Wear and tear refers to the gradual decline in equipment performance over time—a natural part of every assetlifecycle. Over time, belts fray, gears slip and friction begins to build. Left unchecked, wear and tear leads to entirely predictable equipment failure.
This failure is costly: Research shows that 55% of U.S. manufacturers experience unplanned downtime every year, with each hour costing anywhere from $400,000-$1,700,000 per hour. Additionally, impacts extend beyond interrupted operations; catastrophic failures may require capital spending to replace broken machines, and ongoing downtime leads to reduced overall equipment efficiency (OEE), further reducing productivity.
Simply put, even minor wear and minimal tear can quickly escalate into major component failures if left unchecked. The good news? By implementing a predictive and proactive approach to maintenance, manufacturers can detect, address and control the risks of wear and tear.
What causes wear and tear in industrial equipment?
Equipment naturally degrades over time. This is true of industrial machinery, commercial devices and end-user appliances. Consider the simple example of a machine that grinds beans, boils water and brews coffee. If used every day, machine parts begin to wear down. Grinder blades may dull, hopper chutes may clog and water tanks may become calcified.
The result is a slow decline from peak performance to adequate operation and eventually, failure. In a manufacturing context, there are six common causes of normal wear and tear:
- Friction between moving components
- Corrosion from chemical exposure or moisture
- Fatigue due to repeated stress cycles
- Thermal stress from overheating or rapid temperature shifts
- Contamination from dirt, debris or lubrication breakdown
- Misalignment and imbalance create excess load
It’s important to note that these issues do not occur in isolation. Instead, causes have a cumulative effect. For example, continual operation leads to decreased lubrication, which increases the rate of wear and tear on moving parts. Poor lubrication then creates more friction between larger components, which drives an overall rise in operating temperature, followed by the final result: failure.
Types of wear seen in manufacturing
What does wear look like in manufacturing? It depends on the machine and its components.
Abrasive Wear
Abrasive wear occurs when surfaces such as belts or conveyors are scratched or pitted by hard particles. These include dirt, dust and material debris that accumulate over time.
Adhesive Wear
Adhesive wear is tied to friction. For example, bearings or gears that are improperly aligned or incorrectly lubricated can lead to unwanted material transfer and unexpected temperature increases.
Corrosive Wear
Corrosive wear stems from chemical reactions that alter metal properties. This often occurs in pumps or piping. After months or years of continuous operation, material buildups such as limescale or the presence of rust due to oxidation can impact functionality.
Fatigue Wear
Fatigue wear is typically seen in rotating machinery or equipment that handles cyclic loads. Wear starts with microcracks, which can lead to sudden fractures if left unchecked.
Surface Wear
Also called rolling contact, surface wear occurs in bearings, cams and shafts and is the natural outcome of continual use.
Early signs of wear and tear
The earlier teams detect wear and tear, the better. By addressing small issues immediately, manufacturers can prevent larger problems down the line.
Some early signs of wear and tear include:
- Excess heat or vibration
- Increased amperage draw
- Loose fasteners or structural instability
- Debris in oil or filters
- Surface cracks or scoring
Ideally, condition monitoring should be an ongoing process that collects and compares current data with historical reporting. This both increases failure prediction accuracy and lets teams take targeted action to reduce wear and tear.
The cost of wear and tear
Unplanned machine downtime is the most significant cost associated with wear and tear. It leads to reduced performance, lost production time and potential customer dissatisfaction if components are not produced and shipped on time.
But lost machine uptime isn’t the only cost. Take a conveyor belt that has been running for six months without regular maintenance. As dust and debris accumulate, speed begins to suffer, leading to increased energy consumption as more power is required to maintain consistent performance.
Another concern is waste. Tools that cut or shape materials may experience reduced accuracy as components degrade, leading to defects that require costly rework.
Wear and tear doesn’t just affect performance—it can influence safety and compliance. Over time, components under stress may exceed tolerance, creating conditions that require attention. By addressing these risks proactively, manufacturers can maintain a safe work environment, meet regulatory standards and avoid costly disruptions.
Finally, wear and tear reduces overall equipment lifespan. For example, a machine designed to last five years may fail after only three years due to excessive wear and tear. This leads to premature capital spending, not just for replacing the damaged equipment, but also for testing and integrating the new machine into current processes.
Maintenance strategies to manage wear and tear
Proactive maintenance strategies help better manage wear and tear. They include:
- Preventive maintenance: Preventive maintenance involves scheduled inspections and servicing of equipment. This includes tasks such as lubrication, cleaning and alignment checks. If anomalies are found, they can be fixed immediately to prevent further issues.
- Predictive maintenance: Predictive maintenance covers processes such as vibration, thermal or oil analysis. By combining current data with historical information, teams can track emerging trends and address wear and tear issues that haven’t happened yet but will if not addressed.
- Precision maintenance: Precision maintenance is about the details. These may include ensuring correct torque, balance and calibration of machinery to reduce the rate at which wear and tear occurs.
- Conditioned-based monitoring: Condition-based monitoring is tied to the use of operational thresholds that trigger alerts and actions. For example, if a mixing machine experiences a sudden spike in vibration, connected tools can trigger alerts that shut down the equipment for inspection.
- Root cause failure analysis (RCFA): In some cases, routine maintenance treats symptoms rather than root causes of equipment failure. This leads to repeated issues, such as recurring wear patterns on the same parts, time and time again. Root cause failure analysis (RCFA) uses techniques such as the “5 Whys,” fishbone diagrams and fault tree analysis to identify underlying causes.
The keys to any maintenance strategy (or combination of strategies) are standardization and consistency. Organizations must create standardized definitions and approaches and apply them consistently across maintenance operations.
Consider preventive maintenance, which looks to predict when failure will occur. According to a recent study, while 71% of companies prioritize preventive maintenance, the majority of organizations spend less than 40% of total maintenance time on planned repairs.
The result? Costly, reactive operations that remediate rather than anticipate wear and tear.
Technology tools for wear prevention
Effective wear prevention is tied to visibility. The more teams know about machine operations from start to finish, the better prepared they are to combat wear and tear.
Technology enables these efforts. This starts with tools such as computerized maintenance management systems (CMMS), which enable inspection scheduling and work history reporting. Applied in tandem, this data keeps maintenance current and helps track the emergence of potential problems.
Next up is the use of IIoT sensors. These connected devices help capture early-stage wear data, which is then collected and analyzed by CMMS and other tools. For example, manufacturers might install vibration and temperature sensors on critical equipment that provide minute-by-minute reports. Compiled over a day, week or month, these sensors paint a clear picture of wear and tear.
Digital twins are an emerging solution to wear and tear management. Consider a packaging machine running 24/7/365. Using digital modeling software, companies create a virtual “twin” of production line machinery and then simulate common equipment wear scenarios. This helps identify possible failure points and determine optimal maintenance strategies without disrupting current operations.
It’s also worth setting up automated alerts for maintenance teams in addition to preventive maintenance schedules. These alerts help reduce the risk of human error because they rely on embedded and connected sensors to detect physical changes. For example, if a conveyor belt experiences a slow but steady rise in temperature, staff may overlook the trend because performance appears unaffected. Digital tools, however, can track and report the change.
Spare parts strategy for wear components
Another component in wear and tear management is an effective spare parts strategy and inventory optimization using an MRO (maintenance, repair and operations) approach. Here, five components are essential:
1. Criticality-based spare parts restocking: MRO spare parts management starts with an evaluation. What parts are most often required, and which are most critical? This helps ensure that companies aren’t left waiting for essential parts.
2. Supplier alignment for reliable part lead times: It’s impossible to stock every part for every situation. This is why an MRO procurement process is necessary; teams need to work with suppliers to identify reliable part lead times and limit the risk of delays.
3. Standardization of parts for lower inventory cost: Bulk ordering is more cost effective than bespoke production. By standardizing necessary parts across production line equipment, manufacturers can reduce their inventory costs.
4. Integration of wear rate data for storeroom optimization: Wear rate data helps identify which machines will need repair the soonest, and what parts they will require. Using this data lets companies optimize storerooms so commonly-used components are easy to find and transport.
5. Adoption of AI tools: MRO inventory optimization also benefits from the adoption of AI tools that can help anticipate common wear and tear issues and predict what parts are required.
Workforce training and culture
While strategies and technologies help reduce the impact of wear and tear, manufacturers can’t ignore the impact of workforce training and culture.
Skilled teams play a critical role in protecting assets and maximizing machine life. For example, when employees are trained to apply the right torque, ensure precise shaft alignment, and identify machinery lubrication or balance issues early, equipment runs smoother and lasts longer. Without that expertise, wear and tear can accelerate—leading to costly downtime.
As a result, regular training is critical, and it’s worth cross-training staff to ensure there is always someone on-site with the expertise necessary to manage and monitor key machines.
Key takeaways for managing wear and tear
Wear and tear is inevitable, but that doesn’t mean they’re unmanageable.
Reducing the risks of excessive wear and tear starts with early detection. The sooner teams can detect possible problems, the less companies spend on replacement parts and machinery.
Technology helps accelerate predictive processes and improve reliability, in turn boosting production line performance. Finally, it’s worth creating standardized operations and investing in skilled workforces to enhance visibility and take a proactive rather than reactive maintenance approach.
Prevent costly failures and maximize uptime with ATS’s industrial maintenance services. Let’s talk.
References
MaintainX. (2025). State of industrial maintenance report. MaintainX. https://www.getmaintainx.com/state-of-industrial-maintenance-report
Mayer, M. (2025, November 3). Unplanned downtime costs U.S. manufacturers up to $207M: Study. Supply & Demand Chain Executive. https://www.sdcexec.com/sourcing-procurement/manufacturing/news/22953487/fluke-corporation-unplanned-downtime-costs-us-manufacturers-up-to-207m-study
