Overcoming Challenges in Predictive Maintenance ROI Calculation

Practical Tips for Accurate Estimation

Predictive maintenance (PdM) has emerged as a game-changer for industrial manufacturing companies, enabling them to optimize maintenance operations, reduce equipment downtime, and extend the lifespan of critical assets. However, calculating the predictive maintenance ROI can be challenging, primarily due to various obstacles faced during the process. 

In this article, we will explore the seven most common challenges organizations face when calculating predictive maintenance ROI. For each, we will provide insights on how industrial manufacturing companies can overcome them to accurately determine predictive maintenance ROI.

The most common challenges for predictive maintenance ROI calculation and how to overcome these include:

Lack of Historical Data

One of the primary challenges in calculating predictive maintenance ROI is the absence of sufficient historical data. Without a solid dataset, it becomes difficult to establish baseline performance and accurately predict maintenance requirements. To overcome this challenge, companies can leverage advanced IoT sensors, monitoring systems, and data collection tools to gather real-time data. Additionally, collaborating with industry experts or partnering with predictive maintenance service providers can offer valuable insights and access to historical data sets.

Examples of Data to Consider

  • Vibration Monitoring: Installing vibration sensors on machinery can provide crucial insights into mechanical health. Unusual vibration patterns often indicate misalignment, imbalance, or wear and tear, allowing timely intervention before critical failures occur.
  • Temperature and Pressure Sensors: Monitoring temperature and pressure variations can help detect anomalies that might lead to overheating, leaks, or pressure-related failures.
  • Humidity Control: For assets sensitive to humidity, monitoring humidity levels can prevent corrosion, mold growth, and other moisture-related issues that could impact equipment performance.
  • Operational Data: Collecting data on machine operation, such as running hours, cycles, and load conditions, provides a comprehensive view of equipment usage. This data helps in understanding how different operating conditions affect asset health.
  • Enterprise Asset Management (EAM) System: Utilizing an EAM system provides access to data from Original Equipment Manufacturers (OEMs) about maintenance history, failure modes, and recommended maintenance schedules. This system highlights past issues and helps in identifying recurring problems.
  • Service Ticket System: Historical service tickets offer insights into past maintenance activities, repairs, and issues. Analyzing this data can uncover recurring problems and provide valuable context for predictive maintenance strategies.
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By combining real-time data from IoT sensors with historical data from EAM and service ticket systems, industrial manufacturing companies can build a comprehensive dataset for predictive maintenance analysis. This rich data pool enables accurate prediction of asset health, timely identification of potential issues, and informed decision-making regarding maintenance strategies.

Collaborating with experts further enhances data utilization. Predictive maintenance service providers, with their domain knowledge and experience, can offer guidance on data analysis techniques and help interpret complex patterns that might not be immediately obvious. This collaborative approach ensures that even companies with limited historical data can embark on a successful predictive maintenance journey, accurately estimating ROI and reaping the benefits of optimized maintenance operations.

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Data Quality and Integrity

The effectiveness of predictive maintenance relies heavily on the quality and integrity of the data used for analysis. Inaccurate or incomplete data can lead to faulty predictions and unreliable ROI calculations. Industrial manufacturing companies should invest in data validation and cleansing techniques to ensure data accuracy. Implementing data governance practices, such as regular data audits and validation checks, can help maintain data integrity and improve the overall ROI calculation process.

Examples of Inaccurate or Incomplete Data Challenges and Solutions

  • Outliers and Noise: Inaccurate sensor readings, anomalies, or noise in the data can distort predictive models. For instance, a sudden spike in temperature readings might not indicate a genuine issue but rather a sensor malfunction. It is important to employ statistical methods to identify and remove outliers. Applying smoothing techniques, like moving averages, can help mitigate noise and make the data more reliable.
  • Missing Data: Gaps in the dataset due to sensor malfunctions or communication failures can disrupt predictive analysis. Missing data can lead to incomplete insights and unreliable predictions. To avoid this it is important to utilize interpolation or extrapolation techniques to estimate missing data points. Machine learning algorithms can help predict missing values based on historical patterns.
  • Data Formatting Inconsistencies: Inconsistent data formats or units can lead to confusion during analysis. For example, if different sensors measure temperature in Fahrenheit and Celsius, merging the data could result in errors. To solve this, standardize data formats and units before analysis. Use data preprocessing tools to convert all measurements to a common format for accurate comparison.

By addressing these challenges through data validation and cleansing techniques, industrial manufacturing companies can enhance the accuracy and reliability of their predictive maintenance analysis. Establishing strong data governance practices, including regular audits and validation checks, is essential to maintaining data integrity over time. By ensuring the quality of input data, companies can have confidence in the output of predictive models, leading to more informed decision-making, optimized maintenance strategies, and ultimately, a more accurate ROI calculation.

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Complex Data Analysis

Predictive maintenance involves analyzing vast amounts of data, including sensor readings, performance logs, and maintenance records. However, many companies struggle with the complexity of data analysis, as it requires advanced statistical models, machine learning algorithms, and domain expertise. Investing in data analytics tools, hiring data scientists, or partnering with analytics firms can provide the necessary expertise to effectively analyze complex data and generate actionable insights.

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Identifying Key Performance Indicators (KPIs)

Defining relevant Key Performance Indicators (KPIs) is crucial for measuring the success of predictive maintenance initiatives. However, industrial manufacturing companies often face challenges in identifying the most appropriate KPIs to evaluate the impact of predictive maintenance on their operations. To address this challenge, companies should align KPIs with their specific business goals and objectives. Common KPIs include asset uptime, mean time between failures (MTBF), mean time to repair (MTTR), and overall equipment effectiveness (OEE).

Examples of Industry-Specific KPIs

Rail Industry – Vertical Displacement Monitoring

In the rail industry, measuring the vertical displacement of the rail can serve as a powerful KPI. This indicator helps in predicting rail wear and deformations, which can directly impact train stability and safety. A certain level of vertical displacement might indicate a need for maintenance to prevent accidents and disruptions.

A way of doing this is to install IoT sensors along the rail tracks to monitor vertical displacement. Implement predictive algorithms that analyze the displacement trends and patterns. When the displacement reaches a predetermined threshold, it triggers a maintenance alert, allowing timely interventions. Check out how a leader in the industry, has already modernized its railway operations with Sensorfy’s technology

vossloh iot smart sensor system for predictive maintenance

Industrial Door Monitoring – Cycle Counter

For industrial door monitoring, tracking the door cycle counter provides valuable insights into door usage patterns. Monitoring how many times a door is opened and closed helps in predicting wear and tear, potential malfunctions, and the need for maintenance.

The way to go is to equip industrial doors with cycle counters or sensors that detect door movements. Analyze the cycle count data over time to identify usage patterns. Implement a predictive model that estimates maintenance requirements based on the door’s historical usage and alerts maintenance teams when thresholds are exceeded. This solution helped one of the big players optimize the maintenance routes to deliver better maintenance to their customers 

By customizing KPIs to specific industries and operational needs, industrial manufacturing companies can gain a more accurate understanding of the impact of predictive maintenance on their assets. These industry-specific KPIs, such as vertical displacement monitoring for the rail industry and cycle counters for industrial doors, enhance the ability to predict maintenance needs, optimize operations, and ensure a more precise ROI calculation.

smart maintenance_Sensorfy_automatic doors

Cost of Implementation

Predictive maintenance implementation involves upfront costs, such as purchasing and installing sensors, upgrading systems, and training personnel. These costs can be a significant barrier for some industrial manufacturing companies. To mitigate this challenge, companies should conduct a comprehensive cost-benefit analysis to determine the potential savings and benefits associated with predictive maintenance. This analysis can help justify the initial investment and secure the necessary funding for implementation.

At Sensorfy, we understand the financial constraints that organizations might face when adopting predictive maintenance. To assist companies in getting started on this transformative journey, we have developed a low-cost model designed to address these challenges effectively.

1. Proof of Concept

Our approach begins with a proof of concept, where we utilize the Sensorfy Standard Platform for a swift and seamless setup. This platform provides real-time data visualization of your assets, allowing you to witness the potential benefits of predictive maintenance firsthand. This low-cost solution serves as a validation tool for the service business model without incurring substantial upfront expenses.

2. Data Analysis

By leveraging our proven technology, you can significantly reduce technology risks associated with custom software development. You don’t need to spend time and resources building complex software before the value of predictive maintenance is proven. This approach accelerates your time to market from potentially years to just months.

3. Scale Up

Once the value of predictive maintenance is demonstrated through the proof of concept, we seamlessly scale the solution. This means you only invest in expanding the system once you are certain of the benefits. Our deep IoT expertise and experience ensure that the scaling process is efficient, avoiding costly mistakes that often come with building custom solutions in-house.

Incorporating Sensorfy’s offering into your predictive maintenance strategy can help overcome the financial barriers associated with implementation. Our cost-effective approach allows you to experience the advantages of predictive maintenance without an overwhelming initial investment. With the ability to validate the value and then scale based on proven results, you can optimize your maintenance operations, reduce downtime, and ensure accurate ROI calculations while minimizing risks and costs. Our experts are happy to provide you a quick demo to define the benefits for your organization.

predictive maintenance dashboard reviewed by team member

Change Management and Workforce Adoption

Adopting predictive maintenance often requires a cultural shift within the organization. Resistance to change and lack of workforce buy-in can hinder successful implementation and ROI calculation. To address this challenge, companies should focus on change management strategies, such as employee training and education programs, to create awareness about the benefits of predictive maintenance. Involving employees in the decision-making process and highlighting success stories from early adopters can foster a positive attitude towards the new approach.

Predictive maintenance offers tremendous potential for industrial manufacturing companies to optimize maintenance operations and improve overall productivity. However, calculating the return on investment (ROI) for predictive maintenance can be a complex task, hindered by various challenges. By addressing the seven most common obstacles discussed in this article, companies can ensure accurate ROI estimation and maximize the benefits of predictive maintenance.

By leveraging advanced IoT sensors, investing in data validation techniques, and employing data analytics tools, companies can overcome challenges related to data availability, quality, and analysis. Furthermore, aligning KPIs with business goals, integrating predictive maintenance with existing systems, and conducting comprehensive cost-benefit analyses help organizations realize the true value of their investment. Lastly, a focus on change management strategies and fostering workforce adoption can ensure smooth implementation and successful ROI calculation.

maintenance employees reviewing predictive maintenance dashboard

Take your first steps into predictive maintenance

In this journey, it is crucial for industrial manufacturing companies to collaborate with experts and leverage the knowledge and experience of predictive maintenance service providers. Our experts are committed to supporting you throughout this process, offering guidance, technical expertise, and technical solutions to overcome challenges and achieve accurate ROI calculations. Embracing predictive maintenance and accurately estimating its ROI will not only enhance operational efficiency but also position your company for long-term success in an increasingly competitive landscape.

Remember, the path to predictive maintenance ROI begins with addressing these challenges head-on and adopting a proactive approach to maintenance. Embrace the power of data, leverage advanced analytics, and seek expert guidance to unlock the full potential of predictive maintenance for your industrial manufacturing operations. The future of maintenance is here, and together, we can navigate the complexities and realize the substantial benefits it offers. If you need any support in getting started with predictive maintenance, get in touch with us. Our experts are happy to guide your predictive maintenance journey! 

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