What does it cost to operate and maintain an industrial facility over its lifetime? If you guessed 6 to 10 times what you paid for it, you would be correct. If you are surprised, you are typical of most business owners and management teams. The next question that most decision makers ask is related to the magnitude and timing of the expenditures.

The best forecasting approach in situations such as the post-COVID19 recession is to use spreadsheet models with Monte Carlo simulations. According to the international risk standard, ISO-31000, an approach using Monte Carlo simulations is the one of the most strongly applicable approaches for quantitatively accounting for uncertainty and risk. A prediction on the back of a napkin or a forecast using a spreadsheet with best-guess point estimates will not get the job done – at least in situations with complexity (multiple parts) and high amounts of future uncertainty.

What is this Monte Carlo stuff?

This is a common question that is asked by a wide range of business managers and with wide ranges of educational backgrounds. First, it originated in the 1940s by a nuclear physicist working on the atomic bombs that ending World War II. That makes it old. And probably smart (technical) too.

Second, it has nothing to do with gaming and a lot to do with statistics. The name comes from one of the creators and named for the habit of gambling through the ages at Monte Carlo. In essence, if we play enough hands, shoot enough dice, and spin the roulette wheel many times, then our results (possibilities) can be associated with frequencies (probabilities) that are driven by statistics and physics.

Third, it is not that hard to do. There are a number of affordable spreadsheet add-ins or simulations can be organically developed in Excel. The computer has taken the manual simulation aspects out of it. Monte Carlo methodologies went “quite” until computing became cheap in the latter 1990s.

What is wrong with traditional point estimates?

The short answer is nothing when situations are simple and there is limited uncertainty. This is not the case with most industrial facilities where there are thousands of parts and equipment and where products, operating environments, and maintenance practices are regularly changing.

The long answer to what is wrong with traditional point estimates is multi-fold and has several important ramifications. First, it is left up to the forecaster to select the value of each input variable. Most technical professionals tend to be conservative in order to avoid shortfalls or simply being wrong. This conservatism in the input parameters leads to overstated financial needs and/or early spending needs.

Second, the need to have really good estimates of input parameters leads to collecting vast amounts of data. Collecting data takes time and money – usually lots of both. It can be assumed that developing probability distributions takes time and money too; however, the difference is that existing history can be used or approximated so that a ‘spin of the wheel’ can be generated. Decision makers, not analysts, can then decide on the importance if, and what types, of data is needed to improve the decision.

Third, forecasts based on point estimate inputs generate a single output. The result is a ‘beat the budget’ which is the product of the analyst rather than the probabilities and probabilities. With a Monte Carlo approach, we understand the full range of probabilities and, more importantly, inform us of the chance of success if we only have a fixed amount of money for our facility.

Summing It Up

The best forecasting approach in situations such as the post-COVID19 recession is to use spreadsheet models with Monte Carlo simulations. No one can predict when a piece of equipment will exactly fail, the degree to which operating conditions will change based on market conditions, or the amount of organizational capacity that will be available to maintain our systems. The possibilities and probabilities are marked with high degrees of complexity and uncertainty. Our methods for forecasting our financial needs must be aligned to meet the challenge.

This document provides guidance for good practice asset management. It is part of a suite of Subject Specific Guidance documents that explains the 39 subject areas identified in “Asset Management – an Anatomy”, also published by the Institute of Asset Management. These subject areas are also acknowledged by the Global Forum for Maintenance and Asset Management as the “Asset Management Landscape”.

I was pleased to have served as the project manager for this effort. Our international team of more than a dozen subject matter experts worked diligently for more than a year. All of our team members made meaningful contributions. In particular, Georgia Smart, Adam Lea-Bischinger, Mick Saltzer, and Harry Sellers were extremely committed to the work from cradle to grave.

A very mature organization may choose a simple solution where a developing organization may think that a complex solution will solve all its problems. In truth, there is no universal best practice in Asset Management – only good practice that is appropriate for the operating context of any particular organization. What is good practice for one organization may not be good practice for another.

One of the most interesting aspects of this guidance document is that in blends Maintenance and Operations into an integrated approach. For those of us who work in both areas, we well understand that integrated Maintenance and Operations is a form of nirvana – much talked about but only seen in practice for a fleeting period, if at all. Yet intuitively we all know it is essential for organizations realizing full asset value and achieving full reliability of their facilities and infrastructure.

As the SSG explains, the delivery of effective maintenance is a key element in ensuring the reliability of an asset through its Life Cycle with optimal use of resources. The amount and type of maintenance and maintenance support depends on the asset needs, the nature of the maintainable item, its condition, its required availability, and a range of other factors. As these components change over time, the level and type of maintenance support must be adjusted.

Asset operations is defined as the day-to-day activities to ensure value is delivered from your asset(s). These activities will focus on the varying demands placed on the assets and will vary based on industry and organization context. For a manufacturing plant, operations are focused on ensuring assets produce the required product at the desired volume and quality. For a hospital or school, operations are focused on ensuring the asset condition and environment is at the optimum level for the users. Historically asset operations were focused on delivery of the end product and/or service, with minimal consideration to the interrelationship between maintenance, skills and competency, business longer term objectives and process optimization.

The integration of maintenance and operations into an integrated guidance document is a crowning achievement that almost did not happen. The original team was chartered to develop the Maintenance Delivery SSG. About 6 months into the process, the fledging collection of experts that were to deliver the Asset Operations SSGs needed a leader. IAM noted that I, as project manager, had both the maintenance and operations background to pull off an integrated guideline. I agreed to give it a shot and more importantly the two teams agreed to give it a shot, albeit with some reservations. Like many aspects of asset management, achieving lofty goals is a function of context, creativity, expertise… and necessity. Timing is everything.

There are many useable observations and recommendations that make the SSG worth the read. The limitation, as this and all the SSGs state, is that this is a bridge document between the higher-level ISO 55000 international standard and the many detailed books, articles, and research. The Maintenance Delivery and Asset Operations Subject Specific Guideline is indeed a guideline. Practitioners will find its many ‘gold nuggets’ helpful in the asset management journey.

There is a good chance that you will encounter an algal bloom this summer if you are around the water. That is not all bad because algae is naturally present in all water bodies and algae is an important part of the ecosystem. The part that makes algal blooms a tough subject is that the factors that cause both ‘good’ and ‘bad’ algae are overlapping and non-linear. Solutions require both short-term and longer-term approaches.

Blue-green algal blooms (cyanobacteria) are the bad kind. Commonly referred to in the business as HABs (or Harmful Algal Bloom), cyanobacteria is a photosynthesizing bacteria which releases toxins that can produce headaches, fever, nausea, vomiting and severe liver damage in humans and animals. Undesirable conditions in water chemistry, such as low dissolved oxygen and high pH, are usually present in conjunction with HABs.

The combination of high nutrient levels, light, high water temperatures, and stagnant conditions produce algal blooms. However, it is not quite that simple because these conditions are frequently present but harmful algal blooms do not exist. Stagnant water, high temperature, and excessive nutrients stimulate blue-green algae growth.

Regularly monitoring the water column for temperature levels, dissolved oxygen (DO), pH, and turbidity provide the best indicators for forecasting a harmful algal bloom. Monitoring for chlorophyll-a, a surrogate for phosphorus and nitrogen, can be helpful; however, analysis of the actual species of nutrients and algae are much better predictors than proxying from a surrogate measure.

The solutions are not straightforward for preventing algal blooms in our surface water bodies – ponds, lakes, reservoirs, and water supply canals. Reducing the presence of stagnant water is an obvious choice, but there are different users, use patterns, and natural water availability depending on the seasonal period. Eliminating excess nutrients in the water body is also an obvious choice but in complicated by policy and property rights issues related to nonpoint source (stormwater) controls and atmospheric deposition (rainwater) from sources that are non-adjacent to the water body. In recreation applications, implementing good practices to minimize bottom disturbance, which avoids stirring the nutrient food source for algae into a suspended state, can be difficult to show to policy makers the direct correlation to harmful algal blooms.

The most effective things to do to minimize harmful algal blooms is to maintain a normal flow of water and to reduce the number of algae-feeding nutrients that make it into the water. Long-term success requires extensive changes in policies and human activities. The ‘what’ to do is fairly well understood. The “how” to do it is the confounding part.

Some solutions for algal blooms once you have them include aeration, chemical additives such as alum, and mixing. Aeration replenishes crucial dissolved oxygen but can be expensive or difficult at large scales. Chemical additives include alum (yes, the same stuff used to make pickles and used as part of drinking water treatment) which drags nutrients to the bottom of the water column. Another form of chemical additive is aquatic herbicides (algaecides) that are typically copper based; however, algaecides are quite expensive at large scales. Finally, using mechanical mixers to eliminate stratified layers is a third major solution. In practice, aeration, chemical additives, and mixing are often used in combination depending on the scale and scope of the problem.

Preventing algal blooms in water bodies is a year-round thing. Like remembering to fix the roof only when it starts raining, it is often when the weather heats up and we see the effects of algal blooms that we think about the problem. The key to viable solutions is combining short-term mitigation with long-term good practices, and of course with ample monitoring for feedback loops. Water bodies are complex, biological systems. Timing, and expertise, is everything.