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MARCON 2026 Presentation and Conference Review

Thu, 26 Mar 2026 13:30:04 GMT

2026 and Beyond – How to Transform Your Reliability Journey with Artificial Intelligence (AI), Machine Learning (ML), and Other Transformational Tools

MARCON 2026 just completed on March 5, 2026. It was the first time since pre-Covid that I had attended MARCON. No real reason for not attending, as I have always enjoyed this conference. It was good to be back. Kim Kallstrom, Klaus Blache, and the team from the University of Tennessee Reliability and Maintenance Center (RMC) always put on a great event. Having served on the conference team at Reliabilityweb.com for several events, I have seen the good, the bad, and the ugly of conference planning and know the hard work involved so attendees feel they are getting good value for the time and money spent to attend.

The initial abstract that I submitted was for a one-hour presentation, Kim contacted me right away to gauge my willingness to expand my presentation into a three-and-a-half hour workshop. Given the scope of the material that I hoped to cover, I eagerly agreed and asked Michael Guns to be a co-presenter. Our workshop was titled 2026 and Beyond – How to Transform Your Reliability Journey with Artificial Intelligence (AI), Machine Learning (ML), and Other Transformational Tools. A few weeks before the conference there was an email from Kim asking if we could present to a larger group than first planned. We assured her that we could and the session was moved into a larger room for the presentation. Good problem to have. Seventy-eight conference goers committed three-and-a-half hours to interact and hear what we had to say.

The agenda was as follows:

Introduction: slides 1-10
Maintenance & Reliability Building Blocks: slides 11-30
AI Foundation and Fundamentals: slides 31-48
Tools & Resources: slides 49-59
Reliability & AI Journey: slides 60-65
The Case Study: slides 66-77
What’s Next: slides 78-83
Discussion and Q&A: slides 84-92

The first item on the agenda is the Introduction, and the normal items are there – presenter picture and info; agenda; and course description. Prior to the conference we conducted a survey of the participants registering for the workshop and got over 50% involvement. The survey was a quick quiz on AI fundamentals and gauged the participants’ AI experience and plans/budget/etc. for 2026. Quiz and results were reviewed in the Introduction section of the workshop and are posted in Chart #1. The results showed on average that:

Overall quiz score was 55%
Self-assessed AI knowledge level was between low and average
Average use of AI in the six choices given was 39%
Questions on AI training, current projects, budget, and long-term plan were all closer aligned to a “No” answer

As a lead into the presentation material, we review the Piper Alpha oil and gas platform fire incident to show the effects of poor maintenance and safety practices.

The next section is a nineteen-slide Reliability and Maintenance building blocks segment. The first item covered is “Where are you on your reliability, maintenance, and AI journey?” and what is needed to move forward. Other things presented/reviewed are:

Defining success
Reasons to proceed and not to proceed
Organizational considerations
Standards, framework, benchmarking, and assessments
Technology
Change management
Communications
Data, data, data
Governance
Process, procedures, checklists
And more

These items are all foundational pieces that need to be in place, with a clear vision and strategy, to prepare for progress into AI and other advanced technologies desired for implementation.

AI Foundation and Fundamentals is an eighteen-slide section that starts with the history and evolution of AI. It then proceeds into current statistics on AI proficiency and the roadblocks to implementing AI projects, with suggestions on how to overcome these pitfalls. Proficiency is limited and the impeding elements include poor data quality (<30% data readiness), unreasonable AI project deliverables, and poor overall AI plan/approach/training. There was a deep dive of AI and ML applications. Definitions, limitations, examples, and subtopics are examined. AI is further broken down to traditional, predictive, generative, and agentic. That list is not complete but was included to show the most used applications and the complexity associated with AI. Speaking the same language on this topic will be important as forward progress continues. AI is examined from the standpoint of weaknesses and mistakes (hallucinations). Additionally, built in bias, stereotypes, and ethical issues are explored.

This final section will be encompassed by breaking down the remaining five areas of the presentation:

Tools & Resources – Provides detailed information on resources, tools, and certifications available in the areas of maintenance, reliability, and AI. Examples are provided, as well as contact information for further details. Several company examples are highlighted to provide additional material.
Reliability & AI Journey - There are several tools provided to tie together material previously discussed and material to facilitate the journey. These include:
Common Sense ReliabilityTM Framework breakdown
CMMS Best Practices
Example 5-year Roadmap to Excellence
Detailed list of deliverables
Applicable research example
The Case Study – Detailed AI case study requirements which cover everything from RACI, governance, readiness audit, action matrix, business case, decision framework, success definition, pilot, and tracking results/Return on Investment.
What’s Next – Information from respected sources on what is forecast to be on the horizon for the next several years in our industries. Sources include Gartner, UpKeep, and industry specific studies.
Discussion and Q&A – Four participants volunteered to be part of a Q&A panel, and we dove into the questions that were listed on the slides in this section. There was good audience participation. The questions were from the Reliability Academy and Erik Huple.

Conference Reflections and Closing Thoughts

All of us stay extremely busy meeting the daily demands of our jobs and most find it very difficult to stay up to date on all the changing technologies and methodologies involved. Conferences, such as MARCON, play an integral part in keeping us abreast of these changes. At first glance, it might seem a huge investment both in time and finances but the knowledge gained, connections renewed, and introductions made can all have a lasting impact on the day-to-day success of your organization.

MARCON 2026 was an enormous success, and it was fun to be involved. The presentations and workshops I attended were all well-presented and provided tools that can be used to improve day-to-day business. It was great to renew old friendships and make new ones. This is one of the best conferences you could attend and one of my favorites. Mark your calendars for MARCON 2027 for March 1-4, 2027. Check the website at https://marcon.utk.edu/ for further information and updates. Hope to see you in Knoxville next year!

Data - the New Gold

Fri, 20 Feb 2026 15:44:50 GMT

What is data and why is it the new gold?

Ramesh Gulati tells us that data is a piece of information; in a raw or unorganized form; used as the basis for reasoning, discussion, calculations, further processing, and communication (Reference #1). It can be quantitative, qualitative, structured, unstructured, semi-structured, analog, digital, etc.

Why is data such a hot topic?

In the maintenance, reliability, operations, and Artificial Intelligence (MROA) world, data plays a significant role. Because of its importance, leaders need to understand why a large portion of businesses today are dissatisfied with their data quality. This is especially true as the value and importance of data continues to increase as technology advances.

Data is the key component to decision making. Most companies do not lack data. In 2019, there were over 8 billion connected devices. By 2025 that number exceeded 25 billion and 50 billion is the estimate by 2030. Each device potentially creates multiple data points on a second/minute/hourly/daily/weekly/monthly/etc. basis. The total volume of data generated was estimated at 180 zettabytes (ZB) in 2025. A ZB is equal to one trillion gigabytes (Reference #2). Reliabilityweb.com conducted a survey in 2024 and found that less than 60 percent of respondents trusted their data (Reference #3). Most companies are lacking the quality data necessary to make informed decisions. The saying is “garbage in/garbage out.” Therein lies the problem.

Why is poor data quality a problem?

In addition to poor decision-making opportunities other effects of poor data include:

Production losses
Delayed or incorrect maintenance actions
Inefficient use of labor and materials
Weak reporting
Audit risk
Safety incident/injuries
Environmental impact
Equipment breakdowns
Negative financial impacts
Regulatory violations

How does a company improve data quality?

First, there needs to be a data plan. Second, they need a governance process in place to ensure the data plan is followed and continuously updated. Figure #1 shows there is a great amount of detail, planning, work, and execution required to deliver a plan resulting in clean, reliable data (Reference #4). Once the data plan is in place it needs to continuously progress while remaining flexible as technology changes. Governance is the foundation piece of data science. It establishes the rules behind how data is obtained and handled. Governance is the discipline to achieve the business outcomes desired from data capture.

Figure #1 shows that there are many pieces to the data puzzle and many decisions involved. There is no one size that fits all. For a data plan to work, each company will need to account for financial considerations, workforce maturity, skillsets (current and future), desired outcomes, strategic plans, and more. There is no silver bullet that will immediately transform data quality from “garbage” to “gold.”

Figure #2 is an example of information that is entered into a Computerized Maintenance Management System (CMMS) for an organization (Reference #5). The items listed in Figure #2 are all tasks/data points determined to be important by MROA groups. In most cases, in addition to these data points, subitems under each one also require data capture. Much of the information will be used to gauge performance of the various departments based on key performance indicators (KPIs) and reporting. Additionally, there is a desire in many companies to incorporate AI into the data process. When combined, all of these factors create an environment where data needs to be as accurate as possible, and the information in the CMMS will need to be “cleaned up.”

Why does the CMMS data need to be cleaned up to use Artificial Intelligence (AI)?

Figure #3 shows the connection between the CMMS, AI, maintenance results, and improvement (Reference #6). There is a misconception in organizations that a CMMS full of information/data means they have good data. Volume does not mean quality. AI models learn from patterns and if those patterns are in chaos, AI will give you garbage predictions. Examples of CMMS data issues that are commonly found in CMMS systems include free-text fields with no standards, asset hierarchies missing or inconsistent, failure codes unused or misapplied, bad timestamps, and incomplete records. AI cannot fix what is not properly documented (Reference #7). Figure #4 shows that less than 30 percent of respondents to UpKeeps 2026 State of Maintenance survey believe their data is ready to use AI. These results further emphasize the urgency to improve data quality (Reference #8).

Only after a company has created a data plan, governance, usable data, and an AI vision, can they move forward. The strategy is not “go deploy AI,” but to make a workflow AI-ready, prove value, and scale up. Examples of the potential early AI wins when implementing a good adoption program may include:

Work request triage
Work order structuring
Suggested troubleshooting
Schedule optimization
Parts forecasting
Documentation prompts

A detailed graphic on page 26 of UpKeep's State of Maintenance 2026 Report gives more information on what each application does and why it works.

Are there further steps once data is clean?

After achieving quality data and AI adoption there will still be needed improvements. The digital transformation journey will have taken a significant step forward, but like most other aspects of the MROA process this will be a continuous improvement reality. The perfect plan does not exist, but companies should continue to strive to ascertain and achieve the plan that works best for their organization. At Russ Parrish Consulting (RPC) we practice Common Sense Reliability. Figure #5 shows our RPC Framework. The items in red correlate with data actions. Data is a key concept to any successful reliability program. Have questions on how your organization will benefit from a strong data plan, please reach out, we would welcome the opportunity to guide the process.

References

Ramesh Gulati (2015), Certified Reliability Leader Pocket Dictionary , pg.29
Google AI Search 2-17-26
© Copyright 2024, Reliabilityweb.com. All rights reserved. Reliabilityweb.com®, Uptime®, and A Reliability Framework and Asset Management SystemTM are the trademarks or registered trademarks of Reliabilityweb.com and its affiliates in the USA and in several other countries.
Graphic from @orcaorgit on LinkedIn
Graphic provided by Bill Leahy from Unturning Steel, The MADE IN AMERICA Community
Graphic from Akshdeep Singh Rakhra on LinkedIn
Maven Asset Management information from presentation material
UpKeeps 2026 State of Maintenance survey

Putting the Pieces Together: Planning and Scheduling

Wed, 28 Jan 2026 15:38:40 GMT

What Are Planning & Scheduling and Why Is Being Successful at Them Important?

2026 is upon us and it is an ideal time to look at one of my favorite topics – planning and scheduling (P&S). P&S are two of the most important foundational pieces in both my business and personal life. With the new year just beginning this is the ideal time to dive in.

What are P&S and why is being successful at them important? The idea behind P&S is that we do the right thing, at the right time, at the right cost, and with the minimum acceptable risk. Ramesh Gulati defines them as:

Planning – The process of determining the resources and methods needed, including precautions, tools, skills, and time necessary to perform work efficiently and effectively.
Scheduling – Deciding what gets done, by whom, and when based on priorities, resources, and availability.
Planning is the what and how, while scheduling is the who and when.

These definitions point to maintenance and reliability practices but can easily be modified for personal use. Think about the processes one employs if they are planning a vacation, juggling multiple children involved in various activities, balancing whose family they are visiting during holiday time, etc. Planning and scheduling play a big part in day-to-day, week-to-week, and month-to-month life. In life and in work, P&S allows a shift from being reactive to proactive. Figure #1 shows SMRP benchmarking, which captures the cost of being reactive. A reactive response takes up to three times longer and can cost 4-10 times more!

The best way to look at P&S is to see how one would incorporate it with other items to improve proactivity. The first thing to understand is that P&S are pieces, particularly important foundational pieces in a much larger overall puzzle. The next thing to realize is that P&S is both an art and a science. Want to increase uptime? P&S can play a significant role. By themselves P&S are going to improve desired results, but not as effectively as when combined with other benefit pieces. Figure #2 shows the impact of P&S on uptime; P&S with a preventative/predictive maintenance program (PM); P&S, PM program, and defect elimination (DE). With P&S, a 1.3% increase in uptime is produced. While a PM program adds 2.4%, combining P&S and PM will increase uptime by 5.1%. The biggest variable here is implementing DE analysis. Combining it with all the other pieces mentioned increases uptime by 14.8% to 98.3%.

Figure #3 - Proactive Shift Phases (Allen Inapi graphic)

When we look at planning and scheduling in our personal lives it is similar to aspects we have examined in the business aspect, but there are obvious differences. Figure #4 shows examples of different tools available to support one’s P&S in their personal life. Similarities between personal and professional P&S include goalsetting, developing plans (daily, weekly, monthly, long-term, etc.), posting information, communicating expectations, learning from mistakes, and continuous improvement. The need to not waste time or money is paramount in many homes. Lastly, the key to success here is the effort and discipline to follow what you have planned. There will need to be more flexibility in personal P&S as there are more opportunities to deviate from the plan. Being successful at P&S is a skill that is honed and developed over time, but one that is a key contributor to personal and family achievement.

To an extent, success is measured by the goals one achieves. P&S are foundational tools that can be used both personally and professionally to more effectively reach one's goals. In this technological age, there is no shortage of P&S tools available. Many of RPC's company partners have developed cutting edge tools to help in this area. AI can be a significant asset in scheduling, analysis of information and data (including financial), and continuous improvement. As stated earlier, combining tools and processes will help create a powerful P&S approach. However, technology alone does not do the work, success still hinges on human implementation, use and discipline.

If you or your company need help in choosing the right tools to achieve your goals, Russ Parrish Consulting would be happy to help you sort through the myriad of choices to find the right fit for your needs.

Reliability and the Age of Artificial Intelligence

Thu, 18 Dec 2025 13:15:11 GMT

Reliability and Artificial Intelligence (AI), a match made in heaven?

We are in an age of ever changing and rapidly evolving technological advancement. Over the last few years, technology has advanced from Digital Transformation to Internet of Things (IoT) and now to Artificial Intelligence (AI). As the usage of AI increases, it will come as no surprise that information technology change and advancement are only going to move more rapidly. This blog will help to define terms and information around reliability and AI. We will evaluate the need for combining reliability practices with AI and define practical applications of each. As we wrap up, we will explore the future applications and implications of AI.

Most believe that AI is a recent technology; however, AI has been around since the 1950’s. Figure #1 shows the evolution of AI during the previous eight decades.

Figure #1 – AI to Generative AI (Stryker & Kavlakoglu, 2024)

To begin, let us level set on terms and definitions, specifically those associated with AI. I believe this audience has a knowledgeable understanding of terms associated with reliability. [If a review is needed on reliability terms, a great resource is Ramesh Gulati’s Certified Reliability Leader Pocket Dictionary. ] AI terms to be defined:

Artificial Intelligence (AI) – The study of how to build, create, and use:
Systems that think like humans.
Systems that think rationally.
Systems that act like humans.
Systems that act rationally.
Simplified AI is technology that enables computers and machines to simulate human learning, comprehension, problem solving, decision making, and autonomy.
Machine Learning (ML) - The use of data to improve performance of associated, targeted and AI systems over time. It is a system that learns from historical data.
Deep Learning (DL) – ML with deep neural networks. These are ML models that mimic human brain function.
Neural Networks – Artificial networks that are modeled after the human brain’s structure and function. These networks consist of interconnected layers of nodes that work together to process and analyze complex data.
Generative Artificial Intelligence (Gen AI) – AI used to generate content – i.e., text, image, videos, etc. These are DL models that create original content.
Natural Language Processing (NLP) – Language modeling that correlates probability distribution or a collection of rules to capture properties of human language models. The most common of these is the Large Language Model (LLM), which is the foundation of products like Chat GPT. LLM’s are a type of DL architecture used for language processing (USF, 2025).

These concepts can be a bit overwhelming. The key takeaways are that applications and devices equipped with AI can:

see and identify objects
understand and respond to human language
learn from the latest information and experience
make detailed recommendations to users and experts
act independently, replacing the need for human intelligence or intervention (a classic example being a self-driving car)

However, AI is not perfect and has weaknesses.

It makes mistakes (called hallucinations).
There are bias/stereotypes built into AI responses.
There are ethical issues surrounding AI use.
AI’s responses are only as good as the information it has evaluated, or the prompt used to create the output.

The AI weaknesses are the catalyst for using reliability practices and AI together (Stryker & Kavlakoglu, 2024).

Reliability is the foundation for the assets that are driving operations and maintenance practices. Reliability is defined as, “the probability that an asset, item or system will perform its required functions satisfactorily under specific conditions within a certain time period. (Gulati, 2015, pg. 85)” Expand this to include Reliability Centered Maintenance (RCM); which is a systematic, disciplined process for establishing the appropriate maintenance plan/requirements for an asset/system to minimize the probability of failure (Gulati, 2015, pg. 86). When businesses account for things like Failure Modes Effect Analysis (FEMA), Root Cause Analysis (RCA), process/procedure/checklists, planned/predictive maintenance, continuous improvement, CMMS, and planning/scheduling, they are developing the data from which AI will evaluate and deliver. The quality of all this information, and more, will determine how effective AI becomes. Just as maintenance, operations, reliability (MOR) can be continually improved, so can the accuracy of AI. Again, these are the foundational pieces of an AI and reliability partnership. Company maturity will have a significant impact on the success of both AI and reliability. Additionally, buy-in of leadership, skillsets of employees, investment in training, discipline in following process, and strategic planning are examples of items that will also impact success.

Figure #2 - 2025 Top 10 (Gartner)

Reliability is a defined and stable set of practices and expectations. Technologies including AI, ML, etc. are evolving quickly and reliability must also adapt. At this point, AI is the larger variable. AI hype must be balanced with AI caution. Sixty-five percent of respondents in a recent survey are anxious about how fast AI is moving (USF, 2025). This has led to a bigger problem known as AI FOMO (Fear of Missing Out). AI FOMO is the fear of missing the rapid changes and immense promise AI brings (USF, 2025). AI and ML will continue to advance and have a greater impact on industry in the future. The quicker companies adapt and start moving on the AI journey; the quicker they will see improvement. Companies who decide not to move forward will be at a competitive disadvantage. Those that choose to wait will be left behind. Figures # 2-4 show how Gartner saw innovation in 2025; sees innovation in 2026; and what CIOs will need to do to successfully move forward.

Good luck!

Figure #3 – 2026 Top 10 (Gartner)

Figure #4 – CIO Priorities (Gartner)

References

Various Instructors (2025), University of South Florida, Gen AI in Action: Impact and Possibilities Course . Taken November 2025 – December 2025
Cole Stryker & Eda Kavlakoglu (2024), What is Artificial Intelligence?
Ramesh Gulati (2015), Certified Reliability Leader Pocket Dictionary , pgs. 85-86
Gartner Presentations (Various) – sited on graphics.

Common Sense Reliability™ Expanded & Explained

Wed, 19 Nov 2025 16:37:41 GMT

Are you confident you're on the right path in your maintenance, reliability, and operations journey?

To create meaningful change, it is essential to understand both your current position and your desired destination. One of my favorite quotes sums this up: “You can’t go back and change the beginning, but you can start where you are and change the ending.” This sentiment is foundational to any conversation about improving a company’s performance or overall Maintenance, Reliability, and Operations (MRO) program.

This principle is also the cornerstone of change and Common Sense Reliability (CSR) is the mechanism to produce the change. Of course, additional tools - such as change management, effective communication, and knowledge sharing - are also needed. Russ Parrish Consulting (RPC) aides its clients in implementing all these elements to achieve a high degree of success.

Common Sense Reliability - What is CSR? It is the model developed and used at RPC. It consists of three sections – RPC Common Sense Process; Customer Requirements; and 8 Pillars of Asset Management (From Asset Operations – Maintenance, Reliability, and Operations in the Era of AI by Ryan Chan @ UpKeep) . RPC Common Sense Processes are the steps we follow in most customer projects. The steps are:

Listen to the client's wants/needs – What are their problems, pain points, etc. Also, what are their needs and wants and why.
Evaluate available information – CMMS, ERP, KPI, performance, downtime, etc. Preferably as much as is available, a minimum of 12 months' worth.
Perception vs. Reality – Our observations of what the client thinks is happening versus what is actually occurring. This will be based on data and conversations.
Identify opportunities – Actionable items that impact the wants and needs from above. Additionally, those items that could affect longer term strategies.
Create an achievable & sustainable plan – Develop a plan that encompasses short, medium, and long term actionable deliverables to achieve defined opportunities.
Stress technical, communication & change – Insure these three items are included in any project plan. Create milestones to track important goals.
Instill discipline to achieve desired results – Establish accountability for action items.
Put tools in place to sustain success – Have a strong governance policy and adhere to established standards.

Customer Requirements - A solid foundation for maintenance and reliability is built on four key areas:

Execute basic foundational maintenance & reliability requirements well.
Operate in a data driven and proactive decision-making manner.
Embrace innovation and technology.
Invest in employees.

In the September edition of the RPC Newsletter, we did a deep dive into the four areas of the Customer Requirements in the blog article, Four Areas Needed to Build A Solid Maintenace and Reliability Program , refer back for details.

Figure #2

Eight Pillars of Asset Operations Management - The 8 Pillars of Asset Operations Management bridge foundational MRO practices with advanced best practice/world-class aspirations, including advanced technology areas like Internet of Thing (IoT), Artificial Intelligence (AI), Digital twins, and others:

Maintenance, reliability, and operations must work together and be aligned on achieving a common goal.
Data must flow into a single repository.
Measure teams based on why they do something rather than what they do.
Collect the right data, display the best insights, and provide actionable feedback through a centralized command center.
Continuous improvement as an abundant lifecycle, not just a point in time.
Everything measured can be improved.
Data accessible from wherever you are.
Maintenance, reliability, and operations are revenue drivers, not cost centers.

These three areas, when combiined, form a comprehensive approach (Figure #2). Not every item will apply to every organization; it is important to identify those that apply to your business model, current operational/maintenance state, and the urgency of your needs. By implementing these three areas businesses will have a strong MRO foundation; improved decision making, with better data quality; more skilled/satisfied employees; increased opportunity to successfully innovate and use advanced technologies; and find success in continuous improvement.

The ultimate goal is to note improvement each operation, shift, and day. Continuous improvement opportunities should be implemented and built upon. ReliabilityX has defined a great approach with six principles, stating: “ Meaningful progress is attainable through a few core principles, paired with consistent execution. These principles are not revolutionary, but they are powerful when applied with intention. Here are several that can help drive real improvement within your organization:” This is a great take on this fundamental concept. Well done to the ReliabilityX team!

By combining continuous improvement with the other three aspects of CSR, businesses create a holistic, dynamic, and achievable MRO approach. If you have comments or questions, reach out to us at RPC. Have a blessed day.

The Business Approach to Maintenance

Tue, 14 Oct 2025 20:17:20 GMT

Telling Leadership What They Want To Hear

Why is it so hard to get a company to take on maintenance and reliability initiatives and why are the initiatives usually not “successful” (Fig #1)? This is a common occurrence/problem in the maintenance, reliability, and operations (MRO) arena. Is there a way to increase the chances of success? The answer is yes, with several factors impacting both success and failure.

Fig#1 – Success or Failure

© Copyright 2024, Reliabilityweb.com. All rights reserved. Reliabilityweb.com®, Uptime®, and A Reliability Framework and Asset Management SystemTM are the trademarks or registered trademarks of Reliabilityweb.com and its affiliates in the USA and in several other countries

There are three factors that must be addressed successfully in every MRO initiative - the technical aspect, communication, and change management. In my opinion, the weakest of these is communication. In most cases, those in technical roles do not speak with a business acumen that justifies the cost for the request being made. Since the initiative can only be as successful as the weakest point, communication to the decision makers must be improved and reflect business outcomes. There are two other items we need to dive into as well – properly defining what success should look like and the discipline to make the necessary changes for a successful outcome. All of these factors must be defined in terms that can be effectively communicated to MRO and organizational leadership in order to gain approval and funding. Examples include:

1. Upgraded/Improved asset performance

2. Improved software performance

3. Automation leading to less personnel

4. Increased production time and/or throughput

5. Decreased planned and unplanned downtime

From a technical viewpoint, there is great value in these results; however, they are not the improvements that the C-Suite measures. To get the necessary funding and approval, correlation must be shown between MRO initiative deliverables and organizational business goals, requirements, key performance indicators (KPIs), etc. While Fig #2 is a comprehensive list of improvements, I have bolded several that will appeal to the C-Suite.

Fig#2 – Outcomes

* Information from SMRP Benchmarking

Once the applicable deliverables germane to the initiative are identified, how do we speak in “business” terms that align with our corporate needs? Money talks - how does this initiative increase revenue, reduce costs, improve efficiency, etc? What value does it bring – real and potential? A great example to use here is that in 2023 OSHA reported that for every $1 spent positively impacting safety, the company saves $3. Next, how does the initiative align to increase advantages and decrease the disadvantages of our business strategy? Lastly, how does it positively impact decision-making capabilities? Understanding that data is a key asset to decision-making, one must be able to communicate that impact and value. The last step is to take the information defined above and create an overall business case that captures the business and technical aspects of what this successful initiative will deliver; making sure to define thoroughly what factors will be necessary for success and how that success will be measured. Remember, an initiative does not have to be one hundred percent complete to create value. As an example, a baseball hitter must be successful three out of ten times to be considered a great hitter.

If all steps are followed to completion, is success guaranteed? The answer is no. All will have both successes and failures throughout their career. By covering all their bases, an organization is in a better position to achieve successful outcomes. The idea is to learn from both successes and failures, have more success, and increase the odds of being successful. There are things that can be done to create a “successful” mindset. Foremost, do not be afraid to fail. Anticipate and adapt while continuously trying to improve. Get better every day. Set an expectation for sustained success. Realize that the perfect program does not exist so look for opportunities to improve. Educate yourself, not just technically. As I was often reminded in my career, businesses are in the business to make money. Anything positively, ethically, and proactively one can do to help attain this goal will further the chance of achieving success.

Four Areas Needed to Build A Solid Maintenance and Reliability Program

Thu, 28 Aug 2025 19:24:07 GMT

"What do I need to do to improve my maintenance and reliability program?"

“What do I need to do to improve my maintenance and reliability program?” is our most commonly asked customer question. There are a myriad of ways to answer that question and some are customer or industry specific. This month we are reviewing Ryan Chan’s book, Asset Operations – Maintenance, Reliability and Operations in the Era of Artificial Intelligence, in the September Common Sense Reliability Newsletter. The book, along with the understanding of the eight pillars of asset management defined in it, are a great supplement to this month’s topic. At Russ Parrish Consulting (RPC), we believe there are four areas of concentration needed to build a solid foundation for maintenance and reliability. The four areas that will be explored are:

1. Do the basic foundational maintenance and reliability requirements well.

2. Operate in a data driven and proactive decision making manner.

3. Embrace innovation and technology.

4. Invest in your employees.

Most of us are inheriting a maintenance and reliability system and we will center this discussion around those existing platforms. If one is lucky enough to create something from the ground up, we would use a “design for reliability” method and all the items we discuss here would need to be a part of this method as well. First, we need to determine our starting point by performing an assessment. This is a line in the sand moment and all forward movement will be measured against it. Not every item in the area write-ups will be listed; however, several examples will be given of each.

Area One: Do the Basic Foundational Maintenance and Reliability Requirements Well Once the initial assessment is complete and the starting point and opportunities are identified, the definition of success must be accurately established.

Create a plan. This plan is our roadmap but it will need to be updated and adjusted as we move forward.
Have documented processes, procedures, and checklists.
Adhere to a strategic asset management plan (SAMP).
Ensure all assets are entered into the system of record. For this discussion, we will assume use of a computerized maintenance management system (CMMS).
Level of details for the assets should include standardized naming convention, name plate data, criticality, spare parts, condition, estimated life expectancy, maintenance plans, history, and more. The goal is to have all assets entered in the CMMS; this will be a continuous improvement activity to maintain 100%.
Plan and schedule maintenance and repair activities. Ensure all planning, scheduling, technician work performed, and operations information is captured.
Maintain an adequate and accurate backlog of work.
Make certain maintenance, reliability, and operations (MRO) personnel operate together and are aligned to achieve common goals.
Other items to consider: key performance indicators (KPI) to monitor; budgetary and spend items; and capital planning. Capture as much practical information in the CMMS as possible and have an “everything measured can be improved mentality.”

Area Two: Operate in a Data Driven and Proactive Decision Making Manner It is said that data is the new gold or oil. The premise is that it is of immense value both in use and financial gain. Couple “good” data with a proactive way of doing business and there is a terrific opportunity to save time and money; thereby, increasing efficiency and gains. Let’s examine data and what will be needed to make these gains.

Collect the right data, capturing the needed insights.
Have a strict data plan and governance policy in place with good adherence.
Use a single repository for all data and make it accessible from wherever needed. Not only is having clean and good data important but having access when needed is essential.

Next, let us define the standard for proactive vs reactive work. The baseline for world class proactivity is eighty-five percent proactive. This is the minimum ratio and the target to chase. Beyond that percentage, the benefit must outweigh effort, cost, and return - a fine line to maintain. The combination of the above data recommendations and achieving elevated levels of proactivity will enhance the information available, lead to better decision making capability, and produce improved results.

Area Three: Embrace Innovation and Technology We live in an environment of quickly evolving technology and innovation. It is an exciting time to be in our industry, simultaneously, it is a challenging time. New software and technology systems, real time sensor information via IoT, digital twins, and others are constantly changing the maintenance and reliability landscape. Data changes made in Area Two provide the necessary groundwork to tackle Artificial Intelligence (AI) and all the opportunities provided by its implementation. To be successful, we need to choose wisely from the myriad of innovations currently available. Choices should bring value and make sense for our circumstances. Not every new product or procedure will be a good fit, we should be judicious in our approach. We must have a plan, train/hire the appropriate skill sets and employees; and adjust to new and greater things. Area Three will be a constant area of studying, learning, and evaluating as we move forward.

Area Four: Invest in Your Employees It is said that a business’s greatest asset is its culture and its people. I agree and lean toward the people being most important. If the value of our human assets is that high then there should be a willingness to invest in our teams.

Begin in the hiring process and all the tools exercised to get the correct resources hired - job descriptions, benefits packages, fair compensation, etc., are all key.
Give new hires the resources necessary; positioning them and helping them to achieve success.
Put forth effort to train the employee and continue to enhance their skill sets as equipment, technology, and innovation advance.
Help employees to advance in their career path.
Create and maintain an environment where the employees want to work and bring value - company culture is essential.

“What do I need to do to improve my maintenance and reliability program?” What needs to be done depends a great deal on where you are starting. Following a common sense approach and the steps outlined in this blog, you will be on the right trajectory. Additionally, ancillary improvements should also be noticed – enhanced safety, decreased downtime, improved asset performance, etc. Ultimately the goal is to get better each day and continuously improve to achieve the desired results. If you have any questions on what your next steps should be, please reach out to RPC. We would be happy to help.

Meet Russ

Thu, 31 Jul 2025 13:25:24 GMT

Russ, what exactly do you do? And, what is common sense reliability?

“What exactly do you do?” I hear this question a lot from both family and friends. Many people, even those in the field of reliability, may find it difficult and complicated to explain. However, we at Russ Parrish Consulting (RPC) disagree and want to simplify both processes and programs for our clients. My name is Russ Parrish, Principal at RPC, and I am a maintenance and reliability junkie.

I have worked in maintenance, reliability, and operations (MRO); as well as; engineering, management, and leadership for over forty years. When I began as a submarine electrician in the US Navy, the avenue to become a Certified Maintenance and Reliability Professional (CMRP) did not yet exist. However, as my career progressed and I was made aware of the reactive nature in manufacturing and business, I became intrigued by the ideas and concepts of Reliability Engineering. Each step of my career and educational path developed and enhanced my passion for the field. Throughout, there was a purpose for each academic endeavor and an urge to continue the development of my maintenance and reliability professional (MRP) skillset. I have been fortunate to have great mentors and other professionals to encourage me in these pursuits. They often saw more in me than I saw in myself. All these factors have contributed to a unique and eclectic blend of skill, knowledge, intuition, and drive that has led to the creation of RPC.

So, exactly what makes me a junkie and drives my passion? I love the opportunity to identify and improve performance, optimize the process, and deliver results. I have experience and enjoy working with all levels and parts of the organization. My process may seem long and a bit complicated but in practice it is just a matter of common sense:

listening to the client’s wants/needs
evaluating all the information available
determining perception vs. reality
identifying opportunities
creating an achievable and sustainable plan
driving communication and the necessary change management
helping to instill the discipline to achieve the desired results
putting tools in place to sustain the objectives we set out to accomplish

At this point in my career, this is why I get up and go to work. It is exciting to see how simple, common sense changes can have an immediate impact for my clients. Each opportunity to work with a customer is new and different. I also get to partner and work with some of the best professionals in the maintenance and reliability field.

“But still, what exactly is it that you do?” In simple terms, I am a reliability visionary. I lead clients in trying to facilitate reliability as a core aspect of their business by driving performance, efficiency, and success. This is accomplished by helping our clients become more proactive, focusing on preventing problems before they occur. RPC’s goal is to assist our clients in delivering the right work, at the right time and cost, and with the appropriate (or acceptable) amount of risk. The Society of Maintenance and Reliability Professionals (SMRP) benchmarking has determined that reactive work takes up to three times longer and costs four to ten times more than proactive work. The more reactive a company becomes the more time and money they waste. Experience has shown that the average company is more reactive than proactive; RPC welcomes the opportunity to help its clients become more proactive. Finally, the perfect MRO program does not exist, what works for one client may not work for another. At RPC, we have the knowledge and expertise to design and help our clients implement their best individualized program.

As we strive to improve client performance, the playing field is constantly changing. We are working in an exciting time for MRO. There are many new and evolving aspects of our field. Where Artificial Intelligence (AI), Internet of Things (IoT), Digital Twins (DT), Technology, and others will take us is still to be defined. We need to be ready and take advantage of all the opportunities that are, and will be, available. Most importantly, we want to build a solid foundation and echo two thoughts provided us by Reliability Academy, this month’s exceptional highlighted partner. First, reliability has become too complicated. Second, we should strive to keep reliability as simple as possible. Those may sound very similar, when in fact, they are not. One is a cause and the other an effect. Both are important as we move forward. We at RPC are excited for what the future holds and are looking forward to working with our clients as we make every effort to deliver the individualized maintenance and reliability program each deserves.

RPC was envisioned over a decade ago and finally went live in early 2025. Our mission is to provide superior performance and drive sustainable outcomes for our clients. Through exceptional service and integrity, RPC will align with our clients to achieve lasting success. Additional information on our service areas can be found at https://www.russpconsulting.com . Thank you for your time spent reading this and have a blessed day.