Augmented Reality in Maintenance
Augmented Reality in
Maintenance
Why
Siemens Is Already Showing What Is Possible - and What Canon Production
Printing Can Gain from It
|
Executive
takeaway: augmented reality
delivers the strongest return in complex failures, high downtime-cost
environments, and situations with scarce expertise. Siemens shows that the
technology is already operationally applicable; for Canon Production
Printing, the strongest business case lies in standardization, knowledge
retention, faster troubleshooting, and remote support. |
What
exactly is augmented reality?
Augmented reality (AR) is a technology that
adds digital information to the real world rather than fully replacing it.
Through a tablet, smartphone, or smart glasses, the user still sees the real
machine or work environment, but with additional digital layers overlaid on
top, such as instructions, arrows, 3D models, checklists, or live machine data.
Unlike virtual reality, the technician continues to work in the real
environment; AR enriches that environment with context at the moment it is
needed.
Why
this matters for maintenance
The biggest maintenance challenges usually do not arise in simple routine tasks, but in complex and infrequent interventions. That is exactly where time is lost searching for information, translating 2D documentation to the machine, and correctly applying tacit knowledge from experienced colleagues. Research shows that AR delivers the greatest return precisely in this heavier context. A recent study specifically recommends AR for high-demand maintenance tasks because the time savings and learning value are greater there than in simple tasks (Alessa et al., 2023). In a practical study with experienced workers, an AR-supported repair workflow was even completed 21% faster with 26% lower perceived workload than the paper-based approach (Eversberg & Lambrecht, 2023).
The conclusion is therefore strategically
relevant: AR should not primarily be positioned as a general innovation, but as
a selective performance lever for complex maintenance situations. That makes
the technology relevant for companies where uptime, quality, and knowledge
availability have a direct operational impact.
Where
is the first promising pilot opportunity?
·
Complex
failures on critical assets: this is where the potential for direct MTTR gains
and less unplanned downtime is greatest.
·
Training
and onboarding of maintenance technicians: this is where AR quickly creates
value because senior knowledge can be captured and transferred faster.
·
Remote
support between maintenance, engineering, and service: this is where specialist
knowledge becomes deployable without physical presence at the workplace.
What
does it concretely deliver in maintenance?
|
AR capability |
Operational effect |
KPI impact |
|
Step-by-step visual instructions |
Less search time and fewer interpretation errors during
diagnosis and repair. |
Lower MTTR, higher first-time-fix, less variation between
technicians. |
|
Remote expert support |
Faster escalation without travel time and direct live
guidance during failures. |
Shorter downtime, faster response time, lower external
service costs. |
|
Real-time data in context |
Machine data and history are available at the point of
execution. |
Better diagnostic quality, fewer repeat failures, higher
compliance. |
|
Training and onboarding |
New employees learn in a task-oriented way on the shop
floor. |
Shorter ramp-up time, less dependence on senior experts. |
Siemens
as proof from practice: where does AR already stand today?
Siemens is a strong reference case because it
does not position AR only as a future vision, but already links it to existing
maintenance solutions. With COMOS Mobile Worker, Siemens combines real-time
data, augmented reality, navigation functions, mobile data capture, and the
digital twin in one maintenance environment. The promise behind this is clear:
digital reliability, paperless maintenance, and access to current plant
information at the location where maintenance is performed (Siemens, n.d.).
5W2H:
a sharper view of the technology
|
Question |
Elaboration for augmented reality in maintenance |
|
What |
AR adds digital information to the real world through a
tablet, smartphone, or smart glasses. In maintenance, this means live
instructions, 3D overlays, component markings, checklists, and machine data
directly on or next to the object. |
|
Why |
The core value lies in less search time, lower error
probability, and faster knowledge transfer. The business case is especially
strong for complex, non-routine tasks with high downtime costs (Alessa et
al., 2023; Eversberg & Lambrecht, 2023). |
|
Where |
Siemens is already applying AR today in mobile
maintenance and remote services. For Canon Production Printing, the logical
landing zone is the high-tech production and maintenance domain in Venlo,
where R&D, manufacturing, and logistics come together (Siemens, n.d.;
Canon Production Printing, n.d.). |
|
Who |
The stakeholders involved are maintenance technicians,
senior experts, engineering, IT, production, and management. With AR, part of
senior expertise shifts from informal shadowing to digitally captured
instructions and remote support. |
|
When |
Already useful today for training, troubleshooting, and
remote assist. In the medium term, AR becomes stronger through better
integration with CMMS/ERP, sensor data, and digital twins. In the longer
term, the value shifts toward AI-supported diagnostics and context-driven
decision-making. |
|
How |
A realistic implementation does not start broad, but with
one or two critical use cases, for example complex failures, first-time-fix
support, or onboarding. After that, scale via content management, KPI
measurement, and system integration. |
|
How much |
The benefits lie in lower MTTR, less downtime, shorter
training time, and higher process stability. The costs lie in hardware,
software, content development, integration, governance, and change
management. The business case must therefore be substantiated per use case. |
What
does this specifically mean for Canon Production Printing?
Canon Production Printing operates in a
context in which AR can be relevant not as a gadget, but as a performance
instrument. Official company information shows that Venlo is a high-tech site
with R&D, manufacturing, and logistics. In addition, Canon Production
Printing emphasizes the importance of Lean Manufacturing and continuous
improvement. In exactly this kind of environment, maintenance is not a
supporting side activity, but a direct driver of availability, quality, and
process stability (Canon Production Printing, n.d.; Careers at Canon Production
Printing, 2024).
|
Dimension |
Opportunity for
CPP |
Operational
benefit |
Key precondition |
|
Product |
More stable output from high-tech equipment and fewer
disruptions in critical production steps. |
Less quality loss due to equipment problems and fewer
unplanned stops. |
Select assets where downtime is directly visible in
output or delivery reliability. |
|
Process |
Digital work instructions, visual troubleshooting, and
uniform execution of maintenance. |
Shorter search time, less variation between shifts, and
more reproducible interventions. |
Work processes must first be standardized sufficiently to
be digitized. |
|
Work environment |
Faster training, safer support, and less dependence on a
single experienced colleague. |
Shorter onboarding, less travel time for experts, and
better collaboration between teams. |
Adoption requires ergonomic hardware, clear governance,
and user acceptance. |
|
Engineering |
R&D knowledge, drawings, and 3D data can be brought
closer to maintenance. |
Stronger feedback loop between design, production, and
maintenance; better knowledge retention. |
Engineering data must be current, accessible, and
properly governed. |
Pros
and cons: the business case is strong, but not automatic
|
Advantages |
Risks /
disadvantages |
Managerial
conclusion |
|
• Less search time and faster maintenance in complex tasks |
• Investments in hardware, software, and licenses |
AR should be treated as a selective operations investment, not
as a broad hype implementation. Therefore start with one critical maintenance
context, define KPIs in advance, and scale only when measurable results are
visible. |
Where
can AR evolve next?
The literature shows that AR currently proves
itself mainly in assistance and training, but the strategic horizon extends
further. The combination with sensor data, digital twins, and later
AI-supported diagnostics increases the value step by step (Morales Mendez &
del Cerro Velazquez, 2024; Siemens, n.d.).
|
Now to 5 years |
5 to 10 years |
10+ years |
|
Pilot use cases, remote assist, digital work
instructions, onboarding, and paperless maintenance. Focus on rapid
operational gains and adoption. |
Stronger integration with ERP/CMMS, sensor data, and
digital twins. AR shifts from visual instruction to context-driven support
and better decision-making. |
AR becomes part of a data-driven maintenance operating
model, with AI-supported diagnostics, predictive context, and reusable
knowledge at scale. |
TechniShow - 2026
These organizations are specifically looking for universal tools that are already widely adopted in the industry, so they can align with existing standards and workflows. For example, one company is working with PTC Vuforia, which uses AR file formats that are standardized through ISO.
This development means that customers who purchase a new machine will not only receive a traditional user manual but increasingly also AR files or instructions for AR-based applications. At the same time, there are companies that deliberately choose to have maintenance performed only by certified specialists as part of their business model, making the use of AR less essential in those cases.
How
this blog is structured and why the sources are strong
The 5W2H framework was intentionally used to
structure this blog. As a result, the analysis explains not only what AR is,
but also why it is relevant, where it already stands today, who is involved,
what implementation looks like, and under which conditions the business case
can turn positive. This aligns the analysis with both the technology content
and the research process.
·
Official
Siemens sources were used to show where AR is already being applied concretely
in maintenance and remote services.
·
Official
Canon Production Printing sources were used to substantiate the application
context in Venlo: high-tech production, maintenance roles, and a Lean
organization.
·
Peer-reviewed
studies were used to substantiate benefits, limitations, and adoption factors
with literature rather than vendor claims alone.
·
The
combination of practical sources and scientific sources makes the analysis both
current and analytically strong.
Where
to look next for further depth
·
Google
Scholar: search terms such as "augmented reality maintenance digital
twin," "industrial AR remote maintenance," and "AR work
instructions manufacturing."
·
Vendor
documentation from Siemens, Microsoft, PTC, or comparable parties to see how
integration with existing industrial systems is designed.
·
Interviews
with maintenance engineers, reliability engineers, and production leaders
within the organization itself to sharpen use cases and boundary conditions.
·
Pilot
data from the plant itself: MTTR, first-time-fix, training time, number of
escalations, and downtime per critical asset.
Conclusion
Augmented reality is especially valuable in
maintenance when complexity, knowledge intensity, and downtime costs come
together. Siemens shows that the technology is already operationally applicable
today in the form of mobile maintenance, real-time data access, and remote
support. For Canon Production Printing, AR is therefore not a futuristic
gimmick, but a plausible next step in a Lean and high-tech maintenance
environment.
The most convincing positioning for Canon
Production Printing is not a broad rollout across everything at once, but a
phased pilot strategy: start with complex failures or training on critical
assets, measure KPI impact rigorously, and then scale in a controlled way. That
way, AR does not become a standalone innovation project, but a focused
capability that strengthens process stability, knowledge retention, and
maintenance performance.
References
(APA style)
1. Alessa, F. M., Alhaag, M. H., Al-harkan, I. M., Ramadan, M. Z.,
& Alqahtani, F. M. (2023). A neurophysiological evaluation of cognitive
load during augmented reality interactions in various industrial maintenance
and assembly tasks. Sensors, 23(18), 7698. https://doi.org/10.3390/s23187698
2. Canon Production Printing. (n.d.). About Canon Production
Printing. https://cpp.canon/about/
3. Canon Production Printing. (n.d.). Lean Academy.
https://cpp.canon/about/lean-academy/
4. Canon Production Printing. (2025, June 17). Venlo, Netherlands.
https://cpp.canon/about/corporate-information/worldwide/netherlands/
5. Careers at Canon Production Printing. (2024, August 2).
Maintenance Technician. https://careers.cpp.canon/maintenance-technician/
6. Eversberg, L., & Lambrecht, J. (2023). Evaluating digital
work instructions with augmented reality versus paper-based documents for
manual, object-specific repair tasks in a case study with experienced workers.
The International Journal of Advanced Manufacturing Technology, 127, 1859-1871.
https://doi.org/10.1007/s00170-023-11313-4
7. Havard, V., Baudry, D., Jeanne, B., Louis, A., & Savatier,
X. (2021). A use case study comparing augmented reality (AR) and electronic
document-based maintenance instructions considering tasks complexity and
operator competency level. Virtual Reality, 25, 999-1014.
https://doi.org/10.1007/s10055-020-00493-z
8. Morales Méndez, G., & del Cerro Velázquez, F. (2024). Impact
of augmented reality on assistance and training in Industry 4.0: Qualitative
evaluation and meta-analysis. Applied Sciences, 14(11), 4564.
https://www.mdpi.com/2076-3417/14/11/4564
9. Mourtzis, D., Siatras, V., & Angelopoulos, J. (2020).
Real-time remote maintenance support based on augmented reality. Applied
Sciences, 10(5), 1855. https://doi.org/10.3390/app10051855
10. Palmarini, R., Erkoyuncu, J. A., Roy, R., & Torabmostaedi,
H. (2018). A systematic review of augmented reality applications in
maintenance. Robotics and Computer-Integrated Manufacturing, 49, 215-228.
https://doi.org/10.1016/j.rcim.2017.06.002
11. Siemens. (n.d.). COMOS Mobile Worker - Smart mobile maintenance.
https://www.siemens.com/en-us/products/comos/mobile-worker/
12. Siemens. (n.d.). Remote Services - Remote support and
maintenance.
https://www.siemens.com/en-us/products/industrial-lifecycle-training-services/remote-services/
13. Siemens. (n.d.). App for the maintenance of process plants with
integrated augmented reality function.
https://press.siemens.com/global/en/pressrelease/app-maintenance-process-plants-integrated-augmented-reality-function
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