This Netflix series showcases the amazing engineering effort behind the top performing racecars in the world.  But why are we discussing F1 racecars in a blog about AI (Artificial Intelligence)?  This Netflix series tells a story about decisions, how they affect outcomes for every race, and how the use of data and information affects the quality of those decisions.

The current state of AI is like Formula One racecars. These machines depend on advanced technology and specialized skills.  They require a highly technical engineering team coordinating their efforts to ensure the racecar goes around each lap of the track, in the least amount of time possible.  This is a process of continually compounding the team’s collective understanding of their cars, the tracks they race on, their competitors, and the conditions they need to adjust for before and during the event.

Harnessing the power of AI is similar to harnessing the power of an F1 race car.  The team of engineers, subject matter experts, and data scientists precisely coordinate their efforts to use information they have (historical data) to produce information they don’t have (predictions).  The quality of these predictions directly enables the quality of decisions made by heads of the team.  At this elite level, the aggregation of marginal improvements is what makes the difference between a podium finish or not scoring any points at all.  This elite and prestigious level of performance can attract and retain the top available talent to be part of this team.

Similarly, to harness the power of AI, a team of data scientists, subject matter experts, and specialized software engineers are required.  This means that only those organizations that can attract and dedicate such a team are going to reap the rewards promised by AI. At least for now.

When you consider the application of AI within industrial facilities, building a team to drive successful AI projects can be even more challenging.  A skills gap due to an aging and retiring workforce, the need to collaborate across many organizational functions such as operations, maintenance and IT can create significant burden on already stretched resources.  One of the keys to being able to successfully leverage AI at scale, is to put the power directly into the hands of equipment and process experts.  Yes, AI as an enabling technology provides great productivity gains by rapidly assessing large volumes of data on a continuous basis, but the true value drivers are not algorithms.  It’s the people and their knowledge of how the production process operates, how equipment can fail, the symptoms that indicate potential failure and most importantly what action to take when anomalies are detected.

In the APM space, we have seen AI applied to the most critical and complex assets, yet these represent a very small percentage of the total assets in an industrial facility.  If we could turbo charge the process by enabling equipment experts to directly build anomaly detection models at a faster rate, we can unlock much more value across a broader set of challenges related to the risk, cost, and performance of assets.

It wasn’t the sophisticated engineering that went into the car that brought about the era of motorized transportation. It was Henry Ford’s assembly line production which brought the car to the masses.  AI is going to continue to advance and will do so at an increasing rate.  But until we solve the challenges of practicality and availability, we will be dependent on precision-coordinated teams of highly skilled experts.

Look out for our next post where we will offer some insights into approaches to fully harness the power of AI.

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

The industrial market has exploded with new sensors and solutions which can quickly and affordably monitor vibration in rotating machinery and predict potential failure.  From wireless sensors to handheld devices to completely outsourced condition monitoring services, you now have many options when looking to mitigate one of the most common failure risks in rotating machinery, high vibration.

In addition, we are seeing industrial organizations increase their adoption of standards as they can provide reliability and maintenance teams with the opportunity to take advantage of expert perspectives and years of learning at a very low cost.  These technical standards, in particular, provide a solid starting point for organizations looking to implement basic condition monitoring.

As a technology provider to the industrial market, Itus Digital leverages standards as they allow our customers to quickly develop best practices built on what experts across industrial sectors, have developed over the years.  Our latest work here is the inclusion of ISO 10816 compliance elements into the Itus Asset Twin Library.

This article offers insights into the ISO 10816 Standard and provides a walk through on how Itus Asset Twin capabilities enable the requirements of this standard.  As a technical footnote to this article, we will limit our discussion to the use of overall vibration readings as defined in the ISO Standard.  We will not be addressing the use of detailed vibration data (such as waveform) which are also a key aspect of vibration data diagnostic activities.

What is the ISO 10816 Standard?

The ISO 10816 standard provides guidelines to evaluate the severity of overall vibration levels of machinery taken from sensors or handheld devices.  The standard has seven ‘parts’ which offer guidance for a variety of machine designs and applications.  For the audience reading this blog, ISO 10816-3 (Part 3) will be of particular interest as it specifically applies to industrial machines with nominal power above 15kW and speeds between 120 and 15,000 revolutions per minute (RPM).  Typical machines included in this standard include pumps, fans, motors, blowers, and rotary compressors which can often be significant drivers of repair cost and downtime.

One of the most valuable aspects of ISO 10816-3 are guidelines on how best to interpret the results from vibration readings taken on non-rotating parts of the machine (such as the housing).  The chart below provides a concise summary of how to interpret the severity of overall vibration readings based upon the machine group and foundation type.

Once readings are taken, the standard can be applied to evaluate if further action should be taken such as a more thorough diagnostic, operator check or specific maintenance activity to prevent equipment damage.  It can also be a trigger to check the execution of standard preventative maintenance activities.

Applying the ISO 10816 Standard

Here is a common situation we see with our customers.

• Vibration monitoring programs are recognized as a valuable method for early detection of failure risk across various equipment types to prevent catastrophic failure, unnecessary repair costs and production downtime.
• Investments in continuous vibration monitoring solutions have been made in highly critical equipment which represent a small percentage of all rotating equipment.
• Expansion of vibration monitoring programs is desired on medium and low criticality equipment leveraging low-cost sensors and handheld devices.

The key challenge they face is how to leverage these new equipment condition data streams to their fullest potential without putting more burden on already stretched resources?  Additionally, how to implement these new data streams without creating another siloed data source.

This is where enabling technology such as the Itus Asset Twin can be leveraged to develop best practices such as those that can be enabled with ISO 10816.  With Itus technology, not only can the prescribed decision criteria be automated, but the results of the evaluation can also be driven into broader reliability and maintenance processes in your organization, realizing more value from your assets:

• Centralized management of all vibration monitoring advisories from your various sensors, field data collectors, and solutions to drive collaboration among your equipment experts.
• Integration of additional equipment conditions from other systems such as thermography, oil analysis, and process monitoring to enable broader failure mode coverage and overall asset health scoring.
• Automatic generation of requests in your maintenance management to drive appropriate follow up or repairs as needed.

Let’s walk through a quick example of how ISO 10816-3 requirements are enabled with an Itus Asset Twin.  Below is the configuration of the standard’s requirement for Machine Group 1 on a flexible foundation.

In this configuration, the Itus Asset Twin is utilized as a screening tool for machinery vibration to identify situations which require more diagnostic tests or the initiation of an inspect/repair activity.  Here are the key aspects of the Protection implementation.

• Tag Aliases – The incoming vibration data stream from sensors, handheld devices or historians. This template is set up for readings with units of in/sec but mm/s is also supported.
• Thresholds – The definition for the analytical evaluation of the vibration readings. This example is very straightforward with two threshold evaluations, .28 in/sec and .44 in/sec.
• Actions – The prescribed action which will be recommended when the Threshold condition has been met. In this case, when the ISO defined Threshold of .28 in/sec is met we recommend performing a more detailed vibration diagnostic at a high priority within the next 3 days.  If the vibration readings exceed .44 in/sec, we recommend an operations/maintenance intervention as the standard indicates the machine may experience damage.

This example of an Asset Twin demonstrates how ISO 10816-3 requirements can be technically enabled ‘in kind’ but you can easily adjust this definition based upon your specific operating experience and context as needed for your business needs.

Itus Libraries quickly enable the requirements of this standard

Our use case for this article has focused on one specific aspect of ISO 10816, Part 3, more specifically, machine group 1 with a flexible Foundation.  Beyond this example, subscribers to the Itus solution have access to every scenario covered in Part 1 and 3 of ISO 10816 through the Itus Asset Twin Library.  For subscribers of the Itus solution, the requirements of the ISO standard can be enabled for a piece of equipment within minutes by accessing the library, selecting the appropriate machine group and foundation type, and then applying the model to the vibration data feeds from sensors, devices or monitoring solutions.

The Itus Asset Twin Library also provides Asset Twin templates for the most common failure modes across over 200 types of industrial equipment.  Leveraging this library allows maintenance and reliability teams to standardize their asset strategies and drive a consistent approach to preventive maintenance activities, condition monitoring and evaluation of equipment health.

Minimize failure risks by enabling standards requirements with Itus technology

Vibration monitoring is a very effective approach for early detection of potential failure in rotating machinery.  Historically, applications of vibration monitoring have been limited to highly critical equipment due to the cost and required resources.

Advancements in sensor technology and handheld data collection have dramatically reduced the cost of assessing machinery vibration which now opens the opportunity to expand usage for medium and low criticality equipment.

The downside is organizations are now drowning in sensor data and need simple methods to analyze and interpret that data from a myriad of equipment and condition data sources.  Fortunately, ISO 10816 provides a practical model to assess the severity of vibration readings and should be considered when implementing a vibration monitoring program.

Evaluation of vibration data and compliance to the ISO 10816 standard is just one simple use case of how the Itus APM solution can be used to drive optimal asset strategies to reduce maintenance cost and lower equipment failure rates.  If you would like to learn more about our approach and solution, please connect with us here.

The post Rapid Compliance to ISO 10816 Vibration Monitoring Standard appeared first on Itus Digital.

The problem with this simple approach is context.  Can it really be said with confidence that a rare but catastrophic event is as low a risk as a negligible but frequent event?  No, because the matrix above lacks meaningful context.  I’ve learned that different risk contexts require different risk visualizations.

I used to think that a risk matrix was the ultimate tool in evaluating criticality and risk for asset management.   I used to think that all categories of risk could be evaluated congruently in a common risk matrix across an organization.  In my past, I even led an entire product line of asset management tools upon this principle, thus forcing many subject matter experts in asset reliability, mechanical integrity, and process safety to attempt to align to a common risk matrix.  Those were some very spirited discussions usually turning into debates, with alignment not always the result.  As William Blake once said, “The fool who persists in his folly will become wise.”  Indeed, I have learned this lesson, with age and experience comes wisdom and humility.

In my defense, the risk matrix is a very good tool.  It provides a visual representation to communicate risk concepts simply while providing a framework for prioritization.  It can be customized for specific organizations with specific definitions of risk.  It is no wonder industry so quickly wants to adopt it to understand risk across our organizations.  Unfortunately, it is not a good tool for comprehensive risk quantification, nor its mitigation.  It is limited to discrete ranges of probability and consequence.  It is often over-simplified, subjectively qualitative and does not consider how risk changes over time.

Let’s consider a couple of common categories of risk in asset intensive industry – Safety and Operations.

First and foremost, let’s discuss Safety risks which are focused on consequences from minor injury up to very severe injury, including fatality.  Naturally, the protection of people is of utmost importance.  A safety risk assessment considers events that could lead to personnel harm or injury.  For example, could the event occur, can the event cause a chemical leak and/or fire, and is there a possibility that someone could be exposed to the leak or fire.  When determining a risk matrix, the consequence categories can scale reasonably from “minor first aid” to “fatality”.  But in this context our probability scale actually factors in multiple probabilities for every row (i.e. event, leak, fire, and exposure).  Thus, the matrix probability could exponentially scale from an occurrence of once per year to an occurrence of once every 10,000 years.  It can be difficult to practically understand something occurring only once every 10,000 years, but this makes total sense to a safety engineer or likewise to a mechanical integrity engineer. They are responsible for mitigating severe consequences that should never happen which include fatality, loss of containment, fire, and hazards to the environment.

Safety and integrity engineers use methods of risk mitigation dictated by process safety management standards such as Hazards Analysis, Safety Integrity Systems (SIS), Layers of Protection Analysis (LOPA), Risk Based Inspection (RBI) as well as compliance to jurisdictional standards.  These methods are relatively complex methodologies that drive recommended and mandatory actions as part of an overall safety and integrity plan.   While you can use a risk matrix as a visual representation of the result these methods, you cannot easily use the same risk matrix to represent what I will next call Operational risk.

For the context of this article, I define Operational risk as the risk of unplanned production downtime and associated costs, which may include maintenance, overtime, lost production, rework, and scrap.  Often unplanned production downtime is caused by asset failures.  Reliability engineers seek to mitigate the risk of these asset failures, especially those that impact production.  There is typically an immediate cause and effect of a critical production asset failure.  The asset fails and production is immediately impacted.  Imagine assets that failed every 10,000 years, reliability engineers would not be needed to determine how to improve those failure rates!  That is the difference of probability scale for a reliability engineer.  They are dealing with asset failure consequences of production loss and costs which could occur multiple times a year up to once every several years.  It is a completely different context to that of the Safety or Integrity engineer.

Thus, I have learned that a risk matrix is just a defined set of intersections of probability and consequence chosen to represent a category or context for risk.  As described above, contexts for Safety and Operations are different, thus it is reasonable for risk matrices to differ.  Remember it is a tool for visualization and prioritization more than assessment and mitigation.

Back to the reliability engineer focused on Operational risks, there is another key aspect to consider for risk assessment that I call “mission time”.  The mission time for an asset or a group of assets, such as a production unit, can be thought as the time between major shutdown events where restorative maintenance or asset replacement is performed.   If a risk matrix is used to assess risk in this context, the assessment will be limited to just the ranges defined for each intersection and it will not have any context of risk over time.

For the reliability engineer it is best to assess risk with a more quantitative probability estimate over a mission time multiplied by an estimate of the overall cost of the potential failure to the business.  This allows the engineer to focus improvement efforts on the assets that will return the most value to the business.  These include efforts leveraging reliability methodologies such as failure mode-based strategy development, maintenance optimization, reliability modeling, asset health monitoring and advanced analytics.

A better risk assessment method is to estimate a failure probability quantitatively for the asset.  This can easily be done with an estimate of failure rate experienced (or expected) combined with a desired mission time.  A simple calculation can be used to represent the probability over time such as a random Weibull or exponential distribution.   Plotting the distribution will provide a simple visual over time.

Consequence can also be estimated based on the overall cost of failure, which would include all costs including repair costs and production losses.  Combining this cost-based consequence with our failure probability, overall risk for our mission time can be easily calculated.

Now if you have a set of production assets to evaluate for a system or unit, you could compare them across a mission time between shutdowns.  A comparison might look like the chart below; note the riskiest asset is not always the one to focus on during the mission time.

Assets with a higher probability of failure but lower cost, might need more attention during the mission time than an Asset with a much lower probability of failure but much higher cost.  This is a risk comparison tool to evaluate assets in a specific context over a specific time.  Also, by estimating failure probability and cost of failure, you are better equipped to leverage other reliability methods to determine the best course of action to improve assets.

Now for those of you that have assessed asset criticality with a qualitative risk matrix approach, take heart because all is not lost.  You are a step ahead.  Consider your risk matrix intersection an initial assessment that can be leveraged in the more detailed assessment above.  You can simply use your ranges as estimates of failure rates and costs to plug into the same formulae and then adjust as needed.

With this comparison, the next steps can be taken to mitigate risk and capture its associated value to the organization.  There are several mitigation methods to improve an asset’s performance or reduce the cost of unplanned failures.  These include addressing problems such as:

• The asset is unreliable with low inherent MTBF
• My asset strategy does not cover all failure modes
• My asset strategy does not effectively cover failure modes
• My asset strategy interval is too low (doing too much)
• My asset strategy interval is too high (doing too little)
• My asset strategy is not being executed properly
• My asset strategy is not addressing the root cause of the failures

In summary, remember an initial risk assessment is just the starting point to any form of active risk management.  You must use the assessment to drive prioritization and improvement of asset performance in support of the operation of your business.  A risk matrix is a tool that can be used, but we believe a quantitative approach is better.  The better the assessment, the better decisions you will make regarding risk mitigation.

If you want to take your first, or next, steps to assess operational risk, we can help!

Register for our FREE Asset Risk Analyzer here and start managing and mitigating your risk in minutes!

The post Confessions of an Old School Risk Matrix Zealot appeared first on Itus Digital.

Mean Time Between Failure (MTBF) Basics

The purpose of this blog is to introduce the concept of calculating Mean Time Between Failure (MTBF) and offer our FREE tool to calculate MTBF directly from EAM solutions such as SAP and Maximo.  When it comes to estimating how often an asset fails, there are many layers of sophistication from preparing data, calculating values, and then interpreting results.  The level of analysis rigor to apply in an MTBF calculation is driven by the specific use case, an organization’s maturity, and the required level of accuracy in the results. We are just scratching the surface on this topic in this blog so please reach out to us here if you would like to learn about more sophisticated approaches to managing equipment failure rates.

Want to skip the MTBF intro and start getting reliability insights right away with our FREE MTBF Calculator?  Scroll to the bottom and download!

Managing the Mean Time Between Failures (MTBF) of your equipment is one of the most basic yet effective approaches to measure performance, identify bad actors and drive reliability improvement programs.  Typically represented in years, months, or days, MTBF measures the average length of time that an asset has operated without interruption.  The metric provides key insights to help drive tactics to ensure assets are operating to their fullest potential at the optimal cost profile and should be part of any maintenance and reliability professional’s toolkit.

Unfortunately, the MTBF calculation is not always well understood and while the mathematical equation is simple, many still struggle to gain visibility to their equipment failure rates.  We see the MTBF measure as a cornerstone to driving equipment reliability improvements and are often asked to help organizations determine the best approach to calculate the failure rate of their equipment.  To help customers gain visibility to their failure rates, we offer several approaches from simple to complex depending on input data quality, required accuracy and level of calculation sophistication.  This blog introduces our simplest approach (which is also FREE) to quickly calculate equipment MTBF utilizing a Microsoft Excel template.

Data Inputs to Calculate MTBF

The most common source of data for calculating MTBF is work order data from a computerized maintenance management system (CMMS) or Enterprise Asset Management (EAM) system.  Work orders are typically utilized to plan repair activities, order replacement parts and source labor which typically provides a good record of when a failure has occurred.  While the work order might be the most common approach to determining when an asset has failed, they can present calculation challenges depending on the completeness of the information supplied during the order closure process.  The subject of work order data quality is, and will most likely be forever, debated amongst maintenance and reliability professionals.  Some will take the position that the data must be perfect to be utilized in an MTBF calculation which unfortunately will prevent them from utilizing this very valuable measurement of equipment performance.  “Perfect is the enemy of good” is an aphorism used to describe this engineering perspective, an insistence on perfection often prevents implementation of good improvements.  The good news is that our experience in leveraging work order data for reliability measurements over the last 20 years, tells us that many of you now have work order data that is appropriate for the use case most reliability programs need today:  an estimation of rate of failure to make better reliability and maintenance investment decisions.

Other technological advancements, such as the rapid expansion of sensors and monitoring systems, are making it more common to automatically identify and document equipment failures with greater accuracy.  For equipment that is already monitored by a control system or process historians, you may already have direct access to the running state of the machine which can help you determine when an asset has failed in addition to automatically calculating run times which are key inputs to MTBF calculations.  For assets that are not actively monitored, it is now possible to install affordable and non-invasive sensors which can monitor conditions (such as energy usage) to help automatically detect when an asset is not running and document each time the asset starts or stops.

MTBF Calculation Approaches

In its simplest form, MTBF is calculated by taking the total time an asset is running and dividing it by the number of failures that happened over that same period of time or:

MTBF = Running time between installation date and last failure / # of Failures

Running time:  This is the total amount of time an asset is running over a specific time period.  Note that if you have assets that are not operating 24×7, running time is not represented by calendar time.

# of Failures:  This is the total number of equipment failures or breakdowns which have occurred over a specific time period.

Here is a very simple example.  Take a cooling water pump that continuously operates at a manufacturing facility.  The pump does not have a standby spare, and we want to calculate its specific MTBF over a 5-year period.  The asset has experienced 3 failures over the 5 years.

Running Time = 5 years

# Of Failures = 4

MTBF = 1.25 Years (5 years / 4 failures)

For this blog, the focus is on a simple method to calculate MTBF directly from work order data.  If you have more descriptive data sets and supporting calculation tools, you may want to utilize a Weibull Distribution for your failure rate analysis.  A Weibull analysis will provide a ‘beta’ value (or shape factor) which offers additional insights on the failure pattern (infant mortality, random, wear-out) associated with the MTBF value.  If you are interested in learning more about Weibull analysis, we highly recommend this tutorial from our partners at Prelical – it’s a great introduction to utilizing this mathematical technique for estimating failure rates.

MTBF is calculated – Now What?

With MTBF calculated, there are many ways to utilize this information to make better reliability and maintenance decisions, in fact too many to offer a complete list in this blog.  Here are a couple of common use case’s reliability professionals are driving once they have calculated MTBF on their equipment:

Identify poor performing equipment.  Evaluate the performance your equipment by comparing against similar equipment in a similar operating context.  In the previous example, MTBF was estimated at 1.25 years for the cooling water pump.  With this information it is now possible to benchmark it’s specific performance against industry standards such as the Itus Asset Twin Library, OREDA database (Oil and Gas Industry Specific) or OEM specific performance guarantees.  Once compared, it is easy to identify bad actors in your equipment population, evaluate reasons for the poor performance and establish a plan to improve the asset strategy.

Measuring the effectiveness of your reliability initiatives.  While MTBF is a lagging indicator, it is useful in assessing the effectiveness of a reliability and maintenance improvement program.  The pump example we reviewed demonstrates how we can measure the specific performance of one asset over time.  If you have leveraged a strategy development process such as a Failure Modes and Effects (FMEA) analysis and developed an optimized strategy or preventative maintenance plan for an equipment class (i.e., centrifugal pumps), the MTBF calculation can also be used to measure the effectiveness of that strategy.  As you implement your maintenance and monitoring strategy, the equipment class should become more reliable over time and MTBF should increase.

Analyzing future operational risk.  For equipment failures which directly impact production, MTBF can be a key input to evaluating future operational risk.  Keeping with our simple cooling water pump example which has an MTBF of 1.66 years.  If this pump is needed to run for the next 2 years to meet projected demand, it is highly likely it will experience at least one failure during that run cycle.  With a calculated MTBF value, it is possible to utilize solutions such as Asset Risk Analyzer to determine future operational risk, communicate potential downtime implications to management and justify investment in a reliability improvement initiative.

MTBF provides a practical approach to measure the current and historical failure rates for industrial equipment.  With visibility to MTBF information, maintenance and reliability professionals can make wise decisions on where to focus efforts and investments to meet business objectives.  Historically, the MTBF measurement has been considered an advanced reliability technique but is much more common today as organizations have implemented foundational asset management systems and processes.

If you are not currently utilizing MTBF to enhance your decision making, consider using our FREE Spreadsheet to get started.  Register below to get instant access to our template.

Download MTBF Calculator

Our calculator will walk you through the entire process of getting data from your EAM system, how to further classify records as breakdowns vs preventative maintenance, as well as specifics on how the calculations work and what to do with the results.  Register below and get instant access to start actively managing your equipment failure rates!

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Roanoke, Va. – August 1^st, 2022 – Itus Digital today announced the general availability of Asset Risk Analyzer, a free and easy to use solution to assess industrial asset risk and criticality.  The solution analyzes maintenance history, calculates operational risk, and identifies assets not performing to industry benchmarks.

Designed for risk, reliability and maintenance professionals, the solution analyzes data across large populations of assets and calculates the operational risk due to equipment failure.  The insights generated help asset managers prioritize and justify improvement efforts by answering key questions such as:

• Is my current asset performance aligned with industry norms?
• Which assets are likely to fail during my next planning or production cycle?
• What assets will drive the biggest impact if performance is improved?

Asset Risk Analyzer is an ideal starting point for any reliability improvement initiative by identifying critical assets which are candidates for optimized asset strategies.  In a seamless workflow, Asset Managers can quantify their operational risk and deploy mitigation strategies through failure prediction, predictive maintenance, and asset health management use cases.

“Ensuring industrial assets are productive, safe and efficient through optimal asset strategies is core to our mission, “said Joe Nichols, President, Itus Digital.  “By offering this capability free, it is now possible for anyone to quickly pinpoint risky assets and implement asset strategies to ensure optimal performance.”

The solution is immediately available to asset operators, service providers and OEMs looking to start or reenergize stalled asset performance management initiatives.  Accessible from any internet connected device at: www.assetriskanalyzer.com there is no software to download, and registration can be completed in minutes.

“Operations as well as maintenance managers are under increasing pressure to maximize asset availability and production while minimizing costs and risks,” said Malavika Tohani, Verdantix Research Director.  “With Asset Risk Analyzer, firms can optimize their asset maintenance planning strategies by prioritizing which maintenance tasks need to be carried out in what order as the solution quickly analyses the work history to identify the riskiest as well as the most critical assets; thereby improving asset reliability, efficiency and environmental performance.”

About Itus Digital

Itus Digital is a team of industrial software veterans maniacally focused on optimizing the performance of industrial assets by fusing apps, industrial engineering expertise and analytics within simple to use and scalable software solutions.

The post Itus Launches New Solution, Asset Risk Analyzer™ 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!

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The term “digital twin” is seemingly used today to cover any possible use case where there is a digital representation of a physical object.  In the industrial space alone the term is used to describe digital capabilities which can include 3D modeling, virtualization, augmented reality, and machine learning analytics.  For the management of industrial assets, the term Asset Twins better describes the overall strategy and plan to optimize the performance of assets over its lifespan.  As a digital, virtual representation of a physical asset, the Asset Twin includes:

• Design characteristics
• Failure Modes and consequences to the business
• Expected mission time
• Preventative Maintenance Plan
• Inspection & Monitoring Plan
• Historical failure rates and costs

More importantly, it defines the key components for maintenance, reliability, production, and operations professionals to manage the asset strategy and monitor asset performance in the context of its use. Going one step further, asset twins constantly assess failure risk, proactively identify emerging threats and automatically prescribe corrective actions.

An Asset Twin focuses on operationalizing your asset strategy, both in terms of monitored conditions and activities performed.  It brings your asset strategy (or maintenance tactics) together seamlessly with asset health monitoring of conditions and allows you to strategically manage industrial assets to ensure optimal performance at an ideal cost profile.

The Itus Digital Asset Twin Technology

Our Asset Twin actively monitors the asset and advises you of emerging failure risk and associated business consequences through three core constructs:

• The Asset and its business objectives.  We start with the virtual definition of the Asset and provide a classification, mission time, and typical failure rate.  Armed with this knowledge, you can assess the overall failure risk in the context of operational consequences. Further, you can gauge the financial impact of the potential failure in terms of lost production, repairs, and other costs.

• Dominant Failure Modes that point to the asset’s vulnerabilities. Our technology provides a definition of dominant Failure Mode definitions for the asset and presents it clearly and straightforwardly.  Also, for each Failure Mode, it gives the best approach to mitigate the failure risk.  By defining the conditions to monitor and best maintenance activities, you can protect the asset and extend its value and lifecycle.

• Protections employed to guard against the failure modes occurring unexpectedly.  Protections are more than just a statement of intent to perform an activity or collect data; they are essentially the operationalization of the strategy.  Protections include active monitoring of data with analytics that trigger prescriptive and proactive action to mitigate emerging threats.  Two primary forms of protection can manage failure risk:  condition-based and activity-based.

Together, the (1) Asset, (2) Failure Modes, and (3) Protections provide an active digital model of current condition, risk, and prescriptive remediation as needed.  This Asset Twin provides the “operationalization” of the asset strategy to ensure business objectives are not interrupted.  It virtually models your asset, improves equipment performance, and optimizes maintenance without the cost and complexity associated with a Digital Twin.  We based our approach on core reliability engineering principles that our platform applies to assets across all levels of criticality and consumable by industrial organizations of all sizes and maturity.

Learn more about how Asset Twins drive the management of Asset Strategies.

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There is no better time for a new approach to asset performance management. Why?

The fundamental goal of asset performance management is improved operational performance and productivity. Overall, it serves the business purpose of mitigating risks, reducing costs, and maximizing margins. Most production and infrastructure assets have a life span of 25+ years. So, organizations need to make the best financial decision at every stage of an asset’s life. Leveraging asset performance management, you can accurately determine the maintenance cycle and necessary investment levels. As a result, you not only extend the asset’s useful lifetime but its end-of-life value.

An effective APM platform should translate your asset strategy from a written plan into a dynamic digital reality. It needs to include an optimized set of monitored conditions and specific maintenance actions. And a successful asset strategy must ensure that you stay in front of unplanned failures and maintenance costs. Toward that end, asset performance management centers on asset strategy, implementation, and ‘active’ management and governance.

Itus offers a purpose built approach to APM

Itus Digital offers a seamless process to manage and govern the asset strategy lifecycle.  We believe that industry experience combined with a practical business process is critical in developing and maintaining a successful asset strategy. Further, our process engages and integrates all of the key asset stakeholders such as engineering, reliability, maintenance and operations.  The business process connects the data and people to drive the active management of asset strategies over the equipment’s lifecycle:

• Start by assessing the risk of credible failure modes on asset that impact business objectives.
• Develop strategies to protect asset from potential failure risk through mitigation activities such as PM’s, Inspections, Monitoring and Overhauls.
• Leverage existing data to monitor asset and operational conditions and automatically prescribe mitigations when threats emerge.
• Ensure appropriate corrective action is take to minimize risk to the business and ensure learnings drive optimization of asset strategy.

Our method guides you through the design, implementation, and management of asset strategies, including digital Asset Twins.  We know that the asset twin must be accessible across the organization and for the asset’s entire life span.  Further, we believe what can’t or doesn’t get measured doesn’t get improved. That is why we designed our process to enable maintenance, reliability, and operations professionals to easily measure their APM program’s effectiveness. By helping you drive a continuous asset optimization cycle, our model provides increased equipment reliability while lowering operating costs.

Overall, with our approach, you can balance maintenance activities and asset lifecycles within budget constraints. The result is a dramatic drop in unexpected and costly failures and asset downtime. There is no better time for a new approach to asset performance management.

See how Itus Digital has purpose built an APM solution focused on optimizing your asset performance!

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