SIGNAL / TYRE PRODUCTION INTELLIGENCE
One factory.
One operational truth.
Follow every tyre from building through transit and curing. See production, losses and verified engineering gains without confusing the evidence with another process.
Production by shift
Five teams. The same evidence.
Compare output against the 105-tyre target while retaining the machine, fault and downtime context behind each result.
Team Alpha104 average
99% of targetTeam Bravo101 average
96% of targetTeam Charlie105 average
100% of targetTeam Delta98 average
93% of targetTeam Echo103 average
98% of targetProduction by machine
Ten tyre-making machines, clearly identified.
Output, current state and principal loss are visible by machine for the selected date range.
Top-down loss breakdown
Start with the largest loss. Drill into the cause.
Event register
What happened, where and what it cost
MechanicalCT-03 bead clamp46 min · 21 tyres lost
ElectricalTTS elevator drive trip34 min · 17 tyres delayed
QualityCT-07 splice position12 rejected · investigation open
SoftwareICP-02 robot handshake18 min · reset completed
PlannedCP Robot 4 service56 min · completed to plan
Size changeCT-06 product change42 min · 6 min above standard
MeetingTeam Delta safety brief22 min · planned operational loss
Machine modification impact
Record the change. Measure the result.
SIGNAL compares production before and after each modification so engineering improvements—and unintended losses—remain visible.
Average output increased from 99 to 107 tyres per shift across 15 comparable shifts.
Mechanical stoppage reduced by 22 minutes per shift after commissioning.
Tyre-hotel congestion increased. Change flagged for engineering review.
Watson / Tyre-production example
Follow the evidence before blaming the shift.
Team Delta is below target. Ask Watson whether the team, CT-03 or the Tyre Transit System best explains the loss.
Illustrative SIGNAL dataCT-03 · Team Delta
Target105tyres
Actual84tyres
Downtime52minutes
Quality98.7percent
Watson is ready
Select a question—or ask your own—to begin the investigation.
Michelin production proof point
31%
More output, with no new equipment.
A Pareto-driven approach across all departments increased output from 80 to 105 tyres per shift.
Before80tyres / shift
After105tyres / shift
Gain+31%no new equipment
SIGNAL in other production environments
The same discipline, applied to a different process.
Each example has its own equipment, measures and loss model. The production data remains separate.
Example 02Industrial bakery3 production lines · 5 shift teams
Mixing→Proofing→Baking→Cooling→Packing
Output42,860units / shift
Waste1.8%−0.6% this week
OEE84.2%target 86%
- Oven-zone temperature deviation
- Dough-weight quality faults
- Recipe and allergen changeovers
- Packaging film and checkweigher stops
Example 03CNC workshop18 machining centres · 3 shifts
Material→Setup→Machining→Inspection→Dispatch
Parts1,284in range
Utilisation78.6%+4.1% this month
Scrap2.1%27 parts
- Cycle time by machine and component
- Tool-life warnings and tool changes
- Setup time and first-off inspection
- Machine alarms, scrap and rework causes
Discuss SIGNAL