Predictive maintenance is a proactive maintenance strategy that uses real-time data and predictive analytics from the Industrial Internet of Things (IIoT) sensors, Artificial Intelligence (AI) and machine learning to continuously monitor machinery and equipment. The objective is to locate and identify production line issues before they lead to unexpected machine shutdowns and work stoppage.
Predictive maintenance programs create measurable business value for organizations by shifting asset management and equipment maintenance from a reactive, break-fix situation to a targeted, data-driven strategy. This can reduce overall maintenance costs, lower the risk of unplanned downtime and improve machine uptime.
Implementing a predictive maintenance program helps manufacturers avoid unexpected machine downtime, extend equipment lifespans, and improve quality and throughput in the production line. Since organizations have fewer emergency repairs, they can better allocate personnel to handle more high value initiatives instead of break-fix repairs of equipment.
Measuring predictive maintenance Return On Investment (ROI) can be difficult if manufacturers rely on fragmented legacy systems, operate in non-linear environments, and lack the necessary data infrastructure and IIoT technology.
Measuring predictive ROI requires bridging disconnected operational and financial data to isolate cause and effect.
What maintenance strategy works best for your manufacturing facility will depend on your organization’s particular evaluation of asset criticality, downtime costs, failure frequency, sensor strategy, and the ability to act on alerts. Read on for Advanced Technology Services (ATS) industry best practices to help maximize your facility’s predictive maintenance ROI.
Predictive maintenance ROI formula
Calculating predictive maintenance ROI involves comparing the financial gains of preventing failures against the total costs of implementing the program. There are several formulas industrial manufacturers can use.
Basic predictive maintenance ROI formula
- Predictive maintenance ROI = (Downtime Avoided + Repair Cost Savings + Productivity Gains + Quality Gains + Inventory Savings + Asset Life Extension) – Predictive Maintenance Investment.Â
Percentage ROI formula
- ROI % = [(Total Predictive Maintenance Benefit – Predictive Maintenance Investment) ÷ Predictive Maintenance Investment] × 100.
Downtime cost formula
- Downtime Cost = Lost Production Value + Labor Cost During Downtime + Repair Cost + Expedited Parts Cost + Scrap/Rework Cost + Schedule Impact.
The formulas should be applied first to the assets with the highest downtime cost, longest repair time, most frequent failures, or greatest production impacts. Which option works best for your organization’s facilities will depend on your particular equipment types, critical assets, repair costs, manufacturing environment, goals, and IIoT and AI sensors used.
Predictive maintenance ROI drivers
Predictive maintenance programs deliver organizational value by increasing equipment lifespans through proactive maintenance health monitoring. This keeps production lines running and increases output.
ROI driver | How it creates value |
Downtime avoided | Keeps production running and protects output |
Repair cost reduction | Prevents failures from becoming major repairs |
Labor efficiency | Reduces emergency work and improves planning |
Parts readiness | Reduces expedited orders and stockout risk |
Asset life extension | Protects capital equipment |
Quality improvement | Prevents defects caused by equipment degradation |
Energy efficiency | Detects abnormal operating conditions |
Safety improvement | Reduces risk from failure-related hazards |
Predictive maintenance costs to include
The initial investment to implement a predictive maintenance program is often higher than preventative maintenance costs at first. Once the system is in place and running, the ROI will add up. Predictive maintenance cost should be compared against the cost of downtime, not just the cost of traditional preventive maintenance tasks, as this is a more effective measurement.
Organizations should know that sensor-only programs may look cheaper upfront for facilities but can underperform if data is not interpreted or converted into maintenance actions. Facilities looking to reap the full cost saving benefits of a predictive maintenance strategy will need an integrated approach combining software, hardware, and sensors as part of a holistic approach. Â
Components of a predictive maintenance plan
- Embedded IIoT sensors
- Integrated data platform
- Analytical AI tools
- Equipment condition monitoring tools
- Computerized Maintenance Management Systems (CMMS)
- Enterprise Asset Management (EAM) software
- Operator training
- Defined workflows
How to calculate downtime avoided
Industrial manufacturers can calculate avoided downtime by using a step-by-step evaluation of what failure and downtime actually costs an organization. Â
Steps to calculate downtime
1. Identify the asset and failure mode.
2. Estimate expected downtime following a failure.
3. Calculate lost production value.
4. Add labor and repair costs.
5. Add expedited parts or overtime costs.
6. Include scrap, rework or quality impact if applicable.
7. Compare against monitoring and corrective action cost.
Example calculation
A critical machine would cause $10,000/hour in lost production.
Predictive maintenance avoids 12 hours of downtime.
Formula
Lost Production Cost Per Hour x Hours of Downtime = Avoided Downtime Value
- Calculation: $10,000 × 12 = $120,000
If repair and monitoring costs were $20,000.Â
Formula
Avoided Downtime Value – Repair and Monitoring Costs = Predictive Maintenance ROI
- Calculation: $120,000 – $20,000 = $100,000
The predictive maintenance ROI is $100,000.
When calculating, the best practices are to use conservative estimates, document any assumptions, and validate against actual downtime history wherever possible.
Which assets generate the highest predictive maintenance ROI?
Manufacturers should apply predictive maintenance to highly critical assets and frequently failing assets. These are assets where failure is expensive, disruptive, difficult to recover from, and currently causing problems.
Predictive maintenance ROI is usually strongest for assets with:
- High downtime cost
- Recurring failures
- Long repair times
- Difficult troubleshooting
- Expensive replacement parts
- Safety or quality risks
- Measurable failure indicators
- Production bottleneck status
Equipment type | ROI opportunity |
Motors | Avoid bearing failure, overheating and overload |
Pumps | Prevent seal failure, cavitation and pressure issues |
Compressors | Avoid utility disruption and expensive repairs |
Gearboxes | Detect wear and lubrication issues early |
Conveyors | Prevent line stoppages and product flow issues |
CNC machines | Protect precision, spindle health and quality |
Packaging equipment | Avoid bottlenecks, jams and lost output |
Ovens and dryers | Prevent process interruption and production loss |
Predictive maintenance ROI by maintenance maturity level
Predictive maintenance ROI heavily depends on maintenance maturity levels. The financial payoff of a predictive maintenance program is directly linked to an organizations shift from break-fix repairs to structured, proactive strategies.
Maintenance maturity level | Predictive maintenance ROI potential | Recommended next step |
Mostly reactive | High potential, but foundational gaps may exist | Start with critical assets and work order discipline |
Preventive maintenance in place | Strong potential | Add condition monitoring to bottleneck assets |
CMMS/EAM established | Stronger scalability | Integrate alerts with workflows |
Multi-site reliability program | Enterprise ROI potential | Standardize monitoring and benchmarking |
Data-driven maintenance | Highest optimization potential | Expand analytics and continuous improvement |
ATS predictive maintenance ROI use cases
ATS provides industrial clients predictive maintenance solutions that are tailor fit to each organization’s individual needs. See the below table for documented ROI use case successes. Individual facility results will vary based on each organization’s unique asset criticality, downtime cost, response time, and maintenance execution. To find which predictive maintenance options are recommended for your organization’s facilities talk with an ATS expert today.
ATS use case | Industry / Context | Reported outcome |
Metal products / wire and cable production | Achieved 14x ROI after ATS helped avoid a downtime event using sensor technology and reliability engineering expertise. | |
Consumer packaged goods / bakery operation | Achieved 32x ROI in the first 90 days of R360® Machine Health Monitoring, with avoided losses reported at $138,000. | |
Enterprise deployment across 100 plants | Avoided 1,600+ hours of downtime, generated $2.1M in avoided losses and achieved 2x ROI in the first six months. | |
Can and glass bottle manufacturing | Avoided 32 hours of unplanned downtime and $86,000 in losses at one can plant during a 30-day trial, with nearly $100,000 in downtime costs avoided at a glass bottle plant.  | |
Production equipment monitored through Reliability 360® | Avoided $7M+ in production losses and prevented 240+ hours of potential downtime. | |
Press monitoring / abnormal frequency alerts | ATS detected abnormal frequencies across several presses, enabling preventive action with no downtime, 12 downtime hours avoided and $15K in avoided losses. |
How ATS helps manufacturers measure and improve predictive maintenance ROI
Predictive maintenance ROI is strongest when the program focuses on critical assets, measurable failure modes and clear maintenance workflows. The best starting point depends on downtime cost; failure history; asset criticality; maintenance, repair, and operations (MRO) readiness; and maintenance team capacity. Manufacturers should start by identifying high-risk assets, calculating downtime cost, and estimating the value of avoided failures. Organizations don’t have to do this alone. This is where ATS can help.
ATS partners with organizations looking to improve asset lifespans and increase predictive maintenance ROI. We can evaluate predictive maintenance opportunities for your organization by identifying where equipment failures are creating the greatest production bottlenecks, repair expenses, quality issues, and safety impacts.
We can provide comprehensive predictive maintenance analytics and remote reliability monitoring of critical assets at your facility. ATS will then prioritize the assets with condition and machine health monitoring, expert data analysis, and planned corrective actions that will produce measurable savings for your facilities.
Talk with an ATS expert today.
