Stop guessing why your factory is losing money.
Flarehex mines your existing maintenance and production data to find bottlenecks, predict failures, and optimise your resources. Start from a CSV you already have.
The problem
Your factory generates thousands of data points every day. You're reading none of them.
- Reactive fire-fighting
A machine breaks. Production stops. You call a technician. You wait. You lose money. Then it happens again next month.
Industry estimates suggest unplanned downtime costs SMB manufacturers an average of $125,000 per year.
- Invisible bottlenecks
You know output is lower than it should be. But which step is the problem? Which machine? Which shift? Without data, you're guessing.
In industry surveys, most SMB factory managers report they cannot identify their top bottleneck within 24 hours.
- Wasted resources
Technicians are assigned by habit. Machines run at wrong times. Energy is wasted on peak tariffs. Nobody optimises because nobody has the full picture.
Industry research indicates suboptimal scheduling wastes 15-25% of available capacity in most factories under 150 people.
The solution
Start with the layers you can use now. Expand only after proof.
Layer 1 and Layer 2 are the public starting point: secure data review, guided kickoff, and an action plan from the data you already have. Sensor and ERP-heavy stages come later as guided pilots.
Reactive Intelligence
Available now
review what already broke and which actions recover time first
Preventive Compliance
Available now
check whether scheduled maintenance is really happening
Predictive Maintenance
Guided pilot
add sensors only after the first review proves where deeper monitoring helps
Process Intelligence
Guided pilot
connect maintenance findings to production impact for qualified expansion
How it works
Secure review. Guided kickoff. Proof. Practical next step.
Request secure data review
Share a maintenance export through a secure upload flow. A basic log with case ID, activity, and timestamp is enough to start.
Guided kickoff
We run a structured kickoff with your team to map the real event flow and align on what success looks like first.
Get proof
You see evidence from your own data: where time is lost, which failure paths repeat, and what is costing you most.
Take the next step
We translate findings into an execution-ready action plan with clear owners, priority, and expected impact.
Scale when ready
Then you can add production logs, IoT, and ERP inputs in phases as your team proves value.
Proof
Proof from a real guided kickoff.
In an anonymized kickoff, we surfaced three repeat-failure paths driving 41% of downtime impact and delivered a prioritized, execution-ready next-step plan in the same week.
Output Snapshot
Top downtime contributor: Machine 3 bearing path
Observed avg repair time: 6.2h
Best technician median: 3.1h
Recommended assignment change: Tech A on bearing faults
Estimated monthly recovered time: +18 hours
What this proves
- We can normalize real maintenance histories into an action-ready timeline.
- We can prioritize failure paths by operational impact, not guesswork.
- We can turn findings into practical execution steps with your team.
Your next operational win is already in your data.
Upload your maintenance log, walk through a guided kickoff, and leave with proof plus one practical next step for your factory.