Book a Demo
Extending Mobile Capability and Building AI Readiness
Before partnering with iMaintain, the client already had a digital maintenance system in place. Rather than replacing existing infrastructure, their strategic objective was to extend mobile capability and introduce everyday AI readiness directly on the shop floor.
This approach would give engineers faster access to critical information, better visibility of asset history, and stronger reporting capabilities across multiple manufacturing sites. The focus was on practical enhancement rather than disruptive replacement, ensuring that existing investments were leveraged whilst building towards future capability.
About the client
The Aluminum CNC supplies high-quality aluminium windows, doors, and glazed roof systems to clients nationwide. All of our products are designed and manufactured in the UK.
With decades of experience in aluminium fabrication, and currently the largest Smart Systems fabricator in the UK, they combine technical expertise with industry-leading engineering to deliver reliable, high-performance aluminium systems.
They work with 1,000+ trade customers across the UK whilst also supporting hundreds of thousands of homeowners through a trusted network of installation partners.
Whether you're a trade professional looking for a dependable supply partner, or a homeowner seeking a complete supply-and-install solution, they have you covered.
Manufacturing Excellence
  • Largest Smart Systems fabricator in UK
  • UK-designed and manufactured products
  • 1,000+ trade customers nationwide
  • Hundreds of thousands of homeowners served
  • Decades of aluminium expertise
The Challenge: Common Pain Points Across Manufacturing
The SME faced a set of challenges that resonate across the manufacturing sector. They experienced a heavy reliance on senior engineers to answer questions, diagnose faults, and support less experienced team members. This created bottlenecks and made institutional knowledge difficult to scale.
Their reporting workflows were largely manual, requiring data to be exported from multiple systems into Excel spreadsheets, then reworked into charts, graphs, and management reports. Legacy platforms limited their ability to explore or deploy AI in any meaningful, day-to-day capacity on the shop floor.
Without better structure and visibility, these inefficiencies would continue to scale with the business, creating increasing drag on operational performance and engineering capacity.
Productivity Loss
Unplanned downtime consumes 5–20% of productive capacity across manufacturing
Engineering Time
Engineers spend 30–50% of their time diagnosing repeat issues and searching for information
Scalability Risk
Without better structure and visibility, inefficiencies scale with business growth
What We Did: Rapid Deployment with Practical Focus
iMaintain was onboarded in under one week, including an in-person training session to ensure engineers and stakeholders were comfortable using the system from day one. The implementation was designed around reducing friction, not adding complexity.
The focus was on creating a solid digital foundation that engineers would actually use, not just deploying software for the sake of modernisation.
Asset QR codes enabled engineers to raise jobs directly from the shop floor, eliminating ambiguity during fault reporting. Every asset could be quickly identified with uploaded photos, ensuring consistency across the maintenance function.
This practical approach improved data quality at the point of capture whilst giving engineers the tools they needed to work more efficiently.
01
Full multi-site setup across assets and users
02
Printing and deployment of asset QR codes for fast, consistent job raising
03
Uploading asset photos across sites for quick identification
04
In-person training ensuring immediate system confidence
Early Data Insights: Where Engineering Time Was Really Lost
One of the first critical insights to emerge from the data revealed where engineering time was actually being lost. Initial performance metrics showed high Mean Time to Acknowledge (MTTA), high Mean Time to Repair (MTTR), and low Mean Time Between Failures (MTBF) values across a subset of assets.
Importantly, this downtime was largely driven by facilities and support assets, not by machines that directly add component value to the production process. This distinction was critical for understanding the true challenge. The issue was not production capability, it was engineering capacity leakage.
Valuable technical expertise was being diverted to support assets rather than focusing on value-adding production equipment. Engineers were being repeatedly pulled away from production support to address facilities issues, and the same facilities issues kept reoccurring due to limited historical visibility and context.
This visibility transformed how the maintenance team understood their workload allocation and enabled strategic decisions about resource deployment.
The Reality: Not Perfect on Day One
iMaintain is an evolving platform, and the early stages of implementation were no exception. operating a multi-site maintenance team, and early on there were challenges around notification handling and visibility, particularly in ensuring the right people were informed at the right time across different locations.
Rather than masking these challenges, both teams worked closely together to address them systematically. This collaborative approach led to meaningful platform improvements that now benefit all iMaintain users.
This real-world collaboration directly influenced how the platform now handles manufacturing complexity across multiple sites. The improvements made for the CNC Client strengthened the product for every customer that followed.
Strengthen Notification System
iMaintain hardened the notification architecture for reliability across all operational scenarios
Expand User Coverage
Notifications expanded to include all requestor users, not just engineers, improving visibility
Improve Cross-Site Visibility
Enhanced visibility reduced confusion and duplicated effort across multiple locations
Measurable Change: Before and After Stabilisation
As system stability and operational visibility improved, the performance data began to shift significantly. These improvements were achieved before any AI deployment, the gains came from clearer visibility, better notifications, improved asset context, and more consistent recording practices.
Mean Time to Acknowledge dropped from 198 to 90 hours, a 54.5% improvement in response time. Mean Time to Repair decreased from 224 to 93.2 hours, a 58.4% reduction in repair duration.
Mean Time Between Failures increased from 50.8 to 92.9 hours, indicating 82.9% improvement in asset reliability. Total MTTR reduced by 431 hours, representing a 40.4% decrease in overall engineering time lost.
They reduced the amount of engineering time lost to repeat failures and slow response, particularly across non-value-adding facilities assets. This freed up skilled engineers to focus on production support and continuous improvement activities.
Reporting and Management Visibility
To support ongoing improvement and informed decision-making, iMaintain delivered a custom dashboard specifically designed for the Head of Technical. This transformed how leadership accessed and understood maintenance performance data.
Previously, reviewing engineering performance required a manual process: exporting data from multiple systems, building spreadsheets, manipulating data formats, and stitching together reports. This consumed hours every week and meant management visibility lagged behind real-time operations.
With the new dashboard, engineering feedback and performance metrics could be reviewed instantly. Leadership could now focus on identifying trends, making strategic decisions, and supporting their teams, rather than spending valuable time on data preparation and report formatting.
60%
Time Savings
Management reporting time reduced from hours per week to minutes
100%
Real-Time Insight
Instant access to performance trends enables proactive management
Outcome: A Robust, User-Friendly Foundation
Through collaborative development and real-world refinement, the client and iMaintain shaped the platform into a robust, user-friendly CMMS that excels in practical, everyday use across manufacturing environments.
Most importantly, the client now possesses a clean, structured, and reliable data foundation. This is not just about having software, it's about having trustworthy data that can support genuine business intelligence, continuous improvement, and future technology adoption including artificial intelligence.
Multi-Site Communication
Strengthened notifications and communication across sites ensure the right people have the right information at the right time
Asset Visibility
Complete asset visibility and historical context enable faster diagnosis and better decision-making
Shop Floor Usability
Practical usability designed for engineers working on the shop floor, not office-based administrators
Looking Ahead: Positioned for AI Deployment
With a solid data foundation now in place, the client is exceptionally well positioned for meaningful AI deployment. Rather than attempting to apply AI on top of fragmented, inconsistent, or unreliable data, a common mistake across manufacturing, the business can now introduce AI as a genuine force multiplier.
This foundation enables AI to accelerate fault finding, reduce repeat failures, provide intelligent recommendations, and support engineers at every experience level. Junior engineers gain access to institutional knowledge, whilst senior engineers are freed from answering repetitive questions and can focus on complex problem-solving.
This strategic positioning represents a fundamental shift from reactive firefighting to proactive, data-driven maintenance operations, exactly what's needed to compete in modern manufacturing.
Competitive Advantage
Positioned ahead of the vast majority of SME manufacturers in digital maturity and AI readiness
Operational Resilience
Reduced reliance on key individuals through better systems, visibility, and knowledge capture
Long-Term Competitiveness
Future-proofed for an increasingly cost-regulated and technologically advanced manufacturing environment
Ready to Transform Your Maintenance Operations?
If your site is experiencing similar challenges—heavy reliance on key individuals, recurring facilities issues consuming engineering time, manual reporting processes, or difficulty modernising legacy systems—you're not alone. Whether you want to become an early adopter of manufacturing AI, or simply strengthen engineer records and modernise digital maintenance practices, we'd be happy to discuss what's possible for your operation.