8 important benefits of predictive maintenance

The most significant benefit of predictive maintenance is reducing maintenance costs by preventing sudden machine failures during production. This strategy also improves operational planning and enhances asset performance, yielding additional financial benefits.

These cost savings can have a significant financial impact on any facility. A 2021 study on maintenance found that:

• Maintenance accounts for 15% to 70% of the total cost of goods produced.
• Facilitiies spend $222 billion in annual maintenance-related costs and losses.
• Plants using predictive or preventive maintenance experienced 52.7% less downtime compared to facilities using reactive maintenance.
• Plants using predictive or preventive maintenance experience 87.3% fewer defects compared to facilities using reactive maintenance.

Achieving ROI on a predictive maintenance program is easier now that it doesn’t require as large of a financial investment as it once did. However, it requires careful planning and implementation, new hardware, and changes to data management and maintenance procedures.

Therefore, knowing the benefits of adopting this strategy is useful for getting buy-in from management or maintenance managers. Understanding its potential also  enables you to create a program suited to your specific facility and determine the best metrics to track the program's performance. 

This article:

1. Explores predictive maintenance benefits from financial and operational perspectives. 

2. Explains predictive maintenance and how it compares to other maintenance strategies. 

3. Offers guidance for building a predictive strategy and implementing it. 

How does predictive maintenance work? 

As a real-world application of IIoT, Predictive maintenance (PdM) integrates sensors, networking infrastructure, data analytics, machine learning, and Artificial Intelligence (AI) to prevent costly equipment breakdowns by identifying potential failures before they happen. 

PdM requires collecting real-time sensor data from critical equipment, including vibration, temperature, pressure, and electrical performance. Algorithms and machine learning analyze the real-time data collected to identify patterns indicating developing problems. The system alerts teams when abnormal conditions suggest impending failure. 

This data analysis approach is particularly valuable for assets with significant operational impact, where failures or unexpected downtime severely disrupt business operations.

Predictive maintenance vs. other maintenance strategies

Maintenance management strategies range from reactive to proactive. Here is how predictive maintenance compares to them: 

1. Reactive maintenance addresses problems after a failure occurs. PdM identifies issues before breakdowns.

2. Preventive Maintenance follows fixed schedules regardless of equipment condition, often requiring planned downtime. PdM schedules maintenance only when necessary.

3. Condition-Based Maintenance monitors equipment parameters and provides real-time alerts about degradation of  machine condition. PdM analyzes data patterns to anticipate future failures and predict when a failure will occur.

Advantages of predictive maintenance 

​​​​​​​PdM delivers these benefits by:

• Increasing machine uptime and boosting OEE

• Optimizing maintenance strategies and reducing associated costs

• Preventing machine failures that cause product defects, injuries, downtime, and wasted resources

Maximize revenue generation

Machine downtime can cost manufacturers $22,000 to $260,000 per minute. These translate to over $1 million in lost revenue per hour of downtime. Use predictive maintenance to: 

• Increase machine uptime: Addressing minor issues before they cause breakdowns extends operational time.

• Boost OEE: Maintaining optimal working conditions over longer periods improves Overall Equipment Efficiency (OEE), accelerating production.

• Enhance product quality: Well-maintained machines retain precision for consistently high-quality products.

• Eliminate penalties due to missed contracts: Reduced downtime keeps production schedules on target, eliminating financial penalties from customers for missing deadlines

These improvements are measured by Mean Time Between Failures (MTBF) and Mean Time to Repair (MTTR). One automotive manufacturer estimated that it prevented $500,000 in maintenance costs and 5 weeks of downtime on a stamping press, achieving ROI in less than three months. 

Reduce operational costs

Unoptimized maintenance reduces plant productivity by up to 20%, costing industrial manufacturers $50 billion annually. To reduce that number, use PdM to:

• Optimize maintenance scheduling: Performing maintenance only when necessary eliminates planned downtime from potentially unneeded time-based repairs.

• Minimize emergency repairs: Anticipating problems prevents costs from overtime, production line reconfiguration, and expedited shipping of replacement parts.

• Reduce spare parts inventory cost: Better asset health forecasting enables parts ordering as needed, reducing inventory investment.

The savings can add up quickly: A leading clothing retailer with 1,000 locations saw $250,000 in cost savings per location with a customized solution focused on vibration monitoring. 

Increase ROI on machines

Maintenance cost (MC) as a percent of estimated replacement value (ERV) ranges from under 3% in some industries to 11-36% in others. PdM reduces this percentage by:

• Extending machinery service life: Preemptively fixing mechanical issues, avoiding run-to-failure, prevents breakdowns, and can add years to service life.

• Preventing additional component damage: Preventing minor repairs from becoming major ones by avoiding cascading damage where one broken part affects others.

• Improving capital investment planning: Data-driven insights on equipment performance help organizations make better-informed repair-versus-replace decisions.

Optimize maintenance team productivity

Implementing a predictive strategy helps maintenance teams reduce costly downtime by:

• Performing more efficient repairs: Advance warning enables better scheduling, time to gather tools and parts, and faster work completion.

• Shifting to planned maintenance: PdM reduces disruptive emergency repairs that pull technicians away from scheduled tasks.

• Focusing on value-added activities: Teams with fewer emergencies can pursue improvement projects and training that deliver long-term value. 

Enhance workplace safety

As of 2021, workplace injuries and diseases cost U.S. companies $250 billion annually or nearly 2% of GDP. Predictive maintenance can: 

• Prevent injuries from catastrophic failures: PdM prevents dangerous equipment failures that could cause flying debris, chemical leaks, or structural collapses.

• Reduce emergency repair risks: Planned maintenance allows for proper lockout/tagout procedures, PPE preparation, and thorough risk assessments.

• Improve compliance with safety regulations: Regular equipment monitoring ensures adherence to safety standards, preventing violations, fines, and increased insurance premiums.

Improve customer satisfaction

• Maintain high product quality standards: Equipment operating at optimal performance produces more consistent quality products. PdM preserves precise tolerances customers expect, reducing returns and complaints.

• Ensure consistent delivery schedules: Minimizing unexpected downtime prevents schedule disruptions that damage customer relationships.

• Offer competitive pricing: Companies can pass operational cost savings from reduced downtime and maintenance expenses to customers without sacrificing profit margins.

Support sustainability goals 

Sustainability impacts both the environment and profitability. From 2021 to 2022, manufacturing executives viewing sustainability as competitively essential increased from 38% to 58%, with 68% implementing company-wide initiatives. A PdM strategy can:

• Decrease energy consumption: Maintaining equipment at optimal parameters reduces energy usage and carbon emissions. 

• Reduce waste: Prevent failures that cause scrapped materials to reduce landfill waste, and identify inefficiencies to conserve raw resources.

• Extend resource lifecycles: Maximizing equipment lifespan reduces environmental impacts from manufacturing replacements, including raw material extraction, transportation, and installation.

These goals are also tied to financial impacts: A global manufacturer of braking systems saved over $230,000 annually in scrap costs per line. They also reduced scrap from 7-10 parts per tool break to 0-1. 

Shifting from reactive to predictive maintenance

Shifting to predictive maintenance from reactive, preventive, or condition-based maintenance requires investment in sensors, IIoT infrastructure, and training, plus potential production disruption during installation.

Start with pilot projects on critical equipment with the highest downtime costs or safety implications to build momentum for widespread implementation. Select technologies tailored to your specific equipment and failure modes.

Creating a Successful Predictive Maintenance Strategy

Successful implementation begins with clear objectives, such as reduced downtime, extended equipment life, or lower maintenance costs. Well-defined metrics guide investments and clarify ROI measurement.

Transitioning from preventive or condition monitoring is easier than from reactive maintenance. Gradual integration with existing practices ensures a smooth transition.

How to get started

The best predictive maintenance strategy is simple to implement and maintain. It also prepares a facility for the future. The best way to create a customized predictive maintenance program is to start with a proof-of-concept project. For a free consultation with a Solutions Architect, fill out the form below. 

For immediate assistance, contact our service center: 
info.us@ifm.com
800-441-8246