How to build a business case for predictive maintenance in manufacturing

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Predictive maintenance is becoming more important for companies who want to improve operational efficiency and reduce costs. By using data analytics to anticipate equipment failures, organizations can optimize maintenance schedules and minimize unexpected downtime. 

This blog will cover the value drivers for predictive maintenance, outline methodologies for calculating its business case, and provide actionable insights for effective implementation.

What is predictive maintenance and its benefits

Predictive maintenance uses advanced technologies to predict when equipment failures may occur. Unlike traditional reactive maintenance, which addresses problems only after they arise, or preventive maintenance, which relies on scheduled servicing, predictive maintenance proactively identifies potential issues before they lead to breakdowns.

Key technological advancements that enable predictive maintenance include the Internet of Things (IoT) and data analytics. These technologies facilitate real-time monitoring and analysis of equipment, allowing organizations to act on insights derived from data collected over time.

Why organizations should invest in predictive maintenance

4 reasons why organizations should consider predictive maintenance:

  • Reduced downtime: Predictive maintenance allows for timely interventions, minimizing unplanned downtime. For example, a steel manufacturer implemented predictive maintenance for its critical centrifugal pumps, which led to a 30% reduction in downtime over a year.
  • Lower maintenance costs: By anticipating failures, companies can optimize maintenance schedules and reduce the frequency of unnecessary inspections. The same steel manufacturer reported savings of €50,000 annually by reducing unnecessary maintenance activities.
  • Extended asset lifespan: Regular monitoring and early intervention help maintain equipment in better condition, prolonging its operational life. In one case for example, predictive maintenance extended the lifespan of aging machinery by an average of 20%.
  • Improved operational efficiency: Enhanced asset performance leads to increased productivity and reduced energy consumption. A manufacturing plant that adopted predictive maintenance techniques saw a 15% increase in overall efficiency.

Common challenges in estimating predictive maintenance benefits

While the advantages of predictive maintenance are clear, quantifying these benefits can pose challenges. Organizations often encounter uncertainties when predicting asset failures and the performance of predictive technologies.

Many predictive maintenance systems cannot perfectly predict failures due to factors such as the complexity of assets and varying operational conditions. Key performance indicators for predictive maintenance, like sensitivity (true positive rate) and specificity (true negative rate), are importantl for evaluating the accuracy of the system.

For instance, a predictive maintenance system with a sensitivity of 90% means it correctly identifies 90% of potential failures, while a specificity of 85% indicates that 85% of non-failures are correctly identified. Organizations should focus on the difference in accuracy between current methods and the new predictive maintenance technologies when calculating potential benefits.

How to assess the predictive maintenance business case

To effectively calculate the business case for predictive maintenance, you can follow a structured approach:

Step 1: Assemble a team of specialists

Gather a team that includes maintenance engineers, predictive maintenance technology specialists, and business case analysts. This collaborative effort will ensure a comprehensive assessment of potential benefits and costs.

Step 2: Analyze failure modes and estimate mean time between failures (MTBF)

Identify the different failure modes of the assets in question. Use historical data and expert judgment to estimate the MTBF for each mode. 

For example, if a centrifugal pump has an MTBF of 10 years for impeller failure and 8 years for bearing failure, these figures will be crucial for calculations.

Step 3: Discuss additional value sources from the predictive maintenance system

Research how the predictive maintenance system may affect inspection costs, maintenance intervals, operational costs, and asset lifespans. Identify and quantify these additional value sources, such as reducing energy usage by identifying inefficiencies early.

Step 4: Perform a detailed business case analysis

Evaluate the initial and recurring costs associated with implementing predictive maintenance. Use ranges for uncertain variables to improve accuracy in your estimates. For instance, consider potential false alarms and their associated costs. If a predictive maintenance system has a specificity of 92%, it may lead to minimal unnecessary maintenance, thus supporting a positive business case.

Key value drivers and a quick-start guide

Organizations should identify the main value drivers for predictive maintenance.

Key drivers include:

  • Improved asset uptime
  • Reduced maintenance and operational costs
  • Enhanced energy efficiency
  • Extended asset lifespan

Four steps to begin with predictive maintenance 

  1. Assess current maintenance practices: Evaluate existing maintenance strategies and identify areas for improvement.
  2. Invest in technology: Consider implementing predictive maintenance technologies that align with your organization’s needs.
  3. Train your team: Ensure your maintenance and operations teams are well-trained in using predictive maintenance tools and interpreting data.
  4. Monitor and adjust: Continuously monitor the performance of your predictive maintenance program and adjust as needed to maximize benefits.

Case studies: changing maintenance strategies in industry

Examples show how updating maintenance strategies can lead to significant cost savings and efficiency improvements. In one case, a centrifugal pump experienced an unexpected breakdown, which had the potential to halt production and incur losses of around €25,000 per hour. By implementing an advanced maintenance system, the company was able to detect the issue early and address the failure mode before it caused costly downtime.

In another case, a company introduced a predictive system for monitoring 10 pumps. As a result, they achieved a payback period of less than two years, while significantly boosting operational efficiency.

These examples highlight how businesses can use data-driven insights to optimize asset management, allowing decision-makers to understand both the potential benefits and challenges of updating their maintenance strategies.

Improving asset management for greater impact

Predictive maintenance is a powerful strategy for optimizing operational efficiency and reducing costs. By understanding the value drivers and implementing effective methodologies, organizations can build a strong business case for predictive maintenance.

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