Automotive · Predictive maintenance consulting
Servicing on a schedule, failing without warning
An automotive manufacturer was losing production time and money to unplanned machine breakdowns. Maintenance was schedule-based: machines were serviced at fixed intervals regardless of their actual condition, and real failures still stopped the line. We delivered a multi-day engagement that assessed their infrastructure, evaluated the applicable technologies and produced a solution architecture for predictive maintenance.
Key results
Client: Automotive manufacturer (under NDA)
A production environment with high-value machinery and significant downtime costs. The manufacturer is under NDA, so it is not named here.
- Industry
- Automotive and manufacturing
- Engagement
- Consulting and architecture design
- Focus
- Predictive maintenance
- Technologies
- Big Data and machine learning, assessed
- Infrastructure
- Microsoft Azure with on-premise hybrid
- Deliverable
- Solution architecture and roadmap
In short
Paying for maintenance and still losing the line
- When a critical machine breaks, the entire line stops, and every hour of downtime is lost output and lost revenue.
- Maintenance was schedule-based, so the company was paying for unnecessary service on healthy machines while still being blindsided by real failures.
- They knew predictive maintenance was the right direction. What they lacked was the expertise to judge which technologies fitted their infrastructure, what data they would need, and how it would integrate with existing operations.
- The engagement answered those three questions and left them with an architecture and a phased plan, not a whitepaper.
The starting point
The challenge
Help an automotive manufacturer understand what predictive maintenance looks like for their specific production environment: assess their infrastructure, evaluate the applicable technologies, and deliver a concrete architecture they can implement.
In automotive manufacturing, unplanned downtime is one of the most expensive problems a production line can have. When a critical machine breaks the whole line stops, and every hour translates directly into lost output and revenue.
The manufacturer’s approach was schedule-based: machines were serviced at fixed intervals regardless of their actual condition. That meant paying for unnecessary maintenance on healthy machines and still being caught out by unexpected failures.
The company knew predictive maintenance was the right direction. What they did not have was the internal expertise to evaluate which technologies applied to their specific infrastructure, what data they would need to collect, and how such a system would integrate with what they already ran.
The build
What we built
The engagement ran as multi-day workshops covering three things in order: what they have, what applies to it, and what to build.
Assessing the infrastructure they already have
The starting point was their existing infrastructure, a Microsoft Azure and on-premise hybrid, because an architecture that ignores what is already installed is an architecture nobody implements.
Evaluating which technologies actually apply
We evaluated Big Data and machine learning approaches against their specific machines and constraints, which is the question they could not answer internally: not what predictive maintenance is, but which parts of it fit here.
An architecture and a phased plan
The deliverable was a solution architecture for predictive maintenance plus a roadmap, designed for their machines, their infrastructure and their operational constraints rather than for a generic factory.
What changed
The results
The manufacturer received a clear, actionable roadmap: not a generic whitepaper, but an architecture designed for their specific machines, infrastructure and operational constraints.
They came out knowing which technologies to invest in, what data to start collecting and how to phase the implementation.
This is how many enterprise AI projects should start: with a focused assessment that answers “what should we build and why” before committing to a large development effort. When implementation then begins, it targets the right problem with the right approach, which is the cheapest way to avoid an expensive misstep.
Questions about this project
Help an automotive manufacturer understand what predictive maintenance looks like for their specific production environment: assess their infrastructure, evaluate the applicable technologies, and deliver a concrete architecture they can implement.
Workshops covering assessment, evaluation and architecture: Multi-day. A full predictive maintenance solution architecture: Architecture. An implementation plan fitted to the existing infrastructure: Roadmap.
Technology used: Predictive maintenance, Big Data architecture, Machine learning, Infrastructure assessment, Sensor data strategy, Solution architecture.
Technology used
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