Digitally enabled reliability: Beyond predictive maintenance
To capture everything digital can offer in
increasing reliability and reducing costs, companies should boost their
digital-maintenance ambitions.
Are we entering a world of
smart machines that can warn their operators before they break down? Advanced
predictive maintenance (PdM), enabled by extensive sensor integration and
machine-learning techniques, is one of the most widely-heralded benefits of the
fourth industrial revolution. The idea is certainly a compelling one, and it is
encouraging companies in asset-intensive sectors to pursue investments in
digital maintenance and reliability.
In our view, however,
treating PdM as panacea for maintenance and reliability challenges may prove to
be short-sighted. In part, that is because today's advanced predictive
techniques can only be practically applied to a subset of use cases. But it is
also because an over-emphasis on one approach means companies won’t position
themselves to capture all the potential benefits of a fully digitized
maintenance and reliability function—one that’s focused on increased uptime and
improved maintenance efficiency.
And those benefits are
significant. Based on our observations of digital maintenance and reliability
transformations in heavy industries, we see the potential for companies to
increase asset availability by 5 to 15 percent, and reduce maintenance costs by
18 to 25 percent.
The opportunities and challenges of PdM
It’s easy to see why
advanced predictive maintenance has been seen as a killer app for Industry 4.0.
The approach combines many of the technologies that underpin the new wave of
industrial digitization, such as networked sensors, big data, advanced
analytics, and machine learning. It is a powerful technique that, by
identifying complex patterns over hundreds or thousands of variables in ways
that traditional analysis cannot, enables operators to develop a deeper,
data-driven understanding of why failures occur. Most seductively, it promises
a very tangible benefit: machines that don’t break down.
But in practice,
economically viable, real-world uses for these advanced PdM techniques are less
than universal. Where a machine is prone to a narrow range of well-understood
failure modes, it is often possible to address a potential problem in a simpler
way, for example by monitoring the temperature or vibration of a component
against a set threshold, or by consistently and rigorously applying data-driven
reliability analysis techniques to address the root causes of failure modes.
Conversely, where a machine can suffer hundreds or thousands of different kinds
of failures (some of them very rare), it can be impractical to create
sufficient models of high-enough quality to adequately predict them all.
When factoring the
effort and expertise required to develop accurate machine-learning models,
model-based predictive maintenance becomes a breakthrough way to solve selected
high-value problems, rather than the whole universe of maintenance
opportunities. The approach has the most potential where there are
well-documented failure modes with high associated downtime impact, for example
in a critical machine on a larger production line. It also works well when it
can be applied at scale to a large fleet of identical assets where there is
sufficient reliability history to spread the development and management costs,
as in offshore wind farms or fleets of locomotives. Thus, equipment
manufacturers are strategically positioned to drive predictive-model development
and deployment at scale for their end users—but these efforts have yet to
materialize widely.
Capturing the digital dividend
Does the relatively
limited scope PdM has achieved mean that maintenance and reliability are
somehow exempt from the digital imperative? Absolutely not. In fact, we propose
that companies press well beyond one particular type of digital tool and think
about how digital and advanced analytical techniques can transform their entire
maintenance and reliability system. This means looking end to end for
opportunities to make better use of data, and apply user-centric design
principles to digitize processes. Sustainable impact will require a combination
of new digital tools, changes in asset strategy, and improved reliability
practices.
An integrated approach to digital reliability
and maintenance
Reliability and
maintenance activity has two basic parts: a program element, which encompasses asset strategies and
maintenance plans, and an execution element,
which encompasses identifying, prioritizing, scheduling, and performing work.
Digital reliability and maintenance (DRM) encompasses both those elements, and
underpins them with a set of enablers—the
infrastructure, processes, and tools companies need to manage their assets,
data, and people, in improving asset reliability and maintenance performance.
Digital reliability enablers
We start from the
foundation up, with enablers. Most importantly, digital processes are fueled by
data. That’s why establishing a robust data backbone is a fundamental enabler
for digital reliability and maintenance. Most organizations already have
systems in place to record maintenance- and reliability-related data, but the
effectiveness of such systems can be undermined by poor housekeeping. The same
assets or issues may be described in different ways in different systems, for
example, making integration difficult. Companies may use free-text fields to
record issues or maintenance actions, making automated search or data analysis
harder. Or critical data may be inaccessible, locked away in spreadsheets or on
paper notes.
Fixing these challenges
often depends not on investment in new technology but on the adoption of more
rigorous standards for asset identification and data recording.
Artificial-intelligence techniques, such as natural-language processing, can
help organizations transform poorly organized historical data into a form more
suitable for automated analysis.
Similarly, the
plummeting cost of data storage and network bandwidth mean that it is now
easier and cheaper than ever to collect data streams from machine-control
systems and external sensors. This data, which may be inaccessible or even
discarded today, is useful for condition-based monitoring, diagnostics, and
failure-mode analysis, either using conventional approaches or the application
of advanced analytics and machine learning.
Once they have their
data in place, companies need a means to access it. For most organizations,
this requires a new step. A consolidated data-services layer, or “data lake,” collects
data from multiple systems and sources, creating a single source of truth and
bridging the information gap between systems to provide a complete picture of
an asset’s health. This critical component of the data architecture has
multiple uses: it provides the basis for digital performance management,
descriptive analytics, and dashboards, while also serving as a unified layer
for new maintenance and reliability applications and supplying the data
required for advanced-analytics models.
The next essential
enablers for DRM are digital tools for reliability-engineering analysis.
Root-cause problem solving, using approaches such as fault-tree analysis as
well as cause-and-effect or failure-modes-and-effects analysis (FMEA), is a
fundamental part of any organization’s maintenance and reliability strategy.
Today, however, these activities are often conducted manually, and their
outcomes are rarely recorded in a centralized manner. Integrating
reliability-engineering tools into an organization’s DRM architecture ensures
that analyses are conducted in a consistent, structured way, accelerates and
simplifies access to input data, and captures analytical outcomes for future
use.
Creating a digital
platform capable of handling the full range of tools and data sources used in
digital reliability and maintenance can be challenging, but getting this right
early in the DRM program will deliver enduring benefits. One oil and gas
company had already started to build maintenance solutions onto an existing
platform. As leaders mapped out their digital-maintenance ambitions, however,
they realized that the system didn’t have the technical capabilities they
required. Since seamless interconnection between tools was central to its
long-term maintenance vision, the company chose to integrate all its
maintenance solutions into a brand-new platform, even though this required
rework in the short term. The result is a DRM function that can expand with the
organization’s needs and digital capabilities, rather than providing only a
temporary boost that quickly falls behind as competition (and technical
capabilities) advance.
Significantly, the
enablers discussed so far focus on the application of digital technologies to
accelerate, streamline, and improve existing reliability-engineering practices.
Digitization is also providing reliability-engineering teams with a plethora of
new tools and approaches. As we have already described, the application of
machine-learning techniques to monitor asset condition has already received
considerable attention, even though their cost and complexity may ultimately
limit their application.
Not all
condition-monitoring techniques require elaborate algorithms or complex models,
however. Data-driven condition-monitoring approaches use simple queries that
are run periodically or in real time against time-series data generated by
machines and external sensors. If threshold conditions are passed, these
systems can trigger investigative or corrective action in the
digital-reliability-engineering workflow, or directly to maintenance execution.
Digital performance management
The enabling
technologies described above establish DRM’s foundation, but don’t actually
improve asset reliability or maintenance effectiveness. Those improvements come
from the way an organization uses its digital data to optimize maintenance
activities: adapting schedules, streamlining plans, and prioritizing resource
allocation.
A digital
performance-management system is central to the operation of an effective DRM
program. This involves the use of descriptive analytics and data visualizations
to provide a real-time view of asset health and reliability performance.
Digital performance management automates the generation and presentation of the
key metrics and qualitative information that companies use in their reliability
programs, such as overall equipment effectiveness (OEE) data or loss reasons.
This kind of automation is a surprisingly powerful improvement lever, freeing
maintenance staff from the time-consuming and error-prone process of data
collection and analysis. And it supports rapid trend identification, fact-based
decision-making, and timely intervention, as well as changes in equipment
investment, processes, and policies.
Sometimes, companies
already have much of the digital infrastructure they need to manage maintenance
performance. One mining company, for example, was preparing to source a new
system to track mobile-equipment maintenance. As it outlined the requirements
for the new system, it realized that the required functionality existed within
its current computerized maintenance-management system. The relevant modules
had even been piloted within the organization, but never scaled up.
The cycle time and
effectiveness of reliability-engineering activities are often hampered by
missing information or poor alignment between operations, reliability, and
maintenance teams. Digital reliability-engineering workflow systems help
address those gaps by tracking the full lifecycle of each unit of work
conducted by the reliability-engineering function. At a minimum, these systems
capture the details of the event or events that trigger an investigation by the
reliability-engineering team, the actions taken in response, and the outcome of
those actions.
Digital asset strategies
New digital tools can
also help to accelerate and standardize the cost-benefit analyses and
decision-making that underpin maintenance and reliability activities. Digital asset-management tools, for
example, help reliability teams plan and manage repair or replacement choices
over the lifecycles of individual assets or entire fleets. Similarly, new
digital tools can support reliability-centered
maintenance, helping teams choose the right maintenance strategy (such
as run-to-fail, planned preventative maintenance, or condition-based
maintenance) for each asset.
Digital work management
New digital tools are
also transforming the way companies plan and manage the execution of
maintenance and reliability activities. Digital work management includes
process digitization and data-driven analytics to improve the effectiveness and
efficiency of maintenance work. Example applications include automated
scheduling algorithms, digitized planning environments, and tablets or wearable
devices for field data entry and retrieval.
Most industrial players
are already on a DRM journey, whether they are aware of it or not. They are
already recording their work orders in an enterprise-resource-planning or
asset-management system, and many of their assets are already generating and
collecting data, even if this data is widely dispersed and little-used.
Right now, however,
this “digitization by default” approach isn’t delivering the full potential
impact it could. When we surveyed a group of maintenance managers earlier this
year, only 50 percent said their current information and operational (IT/OT)
architecture adequately supports their maintenance and reliability processes
and fewer than 20 percent felt that their maintainers have a positive user
experience.
The critical step for
most organizations is the shift to a proactive, comprehensive and
well-thought-out approach to their digital maintenance and reliability
strategy. This involves a detailed assessment of current maintenance and
reliability practices to identify where visibility from improved data capture,
insights from advanced analytics, and increased control from new digital
maintenance execution systems can create impact. The key is to take a broad
end-to-end view of potential applications and to think about how new tools, technologies
and approaches can be integrated and combined.
Like any significant
change effort, moving to this new digital reliability and maintenance world
will require companies to be bold in their aspiration, structured in their
transformation approach, and long-term in their vision.
By Steve Bradbury, Brian Carpizo, Matt
Gentzel, Drew Horah, and Joël Thibert
https://www.mckinsey.com/business-functions/operations/our-insights/digitally-enabled-reliability-beyond-predictive-maintenance?cid=other-eml-alt-mip-mck-oth-1810&hlkid=994c2b4a68814c95bae6f80b003f9081&hctky=1627601&hdpid=d7cf1825-81c1-4a92-9f52-e84804bad89e
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