Predictive Maintenance: Can it achieve Zero Downtime?

In today’s fast-paced industrial landscape, downtime is the nemesis of productivity. Every moment of machinery or equipment downtime can translate into substantial financial losses and operational disruptions. The good news is that modern technology offers a solution: Predictive Maintenance.

The Cost of Downtime

It is true that Predictive Maintenance is the most effective method to reduce unplanned machine downtime and optimise the maintenance process, however, achieving zero downtime is still not a possibility for it. There are multiple external reasons for  and some of them are:

Unforeseen Circumstances

Some events, such as natural disasters or major external disruptions like supply chain, cybersecurity, power failure or economic or market shifts that impact spare parts or service offers, can result in downtime that Predictive Maintenance cannot prevent.

Resource Limitations

Predictive Maintenance requires a well-established infrastructure of sensors, data analytics tools, and skilled personnel. Smaller organizations or those with limited resources might face constraints in fully implementing this strategy. But even large corporations can’t monitor every single component. It’s important to note that the selection of components and parameters to monitor should be based on a thorough understanding of the equipment, its criticality to operations, and the potential failure modes that could lead to downtime or safety risks.

Human Error

Despite predictive alerts, human errors can still occur during maintenance activities or in response to equipment issues, potentially leading to downtime.

Complex Systems

In highly complex industrial systems, some equipment failures may be challenging to predict accurately, especially if multiple factors contribute to the failure.

Initial Implementation Challenges

Transitioning from a reactive maintenance approach to a fully predictive one can be a complex process that takes time to implement effectively.

While Predictive Maintenance can come remarkably close to achieving zero unplanned downtime, the term “zero downtime” is often used more as an aspirational goal rather than an absolute guarantee. The primary aim of Predictive Maintenance is to minimise unplanned downtime, optimise maintenance activities, and improve operational efficiency to the greatest extent possible.

In practice, organisations implementing Predictive Maintenance should expect to see a substantial reduction in unplanned downtime, leading to improved reliability and cost savings. However, they should also remain prepared for the possibility of rare and unforeseen events that could still result in some downtime, albeit significantly less than in a purely reactive maintenance approach.

No Code Tools and Zero Downtime

With the recent developments of No Code tools like Paze Industries , the road to getting closer to Zero Machine Downtime has been slightly shortened. These new age No Code tools provide an intuitive and user-friendly interface that enables personnel from various departments to actively participate in the predictive maintenance process.

For example, Paze Industries’ no code tools makes it easier to deploy sensors, collect data and analysis results. Users can set up sensors to gather data and create automatic workflows to trigger alerts and notifications when anomalies are detected. This democratization of predictive maintenance reduces the reliance on specialized IT or data science teams, making it accessible to maintenance technicians, engineers, and even non-technical staff.

Additionally, these tools often come with pre-built applications and a wide range of professional services, allowing organizations to quickly harness the power of data analytics without the need to develop custom code. This approach accelerates the adoption of Predictive Maintenance, minimizes implementation hurdles, and ultimately contributes to the goal of reducing downtime and optimizing operations. In a world where time-to-insight is crucial, No Code tools like those provided by Paze Industries offer a promising avenue to streamline Predictive Maintenance practices and move closer to achieving the objective of minimal unplanned downtime.


Predictive Maintenance is a dynamic field that continues to evolve, with the ultimate goal of achieving zero downtime being a central focus. While absolute zero downtime may be challenging to attain due to unforeseen circumstances, organisations that embrace Predictive Maintenance can significantly reduce unplanned downtime, optimise their operations, and gain a competitive edge in today’s fast-paced industrial landscape. It represents a proactive, data-driven approach that is vital for businesses seeking to thrive in the era of Industry 4.0.

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7 Predictive Maintenance Metrics You Should Track to Improve Efficiency

No matter what field you are part of, measuring Key Performance Indicators, commonly known as KPIs is crucial to measure success. The simple principle applies to the maintenance of your machines as well. 

KPI for maintenance vary as per the requirements, goals, strategies and action plans of a company. However, there are certain indicators that are common for all. 

In this article, we will cover all the Common KPIs that can be part of your predictive maintenance metrics. Let’s get started….


The purpose of this indicator is to track, monitor and evaluate the asset’s reliability. Downtime tracks the “total time when the equipment was offline”, which means that some problem occurred with the machine which requires intervention from the technician. 

The ideal percentage for this KPI is 10%, meaning, the machine should be fully operational at least 90% of the time. 

When working with Data you will quickly see what downtime was caused due to incorrect handling of the machine. This is the quickest way of eliminating unnecessary downtime.

This KPI also comes in handy when you are working on a predictive maintenance strategy with the objective of keeping downtime below average as well as minimising the risk of unplanned shutdowns.

Maintenance Backlog

It is a time indicator that stands for “maintenance delays”. The backlog records the time needed to perform a reactive, preventive or predictive work order, quality control, improvements or any other activity that promotes a machine’s desirable performance. To calculate this KPI, you need to consider all the workflow maintenance planning and control. 

The formula is:

Maintenance Backlog Formula

NB: Only consider the “productive time” of each technician because they are not executing work orders 100% of the time. 

The maintenance backlog is measured in working days/weeks or months. The ideal duration is 2 weeks, however, for companies that work 24/7, the ideal duration is between 3 to 4 weeks.  The benefit of measuring this KPI is to determine how efficient your team is, as well as determine the causes of unproductivity. 

MTBF - Mean Time Between Failures

This indicator measures the reliability of the machines. It considers unplanned failures, including the ones occurring from software failures and manufacturing defects. 

Since we need to determine the “time elapsed” between each failure, MTBF is measured in time (hours, days, weeks or months). The rule of thumb is that the “longer the MTBF”, the more reliable a machine and vice versa. 

The formula to calculate MTBF is:

As such there is no ideal number for this KPI because it varies from business to business, however, MTBF should be as high as possible. In some industries, this KPI is used to push product sales as a unique selling point.

MTTR - Mean Time To Repair

This indicator measures how much time it takes for your team to perform corrective maintenance after a machine failure happens. Unlike MTBF, MTTR should be as low as possible. The formula to calculate MTTR is:

This way you can calculate the time (hours, days, weeks or months) when a machine was offline.  Similar to MTBF, there is no ideal number for MTTR as well, however, it is important to keep this KPI as low as possible.

OEE - Overall Equipment Effectiveness

The most important KPI for the manufacturing industrie, OEE measures the “overall effectiveness of equipment” which allows you to determine whether your processes are working efficiently or not. A rough benchmark for this KPI is at least 77%. 

One of the key benefits of measuring OEE is understanding how often your machines are available to work. It will help you discover how fast the manufacturing process is as well as how many products/services are manufactured without any unexpected failure. 

The formula to calculate OEE is:

Overall Equipment Effectiveness Formula

Availability is calculated on the basis of downtime and uptime. Performance is calculated by comparing current production against projections. Quality is calculated from total production minus faulty production.

Even though the benchmark for OEE is 77%, however, the top companies in the world like to keep the OEE between 85% and 99%. International companies can provide preset OEE dashboards to subsidiaries or various locations of production in order to keep the KPI comparable.

PMP - Planned Maintenance Percentage

This indicator considers the time spent on planned activities such as maintenance, repairs or replacements. This KPI is directly linked to the company’s Preventive Maintenance Plan. 

The formula to calculate PMP is:

The ideal number for PMP is at least 85%.

Schedule Compliance or Planned Maintenance Compliance

This indicator determines the effectiveness and commitment your technicians and managers showed on their planned tasks. In simple words, Schedule Compliance measures the performance of your whole team. 

The formula to calculate Schedule/Planned Maintenance Complaice is:

Planned Maintenance Compliance Formula

The ideal number for schedule compliance is at least 90%. This means that productivity is high with minimal machine failures.


The primary advantage of calculating these KPIs is that it will help you gain deep insights into internal processes and activities, allowing you to determine what is working and what is not and how can you fix the issues. 

Therefore allowing, you to improve revenues and profits.  An Industrial IoT solution like Paze can help you to stay on top of your numbers and have the most important KPIs live on hand even across multiple locations.

Get IT and OT to Speak the Same Language

Sometimes, similar is very different. Such is the case with Informational Technology (IT) and Operational Technology (OT). Both solutions are built on top of microprocessing power and many layers of software. However, their use cases are much different. Traditionally, they each operated in isolation, but recent technology trends are moving them closer. As a result, manufacturers need a way to close the gap. Here’s how.

Antithetical Designs

Differences between the two approaches start with their origins. IT comes from the computer science field and was built from the ground up to simplify human input and interactions. OT has a mechanical engineering heritage and was created to make machines more efficient.

IT systems are the foundation for enterprise operations. They move information from place to place in support of business processes that run the organization. They work with humans who typically rely on desktops, laptops, and mobile devices to input data. The focus is on making the systems intuitive, so employees work with them easily.

OT systems support manufacturing operations. Here, machines move items along assembly lines and eventually produce goods. They work with different types of equipment, Programmable Logic Controllers (PLC), microcontrollers, Supervisory Control And Data Acquisition (SCADA) systems and Distributed Control System (DCS), These systems only perform what they are programmed to do, so the emphasis is on set functions, such as a robotic arm turning a number of screws.

IT and OT Function Independently

The two markets evolved autonomously, As a result, their infrastructure, operating systems, network protocols, applications, and monitoring tools are not interchangeable. The separation was accepted in the past. At that time, manufacturers divided work up into select tasks and assigned them to departments. Each group completed its piece of the puzzle, usually with little to no interaction among the groups.

This approach had limitations, a major one being how companies managed workflow. Then, they collected information, examined trends, and made changes after manufacturing runs were completed. If a material was late, personnel had limited insight into the problem and were unable to make changes that improved yields.

Those barriers are now being taken down. Both groups are moving to new models where employees have access to real time information. Such a change empowers workers to manage operations proactively rather than reactively. A delay in material shipment impacts not only the factory floor but also the back office. With real time data, changes are made as needed, improving workflow, quality, customer satisfaction, and ultimately revenue.

Building Bridges Among Departments

However, breaking down the walls is challenging. Bridges, both technical and human, need to be constructed.

Industrial Internet of Things (IIoT) sensors provide visibility into machine performance.

Networks have to be linked. Increasingly, IP has become the way that information is transmitted. But a variety of different communications protocols must be integrated in embedded modules, gateways, edge devices, industrial equipment, and business applications.

Companies can use common programming interfaces to connect applications. Suppliers have become much more open and solutions, like low code and no code. Therefore, making such links is easier today than in the past.

Manufacturers need to ensure that all of their data is secure. New solutions are emerging that orchestrate the technology infrastructure’s security and ensure that edge firmware is updated, security keys rotated, and the entire infrastructure constantly monitored for new threats.

An industrial IoT platform serves as the central hub, connecting company data with machine and process data. It enables easy and intuitive access for employees from various departments such as service, maintenance, engineering, purchasing and sales. This seamless integration maximises the value extracted from the data.

Management Challenges Need to be Addressed

Changes are needed in workflow. The process starts by acknowledging the complexity of the IT and the OT infrastructures. In many cases, companies link thousands and even millions of lines of code. Therefore, keeping track of what is happening is challenging.

Suppliers need to educate all stakeholders, managers, technicians, and front line workers, about how the change will impact them. There has to be clear definitions and understanding about the pluses and minuses. The focus should be on how the new model improves their job as well as provides the company with cost savings, higher productivity, better quality, and more satisfied customers.

The process touches upon many areas. 

  • IT terms are not the same as OT verbiage. A common vocabulary needs to be developed, so both teams understand what thoughts are being conveyed. 
  • Cross-functional training enables IT and OT professionals to understand each other’s areas of expertise and better understand each other’s perspectives. 
  • Creating common standards ensures that IT and OT systems have points of convergence. 
  • Collaboration needs to take place: Regular communication among IT and OT teams builds trust and fosters a healthy manufacturing culture. 
  • Recognize each other’s strengths and weaknesses. In many cases, companies lack the expertise to shepherd such projects to completion. So, they should look for help from third parties. 
  • Make needed investments. The changes require clear sponsorship and support from the business leaders. Then, managers establish common Key Performance Indicators (KPIs) and constantly review the progress towards meeting operational objectives with published documentation to all stakeholders.

Turn Information into Action

Rather than a series of autonomous functions, the change creates an interconnected, homogenous entity, one where all of the pieces align rather than splinter. What was once separate islands of digital data becomes cohesive, actionable, real time information. Employees see how materials are moving through the manufacturing process and make changes as needed.

Artificial intelligence algorithms predict when machines are likely to break, opening the door to predictive maintenance, which increases uptime and throughput. Former inefficiencies become new differentiating features.

The IT-OT convergence benefits manufacturers in several ways. 

  • Improve decision making with real time decision support systems
  • Minimize unplanned downtime using predictive maintenance
  • Boost employee productivity  
  • Leverage critical data to streamline operations 
  • Raise first-pass yield rates
  • Enhance workplace safety

IT and OT were raised separately and thrived independently. With manufacturing competition intensifying, they now need to coalesce. The process is complicated because their cornerstones are so different. But by making the change, manufacturers boost productivity, streamline operations, improve production run, and become a stronger, more viable business.

Just in Time Staffing Enhances Manufacturing IIoT Projects

The Industrial Internet of Things (IIoT) introduces unfamiliar tools, terms, and technologies to manufacturers. Consequently, they often need outside assistance to transform their operations. Sometimes, the help comes with a cumbersome, long term contract. Just in Time (JIT) staffing offers suppliers a better option, one where they purchase third party services on an as needed basis.

IIoT solutions provide manufacturers with tremendous opportunities to streamline workflow by gaining significantly more visibility into their operations. Previously, suppliers had limited – and in many cases, no — real time vision into what was happening on the manufacturing floor, the supply chain, or the back office. Only after data was collected, correlated, and reports run and distributed to managers and floor personnel did they understand what occurred. As a result, they managed reactively whenever problems arose.

IIoT’s Immense Potential and Significant Challenges

IIoT offers them the chance to flip that script. However, challenges arise in exploiting such capabilities because of IIoT’s extensive capabilities and its unprecedented flexibility. The advancement shrinks processing power down into small form factors, typically special purpose sensors, that can be programmed to operate just about anywhere and do just about anything. Factory networks then collect new information, so managers recognize manufacturing flows.

Next, they correlate the data to make ongoing improvements that positively impact the business.

Four Step Deployment Process

Adding such capabilities is a four stage endeavor, and each step requires a different type of guidance. The initial requirements definition process is quite different from traditional manufacturing purchases. In the past, suppliers upgraded a piece of equipment or software with a clear objective, for instance produce pieces faster.

IIoT’s impact goes beyond one piece of equipment and a simple objective. These solutions rewrite existing business processes in an unlimited number of ways.

  • Improve OEE 
  • Lengthen equipment lifetimes
  • Streamline workflow
  • Speeds up payments
  • Enhance quality
  • Raise customer satisfaction 

As a result, determining what to do with it can be like falling down into a rabbit hole. A firm gets caught up in so many of the potential bells and whistles that gauging system requirements takes a year or longer. Therefore, they need help setting reasonable expectations and require high level system architecture and project management aid from their third party supplier.

Building the data infrastructure is the next step. Manufacturing plants have a wide and ever growing range of potential data sources, such as machines, applications, and intelligent edge systems. Because their business is unique, each company has to pull the infrastructure together and customize their system and application’s integration pipeline.

In addition, the information has to be stored in a location with the processing power required to deliver real time updates as well as scale with new applications. Finally, the infrastructure has to be managed on an ongoing basis. Because of the complexity, this part of the project is often the longest.

In this phase, the factory requires a cohort able to supply computer infrastructure expertise and ideally a managed service. The manufacturer offloads that responsibility to someone else and focuses on improving their operations.

Phase three revolves around value creation. Companies green light an IIoT project because the change will positively impact the business. Once data is collected and analyzed, sometimes, the initial premise must be changed. Also, new opportunities emerge as reports allow managers to understand how work gets done.

In this phase, cyber models, data applications and analytics become the building blocks for determining how to drive transformation throughout the organization. This case demands business and data analytics help.

Once an IIoT project has been conceptualized, it must be adopted, which is a two-step process. First, machines are connected, applications built, and users onboarded. Next, the employees must integrate the new capabilities into their workday. Here, a supplier needs a system implementor who handles change management and helps organizations overcome any resistance that may arise within the organization.

When more and more people become skilled in manipulating data, the complexity of queries increases, and the organization becomes stronger.

Find the Use Cases

Third parties offer advisory and other services to suppliers. However, consulting firms take a cookie cutter approach. They develop a set of services and then apply it to every project. While good for the third party, the approach is less satisfying for the customer. They are not able to gauge how much help they will need in each step because they do not know how it will unfold. Sometimes, they need more help at one stage and less at another. They require more flexibility than the typical contract offers.

JIT staffing is an approach where manufacturers bring on specialists only when they are actually needed during the project. Like JIT manufacturing, the idea is to deliver the right amount of materials (in this case human brain power) at the right time and not have any extra.

What are JIT Staffing Benefits?

JIT staffing provides organizations with a number of improvements: 

  • Risk Reduction: companies avoid making a set commitment to personnel that they may not need. 
  • Workplace Agility: consulting bandwidth expands and detracts like cloud services, available whenever they are needed.
  • Increased Productivity: employees spend less time trying to figure out how much staff they require and more time how IIoT enhances the business. 
  • Cost Savings: being no longer locked into set pricing results in more efficient service delivery. 
  • Agility:  quickly add talent as new business needs emerge
  • Address the Personnel Shortage: manufacturing has a global labor shortage that could exceed 8 million people by 2030 and result in a $607 billion revenue loss. JIT maximizes personnel usage and reduces personnel needs. 

Manufacturers are adopting IIoT technology in order to improve their operations. The tools offer them a wide range of use cases but managing such projects becomes more challenging. Traditional staffing models were rigid and often incurred unnecessary expenses. JIT staffing is a better fit because it provides more flexibility as well as lowers costs.

Future-Proofing Machine Maintenance: Selecting the Ideal Condition Monitoring System

Did you know that effective condition monitoring systems have the ability to reduce maintenance costs by up to 25%?

In today’s rapidly evolving industries, traditional maintenance practices alone are no longer sufficient to keep up with the demands of modern machinery and equipment. Future-proofing your maintenance strategies requires the implementation of an ideal Condition Monitoring system.

These advanced tools proactively detect potential issues, prevent costly breakdowns, and optimise productivity. But with a multitude of options available, how can you choose the perfect condition monitoring system for your organisation?

In this article, we will explore the essential factors and considerations that will guide you towards selecting an ideal condition monitoring system, ensuring sustainable maintenance excellence while maximising cost savings.

For this, we have divided the article into two parts: the first one will have a look at the internal Factors that need to be known and assessed, and the second one will help you assess the available tools and technologies.

Part 1: Assessment of Internal Factors

Factor 1 - Know your machines

Condition monitoring is commonly used for critical machines whose failure can cost the company a hefty loss both financially and productively. Each industrial process has a list of “bad actors”, which refers to a list consisting of machines that are most prone to breakage and whose failure will result in serious losses.

Therefore, the first requirement of selecting the ideal Condition Monitoring system is to know which are your most critical machines. One way to identify that is using a method called “criticality analysis”.

It is a process used by maintenance teams to assign a ranking to various assets based on the potential loss they contribute to productivity in case of failure. Once you have identified the critical machines, then you can move to other factors.

Factor 2 - Failure Modes

The next crucial step is to conduct FMECA (Failure Modes, Effects, and Criticality Analysis) specifically targeting the top 20% of the most critical machines. Each failure mode exhibits a unique pattern that can be detected through various data sources such as stress waves, vibration, and more.

Certain failure patterns are highly noticeable, enabling sensors to detect them as soon as they begin to emerge. However, there are other patterns that may not become measurable until the system experiences a complete breakdown.

Hence, it is imperative to identify the Condition Monitoring data sources that hold value based on the critical failure modes that need to be monitored. By determining the criticality of these failure modes, we can prioritise the selection of appropriate data sources for effective monitoring.

Factor 3 - Machine’s Environment

Understanding the environment in which your critical machines operate is crucial while selecting the ideal Condition Monitoring system. Today, the majority of the time data collection is performed via wireless sensors and these sensors are delicate pieces of equipment and therefore must be shielded from environmental extremes such as high temperatures, corrosive substances and more.

On top of that, it can be difficult to attach sensors directly on hard-to-reach equipment like those located in ATEX zones and other restricted areas.

Factor 4 - Matching Use Case to Data Source

Matching the use case to the appropriate data source is crucial for effective condition monitoring. Each use case requires specific data parameters to be monitored, such as temperature, vibration, or pressure. Understanding the requirements of the use case and identifying the relevant data sources, such as sensors, IoT devices, or databases, ensures accurate data collection.

Proper alignment between the use case and data source enables meaningful insights, predictive maintenance, and proactive decision-making, enhancing overall condition monitoring effectiveness.

Therefore keep in mind during your hunt for the best tool that’s important to understand: 

  • How each tool collects and measures data
  • What are the requirements to install the tool
  • Whether the tool meets all the connectivity and regulatory requirements

Part 2: Assess the available technologies

Finding the best tool for condition monitoring depends on several factors, including your specific requirements, industry, budget, and available resources. Here are some steps to help you in the process:

1. Low-code development

Look for tools that offer a low-code or no-code development environment. These platforms allow you to build custom monitoring applications and workflows without extensive programming knowledge, enabling faster development and iteration cycles. Evaluate the tool’s user interface, drag-and-drop functionality, and ease of customization to ensure it aligns with your low-code requirements.

2. Integration capabilities

Assess the tool’s integration capabilities with your existing systems and infrastructure. It should be able to seamlessly integrate with your data sources, such as sensors, databases, or other monitoring equipment. Look for tools that support standard protocols and have pre-built connectors or APIs to facilitate data exchange with your ecosystem of applications.

3. Time-to-market speed

Consider the tool’s ability to quickly deploy and start monitoring. Look for features like rapid configuration, easy setup, and automated workflows that streamline the implementation process. Some tools offer templates or pre-configured modules specific to certain industries or use cases, which can accelerate deployment and reduce development time.

4. Compatibility with existing technologies

Assess how well the condition monitoring tool aligns with your existing technology stack. It should be able to work with your current software, databases, cloud infrastructure, and communication protocols. Consider tools that offer flexibility in terms of deployment options (on-premises, cloud, hybrid) to fit your organization’s IT strategy.

5. Scalability and flexibility

Evaluate the tool’s ability to scale as your monitoring needs grow or change. It should be capable of handling a large volume of data, supporting multiple monitoring points, and accommodating future expansions. Look for tools that offer modular architectures or extensibility options, allowing you to add or modify functionality as required.

6. Vendor support and documentation

Consider the level of support provided by the tool’s vendor. Look for resources such as documentation, tutorials, and forums that can help you learn and troubleshoot issues efficiently. Check if the vendor offers responsive technical support, training programs, and ongoing updates or improvements to the tool.

Future Proof Condition Monitoring

If you want a future-proof condition monitoring tool that can be used for multiple use cases such as predictive maintenance and AI, and avoids single-use case island solutions, consider the following factors:

1. Modular and extensible architecture

Look for a tool with a modular and extensible architecture that allows you to add or modify functionality as your needs evolve. This flexibility will enable you to incorporate additional use cases, such as predictive maintenance or AI, without having to invest in separate tools or systems.

2. Data analytics capabilities

Ensure that the condition monitoring tool has robust data analytics capabilities. It should support advanced analytics techniques, such as machine learning and AI algorithms, to derive insights from the collected data. This will enable you to move beyond basic condition monitoring and leverage the tool for predictive maintenance and other advanced analytics-driven use cases.

3. Open APIs and interoperability

Verify that the tool provides open APIs (Application Programming Interfaces) or supports industry-standard protocols for easy integration with other systems and technologies. This will allow you to connect the condition monitoring tool with your existing AI platforms, data lakes, or predictive maintenance solutions, creating a unified ecosystem instead of isolated islands of functionality.

4. Scalable data handling

Consider the tool’s ability to handle large volumes of data efficiently. As you expand your use cases and collect more data, the tool should be capable of scaling up to accommodate the increased data load. Scalable data storage, processing, and analysis capabilities are essential for future-proofing your monitoring solution.

5. Flexibility in data sources

Ensure that the tool supports a wide range of data sources beyond traditional sensors. It should be capable of ingesting data from various devices, databases, IoT sensors, or even unstructured data sources. This flexibility will enable you to incorporate diverse data streams into your condition monitoring and AI workflows.

6. Vendor ecosystem and partnerships

Assess the vendor’s ecosystem and partnerships. Look for tools that have a strong network of partners or integrators who can provide additional expertise and support for different use cases. A robust ecosystem indicates a forward-thinking approach and increases the likelihood of finding complementary solutions for future needs.

7. Future roadmap and innovation

Investigate the vendor’s commitment to innovation and their future roadmap for the condition monitoring tool. Consider their track record of incorporating new technologies and features into their product. Look for indications that they are actively exploring advancements in AI, predictive analytics, and other emerging technologies to stay at the forefront of the industry.

By considering these factors, you can select a condition monitoring tool that not only meets your current requirements but also provides a foundation for future use cases and avoids the limitations of single-use case island solutions.

Ready to revolutionise your operations?

Request a demo now and unlock the potential of intelligent condition monitoring. Don’t miss out on this opportunity to transform your business. Schedule your demo today!


What is Predictive Maintenance and how does it work?

Table of Contents

Predictive Maintenance is a process that involves performance and equipment condition monitoring during regular operations to lower the chances of a breakdown.

The primary goal of predictive maintenance is to predict equipment failures by analysing specific parameters and factors. Based on the prediction report, manufacturers/engineers need to take corrective steps to prevent loss.

Predictive maintenance uses data analysis tools and techniques to identify anomalies in industrial operations and possible failures in equipment and processes so you can fix them before they lead to an outage.

Ideally, predictive maintenance programs allow for the lowest possible maintenance frequency to avoid unplanned reactive maintenance without incurring the costs associated with too much preventive maintenance.
Before we dive deeper into predictive maintenance let’s first understand how is it different from preventive maintenance.

How does Predictive Maintenance differ from Preventive Maintenance?

In today’s fast-paced industrial landscape, equipment maintenance plays a critical role in ensuring smooth operations and minimizing downtime.

As a result, maintenance strategies have evolved over time to better address the needs of businesses and their assets. Among these strategies, predictive and preventive maintenance are two of the most widely recognized approaches.

But how do they differ, and which one is the right choice for your organization?

In the table below, we will delve into the key differences between predictive and preventive maintenance, discuss their advantages and drawbacks, and help you make an informed decision when it comes to maintaining your assets effectively and efficiently.

So, let’s embark on this journey to better understand these popular maintenance methodologies and optimize your maintenance management practices.

Predictive Maintenance
Preventive Maintenance

Occurs as per real-time need based on machine operation data to identify issues at the nascent stage to avoid interruption in production.

Occurs on the same schedule every cycle - whether or not maintenance is needed.

Downtime might be necessary but it can be timed, so that it is least disruptive

Machine downtime is required

Identifies possible machine failure before it can occur

Includes activities such as equipment & component inspection, cleaning, repair, etc.

The demand for spare stock quantity is lower than Preventive Maintenance

The demand for spare stock quantity is higher than the Predictive Maintenance

Requirement for Predictive Maintenance - why it matters?

Predictive maintenance is a proactive approach to maintaining equipment and assets, using real-time data and advanced analytics to identify potential issues before they lead to failures.

 This strategy is crucial for modern organizations as it minimizes downtime, increases asset lifespan, and reduces maintenance costs.

 By anticipating and addressing problems before they escalate, businesses can improve operational efficiency, maintain safety standards, and enhance overall productivity.

In a highly competitive industrial landscape, the requirement for predictive maintenance is more important than ever, as it helps organizations stay ahead of the curve, optimize resources, and maintain a competitive edge.

It can’t exist without condition monitoring. To optimise assets, carry out continuous monitoring under real-time working conditions. Predictive maintenance aims to:

  •   Improve asset reliability to reduce breakdown occurrence and maximising asset uptime
  •   Lowering maintenance work to optimise operational costs
  •   Improve maintenance budgets by lowering maintenance costs and maximising production time.

What are some common Predictive Maintenance Technologies?

There is no singular technology for predictive maintenance yet. Manufacturers/Engineers are dependent upon condition-monitoring devices and techniques to predict failures and raise red flags when needed.

Some of those techniques are:

Acoustic Monitoring

Maintenance staff can identify gas emission, liquid, or vacuum leaks in equipment at the sonic and ultrasonic level by utilising acoustic monitoring.

Although more expensive, ultrasonic technology is considered a more dependable machinery technology, and it has a broader range of applications than sonic technology.

It’s important to note that while these technologies can aid in detecting issues, technicians’ ears remain their most valuable tool.

Sonic and ultrasonic technologies can be utilised alongside regular listening to more accurately identify the root cause of unusual gearbox sounds or potential leak sources.

Infrared Thermography

Predictive maintenance often utilises infrared thermography, which is a non-invasive testing technology. By using infrared cameras, maintenance staff can detect higher-than-normal temperatures in machinery. Faulty components or circuits that are worn tend to generate heat, which can be visualised as a hotspot on a thermal image.

By conducting infrared inspections, these hotspots can be detected early on, and necessary repairs can be made, ultimately minimising the likelihood of more severe problems. Infrared technology is versatile and applicable in various machinery and infrastructure projects.

Oil Analysis

In predictive maintenance, oil analysis is a useful tool that enables technicians to identify contaminants by examining the condition of the oil. Oil analysis involves evaluating viscosity, water content, particle counts, and determining the acid or base number.

One significant advantage of oil analysis is that its initial test results can serve as a reference point for future machinery and maintenance, allowing for early detection of any concerning changes in the oil’s properties.

Vibration Analysis

To monitor the performance of high-speed rotating equipment, vibration analysis is utilised. A technician may use handheld devices or real-time sensors within the equipment to track its functioning.

During optimal operation, a machine emits a distinctive vibrational rhythm. However, as the components begin to wear out, the vibration pattern changes, and a new one emerges.

By constantly monitoring this vibration pattern, a trained technician can compare the readings against known failure possibilities, enabling them to detect and resolve problems early on.

Vibration analysis is capable of identifying a range of issues, including misalignment, out-of-shape shafts, unbalanced elements, loose mechanical components, and motor problems.

However, the technique requires highly skilled technicians due to its complexity. The primary challenge associated with vibration analysis is its prohibitive cost.

How does Predictive Maintenance work?

Predictive maintenance uses historical and real-time data from different areas of your operation to identify problems before they occur. There are three main areas in your organisation that play a role in predictive maintenance.

This includes real-time monitoring of asset health and performance, analysis of work order data, and benchmarking of MRO asset utilisation.

To implement a predictive maintenance program, the following steps are typically taken: 

  1. Assess the equipment’s history (at least 2 years of data analysis required) and based on that create a plan for predictive maintenance.
  2. Review all relevant records, such as downtime, equipment malfunctions, production and energy losses, regulation fines, and workplace safety levels.
  3. Raise awareness among key stakeholders about the need for predictive maintenance, and secure the support of the operations and maintenance teams.
  4. Evaluate the equipment inventory and assess the equipment’s condition.
  5. Choose the equipment to be included in the initial implementation of the program.
  6. Create detailed records of each system and its components.
  7. Evaluate any pre-existing preventive or predictive maintenance protocols.
  8. Determine the frequency and schedule for the predictive maintenance program.
  9. Define personnel roles at each stage and evaluate resource requirements.
  10. Organise the program and integrate it with scheduling systems.
  11. Establish a computerised maintenance management system (CMMS).

What are the benefits of Predictive Maintenance?

Let’s explore the advantages of predictive maintenance and why it has become an essential aspect for organisations today:

1. Reduced incidence of Machine Failures

Extensive research has been conducted to minimise machine failures, and it has been established that regular monitoring of machines and systems can significantly reduce the likelihood of unforeseen, large-scale failures. In many cases, after the implementation of a predictive maintenance program for two years, the frequency and severity of machine failures tend to decrease.

2. Reduced Downtime

The implementation of predictive maintenance leads to quicker equipment repair times. By consistently monitoring and analysing machine conditions, maintenance personnel can easily identify faulty components across all machines and efficiently resolve any issues. This significantly minimise downtime and, in some cases, even eliminates it altogether.

3. Reduced Maintenance Costs

The utilisation of predictive maintenance can effectively reduce maintenance operation expenses, which is particularly crucial when organisations have to bear the costs of labour, replacement parts, maintenance, tools, and equipment required for significant failures.

4. Decrease in Inventory Stocking 

Many companies are faced with significant capital investments in various parts, which can tie up their resources. Furthermore, if these parts remain unused for an extended period, their quality may degrade, leading to waste.

Instead of maintaining a large stock of parts in anticipation of machine failures, ordering parts only when they are required can help reduce the expenses associated with stocking.

5. Extends Lifespan of Machines

By detecting machinery issues before they reach catastrophic levels, equipment lifespan can be extended significantly. Implementing a condition-based predictive maintenance program ensures that equipment is serviced before it deteriorates beyond repair.

The longer lifespan of the machinery provides a better return on investment for the organisation.

6. Estimating Mean Time between Failures

Predictive maintenance offers the added advantage of being able to estimate the mean time between failures (MTBF), which refers to the most cost-effective time to replace machinery.

Some organisations may continue to use equipment despite multiple repairs and faults, believing that purchasing new equipment is an expensive investment.

However, being able to replace machinery at the end of its life can prevent high maintenance costs associated with worn-out equipment.

7. Increase in Production

Robust process systems are necessary to support condition-based predictive maintenance programs and increase their efficiency.

A comprehensive predictive program that includes parameter monitoring can improve operational efficiency, which leads to increased production numbers.

8. Verification of Repairs

Vibration analysis can detect any unintended consequences of a repair that may compromise other parts of a machine. By implementing a predictive maintenance program, companies can analyse data to plan and schedule maintenance shutdowns and maximise the use of machine downtime.

This enables maintenance teams to verify repairs and identify any abnormal behaviour, ensuring that equipment operates at peak performance.

How can Paze Industries help?

Predictive maintenance is a critical aspect of any industrial operation, as it helps to minimise downtime and improve overall efficiency.

The first step in implementing a successful predictive maintenance plan is data gathering and analysis. Developing a data platform is a time-consuming and resource devouring process.

However, Paze Industries has developed a low-code platform that spares you from wasting valuable time, making it easy for you to plan and execute your predictive maintenance plan.

One of those success stories is “Emco” which you can access by clicking here.

The digitization of manufacturing processes results in the networking of the associated machines, production systems and tools. This also affects maintenance. While preventive maintenance still dominates in many areas today, as technologies become cheaper, so-called predictive maintenance concepts are increasingly spreading.

However, machine builders who want to offer their customers added value based on the new maintenance approaches must switch to data-based business models.

Our customers use the SF platform to collect and analyse data from networked machines and systems. The Industrial Internet of Things (IIoT) is currently turning numerous sectors and industrial sectors upside down.

In mechanical engineering, the networking of machines and systems generates data with great potential that industrial companies can use, for example, to optimise their machine development.

Networking allows local machine maintenance to be expanded to include centralised data analysis, from which, among other things, the expected failure time of a component, such as a seal or a bearing, can be derived.

By comparing the machine and system data recorded during operation with other data, such as idealised models, the software uncovers errors and faults as they arise.

Often long before the incident occurs. Such data-based “clairvoyance” not only lowers maintenance costs, it also reduces the machine failure rate.

Anticipate and delay maintenance events

Paze enables continuous 24/7 monitoring of machines and systems in real time. Intelligent sensors integrated into production machines collect the data generated during production and send it to our cloud-based IIoT platform.

This prepares them and enables trained users to draw conclusions about faults in the system from the recorded noises, speeds or temperatures. The models are often easier than you think, especially when it comes to maintenance.

In most cases, the temperature is actually enough as a decisive parameter. In the event of service, technicians can use the collected system data to work on troubleshooting in a targeted manner.

A machine model enriched with historical data can be used to anticipate maintenance events and even delay them to an optimal point in time by automatically changing process parameters. The consequences are reduced maintenance cycles and times.

Automatic alarms when the limit is exceeded

A wide variety of systems and machines, ranging from production systems, wind turbines or aircraft turbines to printing presses, motor vehicles or cranes, can be monitored and maintained worldwide using predictive maintenance via the Internet.

Communication usually starts in the networked systems, where sensors, measuring stations or probes record and transmit conditions such as temperature, vibrations, utilisation or wear.

For the evaluation, product and service experts set certain limit values that must not be undershot or exceeded. If this is the case, the system automatically triggers an alarm and sends a notification, often by email or SMS.

A crane manufacturer, for example, defines wind speed limit values. If the crane driver still tries to load a ship from a critical wind force above the limit value, this triggers an alarm that automatically reaches the responsible crane operator.

This also allows grenade claims to be better evaluated. There is also great potential for savings here.

Lifetime determination by acoustic patterns

An analysis method frequently used in predictive maintenance is what is known as acoustic pattern recognition. The service life of a specific part or component, such as a valve, can be determined on the basis of changes within an acoustic pattern.

Using artificial intelligence (AI) and machine learning, complex measured values are assigned meanings, on the basis of which data scientists can make assessments.

For example, the current state of wear of the drill can be read from the vibrations of a table in a CNC machine. Is it new, already worn or already worn out? More precise predictions are also possible, such as: “The drill has reached 15 percent of its lifetime.”

Efficient production is dependent on the functionality of your plants and technical systems. A technical availability of at least 95 percent of the possible operating time is considered ideal.

As part of predictive maintenance measures, an automatic detection of frequently occurring errors can be implemented in the machine.

For example, the identification of encoder errors in sensors or deviations in machine calibration. Thanks to the data-supported, continuous and always up-to-date insight into the system used, potential for improvement can be identified and implemented at an early stage, for example by comparing it with a digital model, and the availability of the machine can be increased.

Paze can be easily integrated into a wide variety of IT systems

In order to digitise the production processes and manage them appropriately, Paze helps users to have direct, uncomplicated access to the operating and status data of the system or machine.

In order for this to work, Paze can be seamlessly integrated into the IT systems of a wide variety of manufacturers. We provide a REST API that our customers can use to access machine data, query results and other functionalities such as versioning or automation.

The topic of scaling is an important point when choosing the right technology, because the more successful an IIoT application is, the larger the amount of data that has to be processed over time.

Platforms that are not set up to manage ever-increasing amounts of data will quickly reach their limits. With Paze, all services – from messaging to database to API services – can be scaled and run multiple times in parallel, so that resources can be increased (or reduced) seamlessly.

Without data, IIoT would not be possible. But not without trained employees who are familiar with the handling and analysis of data.

Specialists such as data analysts or data scientists are far too seldom to be found in medium-sized companies these days, or are often not available at all.

Without employees with qualified data expertise, however, it will be difficult for companies to create value based on their IIoT platform.

Low-code platform as a game changer

For this reason, Paze offers a platform based on low code. Instead of using classic text-based programming languages, our low-code platform supports the development of processes with visual user interfaces and other graphical modelling techniques.

This makes it possible for our customers, who have a great deal of machine expertise but only limited IT resources, to configure their own applications and apps and independently evaluate the status data of their machines and systems without professional programming knowledge.
Predictive maintenance based on low code is a real game changer for our mechanical engineering customers

Frequently Asked Questions on Predictive Maintenance (FAQs)

1. Why is Predictive Maintenance so important?

When predictive maintenance works effectively as a maintenance strategy, machines are only serviced when it is needed. This means that condition-based maintenance is carried out just before a failure is to be expected. This brings several cost savings, as maintenance time is effectively minimised. This also reduces production downtime and reduces the cost of spare parts and accessories.

2. For which applications is Predictive Maintenance suitable?

Applications that lend themselves to predictive maintenance include those that have a critical operational function and also have failure modes that can be inexpensively predicted through regular monitoring.

3. How is Predictive Maintenance performed?

The aim of predictive maintenance is to determine the best time to carry out work on an asset so that maintenance frequency is as low as possible and reliability is as high as possible without incurring unnecessary costs.

Data analytics for predictive maintenance and leveraging the Internet of Things are key to implementing a successful predictive maintenance program, as is the use of sensors and predictive maintenance techniques.

4. What tools are used for Predictive Maintenance?

Constant condition monitoring helps identify problems as it can reveal the exact reason why equipment is failing, thereby increasing equipment reliability. In addition, condition monitoring can help predict future anomalies and improve asset reliability.

Also the maintenance logs of the individual plants can be filtered and analysed if you have a large amount of data. However, you need many assets of the same or similar type to accurately capture the necessary predictive maintenance process flow.

Maintenance logs also help identify which facility needs more maintenance than required. In addition, it is possible to determine what type of maintenance activities occur, e.g. the replacement of parts, etc.

In addition, the evaluation of based reports is one of the most important tools of predictive maintenance. These reports contain important information and details that are helpful in understanding the plant and predicting plant performance, failure, etc.

The system reading is also applicable to some types of systems that break down periodically or start to overheat after a certain period of time. Many systems only come for maintenance for this reason.

Sometimes it’s hard to keep track of how long a device has been working. With this preventive maintenance software tool, you will be alerted and able to stop the machine and avoid a breakdown.

5. Which services are used for Predictive Maintenance?

The services enabled by predictive maintenance can be divided into four main groups. On-premises and Infrastructure as a Service can be self-managed and Platform as a Service and Software as a Service are managed by the responsible IT provider.

Some platforms like Paze have application templates like OEE for monitoring alarms, congestion or a service control room. This gives you an extra jump start as you can go straight to the customer and start iteration cycles. You can develop value creation very early in the project.

6. How does a Predictive Maintenance program starts?

A predictive maintenance program (PdM) anticipates the future condition of property, plant and equipment and makes timely and informed maintenance decisions. PdM – like the idea of ​​Industry 4.0 – depends on the convergence of information technologies (IT) and operational technologies (OT). The key to a successful predictive maintenance program is to bring people, processes and technology together and to clearly define the desired goals.

SMEs in particular often do not have the resources to develop a completely new solution. Paze offers an end-to-end solution from the edge to the app. Where required, software modules are integrated into the existing IT infrastructure. So there is no rude awakening later when scaling and rolling out.

Predictive Maintenance Steps:

Once the primary purpose and focus areas for a move to predictive maintenance have been identified, assessment of the status quo should begin.

The first task is to document the processes and systems currently in use to identify what is working well and where there are gaps in knowledge and skills. This includes looking at the infrastructure and identifying critical workspaces and data collection points.

The assessment considers and assesses an organisation’s readiness for digital transformation, including a gap analysis that not only documents the status quo of processes and technology, but specifically describes how close or far that status quo is to digital readiness.

This assessment will result in a pilot project, a production test bed that will employ the technologies and processes necessary to demonstrably fill some of the gaps identified in the assessment. Paze’s rapid prototyping function also helps to quickly create a high-quality prototype that has access to all important data.

7. How to improve the performance of Predictive Models?

It’s not just data, it’s people too that make this work. There are likely to be experts in the company who know a machine or process inside out and have been working with it for many years.

The digital data now being collected and used is paramount, but the insights the experts are gaining – about things for which there is no data or has never been measured – can contain invaluable information that helps validate the results. These people are a valuable asset and an important part of any digital strategy.

Examples of using Predictive Maintenance

1. Predictive maintenance in Manufacturing

One industry where predictive maintenance pays off is manufacturing, which regularly uses large and expensive tools and equipment. If such equipment fails, an entire enterprise that depends on manufacturing could incur large losses that may well outweigh the installation and operational costs of predictive maintenance technology.

2. Predictive Maintenance for Mechanical Engineering

For machine builders in particular, predictive maintenance is of great interest as a service for their customers. Assets and devices whose sensors are already integrated into predictive maintenance software can bring customers significant savings in maintenance and service provider costs.

Machine builders can also offer their products with additional predictive maintenance services, which increases the value of the products and can lead to long-term customer loyalty.

3. Predictive Maintenance in the Automotive Industry

In automotive production, planned or unplanned downtime and the associated costs can cause a significant setback. With predictive maintenance, it is possible to constantly monitor the condition of industrial equipment in real time and predict the likelihood of failures. This improves operational efficiency and reduces equipment maintenance costs.

4. Predictive Maintenance in the Oil and Gas Industry

In the oil and gas industry, there is often a lack of overview of the condition of the plants at remote offshore locations. Maintenance technicians would periodically visit these locations to check equipment condition and perform oil analysis, even when not necessary.

With predictive maintenance, oil and gas companies can assess the health and performance of their assets and schedule maintenance only when an abnormal problem is identified.

5. Predictive Maintenance with Machine Learning

Nowadays, machine learning and AI simplify and improve numerous processes and things. Plant maintenance is one of them. Today, you can leverage predictive maintenance along with machine learning statistics and algorithms to prevent large losses and anomalies.

6. Predictive Maintenance and Condition Monitoring

Condition monitoring is the monitoring of the health of a system to detect changes that would indicate damage or impending failure. It enables operators to identify and correct problems (through repair and maintenance procedures) before they lead to equipment failure.

Predictive maintenance refers to the planning of corrective maintenance actions based on predictions about how a system will evolve. These predictions are based on data obtained through condition monitoring and plant-specific knowledge.

In other words, predictive maintenance is one of the ways condition monitoring can be used. The two methods complement each other and relate to different types of use and evaluation of sensor data.

7. Predictive Maintenance and IoT

Today, most modern industrial machines already have a variety of different sensors that continuously collect data and make it possible to set up models for predictive maintenance. With these sensors collecting real-time data across different devices, systems, assets, and locations, IoT-based predictive maintenance enables manufacturers to effectively predict and plan for events such as equipment failures or replacement of spare parts.

8. Predictive Maintenance and the Intelligent Factory

A smart factory or machine park are digitised production facilities that use networked devices, machines and production systems to continuously collect and exchange data. This data is then used as a basis for making decisions to improve processes and resolve problems.

The intelligent manufacturing processes employed in a smart factory are enabled by a variety of technologies including artificial intelligence (AI), big data analytics, cloud computing and the Industrial Internet of Things (IoT) and this provides the optimal conditions for a successful implementation of predictive maintenance.

9. Predictive Maintenance and Industry 4.0

As the Internet of Things (IoT) continues to advance, companies are beginning to adopt an Industry 4.0 approach to the manufacturing sector. The further development of AI and ML will support predictive maintenance and ultimately give companies an extreme advantage over those who do not rely on Industry 4.0.

10. Predictive Maintenance and Edge Computing

Edge computing reduces the load on networks and other IT infrastructure and keeps costs down. The technology involves the use of devices that analyse the data collected directly on the factory floor, rather than sending it to a cloud. Edge computing can also help maximise the uptake of IIoT-based predictive and prescriptive maintenance.

For some IIoT implementations, internet connectivity is not always available due to factors such as remote locations or the unreliability of cellular connections.


Paze offers optimal solutions for SMEs in mechanical engineering and in the manufacturing industry. With our modular end-to-end solution, we have exactly the right building blocks that our customers need to enrich the existing IT systems (such as MES, ERP, ticket, etc.) with the necessary depth of data and evaluate them extensively.

This starts as early as the planning phase, where you can use our rapid prototyping tool to brainstorm use cases and get everyone on board.
The platform can be set up and operated within a few days – with the latest technology and the highest security standards. We have an excellent 1st, 2nd and 3rd support team including ticketing system and telephone support.

We offer both low-code tools for non-coding teams and access to the REST API for developers and system integrators. Our super-efficient start-up out-of-the-box solutions and our customer success team with industry backgrounds can help your users find the most value in their data.

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