Built for manufacturers.
Designed for your specific process.
Flarehex starts with your existing factory data, then expands by proof. Pick your industry to see where the first review usually starts and where pilot work may follow.
Injection Molding
Stop losing shifts to mould faults you could have predicted.
!Pain points specific to injection molding:
- Mould changeover delays are invisible until they cascade
- Barrel temperature faults repeat because root cause is never traced
- Material waste from off-spec runs tied to maintenance-neglected machines
- Quote-to-despatch time consistently longer than promised
How Flarehex applies:
Layer 1 can review your CMMS history to rank fault types by impact, such as barrel heater failures, hydraulic leaks, or mould alignment drift. Layer 2 can compare mould maintenance schedules against actual events. If earlier proof supports it, Layer 3 may be piloted on selected assets and Layer 4 may be scoped to connect findings to active production risk.
Sample insight for this vertical
“Mould #7 has deviated from its 50,000-cycle lubrication schedule by 12,000 cycles on 3 of the last 4 service windows. Correlation with Layer 1 data shows tie bar failures occur 4.1x more frequently on overdue moulds. Two orders using Mould #7 are due this week.”
Metal Fabrication
Your CNC downtime is more predictable than you think.
!Pain points specific to metal fabrication:
- Spindle wear is gradual and invisible until catastrophic failure
- Cutting tool change cycles managed by habit, not data
- Laser / plasma consumable waste from degraded parts running too long
- Production scheduling ignores machine health - orders clash with failure windows
How Flarehex applies:
Layer 1 can show which repair paths and re-work patterns deserve attention first. If that proof points to spindle-health monitoring as the next step, Layer 3 can be scoped as a guided pilot on selected machines. Planning and optimization work stays future scope, not the opening promise.
Sample insight for this vertical
“CNC-4 spindle repairs account for 38% of your unplanned downtime. Technician B resolves spindle faults in 2.4 hours vs the 5.1-hour team average. Assigning Technician B to all CNC-4 spindle events would recover an estimated 14 hours of production time per month.”
Food & Beverage
Compliance isn't optional. Neither is uptime.
!Pain points specific to food & beverage:
- Hygiene-driven PM schedules are legally required but operationally skipped
- Audit preparation takes days of manual log compilation
- Line changeovers hide inefficiency that process mining can surface
- Cold chain equipment failures have both safety and financial consequences
How Flarehex applies:
Layer 2 is the most relevant public starting point here because it can compare regulated PM schedules against the maintenance history you already hold. If the first review proves value, later pilot work can explore cold-chain or production-impact questions with tighter scope.
Sample insight for this vertical
“Sanitisation step on Line 2 was skipped or delayed on 23% of night-shift changeovers last quarter. Cross-referencing with maintenance records shows microbial contamination incidents occur 2.8x more frequently in the week following a skipped sanitisation. Estimated cost of non-compliance: $6,800/month in rejected batches.”
Contract Manufacturing
Multiple customers, multiple deadlines, one factory. Optimise all of it.
!Pain points specific to contract manufacturing:
- Customer orders compete for the same machines with no visibility into health
- Resource allocation is verbal, not data-driven
- A single machine failure triggers multi-customer delays
- Quoting lead times without knowing machine capacity or health is a risk
How Flarehex applies:
Contract manufacturers often start with Layer 1 to identify the maintenance issues causing repeat disruption. If that produces a strong enough proof case, Layer 4 can be piloted to connect machine risk to active orders. Advanced optimization work remains roadmap scope.
Sample insight for this vertical
“Press-2 hydraulic failures have caused delivery delays on 4 of the last 6 occurrences, affecting 3 different customer accounts. The average delay is 2.3 days per incident. Prioritising Press-2 preventive maintenance would reduce multi-customer delivery risk by an estimated 60%.”