing risk from buyer to supplier, and provides a scalable and predictable ROI model that grows with your APM program.

Key Takeaway:  Ensure software licensing structure is a core solution evaluation component:

• Determine how APM solution providers license and charge for their products
• Assess if the licensing model is forcing you to purchase a scope that is not truly align to actual usage of the system
  • Are you being asked to license for a complete facility when you are only concerned about 100 assets?
• Evaluate the licensing model to determine if it drives aligned incentives for both parties

The Asset Performance Management solutions market has evolved significantly over the last 5 years with varied offerings, functional approaches, and technical architectures. When implemented correctly, an APM solution can give an organization a competitive advantage by lowering operational costs, improving asset productivity and reliability, and decreasing overall risk to people and planet. Many industrial organizations can quickly benefit from APM uses cases and this will drive further growth of the APM market. As the market continues to grow, there is a good chance that more APM providers will enter the market further complicating the selection process. The key is selecting the right solution at the right time for your organization and ideally, one that will scale and evolve with your needs.

Before jumping into vendor selection, work with your evaluation team to document your requirements and measurement criteria. Define your primary business objectives, document your use cases, and evaluate potential solutions for rapid user adoption, aligned technical architecture and pricing risk in addition to specific functional requirements. Spending just a little time up front on these basic elements will not only save your team a lot of time but help ensure you are deploying a solution which succeeds in meeting your needs and value targets.

Interested in gaining more insights into the implementation of APM solutions? Itus Digital offers one of the most experienced teams available.

Contact us to gain more insights!

The post Strategies for Selecting APM Solutions appeared first on Itus Digital.

Itus Digital announces its acquisition of nextAPM to support the growing demand for implementing its Asset Performance Management (APM) platform.  This purchase allows Itus Digital to expand client onboarding and provide critical support functions.

Who is nextAPM?

In 2018, a group of APM industry veterans founded nextAPM. Their goal was to provide Asset Performance Management consulting services such as implementing, upgrading, or enhancing APM systems. Since its founding, nextAPM has experienced continued growth.  They have served a deep market need by leveraging their extensive experience in data integration, system configuration, application support, and developing asset strategies and performance dashboards.

“Clients see nextAPM’s experienced team as trusted advisors. And they have a proven record of deploying successful APM projects large and small,” said Joe Nichols, President, Itus Digital. He continued, “As our client base continues to grow, nextAPM’s expertise is a perfect fit to support our rapid onboarding and service model.”  Nichols stated, ” More important, we are excited to have them join the Itus Digital team.”

Our Combined Strength

“We have worked with the Itus Digital team and are impressed with their new APM platform,” said Nate Underwood, Managing Partner, nextAPM.  He continued, “Further, as a combined team, we have a natural fit for our capabilities in data management, systems integration, and application support.” “Consequently, we plan to integrate with the Itus Digital platform fully,”  Underwood explained, “We also see a significant growth opportunity in the APM consulting services market through expansion of our offerings. This shift will accelerate asset strategies and twin models implementation.”  Underwood stated, ” And it is a core component of Itus Digital’s mission and vision. We’re excited to bring our decades of APM implementation experience to Itus Digital. Together we will continue our focus and energy where we believe it needs to be, on our clients.”

About Itus Digital

We are industry veterans maniacally focused on practical asset performance management. Our approach focuses on increasing profit margins while improving asset safety and reliability. By leveraging digital twins, we help companies define their asset strategy, detect emerging threats, mitigate asset risk, and extend asset lifecycle. And, our mission is to protect people, the planet, and profits.

About nextAPM

Founded in 2018, nextAPM helps clients find renewed success in their Asset Performance Management programs. Over the past few years, the APM consulting market has changed. The market has experienced reduced direct support and partnership that clients should demand from their service provider. Our goal is to meet those needs and exceed expectations through integrity and dependable quality.

The post Itus Digital Announces Acquisition of nextAPM appeared first on Itus Digital.

The Platform

The platform includes an extensible Asset Strategy and Digital Twin Library covering 200 of the most common equipment types. This feature enables asset operators and maintainers to rapidly deploy models to support foundational APM use cases. For instance, condition-based maintenance, failure prediction, and operational risk identification are good examples of these cases.

The Itus Digital platform is available as a subscription with on-premises and cloud deployment options. It provides a structured process to manage asset strategies, monitor asset health, and address advisories. Further, it ensures continuous learning of asset management processes and practices.

Best of all, unlike other solutions, customers experience a limited investment risk from the start. An easy pay-as-you-go model allows customers to accelerate the time-to-value. Customers begin with a small number of assets and incrementally grow optimization initiatives as they recognize the value.

The Opportunity

“We founded Itus Digital with the mission to simplify how industrial organizations optimize asset performance, and we have spent the past year doing that,” said Joe Nichols, President, Itus Digital. “Asset Performance Management processes and solutions have become too complex and expensive. As industry veterans, we saw an opportunity to provide industrial companies of every shape and size the ability to optimize maintenance costs and minimize equipment downtime through an innovative APM platform and approach.”

“The APM basics have been lost in the hype surrounding machine learning and analytics,” according to Paula Hollywood, Senior Analyst at ARC Advisory Group. “Asset strategy management is the foundation of APM. Consequently, it is the mechanism for manufacturing organizations to define equipment failure risk, establish maintenance and monitoring plans and ensure alignment of analytic models with engineering principles and business goals. This approach is the key to balancing risk and cost with performance.”

About Itus Digital

We are industry veterans maniacally focused on practical asset performance management. Our approach focuses on increasing profit margins while improving asset safety and reliability. By leveraging digital asset twin technology, we help companies define their asset strategy, detect emerging threats, mitigate asset risk, and extend the asset lifecycle. And, our mission is to protect people, the planet, and profits.

The post Itus Launches Asset Performance Management Solution appeared first on Itus Digital.

What is a digital asset twin, why are they important, and how are they used in driving successful asset performance management programs? Listen to this podcast or read the transcript below as Eddie Amos from Virginia Technology Today interviews Jason Cline and Joe Nichols on this topic and the recent launch of their new company – Itus Digital.

What is a Digital Twin?

Eddie:  Jason and Joe tell us more about ITUS and what is a Digital Twin?

Joe:  Let’s start by defining the concept of a Digital Twin.  A Digital Twin is a virtual representation of a physical object. It is usually modeled in software applications to predict future behavior.  As we are in a very active hurricane season, most of you have probably recently seen examples of Digital Twins.  As Hurricanes form, systems develop forecast models that define the expected path or track of the hurricane.  Weathermen usually refer to these models as spaghetti plots.  A type of digital twin that models hurricanes generate each one of the lines in the spaghetti plot.

In this example, the digital twin knows about the hurricane’s current characteristics, such as wind speed & direction.  Also, the twin monitors other environmental conditions that can influence hurricane behavior– elements such as water temperature and upper-level winds.  And it has historical tracking knowledge on how past hurricanes.  Finally, all this information is then analytically modeled to predict future hurricane behavior.  That’s a Digital Twin!

Our Business

ITUS Digital provides Digital Twin software for industrial manufacturing companies.  Our platform analytically models industrial equipment to predict how it will behave in the future.  We model how the manufacturer designed the equipment and monitor current conditions while the asset is operating. When we detect a potential problem, we provide a warning and direct activities to minimize disruption to the business.

Eddie:  Why are Digital Twins important?

Joe:   Our customers manage large Industrial equipment, which can be complex and very expensive to maintain.  Unexpected failures can impact our customers’ ability to meet their production demands, create very costly repairs, and potentially harm workers or the environment.   We can model equipment behavior, predict potential failures, and avoid expensive accidents with a digital twin.  When an organization applies this approach to large equipment populations, they can save millions of dollars by preventing equipment downtime and ensuring the right maintenance program to avoid equipment failure.

What Makes Itus Digital Different?

Eddie: Several companies in the market claim to have this technology – why are you different?

Jason:  Because of the complexity and cost of developing and managing a digital twin, companies have reserved this approach for very expensive or highly critical equipment.  Think aircraft engines, satellites, and wind turbines.  ITUS has defined a platform that simplifies the process of creating and managing a digital twin.  Furthermore, we have taken advantage of new computing methods and approaches to connect with equipment sensors and data systems.  This technology allows us to offer an affordable platform that customers can use on the most common equipment in many types of industrial facilities.

Now organizations like local water authorities can use digital twins to ensure water delivery to our homes.  For instance, utility companies can monitor transformers in substations and predict potential failures before the lights go out.  And, smaller manufacturing facilities can reduce the amount of money they spend maintaining equipment and improve productivity.  We believe we have built capabilities that can rapidly expand the use of digital twins in the industrial world.

Why Start a New Company?

Eddie:  Both you and Jason are long-time software professionals who played important roles in the success of Meridium.  So why go the startup route instead of working for a more established organization?

Joe:  First, let me state that our experiences at Meridium and later General Electric were amazing.  Consequently, we were able to experience starting and growing a local business with Meridium and then how to deliver software at a global scale with GE.  As our careers progressed, we got further and further from the customers we had enjoyed working so closely with at Meridium.  Also, we were limited in how much we could invest in innovating on new approaches and business models. So, our team had a significant amount of experience and new ideas on solving some huge industrial problems effectively but no easy way to execute them.  The time was right to build a company that allowed us to get back to our roots, work closely with customers, and create innovative software solutions right here in the region.

Where is Itus Digital Heading?

Eddie:  Where do you see ITUS in five years?

Jason:  Having worked in the region over the last 20 years, we believe this is a great place to start and grow a software business.  There is a tremendous amount of local tech talent. The universities continue to provide new graduates knowledgeable in programming, data science, and advanced manufacturing. All of us have witnessed many software companies thrive in the region.  ITUS has recently released our software technology, and the response from the market has been amazing.  We expect to see rapid expansion in Itus Solutions over the next year, providing the foundation for significant company growth.  Our goal is to be a leader in the Industrial Digital twin market in five years, with our global headquarters located right here in Roanoke.

What Has Your RAMP Experience Been Like?

Eddie:  We had Mary Miller on the show recently talking about the new cohort at RAMP.  She mentioned that ITUS was in the program.  What has the RAMP experience been like?

Joe: The RAMP experience has been great. It has challenged us to assess our customers, their problems, and what our solutions need to provide. This process helped shape our solution’s capabilities. Also, the program has given us the vital infrastructure to help us establish our business. For example, its simple but essential elements, such as a great working environment, internet access, and easier access to regional and state-sponsored funding sources, have considerably helped us.

RAMP’s support gave us a foundation on which we are already growing. One of the most significant benefits of RAMP has been the introduction into the regional tech community. I am amazed at the engagement from the local tech community and our program mentors, how supportive they have been, and their desire to see us succeed. The coaching, mentoring, and support we have received have been exceptional, and we are fortunate to be part of the program.

How Can the Roanoke Community Help You?

Eddie:  What help do you need from the local community?

Jason: If you took a map of the region, centered it on Roanoke, and drew a circle with a 100-mile radius, you would be amazed at the number of industrial manufacturing organizations that reside here.  Water treatment facilities, power utilities, paper mills, chemical plants, to name a few.  Many of these facilities can most likely take advantage of our solutions to reduce costs and prevent equipment failures.  We would be interested in working with them to see how we can help them save money and be more predictable in their operation.  The community can help by getting the word out that a local company is providing innovative solutions to help them manage their assets.

Eddie:  Jason and Joe, unfortunately, we are out of time.  Thank you for updating us on RAMP and ITUS Digital.  I want to thank our Executive Producer Joey Self for making the show possible.  So, until next week, I’m Eddie Amos, and this is Virginia Technology.

The post Podcast: Why Are Digital Asset Twins Important? appeared first on Itus Digital.

Where We Began

One of the core motivators we established when launching Itus Digital was to get back to our roots and build solutions through customer and market-driven innovation. We have not been disappointed! Your level of engagement, desire to enhance existing APM processes, and “ideation” have genuinely been invigorating. You provided the Itus team with great requirements for our initial product release and beyond. Looking back on the workshops, functional reviews, and discussions, several key themes have emerged. So we thought it would be helpful to share a few with everyone.

Theme #1 – Traditional Asset Performance Management is Too Hard

APM requires a systematic method to integrate people, processes, and technologies. For many, this requirement represents a significant change in how engineering, maintenance, and operations functions work day to day. It is likely not new news to most who are reading this blog. What we uncovered when we asked “why it is so hard” will be. We were expecting answers centered around people or organizational change. And while these are still elements to manage, the responses focused on gaps in existing solutions. The notable concerns we discovered were:

• APM process definitions that are too broad or narrow and limit APM’s effectiveness,
• Software portfolios stitched together through acquisitions and are loosely integrated, hard-to-use, and challenging to maintain,
• Implementation approaches centered on enterprise-wide, “big bang” deployment models that are risky, difficult, and costly.

Theme #2 – The Asset Strategy is (Still) the Critical Value Driver

Over the past years,  the buzz and energy in the APM space have focused on emerging computing technologies. These technologies consist of various mathematical algorithms and 3D visualizations. They serve to enhance asset performance. However, to drive recognizable business value, these technologies need to anchor to robust asset strategies. As we worked with APM experts, they reminded us that success depends on defining, managing, and governing asset strategies. The industry has indeed made great strides in defining those strategies using RCM, FMEA, RBI, and HAZOP methodologies. But, it still leaves you struggling with strategy implementation and governance across your asset population. Simply put, the ability to assess critical assets, define how they fail, determine mitigations, actively manage emerging threats, and document remediations are the foundation for everything else.

Theme #3 – Enable the Equipment Experts

If you agree with Theme #2, you will probably agree that enabling the equipment and operations experts is another critical element for APM success. After all, an asset strategy is the domain expertise curated to an applied structure and process that you can scale across asset populations. As we worked on our initial product requirements, the COVID19 pandemic forced us all to adjust to a new normal. Further, it pushed several industries into curtailing production, which unfortunately led to the loss of experienced workers and critical equipment knowledge. In addition, many had and still have essential worker protocols that limit site personnel and create a “virtual gap” between the front line and back-office workers.

These new dynamics have further exposed weaknesses in how front-line operators and engineering teams manage assets, share knowledge, collaborate on best practices, and optimize strategies. The feedback here was clear. APM solutions must focus on enabling the equipment and operations experts and capturing their unique equipment knowledge in asset strategies. Ease of use, accessibility from anywhere, collaboration, and documentation of learnings in a structured manner which are easy to leverage, have become essential success factors in Asset Performance Management.

Reflecting on the last ten months, we are fortunate to have a network and community willing to engage and share their expertise. Their generosity has shaped our initial product release and future innovations. Not only have we thoroughly enjoyed innovating with the market again, we successfully validated one of our core beliefs when we founded Itus Digital. APM is much more difficult and complex than it needs to be, and we can fix that! Thanks again to everyone who participated.

To Learn More

If you would like to see some feedback on our pre-release product, follow the link below to see what David Metcalfe, CEO at Verdantix, has to say.  You can also reach out directly to me – [email protected] and I can show you what is coming on September 30th!

Read Verdantix Review on Itus Digital

The post Innovating Again! The driving motivations for the Itus team appeared first on Itus Digital.