Everyone is eager to find ways that AI can improve revenue and profit, and that’s helped drive predictive maintenance to be one of the most popular concepts in the industry. This graph from Google Trends shows that queries about predictive maintenance have been rising over the past 5 years.

Not surprisingly, there was a spike in interest with the arrival of COVID-19, and commensurate drop as the pandemic ebbed, but “interest over time” continued to grow compared with the pre-pandemic level. 

Deloitte succinctly summarized the benefits of predictive maintenance, noting that “The advent of Industrial 4.0 technology, availability of limitless data storage/computing power, and advanced analytical capabilities have unlocked the power to predict equipment failure, reduce maintenance cost, and increase asset life.” According to Deloitte analysts, predictive maintenance is a critical use case for smart operations and digital transformations, with “well-executed predictive maintenance solutions driv[ing] substantial downtime reduction, increase productivity, and reduce overall costs. 

But many people are still asking what is predictive maintenance, and what can it do for your process plant?

We share everything you need to know about predictive maintenance to help you make informed decisions for your company.

What is predictive maintenance?

Predictive maintenance, or PdM, is the most advanced approach to managing maintenance within process plants. It’s part of the rise of industry 4.0, big data, and the Internet of Things (IoT) because it uses the newest applications of artificial intelligence (AI), machine learning (ML), and IoT sensors.

You could think of PdM as a subset of predictive analytics. Predictive maintenance uses AI/ML, the Internet of Things (IoT), and big data to monitor equipment and check for part failure.
Predictive maintenance is sometimes called condition monitoring, or CM, because it uses IoT data to track the condition of your parts.

What types of maintenance are there?

Predictive maintenance differs from other types of maintenance in many ways. Let’s start by looking at various different types of maintenance, such as:

• Reactive maintenance, or run-to-failure
• Preventive maintenance
• Prescriptive maintenance
• Predictive maintenance (PdM), or condition monitoring

Reactive maintenance means that after a part has already failed or when an anomaly or incident is already detected, you react to replace or repair the part, or to investigate what caused the anomaly. That’s why it’s also called run-to-failure, because every piece of equipment is used until it fails, and then it’s replaced.

With reactive maintenance, there’s no risk that you’ll waste time maintaining parts that currently don’t need any attention. However, reactive maintenance keeps you constantly on the back foot, stressfully chasing fires. Reactive maintenance pushes up maintenance costs, because a small early repair could extend the lifecycle of a costly part.

Preventive maintenance, also called time-based maintenance, means that you regularly check the condition of every part and make whatever small repairs are needed before equipment failures occur. You create a strict, condition based, proactive maintenance program that ensures that you don’t overlook any corner of the plant. 

Preventive maintenance can extend the life cycle of your equipment. However, with preventive maintenance, there is a risk that you might waste time and money on parts that don’t need attention yet, and that you could overlook parts that do need your attention.

Predictive maintenance uses Artificial Intelligence (AI) and Machine Learning (ML) to direct maintenance management to the parts that need it most. It analyzes big data from industry 4.0 in real time for condition monitoring, to spot the early signs of equipment failures and detect tiny anomalies before they develop into costly incidents. 

Predictive maintenance helps you save on maintenance costs by addressing only the parts that need attention at the time, instead of using preventive maintenance which involves checking every item whether it needs it or not. Predictive maintenance condition monitoring also guides you to make timely small repairs that extend the lifecycle of your equipment and help reduce downtime.

Prescriptive maintenance takes predictive maintenance a stage further. In addition to condition monitoring to identify the earliest signs of potential part failure, it also recommends what you should do next. Prescriptive maintenance suggests which actions to take to mitigate the anomaly or fix the parts that are showing signs of failure, and anticipates the results of your interventions. 

In the process manufacturing industry specifically, it’s almost impossible to successfully apply prescriptive maintenance because there are simply too many constantly-changing variables.

How is predictive maintenance related to predictive analytics?

Predictive analytics and predictive maintenance both rest on the application of machine learning to real-time big data from IoT sensors and other monitoring systems, but predictive analytics is a broader term.

Predictive maintenance focuses on equipment failures. It uses condition monitoring to track each part individually, spot the earliest signs of failure, and alert you to them. A predictive maintenance program helps prevent you being taken by surprise by sudden part failure.

Predictive analytics encompasses a variety of statistical techniques from data mining, predictive modeling, and machine learning, that analyze current and historical facts to make predictions about future or otherwise unknown events. It’s a wider term which can be applied to different disciplines/verticals: ecommerce, finance, etc. Using analytical tools, predictive analytics can spot small anomalies in production quality, output, part availability, and other ongoing metrics to improve the entire process. It can be used to dive deeper into what is expected to happen in the business, for example predicting fraud, or expected customer demand. 

It’s important to note that although it’s called “predictive” maintenance, it’s almost never possible to predict when a piece of equipment is liable to fail. Process plants simply have too many ever-changing variables, so nothing fails the same way twice. Instead, predictive maintenance helps pick up on early anomalies that are the first signs of failure.

How does predictive maintenance work?

Predictive maintenance begins with IoT sensors on every piece of equipment. The Internet of Things gathers data in real time and sends it back to the central condition monitoring system.

Software like SAM GUARD takes the condition monitoring data showing the regular running of the plant, and automatically analyzes it to establish a baseline for “normal” plant behavior.

The predictive maintenance system then uses ML to quickly process and analyze the flood of new data points coming in all the time from IoT sensors, monitoring equipment condition based on anomalies, and generating an alert when needed.

SAM GUARD’s predictive maintenance solution goes a step further by adding human domain knowledge, which we call human intelligence (HI), to the machine learning system in order to monitor the entire plant. This human enhancement of machine learning helps the PdM system gain a better understanding of which anomalies are serious and which fall within the range of expected fluctuations. In this way, SAM GUARD’s predictive maintenance solution generates fewer, but more targeted, alerts. When a system produces hundreds of alerts a day, it’s inevitable that engineers will learn to overlook them.

Which industries benefit from predictive maintenance?

Predictive maintenance brings important benefits to many industries, such as health care, energy/utilities, banking, cybersecurity and others. One of the biggest beneficiaries of a predictive maintenance program is the process manufacturing industry, since it has an enormous number of interconnected moving parts, with many vital pieces of equipment, and production can never slow down or pause.

Process industries that benefit from predictive maintenance include:

• Chemical processing plants
• Petrochemical plant
• Oil and gas industries
• Refineries
• Cement plants
• Paper and pulp plants
• Beverages
• Pharmaceutical industries

Why does my company need predictive maintenance?

There are many ways that it will help your manufacturing company to make your maintenance predictive instead of using reactive or preventive maintenance. Each plant contains many pieces of equipment, and you rely on each one to perform correctly in order for production to continue smoothly and to avoid downtime.

Replacing a part is expensive, demands a significant amount of time input from your maintenance team, and might require you to stop production while you carry out the replacement. When predictive maintenance issues an early alert about potential part failure, you can deal with the problem while it’s still small and easier and inexpensive to repair. Early condition-based intervention stops small problems from snowballing into major issues without your knowledge.

When you get an early condition monitoring warning, you can move the relevant piece of equipment higher up your maintenance management schedule, so that your team investigates it sooner rather than later. Making maintenance predictive often enables them to repair the part in a way that prevents it from imminent failure, saving you from having to replace costly parts on a more frequent basis. 

If you receive an early alert that a part is not performing correctly, and you investigate it and find out that it will need replacing in the very near future, you probably still have some time before it fails entirely. You can plan to replace it at a time that suits both your maintenance team’s schedule and your production schedule. Thanks to the early condition monitoring warning, the issue isn’t so urgent that it has to be dealt with immediately. This is especially true if you’d have to shut down part of your manufacturing plant to carry out the replacement, because that way you can choose the least inconvenient point and reduce overall downtime.

In contrast, if you’re taken by surprise by sudden partial or complete part failure, or signs of imminent failure, you might not have on hand the replacement parts that you need to deal with it. You could have to suspend manufacturing for a day or two, or more, until the parts arrive. Equally, your maintenance team always has a full schedule with many urgent tasks that need their attention. When a part suddenly fails, they’ll have to put all their other responsibilities on hold while they deal with it, which could lead to a crisis developing elsewhere in the plant.

What are the benefits of predictive maintenance for process plants?

The main way that predictive maintenance benefits process plants is by reducing maintenance costs. When you use predictive maintenance and condition monitoring to get ahead of part failure, you’ll be able to repair parts in a way that extends their lifecycle, avoids downtime, and enables you to replace them less frequently. 

According to Deloitte, companies that use predictive maintenance see a 5-15% reduction in downtime, 3-5% drop in new equipment costs, up to a 20% increase in labor productivity, and as much as a 30% decrease in inventory levels, causing a 5-20% reduction in carrying costs. 

By preventing unexpected part failure, predictive maintenance can also help maintain a positive public brand image for process plants. Leaks, explosions, and pollution incidents can harm your reputation, plus most plant owners are truly concerned to do all they can to protect the environment. Gaining an early warning about imminent part failure helps you replace parts before they’re at risk of causing injuries or ecological damage.

Predictive maintenance also saves you time, because you can optimize a condition-based maintenance program for greater savings and efficiency than if you’re using preventive maintenance. When maintenance is predictive, it ensures that each part is checked and examined when it’s most likely to be necessary, instead of holding your team to an arbitrary schedule that allocates equal time to every single part.

With predictive maintenance, you can decrease frustration among your employees and raise satisfaction levels for ops and process engineering employees. Predictive maintenance helps you and your managers gain control over your plant. Predictive maintenance means you can stop reacting to sudden emergencies, slash downtime, and gain stability across the organization.

Ultimately, predictive maintenance will help you to increase your overall revenue by reducing maintenance costs, overall downtime, and the costly last-minute shutdowns that you need to implement to replace a part that failed unexpectedly.

When would I use predictive maintenance?

Predictive maintenance has a wide number of use cases. Here are just three examples:

Predictive maintenance for pumps

Pump motors need to run smoothly and on full power to keep your plant efficient. When you make maintenance predictive, you can spot slight changes to vibrations in the pump which could indicate imbalance, due to deposits on the impeller or other parts of the pump. Addressing the vibrations via predictive maintenance enables you to clean the pump on schedule to prevent deposits building up enough to damage the equipment and the concrete structure of the pump.

Predictive maintenance for heat exchangers

Various measurements such as temperature in the heat exchanger and other related parts of the plant can indicate partial blockages in the heat exchanger. Predictive maintenance means that the heat exchanger can be cleaned, and the partial blockage removed. Fixing this on time reduces energy costs and other related issues such as erosion that can take place while it’s blocked. Without predictive maintenance, if these issues are not fixed in time, they could eventually lead to a plant shutdown and lost production.

Predictive maintenance for furnace

Blockages in a furnace can lead to damaged product and are a challenge to the operation.  These can be identified through faulty temperature readings that are the root cause of the blockage, and replace the faulty sensor. This prevents the product from being wasted by being cooked at the wrong temperature, and also the considerable damage that would have been caused by the time required to clean the oven, when it cannot be in production. So by identifying the small problem early, predictive maintenance prevented a larger, more expensive breakdown.

What to look for in a predictive maintenance solution for process plants

Choosing a predictive maintenance solution for process manufacturing requires research and careful consideration. These are the main factors that you should bear in mind when you compare your predictive maintenance options to make your maintenance predictive.

Does the vendor have experience working with process plants?

Process manufacturing plants have needs and pain points that differ from other industries. Process plants have thousands of sensors but sparse historical data, which is very difficult for predictive maintenance solutions that aren’t familiar with the conditions. Make sure your predictive maintenance vendor is familiar with these unique requirements.

How long will it take to implement the solution?

Look for a predictive maintenance vendor that has a solution that can be up and running within a couple of weeks, not months.

How much skill is required to manage the solution?

The most effective predictive maintenance solutions are those that can be run by the plant’s operational team, rather than needing to bring in a data scientist. Find out whether you’ll need ongoing support to learn how to master the predictive maintenance system, how long you’ll need it to continue, and what level of internal resources you’ll need to devote to it on a long-term basis.

Will you need a data scientist to analyze the reports?

Data scientists are a valuable resource, and you have many tasks that you need them to carry out. It isn’t cost effective to have to divert your data science team’s time to handling data reports produced by your predictive maintenance solution.

How many alerts does the system generate?

Alert fatigue is already a serious problem in process plants. Process engineers are very busy people, and even if you have a team dedicated to analyzing the alerts, they should not be bombarded with tons of irrelevant alerts or false alarms, as it will reduce the overall effectiveness of the system. Look for a predictive maintenance solution that keeps down the number of alerts it produces so as not to add to the alert fatigue burden.

What is the false alarm rate?

Process manufacturing plants are plagued by “the boy who cried wolf” syndrome, where process engineers respond to so many false alarms that they learn to ignore the system producing them. Predictive maintenance can bring so much value to your plant that it would be a shame to miss it because of false alarms.

Will the solution assist you in identifying the cause of alerts?

Some predictive maintenance solutions generate structured alerts that draw on a number of relevant data points. The combination of these insights can help engineers discover the root cause of the problem, and work out how to resolve it far more swiftly than if they received a generic alert. For example, a rise in vibrations in a pump while power output remains stable is more illuminating than simply hearing about a rise in vibrations.

Predictive Maintenance Changes the Paradigm

One of the main challenges of adopting predictive maintenance solutions in process plants is that process engineers often expect their job to be reactive; management expects them to put out the proverbial — and sometimes real — fires. 

By switching to a predictive mode, process engineers need to think like detectives, examine the clues, and investigate the possible scenarios that the evidence suggests. For example, if indicators show a valve’s position at 90%, together with the temperature of a reformer tube dropping, these early indicators enable predictive maintenance, allowing engineers to take measures to avoid equipment failure and major emergency shutdown.

By adopting a PdM approach to equipment and operations management with the right predictive maintenance solution, process manufacturing companies can cut costs, increase productivity, and improve efficiency across the plant. 

Smart manufacturing is one of the buzzwords in industry in 2022, referring to an almost fully-automated factory that keeps manual processes to the minimum, so as to reduce human error. It’s characterized by the adoption of advanced technologies such as machine-to-machine communication, artificial intelligence (AI), machine learning (ML), and automation. 

2022 is the year that smart manufacturing scales

Towards the end of 2021, Deloitte analysts forecast that 2022 would see smart manufacturing scale, with more companies emulating advanced “lighthouse” factories and ramping up isolated tech projects and pilots to cover the entire organization. 

“Now that we have integrated smart factory solutions, I predict we’ll see a big evolution from organizations having a couple of smart factory components to whole production environments becoming smart,” wrote Jason Bergstrom, smart factory go-to-market leader at Deloitte, adding that organizations will maximize the impact of big data by bringing it together from across the corporation, rather than being satisfied with connecting a single department or just one plant. 

Indeed, this year we’re seeing increasing awareness that smart manufacturing is table stakes and that being left out means being left behind, with Plex’s 7th Annual State of Smart Manufacturing Report claiming that smart manufacturing adoption is rising by 50% year-over-year. Having been valued at $88.7 billion in 2021 by ResearchAndMarkets, the smart manufacturing market is projected to reach $228.2 billion by 2027, growing at a CAGR of 18.5%, and $446.24 billion by 2029. 

This shift owes a lot to groundwork laid by manufacturers over the last couple of years. Prodded on by the pandemic, most companies have completed basic digital transformation projects like deploying Industrial Internet of Things (IIoT) devices, establishing data gathering processes, and implementing cloud storage, and are moving on to more advanced projects that build upon that foundation. 

By now, more than 90% of companies are using or implementing digital manufacturing technology, Fictiv reports, and 75% will have adopted at least some components of smart manufacturing by the end of 2022, according to the Plex Report.

Smart manufacturing adoption drivers

Not surprisingly, the pandemic has been a major driver for smart manufacturing adoption, which has compounded these primary factors that motivate smart manufacturing in 2022:

• Supply chain issues;
• Remote work and labor shortages;
• Sustainability and ESG demands;
• The need to keep up and communicate with customers. Research by Fictiv found that 97% of manufacturers say that customer demands are shifting, specifically towards improved sustainability and quality.

Here are the top 2022 smart manufacturing trends.

Production monitoring 

New solutions use AI and ML together with data gathered by IIoT devices to offer advanced levels of production monitoring. Deloitte notes that this type of close monitoring may be required to help organizations keep up with the fast-moving ESG landscape and quantify moves to lower energy consumption. 

Production monitoring includes predictive monitoring, predictive maintenance (PdM), and PdM as a Service, an important new trend which helps plants onboard to predictive maintenance faster and with less hassle. 

These technologies can detect the earliest signs of impending failures, leaks, or bottlenecks in processes, assisting employees to identify wasteful inefficiencies, spot environmental hazards, and correct them before they become serious. 

Automation 

Automation is rapidly spreading across entire organizations, using AI and ML for robotic process automation (RPA) for soft administrative tasks like invoicing, vendor management, and inventory management, as well as automating manufacturing processes. Plants are adopting collaborative robots, or cobots, which work together with human employees to extend their capabilities and deliver a safer working environment. 

Taking it a step further, one of Gartner’s Top Strategic Technology Trends for 2022 is hyperautomation, which involves rapidly identifying, vetting and automating as many business and IT processes as possible, using a combination of technologies including AI, IoT, and digital twins. 

Digital twins

Digital twins is one of the leading smart technologies, with ResearchAndMarkets’ predicting the digital twins’ market to grow at a CAGR of 68.9% between 2022 and 2027, reaching an estimated value of $43,614.8 million. Digital twins use AI and ML to crunch data from IIoT devices and plant sensors, creating an exact digital copy of the factory which is constantly updated according to real time changes in the plant. 

Digital twins are used to optimize layouts and planning for new factories; by remote engineers to carry out root cause analysis using VR and AR headsets, and even to fix issues in the bricks and mortar factory by changing the configurations on the digital version. 

Supply chain management

Supply chain is the enduring problem child of the pandemic and previously fractured global manufacturing norms, and thus near the top of the list for smart manufacturing solutions. Plex’s study reports that the percentage of plants using a supply chain planning software solution jumped from 30% in 2017 to 78% in 2022.

Tools tackle a number of aspects, including delivering end-to-end supply chain visibility and automating supply chain decisions. Solutions draw on various technologies, including blockchain for transparency and accountability; AI-powered data processing to pull together data from disparate sources, and cloud computing so that data is accessible from anywhere and at all times. 

Data visualizations 

The rise of the connected factory and preponderance of IoT devices have gifted manufacturing companies with a tsunami of data, with plants that have advanced IIoT systems receiving as much as 70 terabytes of data per day from a single assembly line. This data is highly valuable, but only when organizations have the capability to access insights from it. 

Advanced data visualizations, such as 3D visualizations, offer a clearer view of changing metrics and a deeper look into shifting business and plant conditions. With such visualizations, plants can achieve a more accurate understanding of processes and root cause analysis, often in real-time.

Additive manufacturing

3D printing is almost standard for producing exact replacement parts when equipment fails, thereby reducing delays in dealing with incidents, but additive manufacturing is mastering new techniques that support process manufacturing to meet its goals. Additive and conventional manufacturing are “now starting to connect and create a more integrated production environment,” in the words of Bart Van der Schueren, CTO of Materialise.

New 3D printing materials are recyclable and reusable, helping plants improve sustainability, while micro 3D printing can produce ever-more complex and hard-to-source production components, assisting in shortening the supply chain and ensuring that plants have all the items they need. By integrating additive marketing, plants can ensure smoother production runs and fewer interruptions. 

Wearables 

Wearables use data from IIoT devices, GPS location data, and AI to deliver alerts to employees that warn them about potential hazards, remind them about safety or compliance requirements, and notify them about significant changes in the plant. 

For example, smart wristbands can alert the wearer if a surface is too hot to touch or a piece of equipment is malfunctioning, and GPS-embedded items monitored employee movements to enforce safe distancing during the pandemic. The market for industrial wearables is predicted to grow at a CAGR of 25% between 2021 and 2026, rising from an estimated $2 billion in value to $6.1 billion in that timeframe. 

Edge computing 

Edge solutions, wireless connectivity, and 5G/6G system go hand in hand with delivering the latency-free connections needed by IIoT devices, and the systems that rely on them. Digital twins, predictive analytics, supply chain monitoring dashboards, and other smart manufacturing technologies depend upon near-instant data from IIoT systems. 

Research by McKinsey predicts a sharp rise in 5G IoT sales from 2023, with units sold reaching over 22 million by 2030. 

Smart manufacturing is taking off

As Fictiv CEO and co-founder Dave Evans put it, “2020 was about seeing the problems, 2021 was about finding the solutions, and now in 2022, we see companies are making progress towards a future-proofed industry.” 

These 8 smart manufacturing trends, from predictive monitoring and digital twins to wearables and edge computing, build upon the digital transformation foundations laid in the last few years and are set to continue to strengthen throughout this year and beyond.

Digital transformation and industry 4.0 have been on the map for process manufacturing for a number of years. But the pace of adoption accelerated enormously during the disruption of COVID-19, and continued at double-speed as companies struggled to cope with the changes of the post-COVID world. 

But while digital transformation spread everywhere, there are significant variations in the challenges, priorities, and adoption rates among different industries. Each sector has its own story to tell and we’ll be exploring various industries and their transformation journeys in a series of blog posts. 

The State of Digital Transformation in Oil & Gas

The oil and gas vertical lagged behind other process manufacturing sectors when it comes to digital transformation. High oil prices helped blunt the need for operational excellence that drove other sectors towards digitization. Additionally, most oil and gas companies are massive and complex, but smaller, more nimble companies are earlier digital adopters and fare better in digital transformation. Sprawling oil and gas companies faced little competition from agile, digitally-transformed competition. 

COVID-19 forced the sector to embrace digital transformation, when oil and gas prices plummeted due to lockdowns and travel bans. The need to lower operating costs, cut waste, and drive operational efficiency became more acute in the face of a drop in revenue. The market turbulence caused by the invasion of Ukraine and the fast-moving global energy transition helped emphasize the importance of successful adoption of Industry 4.0. That said, progress is slow. According to McKinsey, only 30% of oil and gas companies have scaled digital technologies successfully, and while most are running digital transformation projects, some 70% are stuck in the pilot phase. 

Key Trends in Digital Transformation in the Oil and Gas Industry

Reducing Operating Costs

COVID-19 and the attendant crash in oil prices focused oil and gas companies’ attention on the need to reduce operating costs, cut waste, and increase operating efficiency. That need didn’t disappear with the end of the pandemic. On the contrary; rising energy prices refocused the need for European refineries, in particular, to cut energy usage so as to compete with American refineries whose energy costs are around half of their own. 

Because oil and gas companies are so large and complex, even small leakages and inefficiencies can cost billions of dollars. The average oil and gas company loses between $38 million and $88 million in unplanned downtime every year, and overbuys supplies to the tune of billions of unnecessary dollars because there is no integrated view of upstream operations. 

Oil and gas plants also have to deal with variations in crude and feedstock quality, which make it difficult to predict fouling and corrosion in equipment. Contaminant levels are hard-to-detect and can speed up corrosion and fouling and damage wastewater treatment systems. 

Increasing Efficiency with Advanced Analytics

As a result, implementing an integrated data strategy which encompasses smart sensors, advanced analytics, and integrated data platforms, is a high priority. Advanced analytics is top of the list, with McKinsey consultant Ying Wan Loh pointing out that analytics-driven yield energy and throughput improvement can increase profit per hour by up 50 cents per barrel. Big data analytics is the top trend that executives predict will positively impact their business growth in the next 3 years. 

An integrated data strategy can enable oil and gas companies to spot impending part failure and correct it while repairs are small and inexpensive; prevent overspending on materials and assets; and reduce energy and materials wastage through inefficient processes. 

Increasing Resilience with a Remote Workforce

COVID-19 highlighted the vulnerability of oil and gas companies. The average oil rig depends heavily on a human presence, but the pandemic forced companies to adopt tech solutions that allow rig engineers to work remotely. Process automation adoption was key to helping companies function during COVID-19, and the trend is still strong. 

Plants are also increasingly adopting digital twins, which enable maintenance teams to carry out root cause analysis remotely through smart glasses, and to complete repairs without putting any employees at risk. With digital twins, owners and vendors can track the performance of equipment and look for ways to improve it. 

Improving Environmental and Safety Profile Proactively

Over the past few years, the general public has turned a lot more attention to the environmental damage caused by oil and gas companies. The sector is struggling to recruit the best new talent, because of a widespread negative perception of its carbon footprint and safety issues. 

Oil and gas companies are also encountering new regulatory frameworks designed to limit carbon emissions. Decarbonization is now the second biggest area of concern for oil and gas executives, and the energy transition is gathering pace and weight. 

Digital transformation offers the opportunity for plants to be proactive in cutting emissions and reducing energy waste. Advanced analytics that detect failures and inefficiencies help increase safety, reduce waste, and boost energy efficiency by ensuring that all equipment and processes are always in top operating order. 

What is Holding Back Oil and Gas Digital Transformation

The biggest obstacle is a serious skills gap. According to Bain and Company, there’s been a growth of well over 100% in roles connected with analytics and data science in the industry, but few people with the relevant skills are interested in working in oil and gas. Only a third of energy sector leaders feel confident that they have the skills needed for the energy transition, 

The skills gap is nothing new; in 2018, Deloitte warned that the lack of midstream operative and digital experts, in comparison with the ongoing hiring of more construction, maintenance, and materials experts, would hamper organizations from transforming digitally upstream. 

However, the lack of skills is not the only obstacle. Oil and gas companies preside over legacy assets and outdated infrastructure. A long-term emphasis on growth over maintenance caused companies to neglect the digital upgrade of assets. Oil and gas companies now dream of advanced analytics monitoring centers that can respond quickly to anomalies within pipes and rigs, but first they need the IIoT sensors that would detect leaks and abnormalities. In many cases, the aging pipeline infrastructure isn’t equipped with the technology to gather data. 

Additionally, there’s a lack of digital alignment between different parts of the oil and gas ecosystem. Fuel transportation and storage capabilities are far more advanced than upstream activities, but even here we see that terminal operations are much more mature than tank management systems. Many companies invested heavily in technology for the field, but don’t have an integrated data system. Discrete units operate within extensive oil and gas companies without effective coordination or collaboration, strengthening data silos across the ecosystem. 

In the Oil and Gas Sector, Digital Transformation Has a Long Journey Ahead

The varying pressures of COVID-19, war in Ukraine, and the energy transition drove oil and gas companies a long way down the path towards digital transformation, but there is still a significant gap between them and other, more advanced sectors. The sector needs to build up the necessary talent, culture, and infrastructure for a long-term and effective digital transformation, but the ball is already rolling. 

By 2020, the general approach to predictive analytics has matured. Plants have begun to apply predictive analytics solutions at scale across numerous plants, which demands careful attention to ROI. The value of operational excellence is widely acknowledged. However, calculating ROI for predictive analytics especially for undefined, non-repeating problems, is a challenge. We recently held a webinar explaining how we calculate predictive ROI on SAM GUARD in process plants. You can view the full webinar here.

Boosting Production Generates More Value than Saving on Maintenance

Before we can begin to calculate ROI, we need to have some idea of what we want to achieve. For some plants, the main focus is on fighting failures. They need to cut their losses as much as possible, to make the plant more efficient and stop bleeding money on unnecessarily expensive replacements and last-minute repairs. They apply predictive analytics in the form of predictive maintenance (PdM), to help them prolong the lifetime of each expensive pump, valve, or turbine as much as possible and achieve OEE.In times of crisis, your instincts are to defend that which you already own. In process plants, this means using PdM to protect your existing equipment by spotting the earliest signs of anomalies that could indicate impending part failure. During the current pandemic, it’s fair to say that all plants are in that crisis mode. Cutting costs and improving efficiency are on every plant owner’s mind right now. Additionally, with so many maintenance teams working from a distance, managing skeleton crews, and/or only coming into the plant on a reduced schedule, it’s even more important to catch anomalies before they expand into urgent issues or expensive events. 

However, for other plants, cost-cutting is less of a concern. These plants already have an effective preventive maintenance schedule and backup policies, and the merits of predictive maintenance have less appeal to them. For these plants, their primary concern is production. Plant managers want to avoid failures and produce as much as possible on the current equipment. Boosting production generates significantly more ROI than saving on maintenance, especially in today’s markets when most plants are heavily utilized. Under the unusual circumstances of COVID-19, plants need to keep an eye on both goals: cutting maintenance costs, and maximizing production at a time when the economy is uncertain. 

Operational excellence is a key goal for process plants, and one of the biggest contributors to ROI. When your plant is operating flawlessly and smoothly, without unexpected shutdowns or uneven processes, it elevates production levels to new heights. Predictive analytics plays an important role in the journey to operational excellence. 

How Can Predictive Analytics Improve Production?

Based on our experience working with dozens of process plants, we see several ways to improve production overall ROI:

Solve defined, repeating problems

These types of problems, like optimizing the control loops or repeating the golden batch, are generally much easier to resolve, and their ROI is pretty easy to calculate.

Predict major equipment breakdowns

We see a lot of customers wanting to know when their most expensive piece of equipment will fail, but that happens so rarely that it’s almost impossible to calculate the ROI on their failure.

Prevent frequent, unforeseen failures

SAM GUARD’s focus is on the daily minor failures which have never happened in just this way before. We’re not talking about well-defined repeating events or major breakdowns, but the kind of blockages, failures, and other issues that occur on a regular basis in every process plant. Identifying these in advance requires looking at the entire plant, because you don’t know what’s going to hit you tomorrow. Fixing these daily problems before they escalate can have a significant cumulative effect on the plant’s production.

How NOT to Measure ROI in a Process Plant

Managers like to measure ROI by looking at KPIs such as plant utilization and plant availability. These are lagging indicators of output levels. At first glance, it seems that meeting or exceeding these KPIs is a clear indication of a successful predictive analytics application, and that failing to hit these KPIs shows poor ROI on the application.

In reality, things aren’t that simple, because there’s no way to be sure what would have happened if the predictive analytics system hadn’t been in use. There is no accurate baseline for comparison.

What’s more, there’s no way to be sure that a measured rise in production was definitely caused by actions taken as a result of alerts from the predictive analytics solution. Every plant carries out many activities to improve its workability and productivity, so it’s always possible that one of those activities had a bigger impact on production KPIs than the prediction of the analytics solution. When you calculate ROI, you need to be certain, you can’t base it on informed guesses.

The Correct Way to Calculate ROI

Instead of looking at these lagging KPIs, we measure ROI using what we call “direct indicators.” It means that we examine each event on its own merits by looking at signs that directly indicate the productivity of the system. We investigate the problem that was solved and ask “What would have happened if our predictive analytics solution hadn’t picked up on this event so early?”

Let’s look at an example:

Calculating Downtime Cost

Here’s a step-by-step overview of calculating ROI using this method. We use the example of a problem that SAM GUARD predictive analytics identified with the steam control valve in a process plant.

We ask the user what would the consequences have been if they hadn’t had SAM GUARD there to spot the event so early. The user comes up with a best case scenario, which might be that the plant engineer noticed the problem just a little bit later, so they lost two batches of production. We also ask for a worst case scenario, which could be where no one identified it early, and the plant lost eight batches before the problem was identified. This gives a range of ROI, from the minimum loss of two batches to the maximum loss of eight batches. For advanced users, we add exposure into the calculation. If the plant has ten steam control valves, we multiply the range of potential loss by ten to establish exposure.

This process gives us a reliable range of values for ROI on downtime cost, which is the cost to the plant if the valve broke completely and caused the plant to be closed.

Calculating Maintenance Cost

We also add another component: maintenance cost. The maintenance cost reflects the possibility that even without SAM GUARD, someone could have spotted the problem before it caused the valve to break or the plant to close, so all that was needed was some maintenance work.

SAM GUARD aggregates the range of values for potential downtime cost, and the potential maintenance cost, into a dashboard that reveals the true ROI of using SAM GUARD. For example, in 2 months, SAM GUARD saved one plant between $140,000 and $4 million. Even the lower value of savings is far more than the price of SAM GUARD for the 2 months in question.

Considering the Cost of Effort

Finally, we consider how much effort it costs to apply the solution. Sure, ROI may be high, but if the solution demands a lot of effort from plant employees then that could undermine the financial savings.

Each time there is an alert, someone has to investigate it and think about what to do in response. This requires effort. If there are too many alerts each day, with many false positives, or even legitimate alerts but not requiring immediate action, the effort cost becomes too great.

SAM GUARD produces 1-5 alerts per day on average, flagging just the relevant alerts that require attention. When you set that against savings of between $140,000 and $4 million, the ROI is high and the effort cost is very low.

With SAM GUARD, the ROI is Clear

Preventing small, undefined, non-repeating events can have an enormous impact on production. Continuous improvements like these are what take process plants to the next level of availability, and by calculating the ROI the value can be proven. However this type of calculation can only be done by looking at direct indicators, not lagging KPIs. Calculating ROI on SAM GUARD reveals that the solution produces a great deal of value for a small amount of effort.

For more details and examples of calculating ROI on SAM GUARD in process plants, watch our webinar.

As CEO of Precognize, I’ve just returned from my first, but definitely not my last, business trip to the United Arab Emirates. It was a momentous experience which holds great promise for Precognize and the SAMSON group. 

Precognize was invited to join a delegation of Israeli startups and entrepreneurs that attended Gitex, the Gulf Information Technology Exhibition, which is held every year in the UAE. The delegation from Israel was organized by Bank Hapoalim and the Israel Export Institute, to take advantage of the opportunity to create warm ties with Emirati businesses. 

An in-person conference experience

It was strange and exciting to be at an in-person conference again. There were plenty of precautions, with everyone wearing masks, no handshaking, and more space around the chairs than you’d normally expect. Like every other visitor to the UAE, I had to take a COVID-19 test before I set off and another when I arrived in the country. 

The conference itself was much smaller than normal, with around 1,200 businesses instead of the 4,500 that attended in 2019, but it’s still noteworthy as one of the few in-person tech trade shows taking place in 2020. 

A warm welcome for Israeli innovation

The government and businesses in the UAE are all very enthusiastic about the potential of Israeli innovation, and there’s a general atmosphere of progress and a desire to build and improve the nation. The country itself is filled with people from almost every country, so it has a global feel, and of course beautiful and “larger than life” due to the amazing architecture. 

It was quite amazing to be able to freely wander the streets as an Israeli. I felt fully welcomed whether on the show floor or on my morning jogs. It all feels so different compared to just a few years or even a few months ago, when the UAE seemed like a remote destination. But the country is just a 3-hour flight from Tel Aviv, and we’re thrilled that the gates are open. 

The first steps of Israel-Emirati connections

This warm welcome that Emirati businesses are extending to Israeli startups is extremely heartening, and we are so excited about the new partnerships that lie ahead! Business connections are mushrooming between the two nations, and non-oil trade is soon predicted to reach $4 billion. It feels like every corporation in the UAE wants an Israeli name on their list of clients or partners!

My sense is that SAMSON is on the brink of exciting new developments. We’re bringing the technology that Emirati companies desire, and they’re positive about doing business with us. SAMSON’s main prospects lie in the Emirate of Abu Dhabi, bringing innovative predictive analytics solutions to their oil and gas refineries and other process manufacturing plants. 

I wasn’t able to visit Abu Dhabi on this trip, but I did hold web conferences with local companies who are interested in working with us in the future. SAMSON’s local representative, Ouissem Ouremi, joined me on these meetings, and he’s already working with many customers there. Hopefully, on the next trip, we will take the next step of meeting in person in Abu Dhabi. 

It takes time to build every relationship, and business relationships are no exception, and the first steps are very promising. The sentiment towards Israel in the UAE feels very positive, and there’s a lot of excitement about SAMSON’s solutions and in particular SAM GUARD from Precognize. I’m looking forward to working with new clients and friends in the UAE. 

For some years now, there are rumors that change is in the air: “Industry 4.0”, “digitizing the industry”, “IIOT”, are but a few examples of buzz words we’ve been hearing for the past few years. Headlines dated three and four years back foretell that Predictive Maintenance is embracing analytics (here). Although the rumors are in the air for some time, we now feel a tremendous change. Now it’s time, and let us share with you why.

Let’s begin with the economy. Years of slow economic growth forced tightening one’s belt. Lowering expenses, getting rid of all excess fats, firing personnel, and focusing on production became mandatory. During the 11-year period from 2003 to 2013, the chemical industry in the European Union had an average production growth rate of 0.6%. Just a little bit more than the entire manufacturing which was up 0.3%. EU chemical industry production in 2012 declined by 2.3% compared with 2011. Recovery was slow in 2013, and also in 2014 production levels were slow and sales stagnant (Eurostat and Cefic Chemdata International(2014), slide 23). After applying all other measures, manufacturers are now looking for more sophisticated ways to save money. Manufacturers realize they can apply the sensors data they collect, and is mounted in their database anyway, to achieve great maintenance savings and additional production, while keeping all employees on-board.

It is important for plant managers to understand that the investment in analyzing the data for Predictive Maintenance cannot be postponed. Their operation people are always, busy, have daily problem to deal with, and there always seem to be something more urgent, or not enough people to run the project. But lowering maintenance cost to a minimum, minimizing failure, and being reliable is precisely the plan for a leaner, cost-minded plant.

In addition, past solutions were not good enough. The previous Predictive Maintenance solutions triggered thousands of false positives a day, which operators, as well as plant managers, had become numb to. Today, the reliability of the solutions, such as Precognize, increased immensely and it provides three to four true alerts a week. This significant cut down of noise enables to address the real problems timely and effectively.

And there is something else. Perhaps the key reason for withholding change is due to organizational culture. Embracing change is always a challenge. More so in a conservative environment where work habits, rules, and regulations had been nurtured and cultivated for years. Bear in mind that the average age of a plant manager is 58, 17% are over 60, and only 2.3% are under 30 (here) . It is important to understand that knowledge is disappearing as operation people grow older and retire. The intimate knowledge of the asset, the ability to detect failures as a result of years of experience, will fade away eventually. Their knowledge is essential for implementation of any analytical solution, especially for Predictive Maintenance. The experienced people should not be viewed as a stopper for a change, but as a critical driver of the transition.

We believe that the best way to win the trust of the people of the industry is to make them part of the solution, not the problem. Do not offer them a black box, but harness the experts’ knowledge and draw on their experience. For example, we at Precognize, embed the experts’ knowledge of the plant into a mathematical graph when implementing the system. By applying a mathematical graph on top of the machine learning, Precognize transforms real-time data from hundreds of sensors, into a few actionable alerts. Customer use cases demonstrate that Precognize’s innovative solution reduces maintenance budget substantially by preventing machine failure, saving time spent on false alarms, and calling off the need for several backup systems. The reliability of the plant depends on pursuing the best solutions that are offered today. One cannot stay behind.

The landscape of the industry keeps changing: emerging markets, technological breakthroughs, challenges, and opportunities. To succeed in this ever-changing environment, it is key to look for cutting-edge solutions in order to ride the wave of the digitalization of the industry and not get swamped by it.