Organizations lose thousands of dollars in revenue for every minute their equipment is down—whether in a manufacturing facility, managing a large vehicle fleet, or maintaining critical infrastructure. The maintenance strategy you choose plays a crucial role in determining your operational efficiency.
In this discussion, we'll explore three maintenance strategies that are now widely used in the industrial sector: reactive maintenance, predictive maintenance, and preventive maintenance. While all these approaches aim to reduce the risk of equipment failure, they differ in how they tackle the problem, the costs involved in implementation, and their effectiveness in minimizing downtime. Understanding these distinctions is vital, as making the right choice can help your organization save both time and money.
Preventive Maintenance: The Scheduled Approach
Preventive maintenance is a method in which service is performed on equipment at a schedule, regardless of condition. Preventive maintenance, like changing the oil in your car every 3,000 miles or replacing air filters every 6 months, is based on a calendar and not based on issues.
Preventive maintenance involves conducting scheduled maintenance on equipment and machinery before unexpected failures occur. Instead of waiting for breakdowns, the goal of preventive maintenance is to reduce unplanned downtime by proactively identifying and addressing potential issues through a structured maintenance program.
By performing regular maintenance tasks at predefined intervals, preventive maintenance helps lower the chances of equipment failure, ensuring that assets continue to operate smoothly.
Three primary types of preventive maintenance have emerged, each involving repeatable maintenance procedures, but with varying schedules and tailored to different operational needs within a business.
-
Usage-based preventive maintenance schedules trigger maintenance or inspections based on the usage of an asset, like replacing your car tires after driving 50,000 miles.
-
Calendar or time-based preventive maintenance follows fixed intervals, such as servicing your home furnace annually.
-
Condition-based maintenance is determined by monitoring the asset’s wear and degradation over time.
Characteristics of Preventive Maintenance:
Scheduled Intervals: Maintenance tasks are planned for a specific time, or planned for a specific number of hours, or based on a specific number of miles or miles. It is not dependent on the true condition of the equipment.
Proactive Approach: Instead of waiting for equipment to break down, maintenance is performed before problems arise.
Focus on Equipment Lifespan: The focus is to see the equipment actions are happening at that level and hopefully extend the lifetime of the equipment by keeping on top of wear and tear.
Standardization: Procedures are usually standardized for the same type of asset or same model of asset
Example: A factory might have a preventive maintenance schedule that requires the replacement of filters every six months or a quarterly inspection of machinery to ensure its parts are not worn out.
Challenges of Preventive Maintenance
Despite its many benefits, organizations often encounter challenges when implementing preventive maintenance:
Strict adherence to scheduled maintenance can lead to resource wastage by maintaining equipment unnecessarily. Replacing parts based on fixed time intervals, rather than their actual condition, often results in premature replacement of components that still have useful life left.
Scheduled maintenance can be costly; taking equipment offline for routine servicing requires valuable staff time, even when the equipment is functioning well. This approach overlooks the actual condition of the equipment.
Scheduled
maintenance shutdowns can
impact
production as
the
shutdown
may
interfere with
peak production
times or
critical deadlines.
Also, when
several pieces
of equipment
are
scheduled
for maintenance
at the same time,
there
is
a
strain
on maintenance
personnel
along
with
production
bottlenecks.
Scheduled
or
planned
maintenance
based
on fixed
timeframes
also
restricts the
maintenance
personnel
from
making
flexible
plans
according
to
the operational
needs or
seasonality
of
how
the equipment
is
used.
Routine maintenance can create a false sense of security, potentially overlooking issues that develop between scheduled appointments. Common maintenance practices may also miss certain failure modes, particularly those that progress quickly.
Furthermore, traditional maintenance often focuses on individual components rather than the entire system, which may result in problems going unnoticed.
Documenting detailed information for all scheduled maintenance events can be a burdensome administrative task, leading to an overload of paperwork. Additionally, varying regulations may require different maintenance frequencies, making scheduling more complex.
Other challenges include proving compliance, verifying the effectiveness of maintenance programs, and managing record-keeping. Moreover, ensuring everything is properly audited can be a significant issue.
Predictive Maintenance: The Data-Driven Approach
Predictive maintenance adopts a data-driven approach, utilizing real-time monitoring and analytics to forecast when equipment is likely to fail. Instead of relying on fixed schedules, maintenance is performed only when data shows that equipment performance is deteriorating or failure is imminent.
By leveraging data analytics, sensor technology, and condition-based monitoring techniques, predictive maintenance can foresee potential failures before they occur. Unlike preventive maintenance, which follows a set schedule, predictive maintenance is driven by real-time data from equipment, allowing organizations to base decisions on the actual health of their assets.
Various condition monitoring techniques are used to detect anomalies and provide early warnings of potential issues. These include sound (ultrasonic acoustics), temperature (thermal), lubrication (oil and fluid analysis), vibration analysis, and motor circuit diagnostics.
For instance, a rise in temperature in a component could signal an airflow issue or coolant blockage, while unusual vibrations may indicate misaligned or worn-out parts. A change in sound can provide an early warning of faults that are undetectable by the human ear.
The oil & gas sector was an early adopter of predictive maintenance, mainly to reduce the risk of environmental disasters. Now, other industries are seeing the benefits as well. For example, in the food and beverage industry, undetected food storage faults can lead to severe health risks, while shipping companies can prevent equipment malfunctions, knowing repairs are more cost-effective when made in port than at sea.
Characteristics of Predictive Maintenance:
Condition-Based: Maintenance tasks
are triggered by real-time data, indicating that a
specific part of the equipment is showing signs of
wear or potential failure.
Advanced Technologies: This
approach leverages key technologies such as sensors,
IoT devices, machine learning, and AI to
continuously monitor the health of the equipment.
More Accurate Interventions:
Maintenance is performed only when necessary, based
on insights from ongoing monitoring, thereby
reducing unnecessary downtime.
Cost-Effectiveness: By addressing
issues before they lead to failures, proactive
maintenance helps avoid costly repairs and
unexpected downtime.
Example: A company might use IoT sensors to monitor vibration and temperature in a motor. If the data shows unusual patterns or exceeds predefined thresholds, a maintenance request is triggered, preventing a potential failure before it happens.
Challenges of Predictive Maintenance
While predictive maintenance offers numerous benefits, it also presents its own set of implementation and operational challenges:
Implementing a predictive maintenance program comes with significant costs. Developing and deploying such a program involves expenses for sensors, monitoring technologies, analytical software, and more.
In addition, enabling technologies may require upgrades to IT infrastructure, networking capabilities, and data storage systems. Staff will also need training to interpret data, optimize the use of new technologies, and ensure that condition-based maintenance decisions are based on thorough and accurate analysis. All of these factors contribute to the substantial investment needed for a successful predictive maintenance program.
Combining
all
the
necessary
data in
predictive maintenance can be
a
technical
challenge,
requiring
many
times
the
need
for
specialist
expertise
due
to
this data
coming
from
sensor, systems,
and
other sources,
develop accurate
predictive
model
that
uses advanced
analytics, and
maintain
the
accuracy
of
the
models
over
time
and
as the
analytics
advance.
Pertaining
to reliability
of the system
as
a
whole,
reliability
of
data
is
often
overlooked or
given
less
attention,
because
a
system
could
be
producing
accurate
predictive
analytics
based
on
an
incorrect
sensor
or
erroneous
data
communication
relating
to
that
data.
Sensor accuracy is crucial, particularly in predictive maintenance. Faulty or poorly calibrated sensors can produce unreliable data, leading to incorrect maintenance decisions. Even data from a small number of sensors can quickly become overwhelming, making it harder to identify real trends and patterns.
Additionally, an overly sensitive monitoring system can trigger false alarms, leading to unnecessary maintenance actions and ultimately diminishing confidence in the system’s reliability and effectiveness.
The
success
of predictive
maintenance
depends on
personnel
capable
of
working in
specialized
areas such as data analysis, vibration analysis,
thermography, and other
forms
of diagnostic
thinking.
The job market
can
be
challenging
due
to a
lack of
personnel with
that
skill
level,
and
employing
such
programs
can
make
things
even
more
difficult.
As
technology
is
moving
quickly,
maintenance
teams
must
learn
continuously, and
find
ways
to
develop
their
skills,
in
order to
adapt to
new
tools
and
technology,
and
to
make predictive
maintenance
successful
over
time.
Key Differences Between Preventive and Predictive Maintenance
Scheduled based on time or usage intervals
Data-driven, based on actual asset conditions
Lower initial investment in technology and training
Higher upfront investment in sensors, software, and training
Regular intervals regardless of asset condition
Performed only when necessary, based on data
Relatively simple to implement and manage
More complex implementation requiring specialized technology
Risk of unnecessary maintenance or missed issues
Minimizes risk of unplanned breakdowns
Can lead to unnecessary downtime and resource allocation
Reduced downtime with more efficient resource usage
Requires maintaining large inventories of spare parts
Reduces spare parts inventory through better demand forecasting
When to Choose Preventive Maintenance
Preventive maintenance is ideal for:
Critical safety equipment – Where equipment failure could have a serious safety, financial, or environmental impact, following a scheduled preventive maintenance is a more effective way to ensure safety standards are met.
Low-cost equipment – For equipment that is inexpensive, the cost of setting up monitoring technology may be greater than the value of the equipment.
Simple systems – Where the maintenance procedures are simple enough that implementing a complex condition monitoring system does not make good economic sense.
Regulated industries – Many industries and sectors have compliance requirements that mandate a defined maintenance schedule and procedure.
Limited technical resources – Where the organization may not have significant technical skill or capacity to be implementing predictive maintenance.
Industries that benefit from preventive maintenance:
Food and beverage
Healthcare facilities
-
Transportation (due to compliance and safety of safety-critical systems)
Small manufacturing operations
Facilities management
When to Choose Predictive Maintenance
Predictive maintenance is most effective for:
High Value Equipment – Costly machinery where the impact of an unintended failure is high.
Critical Production Equipment – Machinery that would cause significant delays if it failed.
Complex Systems – Equipment with several failure modes that needs to be monitored routinely.
Variable Operating Condition – Equipment that operates with variable loads, temperatures, and any other harsh physical conditions.
Experienced Technical Teams – Organizations with knowledgeable technicians that can interpret the data and make informed decisions.
Industries that benefit from predictive maintenance:
Oil and gas production
Power generation
Chemical processing
Heavy manufacturing
Data centers
The Importance of Data Management in Contemporary Maintenance Practices
Regardless of which maintenance approach you choose, effective data management is crucial for success. Poor data quality can lead to:
-
Incorrect maintenance decisions
-
Missed maintenance opportunities
-
Duplicate work orders
-
Inaccurate equipment histories
-
Compliance issues
Future Trends in Maintenance Management
The maintenance landscape keeps changing as technology develops further:
Artificial Intelligence: We are seeing more advanced AI algorithms which are better equipped to predict equipment failures along with empowering us to improve our schedule and methodologies for maintenance.
Internet of Things (IoT): More sensors or connected devices create improved data for decision-making in a maintenance sense.
Augmented Reality: AR technology helps technicians see a visualization of equipment problems and procedures in following maintenance.
Digital Twins: Systems which operate in a “virtual“ clone of the original system; they build simulations and predictive models.
Cloud Computing: Meaning I can now access maintenance management systems that are based in the cloud, which affords tremendous scalability on a cloud service, and the possibilities are opening for organizations of every size.
Conclusion
It
is
up
to the organizations these days to
determine
the
best predictive
and preventive maintenance
method
to
use;
both
methods
(predictive
and
preventive)
have
benefits.
Preventive maintenance
is
simple,
often
predictable, and
a
relatively
low initial
investment,
which
is
the
case
in many
organizations and
many types
of
equipment.
Predictive maintenance
looks
for
optimization, efficiency, and cost savings
of
the
entire
process,
especially
in
areas
where
there
is
criticality and
expensive
equipment.
For
many
of
these
organizations
who
have
been
successful,
the
model
is hybrid.
They
have preventive
maintenance as
the
base,
and
apply predictive
maintenance on
a
limited
basis,
to their most
critical assets.
The
organizations
get the
reliability
from
a scheduled
maintenance
program, while
simultaneously
getting the
efficiency in
terms of
workload
from
a
conditioned-based maintenance
(predictive)
program.
While
it
does
not
matter
if you
use
a
preventive,
predictive,
or
hybridized
maintenance
strategy,
you
have
to
understand
this
first
and
foremost.
Clean
and accurate
data and
good data
management practices
are
the
foundation
for
success.
If
you
do
not
have
trusted
data,
then
no maintenance
strategy
–
no
matter
how
sophisticated
–
will
have
credibility.
With
an
advancing and
more cost
effective
technologies,
predictive maintenance
strategies will
get
closer
to
being
what
is
currently
known
as
standards
on more
types
of equipment
and
more types
of industries.
However, preventive maintenance
strategies will
still
be
applicable
in
a
wide
variety
of
possible
application
scenarios:
based
on simplicity,
compliance or cost
for
example.
The key is to start with a clear
understanding of your organization’s needs,
capabilities, and constraints, then choose the
maintenance strategy that best aligns with your
operational goals and available resources. With the
right approach, you can minimize downtime, reduce
costs, and maximize the value of your equipment
investments.
