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If you’re responsible for maintenance in plants or facilities, you’ve likely come across predictive maintenance (PdM). You might wonder if this advanced strategy could enhance your maintenance operations, or perhaps you simply want to know more about how it works and why it’s gaining popularity. You will learn nine key statistics about predictive maintenance today. By the end, you’ll understand PdM’s value, why companies are adopting it, and the requirements for implementing it effectively. Let’s begin. Predictive Maintenance Market Was Valued at $7.85B in 2022 Predictive maintenance is a rapidly growing market. In 2022, its global value was estimated at $7.85 billion. By 2030, this figure is expected to skyrocket to $60.13 billion, according to Grand View Research. That’s an astonishing compound annual growth rate of 29.5% Illustration: WorkTrek / Data: Grand View Research The report reveals that this growth is driven by two major factors: Stricter workplace safety standards The increasing availability of AI and machine learning (ML) tools These advanced tools enable companies to predict when machinery will fail and schedule repairs before that happens, reducing costly unplanned downtime. Not only does this keep the operations running, but it also reduces the risk of accidents. This combination of enhanced safety and cost savings explains why more companies are adopting predictive maintenance and why the market continues to expand. Now, who is leading the charge? Industry giants like IBM, Schneider Electric SE, Siemens, and Microsoft are heavily investing in these technologies, making predictive maintenance more accessible and accurate than ever before. Illustration: WorkTrek / Data: Grand View Research For example, Toyota is already leveraging IBM’s predictive maintenance solution, Maximo, to improve its operations and see impressive results. Brandon Haight, General Manager at Toyota North America Indiana, explains what it looks like: Illustration: WorkTrek / Quote: IBM So, if you’re considering whether predictive maintenance is worth it, this explosive growth tells you that it’s becoming the preferred solution for businesses looking to improve efficiency and save on costs. 30% of Facilities Use Predictive Maintenance Another global survey on the state of industrial maintenance in 2024, involving 1,165 MRO professionals across industries like manufacturing, retail, and hospitality, shines a light on the popularity of predictive maintenance. Of the 1,165 companies surveyed, nearly one-third actively use PdM. Predictive maintenance is the third most popular strategy behind preventive maintenance and the run-to-failure approach. Illustration: WorkTrek / Data: MaintainX Preventive maintenance remains the most widely used maintenance strategy because it’s simple—based on regularly scheduled checkups tied to time or usage. However, it has one fundamental drawback: it doesn’t consider the equipment’s actual condition. This can result in unnecessary maintenance for machines functioning perfectly or failing to detect potential issues that may arise between scheduled checkups. Predictive maintenance takes a more data-driven approach. It analyzes real-time equipment data to detect wear and tear before an issue arises. Source: WorkTrek This reduces unnecessary maintenance and minimizes unexpected breakdowns, saving time and money. Of course, PdM requires a higher initial investment in technology and training. This upfront cost is one reason why preventive maintenance still leads the way for many companies. However, as more businesses recognize the potential for long-term savings, predictive maintenance (PdM) is gaining traction. And this statistic shows that the tide is turning. 79% of Facilities Involve Technicians in Predictive Maintenance When it comes to predictive maintenance; you might wonder who’s taking the lead—maintenance inspectors, reliability engineers, IT specialists, or technicians. According to a PwC and Mainnovation 2017 survey, the answer is clear: technicians play a key role in predictive maintenance. They are directly involved in PdM efforts in 79% of surveyed facilities. Illustration: WorkTrek / Data: PwC This makes sense, as maintenance technicians are the closest to the machinery and understand the nuances of how the equipment operates. However, it’s important to note that most technicians are involved in lower maturity levels of PdM, primarily levels 1 and 2, where visual or instrument inspections are common. Other professionals involved in the process, like maintenance inspectors and reliability engineers, are needed for higher predictive maintenance maturity levels. But how do facilities stand when it comes to PdM maturity? The following statistics will tell us. Only 11% of Facilities Reach Level 4 Predictive Maintenance The same survey revealed that most facilities were still in the early stages of predictive maintenance maturity in 2017. Only 11% reached the highest Level (4), where big data analytics and machine learning drive decision-making. Illustration: WorkTrek / Data: PwC To understand the journey to Level 4, let's break down the four levels of PdM maturity (as per PwC and Mainnovation): Level 1: Visual inspections This is the most basic form, where equipment is checked based on technician observations. Level 2: Instrument inspections Tools and instruments are used to measure conditions. Level 3: Real-time condition monitoring Facilities gather real-time data from their machines to predict failures. Level 4: Big data and machine learning (PdM 4.0) Here, advanced algorithms analyze large amounts of data to provide predictive insights that improve asset availability and reduce downtime. Essentially, maturity levels depend on how much data is being used to forecast equipment failures. To reach level 4, companies must invest in sophisticated technologies like machine learning. While only 11% of companies were at this level in 2017, it's likely more companies have advanced since then, given the growing trend in the use of data-driven technologies. Interestingly, the survey highlighted that certain sectors, like rail, were leading the way. In 2017, 42% of rail facilities had already reached level 4, compared to the 11% of companies in all sectors. Illustration: WorkTrek / Data: PwC The rail industry’s high use of PdM can be attributed to its reliance on similar assets across locations, which makes data collection and analysis more effective. Additionally, political and public pressure to keep transportation running smoothly drives innovation and investments in this sector. Predictive Maintenance Can Lower Costs by 25% We've already touched on how predictive maintenance can save money. However, Deloitte Analytics Institute gives us more specific information: PdM can lower maintenance costs by up to 25%. Illustration: WorkTrek / Data: Deloitte How does this happen? For starters, with predictive maintenance, you avoid emergency repairs, which means you also avoid related costs. When a critical machine in a facility breaks down unexpectedly, you have to pay for emergency repair and associated costs like expedited parts shipping and overtime pay. Another way PdM reduces costs is by extending the lifespan of the equipment. By addressing minor issues before they escalate, your machines run smoothly for longer, reducing the need for replacements. Additionally, compared to preventive maintenance, PdM skips unnecessary checkups that often happen with fixed schedules, focusing only on repairs when they’re truly needed. All of this adds up to significant long-term savings for your facility. 47% Say Improving Uptime Is Their Main Goal With PdM However, lower maintenance costs are not the main driver for adopting PdM. According to the previously mentioned PwC survey, almost half of facilities say improving uptime is their main goal. Illustration: WorkTrek / Data: PwC Why is uptime the main goal? Because downtime costs manufacturers billions each year. When a machine stops unexpectedly, production ceases, resulting in revenue loss. Take Sitech Services, for example, a company providing site services to 22 factories in the chemical industry. Their former director, Richard Schouten, noted that downtime could cost them up to half a million euros daily! Illustration: WorkTrek / Quote: PwC For companies like this, uptime outweighs the importance of shaving a few percentage points off maintenance costs. And it’s not just about money. Downtime affects production schedules, delays deliveries, and can damage customer relationships—hurting your reputation. That’s why more companies are turning to PdM to eliminate unwanted downtime and all of its negative consequences. Predictive Maintenance Boosts Uptime by 10-20% But how much can PdM improve your uptime? According to Deloitte’s internal studies, it can be anywhere between 10 and 20%. Illustration: WorkTrek / Data: Deloitte This is because PdM allows you to plan maintenance activities at the most convenient times. Instead of reacting to breakdowns during production hours, you can schedule repairs during off-peak times or when the right personnel are available. PdM also helps your logistics team by forecasting which parts you’ll need, ensuring you have the right components when maintenance is due. In short, PdM can optimize your entire maintenance process to ensure equipment stays up and running when you need it most. But there’s one critical factor you need for PdM to work. And you probably already know what it is. 60% of Maintenance Professionals Say Data Availability Is Key to PdM Success According to PwC and Mainnovation, 60% of maintenance professionals believe that access to reliable, accurate data is critical for successfully implementing predictive maintenance. Illustration: WorkTrek / Data: PwC And it’s no surprise—PdM depends on continuous data streams to predict equipment failures before they happen. Without this data from sensors, it's impossible to predict maintenance needs effectively. However, many facilities face challenges with data collection. Older equipment may lack the sensors required for real-time monitoring, or the infrastructure to collect data from these machines might not yet exist. This is especially challenging in environments like hazardous industries or in areas where assets, like trains or trucks, move over large distances, which demands robust IoT networks. The quality of data is just as important. To accurately predict failures, you need detailed information such as temperature, pressure, and even more advanced metrics. Without these insights, predictive maintenance is essentially running blind. 51% of Facilities Aren’t Planning to Adopt Predictive Maintenance Despite all the benefits of predictive maintenance, over half of the facilities surveyed in PwC's 2017 study said they weren’t planning to adopt it. Illustration: WorkTrek / Data: PwC Why would so many shy away from such a promising technology? For one, implementing PdM is costly. It requires substantial investments in hardware and software and hiring specialized workers like data scientists and engineers to interpret and act on the data. However, this statistic is from 2017, and since then, the adoption of PdM has grown. Still, many companies aren’t ready to adopt PdM. For those companies, there's still an option to improve reliability through other maintenance strategies, like advanced preventive maintenance.  For example, a modern CMMS like WorkTrek allows you to track meter readings and set advanced preventive schedules, as shown in the image below. Source: WorkTrek This can provide companies with a more reliable maintenance approach even if they are not yet ready for predictive maintenance. Conclusion Predictive maintenance is gaining momentum, bringing the benefits of reduced downtime, cost savings, and improved safety to more and more facilities. However, only some companies are ready to adopt it, and many are still progressing through different maturity levels. Nonetheless, the growing trend toward digitalization, artificial intelligence (AI), and machine learning positions PdM as the future of maintenance. As more companies invest in these technologies, those that embrace predictive maintenance early on will be in a stronger position to reap its long-term benefits.

Computerized maintenance management systems (CMMS). If you want to know some interesting facts about it, you've come to the right place. Today, we'll walk you through key statistics and insights about computerized maintenance management systems (CMMS). You'll see how the market is evolving, why more companies are adopting this technology, and how it can benefit your maintenance operations. The Global CMMS Market Size Is Projected to Reach US $4,2 Billion by 2033 Future Market Insights reveals that the CMMS market is expected to grow significantly over the next ten years. More precisely, it is projected to grow from US $1,636.2 million in 2023 to US $4,215.1 million by 2033, with a growth rate of 9.1% per year. Illustration: WorkTrek / Data: Future Market Insights To put this into perspective, from 2018 to 2022, the growth rate was just 7.0%. So, what’s driving this rapid demand? It’s simple: industries need their machines to run smoothly and last longer, and they need better visibility to the work being performed, which is what CMMS offers. With downtime costing millions per hour, businesses are turning to CMMS to track work orders, monitor performance, and make smarter decisions faster. Source: WorkTrek Another reason for this growth is the general shift toward automation and digital solutions. Gone are the days when maintenance logs were kept with pen and paper. With CMMS, everything is organized digitally, giving businesses more control over their operations. Plus, as digital tools are used more widely, CMMS is becoming more affordable and user-friendly, opening the doors for small and medium businesses to join in. Finally, a modern CMMS can integrate with IoT sensors to analyze real-time data and automatically schedule maintenance based on predictive insights, further reducing downtime and improving asset management. Source: WorkTrek Considering all these factors, it’s easy to see why more companies are jumping on board and investing in CMMS. The CMMS Market Contributes 20% to the Global Asset Management Market CMMS now makes up one-fifth of the global asset management market, and that number is telling. Illustration: WorkTrek / Data: Future Market Insights This shows how vital these systems are for managing and maintaining physical assets. If you're in an industry where your equipment's uptime is crucial—manufacturing, construction, or utilities—CMMS is a game-changer. It ensures that preventative maintenance happens on time, which means fewer breakdowns and more efficient use of your assets. In the long run, this extends the lifespan of your machinery and saves you money. This 20% share shows a trend toward software-based solutions for managing extensive equipment inventories. Companies realize that spreadsheets and manual tracking just don’t cut it anymore. With CMMS, everything is in one place, making it easier to manage work orders, track asset histories, and plan for future maintenance needs. This trend highlights the growing recognition of CMMS as a critical tool for companies that want to stay competitive and efficient. 72% of Companies Use CMMS for Organizing Maintenance This statistic comes from the State of Maintenance Report 2024 by Upkeep, which surveyed maintenance supervisors and managers from mostly small to mid-sized companies. On why they use CMMS, 72% of maintenance professionals answered that they primarily use it to organize their maintenance activities and data in one place. Illustration: WorkTrek / Data: Upkeep This makes perfect sense. Juggling work orders, service history, parts inventory, and asset conditions using different systems or, worse, manually, is chaotic and inefficient. With CMMS, it’s the opposite. Take our very own CMMS, WorkTrek, as an example. It centralizes everything: maintenance tasks, documents, service history, spare parts inventory, asset conditions, and expenses. Source: WorkTrek From one central dashboard, you can easily schedule and assign tasks, track progress, and generate detailed reports. When everything is in one place, it’s easier to manage, which saves you time and minimizes errors. It really can’t get more organized than this! 28% of Maintenance Professionals Say Reduced Unplanned Downtime Is the Biggest Benefit of CMMS Another key finding from the State of Maintenance Report is that 28% of maintenance professionals believe reducing unplanned downtime is the biggest benefit of CMMS. Illustration: WorkTrek / Data: Upkeep It’s easy to see why when we know that unplanned downtime is a huge pain point for many companies. Unfortunately, this pain point occurs way too often. Namely, research shows that over two-thirds of industrial companies experience at least one unplanned outage a month. However, with the help of a CMMS, that can be reduced. Maintenance teams can easily stay on top of their maintenance strategy, schedule preventive maintenance more accurately, and even monitor assets in real time to predict failures before they cause costly breakdowns. As you will soon learn, the latter is growing in popularity across industries. 48% of CMMS Users Have a Predictive Maintenance Regime Even in 2022, 48% of CMMS users have already implemented a predictive maintenance regime. This statistic comes from the CMMS Market Report and Insights by Comparesoft, which analyzed data from 119 CMMS buyers. Illustration: WorkTrek / Data: Comparesoft This shows that almost half of CMMS users are tapping into advanced capabilities to optimize their maintenance strategies further. Predictive maintenance allows CMMS to analyze equipment data and identify patterns that indicate potential issues, letting maintenance teams fix problems before they cause downtime. As more companies adopt Industry 4.0 and IoT technologies, CMMS solutions that connect with sensors to gather real-time data are becoming necessary. With nearly half of users already using predictive maintenance by 2022, it's clear that this trend is set to grow, making these capabilities a must-have for any future-focused CMMS. 21% of CMMS Users Are Interested Only in Core CMMS Functions Not surprisingly, the same Comparesoft report revealed that only 21% of companies were interested in core CMMS functions like asset tracking, job logging, and work order management. With so many companies already adopting predictive maintenance, it makes sense that most users expect more from their CMMS. The majority (79%) are looking for additional features such as service management analytics, parts management, health and safety compliance, and integration with sensors for predictive maintenance. Illustration: WorkTrek / Data: Comparesoft Why is this significant? It highlights that the needs of modern maintenance teams are evolving and that CMMS providers must continuously innovate to stay competitive. It’s no longer enough just to offer basic tracking and scheduling features. As digital technologies and IoT become standard, maintenance teams expect their CMMS to integrate with equipment monitoring software, collect sensor data, and automate maintenance activities. This reinforces the idea that predictive maintenance is becoming a "must-have". 58% of Users Who Sought a CMMS in 2022 Used Spreadsheets In 2022, more than half of potential CMMS buyers still relied on spreadsheets, while 28% weren’t using any maintenance management system, according to Comparesoft. Illustration: WorkTrek / Data: Comparesoft This data highlights a stark contrast between past practices and the future direction of maintenance management. It is easier for companies to adopt CMMS if they are already managing their maintenance somehow, than not at all. Because they can quickly become aware of the drawbacks of their approach. While spreadsheets were once the go-to tool, their limitations are becoming increasingly apparent. They are time-consuming, error-prone, and need more automation and advanced tracking features that a CMMS offers. With a CMMS, maintenance teams can automate tasks, track service history, and manage assets from a single platform—efficiencies that spreadsheets simply cannot match. The growing demand for CMMS shows that more companies are moving away from outdated methods, investing in systems that handle everything from basic asset tracking to predictive maintenance. And you’re about to learn the key reason why this shift matters. 74% of Maintenance Professionals Say Their CMMS Improves Productivity Spreadsheets alone cannot significantly improve productivity, but CMMS can. And this statistic proves it. In 2020, Advanced Technology Services (ATS) partnered with Plant Engineering Magazine to survey the state and common trends in industrial maintenance across U.S. manufacturing plants. Regarding software use, they found that CMMS improves productivity for 74% of maintenance professionals. Illustration: WorkTrek / Data: Advanced Technology Services This statistic highlights how much a well-implemented CMMS can impact day-to-day operations. Here are some ways CMMS can boost your productivity: Automate and standardize work order creation, assignment, and tracking. Schedule and manage preventive maintenance tasks based on equipment data. Track inventory levels, manage spare parts, and optimize purchasing. Use mobile apps to access and update work orders in the field. Implement predictive maintenance using data analytics and algorithms. When you switch from manually managing tasks to an automated CMMS, the boost in efficiency is clear. That’s why so many maintenance professionals notice a significant productivity gain. 7% of Maintenance Managers Say That Poor CMMS Data Quality Is the Biggest Challenge They Face But even with all its benefits, a CMMS isn’t flawless. According to ABL's 2023 Global Maintenance Manager Report, 7% of maintenance managers cited poor data quality as their biggest challenge in using CMMS. Illustration: WorkTrek / Data: ABL While this number might seem small, it points to a serious problem. A CMMS is only as good as the data entered into it. If the data—like equipment details, maintenance history, or performance metrics—is incomplete or incorrect, it can lead to mistakes, delays, and inefficiencies. For instance, wrong model numbers or missing manufacturer information can delay repairs or cause the wrong parts to be ordered, leading to increased downtime. The report further reveals the root cause of this issue: inadequate change management processes, where updates about equipment changes or repairs aren’t properly recorded. Luckily, there is a solution to this challenge. Stuart Murray, Head of Technical at ABL Group Company, suggests that maintenance teams can solve this by collecting accurate data, using standardized processes, and implementing continuous improvement strategies: Illustration: WorkTrek / Quote: ABL Ultimately, it’s not the CMMS itself that causes problems—it’s how we use it. Ultimately, a CMMS can only be as effective as the information it holds. Conclusion These nine statistics tell a clear story—adopting a CMMS can take your operations to the next level. As long as you use it properly, it will keep your maintenance organized, boost productivity, and reduce downtime. With rising demand for predictive maintenance and improved efficiency, now is the time to embrace this technology. You’ll need to if you want to stay competitive. As more companies adopt CMMS to streamline processes and reduce costs, sticking to outdated methods could leave you struggling to keep up.

Manufacturing maintenance is an ever-evolving field, with new processes, technologies, and best practices always emerging. It seems like, just when we think we know everything there is to know about this important process, a new innovation appears, reminding us there's always more to learn and improve. That’s why we’ve dedicated this article to exploring six trends in manufacturing maintenance that you need to be aware of right now. By staying on top of these trends, you’ll not only be able to stay ahead of your competition but also unlock new ways to maximize the potential of your assets. Let’s dive right in. Predictive Maintenance Predictive maintenance (PdM) is a proactive strategy focused on predicting and preventing equipment failure by collecting and analyzing data from the machines themselves. According to a 2024 MaintainX survey, it is currently the third most commonly used type of maintenance, following the traditional reactive and preventive methods. Illustration: WorkTrek / Data: MaintainX So, how does it work exactly? First, data is collected in real-time through various sensors installed on equipment, like the nanotechnology-powered sticker sensor produced by Feelit, as shown below. Source: Feelit These sensors track all sorts of metrics related to the operational condition of assets, like vibration, temperature, operating hours, and so much more. The data is then processed by machine learning (ML) and artificial intelligence (AI) software to detect patterns, identify anomalies, establish correlations between parameters, and assess their impact on equipment health. Ultimately, this enables the system to predict future asset behavior and show it to you in an easy-to-understand way. Source: WorkTrek Plus, these systems can send alerts to users when potential failures are detected, allowing repairs and checkups to be scheduled in advance. Overall, PdM is a real game-changer, ensuring that upkeep is performed only when actually necessary, reducing the risk of both under- and over-maintenance. This, in turn, translates to less downtime (both planned and unplanned), lower repair costs, and more reliable assets. No wonder this type of maintenance is becoming increasingly popular in the manufacturing sector. Take Cintas, for example, an American corporation that provides various products and services to businesses, including uniforms, mats, mops, cleaning and restroom supplies, and more. As their Maintenance Supervisor, Woody Rogers points out; it’s precisely predictive maintenance that empowers them to achieve their high production standards: Because we strive to operate higher than the standard, it’s critical for us to stay ahead of any issues that might impact asset performance or uptime. By monitoring and analyzing historical and real-time data that we collect on the conditions of our assets, we’ve been able to proactively identify, detect, and fix issues before they become bigger problems. After all, in this industry, you have to be able to stay ahead of potential issues if you want to keep your operations running smoothly. And predictive maintenance is all about staying ahead. Maintenance-as-a-Service (MaaS) One of the main barriers to adopting predictive maintenance is its high setup cost, which is driven by the costly, high-tech infrastructure required. Luckily, there's a solution to this problem on the horizon called Maintenance-as-a-Service (MaaS). This new, subscription-based model allows companies to outsource their upkeep to third-party service providers instead of building and managing their maintenance systems and teams. These vendors offer sophisticated predictive tech and the expertise needed to make it work so that the plants have more time to focus on their core activities. The best part? Manufacturing facilities pay only for the maintenance they need, on a pay-as-you-go basis—just like they would for their SaaS solutions like, say, CMMS. MaaS itself is a broad concept, encompassing a variety of sub-services, such as: Fault-Detection-as-a-Service Delivers detailed information on asset status, including predictions of failures based on parameters like End of Life (EOF) and Mean Time Between Failures (MTBF) Recommendations-as-a-Service Provides suggestions on when to perform repairs for specific parts or equipment Simulation-as-a-Service Simulates future asset operation based on historical data in the cloud Training-as-a-Service Offers cloud-based training, including VR (Virtual Reality) and AR (Augmented Reality)-based services  Thanks to MaaS and its flexibility, businesses can tailor their subscription plans based on their actual needs and budget, which enables them to use advanced technologies without breaking the bank. Precognize is one of the providers of such services. They offer SAM GUARD®, an AI-powered predictive maintenance solution that detects issues in equipment and operational processes. In addition to this, their Digital Transformation Experts (DTE) team works closely with clients to implement this tool and help analyze the data it generates. This expert team monitors and evaluates alerts, compiles reports and ensures companies get the most out of the system. Here's a more detailed description of what the team does, as found on their website: Source: Precognize All in all, although MaaS is still in its early stages, it holds immense potential, especially for smaller facilities. It presents an amazing opportunity to experiment with almost anything the maintenance industry offers without compromising the organization’s profitability. So, as it matures, expect it to become a go-to solution for businesses looking to stay ahead of the game while keeping costs under control. 3D Printed Replacement Parts 3D printing, also known as additive manufacturing, enables maintenance teams to create replacement parts on the spot, which eliminates all sorts of concerns related to inventory management. Before you ask, yes, these 3D-printed parts are reliable. In fact, research published in MDPI examined parts produced for Stellantis, a Spanish automotive manufacturer, and found that 80% of the original properties were retained in 3D-printed components. Illustration: WorkTrek / Data: MDPI In other words, they perform just as well as their traditionally produced counterparts. The research also highlights that additive manufacturing plays a key role in preventive maintenance and "will become even more important in the future." We agree. With on-demand spare parts printing, lead times are significantly reduced, unlocking many benefits for manufacturing facilities. Massimiliano Cecconi, Innovation Engineering Director at Baker Hughes, one of the world's largest oil field services companies, elaborates: Additive manufacturing allows us to develop parts and products more efficiently, with better performance and cost-effectively, and it accelerates the speed at which we can bring products to market: production times are drastically reduced—the finished product can be completed in weeks instead of months, significantly reducing production cycles, which ultimately benefits the customer. Faster production, less downtime, and more efficient inventory control are all made possible by additive manufacturing. Even when you need components no longer in production, 3D printing can help. This was demonstrated in 2017, when Siemens reverse-engineered and 3D printed one such component for the Slovenian nuclear power plant, Krško. Source: Siemens Namely, the plant needed a new 108 mm diameter impeller for a fire protection pump that has been in operation since 1981. The problem was that the original manufacturer had since gone out of business. Fortunately, Siemens successfully produced the component, which marked "the first successful commercial installation and continuing safe operation" of such a part in a nuclear power plant. You can see the result below. From left to right, the photo shows the original part, Siemens’ 3D-printed prototype, and the 3D-printed replacement installed and operating in Krško. Source: Siemens Vinko Planinc, Head of Maintenance at the Krško plant, praised the tech’s capability to prolong assets’ useful lives. Illustration: WorkTrek / Quote: Siemens Ultimately, while the high costs of 3D printers mean it may take a little time for every plant to have one, the benefits are simply too powerful to ignore. It’s only a matter of time before we see this technology become a must-have tool for factories across industries, transforming spare part management for good. Use of Augmented Reality For Visualization and Diagnostics Immersive technologies, once the stuff of sci-fi movies, are now becoming part of our reality, and manufacturing maintenance is not left behind. In particular, augmented reality (AR) is gaining more and more traction in this area. In the simplest terms, this technology allows technicians to overlay digital information onto real-world environments and equipment through AR-powered headsets, mobile devices, or wearables. As Drew Bowers, Group Leader for Human Factors in UDRI’s Sensor and Software Systems division, notes, many of us have already seen this tech in action. Illustration: WorkTrek / Quote: University of Dayton Research Institute. In the context of maintenance, it looks quite similar. A technician points a tablet camera at a machine, and the device displays relevant notes on the screen. For example, it could indicate which wire or pipe is which, which drive controls which motor, or which spare parts are required for repair. Some systems show whether these parts are available in the warehouse. This, in turn, dramatically speeds up problem diagnosis and makes repairs more precise, with fewer mistakes. However, AR also improves upkeep processes by enabling remote support. In this scenario, an off-site expert shares the technician’s view in real-time, providing advice and annotating screens with instructions, data, and other helpful information. That’s precisely the service they offer at ABB, says Stuart Thompson, the President of the Electrification Service Division. Illustration: WorkTrek / Quote: Data Centre Dynamics ABB has supported customers in over 20 countries this way, reducing repair time and costs by eliminating the need to send experts to facilities. You can see a demonstration of how it all works in this video: https://www.youtube.com/watch?v=9YUpvD_KoPw Source: ABB Medium voltage products on YouTube It seems futuristic, doesn’t it? Yet, it’s already here. And as AR technology keeps developing, we’ll surely be seeing a lot more of it. Maintenance Robotics Speaking of sci-fi-like technology, there are now all sorts of robots available that can perform various routine upkeep tasks, either alongside humans or even all on their own. And the benefits are almost too many to count. For one, robots can significantly increase the efficiency of your maintenance efforts. Take, for example, Bristola’s remote-controlled submersible robots that clean and maintain liquid storage tanks. Source: Bristola The Bristola team installs its patented equalization chamber entry system, and the machine takes it from there, removing all the sediment and build-up within the tank. No need to drain the tanks, plan for downtime, or send anyone inside for manual cleaning. The process becomes much faster and more cost-effective, taking two days instead of the usual six weeks. In addition to efficiency, robots also greatly improve safety. Maintenance personnel at the global stainless steel manufacturer, Outokumpu, know this very well. The company is currently piloting safety inspection robots, which are expected to reduce employee exposure to hazardous substances by over 80% and cut down dangerous repairs by 20%. Thorsten Piniek, Outokumpu’s Vice President of Health Safety, provides some more information. Illustration: WorkTrek / Quote: Engineer Live He adds that the robots can also shorten malfunction times since they can detect defects earlier through temperature and sound profile measurements. The most fascinating thing about these maintenance robots is that they get smarter daily. Just recently, Boston Dynamics’ robot dog, Spot, learned predictive maintenance. Spot can now perform acoustic leak detection and vibration inspections, helping maintenance technicians identify early signs of bearing failure. You can learn more about Spot in this video: https://www.youtube.com/watch?v=0hrYzgP_Lg4 Source: Boston Dynamics on YouTube Given all these amazing advancements, it’ll be very interesting to see what else the future holds for the field of robotics and its role in manufacturing maintenance. One thing is for certain, though: even more ground-breaking innovations are on the horizon. Green Maintenance With 87% of business leaders acknowledging the growing importance of sustainable manufacturing, we see more and more factories working to reduce the environmental impact of their maintenance activities. Illustration: WorkTrek / Data: Fictiv They are embracing innovative practices to hit those goals. For instance, many are swapping out materials and tools used for upkeep for more eco-friendly alternatives. The market, flooded with greener options, reflects this trend, too. For example, take EcoChem’s Eco-Green Kleen, a water-based industrial degreaser that cuts out the need for harsh chemicals. Source: EcoChem Many, however, go further and evolve their maintenance processes to drive sustainability. According to Bill Zujewski, CMO at NetFoundry, a zero-trust connectivity platform, predictive maintenance is a good example of a maintenance strategy that can yield more sustainable results: There are two use cases around predictive maintenance that jump out at me as win-wins – for the environment and manufacturers. The first one is around the service process. If you can reduce the truck rolls that have to come out to [...]  fix something that’s broken, you’re reducing your carbon footprint – there’s less fossil fuel burned for all those service people coming on-site for routine checkups when they’re not needed. [...]  The other use case is around the parts and the machines themselves. If you can get the machines and part replacements to last longer and not replace parts prematurely, you’re saving scarce resources. You’re not sending them to the dump and creating pollution and waste. Interestingly enough, nearly every trend we’ve discussed in this article contributes to greener maintenance in a similar way. This is because, broadly speaking, all these innovations are developed specifically to decrease the frequency of repairs and help businesses achieve more with less in the long run. This naturally translates to less waste, lower energy consumption, and optimized resource use. 3D printing is another case in point, as it significantly cuts down on material waste. Adam Lea-Bischinger, Partner at Asset One LLP, a company providing Asset Management Advisory services, elaborates: [Subtractive manufacturing] involves starting with a raw material, such as a block of metal, and cutting it down to get the shape you need, creating a lot of waste. An alternative – enabled by new technologies – is additive manufacturing, also known as 3D printing. In this case, you build a product by adding material, rather than subtracting it, so there is very little waste. The bottom line? The environmental impact of manufacturing facilities is no longer being ignored. Companies are finally stepping up, taking responsibility, and actively trying to reduce their negative footprint wherever possible. And when it comes to maintenance in particular, there’s a huge opportunity to make a real difference. Conclusion Looking at all these amazing innovations and trends, it becomes clear that there's never been a more exciting time to work in manufacturing maintenance. Augmented reality, robotics, and the ability to predict future asset behavior—things we used to see only in films—are now our everyday tools, helping us make equipment more reliable, safer, and longer-lasting. And the field is evolving rapidly, which means we'll likely see even more advanced technologies in the near future. In other words, this is just the beginning. The cutting-edge breakthroughs will probably transform the industry in ways we can barely imagine.

In this article, we’re exploring ten statistics about the state of manufacturing maintenance, uncovering the trends they point to and what they could mean for the overall efficiency of plant operations. Understanding these insights can make all the difference in your decision-making, potentially helping you streamline processes and even unlock significant cost savings. So, let’s get started and go over some compelling data about this critical process. In 2018, 57% of Manufacturing Facilities Used a Run-To-Failure Maintenance Method A Maintenance Report from Plant Engineering and ATS offers an insightful snapshot of how equipment upkeep was handled just a few years ago. One particularly interesting data point is that over half (57%) of manufacturing businesses relied on run-to-failure (RTF) maintenance at the time. Illustration: WorkTrek / Data: Plant Engineering Essentially, it means they didn’t have any maintenance strategy but used assets until they failed and needed repair. Companies often choose this reactive approach because it requires minimal to no planning and has lower initial costs, making it the easiest to implement. Plus, maintenance only happens when necessary, so it tends to interrupt production less frequently and reduces planned downtime. However, the irony is that this approach is often cited as a major contributor to unplanned downtime. This is because it overlooks proactive asset care, allowing smaller issues to escalate unexpectedly and disrupt operations. But is that really the case? Is run-to-failure maintenance truly the leading cause of unscheduled downtime? The next statistic may offer some insight. At 44%, Aging Equipment Was the Leading Cause of Unscheduled Downtime in Manufacturing Facilities In 2018 According to the same survey, aging equipment is the leading cause of unplanned downtime, surpassing issues like operator errors, lack of time for maintenance, and neglect of upkeep. Illustration: WorkTrek / Data: Plant Engineering Does reactive maintenance play a role in this? To some extent. After all, older assets tend to break down more frequently. If you rely solely on run-to-failure maintenance, you will inevitably face more frequent production stoppages for unexpected repairs. However, we can't place all the blame on RTF. The truth is aging equipment is a big problem itself. Even with a preventive approach, it can still cause disruptions. Older machines might require no longer manufactured parts, be difficult to handle for younger operators or those that haven’t gotten used to their quirks, or simply be nearing the end of their lifespan. No asset is built to last forever, no matter how effective the maintenance strategy. That's why upgrading machinery was the number one solution survey respondents chose for addressing unscheduled downtime, with proactive upkeep coming in third. Illustration: WorkTrek / Data: Plant Engineering Here’s the bottom line: if you want to minimize downtime, you need reliable machinery running at its best. Aging equipment doesn't really meet that standard, especially if you only use reactive maintenance for its upkeep. So, to improve reliability across operations, invest in preventive upkeep strategies or get new machines, depending on what your budget allows for. The next statistic shows us that the former option is more realistic. For 69% of Maintenance Teams, Proactive Maintenance Is the Solution to Aging Infrastructure The new 2024 Limble report highlights an interesting shift in how we tackle the challenges of aging assets. Back in 2018, we saw that the focus was primarily on upgrading equipment, but now proactive maintenance has taken center stage. Illustration: WorkTrek / Data: Limble That’s because, although it’s completely natural for equipment to degrade over time, preventive upkeep can still significantly slow this process down. And, by addressing minor issues such as leaks, rust, and weakening components through regular checkups and repairs, we can at least postpone those costly replacements. This is why proactive maintenance is the number one strategy for older infrastructure care, while replacements and upgrades are seen as last resort. They are reserved for when there are truly no other options. After all, who wouldn’t prefer to just keep fixing their old, trusted assets rather than having to shell out money for new purchases constantly? A Manufacturing Facility Allocates Approximately 9.7% of Its Annual Operating Budget to Maintenance Processes On average, manufacturing facilities allocated 9.7% of their annual operating budgets to upkeep in 2018. Illustration: WorkTrek / Data: Plant Engineering This is definitely a significant amount, but is it a surprising one? Not really. Maintenance is a costly endeavor. Keeping equipment in top shape demands real investment, from labor and spare parts to tools and downtime costs. What's fascinating, though, is how little this has changed over time. Fast forward to 2024, and a new MaintainX survey shows that most manufacturers spend  5-10% of their annual budgets on upkeep. Illustration: WorkTrek / Data: MaintainX This is close to the 2018 figures. But with inflation and ongoing material and labor shortages, how have plants stuck to these percentages? Have innovations in technology and process efficiency allowed us to achieve more with less, or have companies simply had to raise their overall budgets to keep up with rising costs? The truth is: a bit of both. While technological advances help streamline operations, maintenance still requires a serious financial investment. That much is unlikely to change any time soon. But it’s not just about money—allocating enough time to this vital process is also a must. 31% of Facilities Spend 30 Hours or More Each Week on Scheduled Maintenance The Engineering Plant and ATS survey reveals that nearly a third of plants spend thirty or more hours per week on maintenance. For a factory operating two 8-hour shifts daily, five days a week, that's a significant chunk of total working hours. In fact, according to the survey, it's 11 hours longer than the industry average at the time, which is 19 hours. Illustration: WorkTrek / Data: Plant Engineering While maintenance is undoubtedly important, you don’t want to spend too much time on it. Yes, although that may not necessarily be the case with these survey respondents, there is such a thing as too much maintenance—which can spell trouble. It can lead to delays in production, labor cost increases, and even premature wear of certain components. It’s an easy way to lose time and money without realizing it. So, if you are also allocating more hours to maintenance than the industry standard, ask yourself if this maintenance level is necessary for your operations or if you could be missing out on more efficient practices. Use these maintenance calculators to determine whether you’re spending adequate time on planned maintenance. In 2024, 67% of Manufacturing Companies Are Using Preventive Maintenance to Address Machine Downtime A 2024 Limble report on maintenance in manufacturing and facilities highlights that, for many manufacturing companies (67%), preventive maintenance is the top strategy for preventing downtime. Illustration: WorkTrek / Data: Limble It’s easy to see why. Preventive maintenance focuses on performing regular checkups and smaller repairs to prevent minor issues from escalating into larger, more detrimental ones. As a result, equipment becomes more reliable, longer-lasting, and safer, directly translating to fewer operational disruptions. Previously, one of the main criticisms of this method was its complexity in scheduling and planning, especially when compared to reactive strategies. However, that is no longer the case thanks to advanced CMMS solutions like WorkTrek. These solutions simplify various plant upkeep tasks, making the process more well-timed, efficient, and cost-effective. WorkTrek, for example, enables you to schedule service using predetermined intervals, assign tasks to specific workers, generate detailed work orders, and set up alerts for upcoming or overdue maintenance. As depicted below, the software also documents all these activities, allowing you to see the whole upkeep history at a glance. Source: WorkTrek In other words, preventive maintenance is highly effective and has become much easier to implement. It’s no surprise that so many organizations choose precisely this approach to avoid that dreaded unscheduled downtime. 51% of Maintenance Professionals Say That Machine Downtime and Breakdowns Are One of Their Top Challenges Equipment uptime is one of the most valuable assets for manufacturing companies but, according to the 2024 Limble survey, they seem to have a hard time increasing it. As it turns out, 51% of maintenance professionals agree that downtime is one of their top three biggest challenges. Illustration: WorkTrek / Data: Limble But why is downtime such a big issue? Because it can seriously impact every facet of business operations. It causes production lines to grind to a halt, crippling productivity and cutting into profits, all while damaging the company’s reputation due to delays. On top of that, operational costs soar, particularly as overtime becomes a necessity to compensate for lost time. In an attempt to catch up, manufacturers may even rush production, inviting a host of quality issues into the mix as well. No matter how you look at it, downtime spells all kinds of trouble, which explains why so many plants highlight it as a critical concern in their facilities. But just how often do they have to deal with it? The Average Manufacturing Facility Suffers 20 Downtime Incidents a Month The 2022 Siemens survey titled The True Cost of Downtime offers more detailed insights into this persistent problem, revealing that, on average, unplanned downtime occurs about 20 times a month. Illustration: WorkTrek / Data: Siemens The silver lining, the study emphasizes, is that this figure represents six fewer instances than two years prior. So, does this mean that things are looking better for manufacturing maintenance? Not exactly. Although the number of incidents has decreased, the same research shows that recovery times are still alarmingly high. Namely, the average plant loses more than a full day of production—25 hours to be exact—each month due to unplanned downtime. According to the study, this issue persists because, while dedicated maintenance technology is improving, supply chains face serious challenges. As a result, emergency repairs are often put on hold because it’s impossible to procure critical parts amidst all the material and component shortages. To make matters worse, the industry is grappling with labor shortages, too. There simply aren’t enough skilled workers available to handle these repairs. When you put it all together, downtime costs too much, and our next statistic reveals how much. The Cost of an Hour’s Downtime in an Automotive Manufacturing Plant Was More Than $2M In 2021-2022 In the automotive industry, for example, just one hour of downtime costs a shocking $2 million. In other sectors, such as oil and gas, the figure is around $500,000 per hour. Illustration: WorkTrek / Data: Siemens These expenses are reflected in lost revenue, the cost of emergency spare parts, increased labor costs, and other unnecessary costs. But, what's even more alarming is that, across all industries, the cost of downtime increased by 50% from 2020 to 2022 due to inflation and production lines running at higher capacity. That means today, the cost of downtime could be even higher. It’s no surprise that more and more companies are adopting proactive maintenance strategies and advanced technologies to avoid these costly disruptions. With profits at stake, there’s just no room for error. 91% of Manufacturing Maintenance Professionals Are Prioritizing the Improvement of Their Data Collection and Analysis Capabilities With 91% of manufacturing companies working towards improving their data collection and analysis, it's quite clear that data truly is king, even within the realm of maintenance. Illustration: WorkTrek / Data: Limble We have already mentioned that today, so many different technological and process innovations are emerging, all with the goal of making our maintenance efforts more efficient. But guess what? None of these innovations are effective without accurate data. Take predictive maintenance, for example. Its main objective is to forecast asset failures and schedule maintenance to address potential problems without the risk of over-maintaining proactively. It achieves that through data. Predictive maintenance leverages real-time data gathered from a network of sensors on your machines. This data is then fed into software armed with advanced analytical capabilities that identify patterns and provide users with actionable insights. Many experts, such as Ankush Malhotra, Group CEO at Element Logic, a company providing warehouse optimization tech, believe that this type of maintenance will soon become the norm: Predictive maintenance is becoming a need, not a want, especially as skilled labor is hard to come by and retain. AI offers a clear pathway, and there is a strong belief within the industry that manufacturers who don’t adapt to the benefits are likely to be left behind. Rather than relying on guesswork or ineffective schedules, it focuses exclusively on data to develop better maintenance strategies and plans. It’s natural that manufacturing facilities want to implement these predictive capabilities in their operations, which is why we see so many of them boosting their data collection and analysis efforts. Conclusion Overall, these statistics reveal a significant shift toward proactive, data-driven maintenance. More than ever, companies prioritize upkeep based on real-time asset conditions, moving away from the outdated approaches of simply reacting to breakdowns or sticking to rigid time-based schedules. This is because the consequences of both under- and over-maintenance can be steep, often leading to costly downtime. Looking ahead, we’re likely to see an even greater push toward predictive—and even prescriptive—maintenance models, which will help maintenance professionals ensure assets get exactly the care they need, when they need it.

DFMEA stands for Design Failure Mode and Effects Analysis. Engineers and product developers use it to find and fix potential design problems before they become real issues. DFMEA is a systematic approach to identify, evaluate, and prevent possible failures in product designs. This process helps companies make safer, more reliable products. It examines each part of a design and asks "What could go wrong here?" and "How bad would it be if it did?" Illustration: WorkTrek / Quote: Coast DFMEA is part of the larger FMEA family of risk management techniques. While FMEA can be used for many things, DFMEA focuses on product design. It's often used in automotive, aerospace, and electronics industries, where product failures could have serious consequences. [ez-toc] Overview of DFMEA DFMEA stands for Design Failure Mode and Effects Analysis. It's a key tool in product development and quality control. DFMEA is a type of Failure Modes and Effects Analysis (FMEA) that focuses on identifying potential failures in product design before they occur. The main goal of DFMEA is to improve product safety and reliability. It does this by finding weak points in the design early on. DFMEA follows a step-by-step process: Define the scope Identify potential failure modes Assess the effects of failures Rate the severity of the issues Determine the likelihood of failures Evaluate detection methods Companies use DFMEA to manage risk in their product designs. It helps them spot problems that could lead to safety issues or product recalls. DFMEA can be done at different levels. It can look at a whole system or focus on individual components. Source: WorkTrek The process involves teamwork. Engineers, designers, and quality experts often work together on DFMEA. DFMEA can help companies save money and protect their reputation. It also helps prevent costly mistakes and ensures better product quality. Objectives and Benefits of DFMEA DFMEA aims to identify potential failure modes in a product's design before it reaches production. This proactive approach helps catch issues early. A key objective is to improve product quality. By spotting problems in advance, companies can make design changes to prevent failures. DFMEA also focuses on customer satisfaction. It helps create more reliable products that meet user needs and expectations. Risk mitigation is another important goal. The process evaluates the severity, occurrence, and detection of potential failures, allowing teams to prioritize the most critical risks. Some benefits of DFMEA include: • Reduced warranty costs • Fewer design changes late in development • Improved safety and reliability • Better compliance with regulations DFMEA uses a Risk Priority Number (RPN) to rank failure modes. This helps teams decide where to focus their efforts for maximum impact. Source: WorkTrek Effective risk management is a significant advantage of DFMEA. It provides a structured way to address potential issues before they become real problems. Prevention is at the heart of DFMEA. By considering possible failures, teams can design safeguards and controls to prevent issues from occurring. Components of DFMEA How does DFMEA work? DFMEA consists of several key elements that identify and assess potential design failures. These components help teams analyze risks and prioritize improvement efforts. Severity, Occurrence, and Detection Source: WorkTrek Severity measures how serious the effects of a failure could be. It's usually rated on a scale of 1 to 10, with ten being the most severe. Occurrence measures how often a failure might occur. Like severity, the scale is set from 1 to 10. Detection rates how easy it is to spot a failure before it reaches the customer. This is also measured on a 1-10 scale. These three factors help teams understand the overall risk of each potential failure mode. They form the basis for calculating the Risk Priority Number. Risk Priority Number (RPN) The Risk Priority Number (RPN) is a key metric in DFMEA. It's calculated by multiplying Severity, Occurrence, and Detection scores. RPN = Severity × Occurrence × Detection A higher RPN suggests a more critical issue that needs attention. For example: Low RPN (1-100): Lower priority Medium RPN (101-500): Moderate priority High RPN (501-1000): High priority Teams use the RPN to prioritize which issues to address first. This helps focus resources on the most critical problems. Potential Failure Modes Potential failure modes are how a design could fail to meet its intended function. These might include: Component breakage Software glitches Electrical short circuits Material degradation Teams brainstorm and list all possible ways the design could fail. This step requires creativity and a deep understanding of the product. It's important to consider both obvious and less obvious failure modes. Sometimes, seemingly minor issues can lead to significant problems later on. Potential Effects of Failure This component examines what could happen if a failure occurs. The effects can range from minor inconveniences to serious safety hazards. Examples of potential effects include: Product malfunction Customer dissatisfaction Safety risks Regulatory non-compliance Source: WorkTrek Teams rate the severity of each effect. This helps prioritize which failures need the most attention. Understanding the potential consequences helps teams make informed decisions about design improvements. Potential Causes of Failure Identifying potential failure modes is crucial for prevention. Common causes might include: Poor material selection Manufacturing defects Environmental factors Design flaws Teams analyze each failure mode to determine its root causes. This often involves asking "why" multiple times to investigate the issue further. Understanding causes helps teams develop effective preventive actions. It also aids in risk reduction and improving detection methods for similar issues in the future. By addressing root causes, teams can significantly reduce the likelihood of failures occurring. Executing the DFMEA Process The DFMEA process involves several key steps to identify potential design failures. A systematic approach and cross-functional collaboration are essential for effective risk assessment and mitigation. Cross-Functional Team Formation A diverse team is crucial for a successful DFMEA. It typically includes engineers, quality specialists, and representatives from production and service departments. The team brings together varied expertise and perspectives, which helps identify a wide range of potential issues. Regular meetings and clear communication channels are established. These ensure that all team members can contribute effectively throughout the process. Identification of Potential Risks The team reviews the design thoroughly. They consider all components, functions, and interactions within the system to help reduce system failure. Source: WorkTrek Brainstorming sessions are conducted to identify possible failure modes. These sessions encourage open discussion and creative thinking. Each potential failure is documented, along with its possible causes and effects. This creates a comprehensive list of risks to be evaluated. Historical data and lessons from previous projects are also considered. This helps identify risks that may not be immediately apparent. Evaluation and Prioritization of Risks Each identified risk is assessed based on three factors: severity Occurrence Detection.These factors are typically rated on a scale of 1 to 10. The Risk Priority Number (RPN) is calculated by multiplying these three factors. This provides a quantitative measure for prioritizing risks. Risks with higher RPNs are given priority for mitigation. However, the team also considers the severity of consequences independently. A matrix or table is often used to visualize the risk assessment results. This helps quickly identify the most critical areas for improvement. Risk Control Measures For each prioritized risk, the team develops control measures. These include both prevention and detection controls. Prevention controls aim to reduce the likelihood of failure occurrence. They may involve design changes, material improvements, or process modifications. Detection controls focus on identifying failures before they reach the customer. These may include inspection methods, testing procedures, or monitoring systems. The team considers the feasibility and effectiveness of each proposed measure. Cost-benefit analysis is often performed to ensure efficient resource allocation. Implementation and Monitoring of Corrective Actions An action plan for implementing the chosen control measures is developed. This plan includes responsibilities, timelines, and resource requirements. The team regularly tracks implementation progress and uses CMMS tools like WorkTrek to ensure timely task completion. Illustration: WorkTrek / Data: Pinterest After implementation, the effectiveness of the control measures is evaluated. This may involve testing, data collection, and analysis. If necessary, the team adjusts the control measures based on the results. This iterative process ensures continuous improvement in design reliability. The DFMEA document has been updated to reflect the implemented changes. This document serves as a valuable reference for future projects and continuous improvement efforts. DFMEA in Different Industries Design Failure Mode and Effects Analysis (DFMEA) is used across various industries to improve product safety and reliability. Its application varies based on industry-specific needs and regulations. In the automotive industry, DFMEA is crucial for producing safer vehicles. Car manufacturers use it to analyze brake systems, engines, and other critical components, helping prevent potential failures that could lead to accidents. The aerospace sector relies on DFMEA to ensure aircraft safety. Engineers analyze every part, from wings to landing gear, to identify possible failures. This thorough approach helps maintain high safety standards in aviation. Healthcare uses DFMEA to design medical devices and equipment. It helps identify risks in devices like pacemakers or X-ray machines. This process is vital for patient safety and meeting strict medical regulations. In the defense industry, DFMEA is used to develop reliable military equipment. It helps analyze potential failures in weapons systems, vehicles, and communication devices. Illustration: WorkTrek / Quote: Agilian This ensures equipment performs well in challenging conditions. Industrial applications of DFMEA include: Manufacturing machinery Chemical processing plants Power generation systems By using DFMEA, these industries can create safer, more efficient products and processes. DFMEA Throughout the Product Lifecycle Design Failure Mode and Effects Analysis (DFMEA) plays a key role in every stage of a product's life. It helps catch issues early, boosts quality, and cuts costs. Let's look at how DFMEA works in different phases. Product Design and Development In this phase, DFMEA is crucial for identifying potential failure modes before they become real problems. Engineers use it to spot weak points in the design. They look at each part and ask: How might this fail? What would happen if it did? How likely is it to fail? This helps them make the product safer and more reliable. They can fix issues on paper, which is much cheaper than fixing them later. DFMEA also guides testing plans. It shows which parts need extra checks, saving time and money by focusing efforts where they matter most. Manufacturing and Assembly Processes As the product moves to production, DFMEA shifts focus. Now, it examines how the manufacturing process might cause failures. Teams check: If parts fit together right If assembly steps might damage components If variations in the process could lead to defects This helps improve productivity and product quality. It can lead to changes in how things are made or put together. DFMEA also helps pick the right equipment. It shows where precision matters most, guiding choices about machines and tools. Post-Market Surveillance DFMEA doesn't stop when the product ships. It's a key tool for tracking real-world performance. Teams use it to: Analyze customer complaints Spot trends in product returns Guide updates and fixes This ongoing review helps improve each product version and feeds back into the design process for new products. DFMEA, in this phase, can catch issues that slipped through earlier checks. It's a vital part of continual improvement and maintaining product safety. Integrating DFMEA with Other Quality Tools Illustration: WorkTrek / Data: EZO CMMS DFMEA works best when combined with other quality management tools. This approach creates a more robust quality assurance system. One key tool to pair with DFMEA is design review. Design reviews allow teams to evaluate DFMEA findings and make improvements before production begins. Design verification is another important integration process. It helps confirm that DFMEA recommendations have been properly implemented. DFMEA results can inform quality control measures. Teams can focus QC efforts on areas identified as high-risk during the DFMEA process. Corrective and preventive actions often stem from DFMEA findings. These actions address potential failures before they occur in real-world use. When guided by DFMEA, reliability testing becomes more targeted. Engineers can design tests to evaluate specific failure modes identified in the analysis. Regularly updating the DFMEA enhances continuous improvement. As new information emerges, teams can refine their analysis and mitigation strategies. By combining DFMEA with these tools, organizations create a comprehensive approach to quality management. This integration helps ensure safer, more reliable products. Common Challenges and Best Practices Design Failure Mode and Effects Analysis (DFMEA) involves several key challenges. Teams must avoid common mistakes, implement effective prevention and detection strategies, and foster collaboration to maximize results. Avoiding Common Mistakes   Several pitfalls can hinder DFMEA implementation. One frequent error is focusing too narrowly on known issues while overlooking potential new failure modes. This can lead to incomplete risk assessments. Another mistake is assigning unrealistic severity ratings. Teams may underestimate or exaggerate the impact of certain failures, skewing the analysis. Inadequate root cause analysis is also problematic. Failure to dig deep enough into underlying causes can result in ineffective prevention measures. Teams should use clear, specific language when describing failure modes and effects. Vague descriptions make it difficult to develop targeted solutions. Incorporating Prevention and Detection Strategies Effective DFMEA processes emphasize both prevention and detection controls. Prevention controls aim to prevent failures from occurring. These may include design changes, material upgrades, or improved manufacturing processes. Detection controls help identify failures quickly if they do occur. Examples include sensors, quality checks, and testing procedures. Teams should prioritize prevention over detection when possible. It's better to avoid failures than to catch them after the fact. A balanced approach is key. Robust prevention and detection strategies work together to minimize risks and improve product reliability. Maximizing Team Collaboration Illustration: WorkTrek / Data: UC Today Cross-functional teams are essential for effective DFMEA. Including members from design, manufacturing, quality, and service departments provides diverse perspectives on potential failures. Clear communication is crucial. Team members must share information openly and listen to different viewpoints. Regular meetings help keep everyone aligned. These sessions allow for updates on progress and discussion of new insights. Assigning clear roles and responsibilities ensures all aspects of the analysis are covered. This prevents important tasks from falling through the cracks. Decision-making should be collaborative. Encouraging input from all team members leads to more comprehensive risk assessments and mitigation strategies. DFMEA Documentation and Reporting Proper documentation and reporting are crucial for an effective DFMEA process. Clear records help teams track issues, prioritize actions, and make informed decisions to improve product designs. DFMEA Template Usage A well-structured DFMEA template is essential for consistent documentation. The template typically includes columns for: Item/Function Potential Failure Mode Potential Effects Severity Score Potential Causes Occurrence Rating Current Controls Detection Score Risk Priority Number (RPN) Teams fill out each column systematically, ensuring all potential failure modes are captured and evaluated. A standardized template helps maintain consistency across different projects and facilitates easier comparison and analysis. Generating Action Items Action items are concrete steps to address identified risks. They emerge from the DFMEA analysis and focus on high-risk areas. To generate effective action items: Prioritize based on RPN scores Focus on failure modes with high severity or occurrence ratings Consider detection improvements for hard-to-detect issues Assign responsible team members and deadlines Regular reviews of action items ensure progress and help update the DFMEA as designs evolve. Making High-Priority Recommendations High-priority recommendations target the most critical risks identified in the DFMEA. These recommendations should: Address failure modes with the highest RPN scores Focus on reducing the severity or occurrence of potential failures Suggest improved detection methods for critical issues Consider the cost-effectiveness and feasibility of implementation Teams should present these recommendations clearly, backed by data from the DFMEA analysis. Prioritizing recommendations helps decision-makers allocate resources effectively and tackle the most pressing design concerns first. Advanced DFMEA Topics Software tools, systematic risk assessment, and regulatory compliance can enhance design failure mode and effects analysis (DFMEA), which helps improve product design and reliability. Leveraging Software Tools Software tools streamline the DFMEA process and boost efficiency. These programs offer templates, databases, and automated calculations. They help teams track changes, collaborate remotely, and generate reports quickly. Many DFMEA software options integrate with other design tools. This integration allows for real-time updates as designs change and helps maintain consistency across different analyses. Illustration: WorkTrek / Data: SelectHub Some advanced features include: Customizable risk matrices Automatic risk priority number (RPN) calculations Failure mode libraries Visual mapping of system functions These tools often provide data analytics capabilities. Teams can spot trends and focus on high-risk areas more easily.  Adopting a Systematic Approach to Risk Assessment A systematic approach to risk assessment in DFMEA ensures thorough analysis. It starts with breaking down the product into its components and system functions. Teams then identify potential failure modes for each function. They assess the severity, occurrence, and detection of each failure mode. This assessment leads to calculated risk priority numbers (RPNs). Key steps in systematic risk assessment include: Function analysis Failure mode identification Effect analysis Cause analysis Control evaluation Teams prioritize actions based on RPNs and other factors. They develop and implement risk mitigation strategies, and regular reviews ensure the effectiveness of these actions. Standards and Regulatory Compliance DFMEA plays a crucial role in meeting industry standards and regulatory compliance. Many sectors have specific DFMEA requirements or guidelines. For example, the automotive industry uses the AIAG-VDA FMEA standard, and medical device manufacturers must comply with ISO 14971 for risk management. Compliance often involves: Documenting the DFMEA process Using standardized severity and occurrence ratings Implementing traceability measures Conducting regular reviews and updates Teams should stay updated on relevant standards and adapt their DFMEA processes to meet changing regulations. This approach ensures products meet safety and quality requirements.