SURGE PLUSE/Sewing factory
SIM TIME 00:00:00

INDUSTRIAL IoT / APPAREL MANUFACTURING

Every machine.
A clearer picture.

Turn machine signals into production visibility, faster response and better team planning.

SIMULATION RUNNING
08 connected machines

Illustrative site · synthetic telemetry

VIEW AS
Role previews · no production access control
Factory digital twinSCHEMATIC
LINE 01 · 8 SEWING STATIONSClick a device to inspect · Drag to orbit
RunningAttentionStopped / fault
M01 · Collar attachSelect a machine or use the device list below
SURGE PLUSE
OPERATORIllustrative operator profile
TARGET / HOURpieces
ACTUAL / THIS HOUR
MACHINE SPEEDrpm
SHIFT PIECESat this operation

Live mirror of the mounted screen · synthetic telemetry

See the service journey

Operator waits → mechanic arrives → repair → restart → return.

  1. Alert
  2. Walk over
  3. Repair
  4. Clearance
  5. Return

32 seconds at 1× · scripted inspection and restart clearance.

1× = 1 simulated minute / second
TRY A SCENARIOPresenter controls
Completed operationsSIMULATED TREND
Since session start
Operation completions across stations; not finished garments.HISTORY · LAST 90 SIM MIN
Activity & alerts0 events
    Machine overviewSELECT ANY ROW TO INSPECT

    Demonstration only. Counters and energy are generated by a simplified model. Machine activity does not establish employee attendance, skill or individual performance.

    PLATFORM BLUEPRINT

    One foundation.
    Purpose-built workflows.

    Proposed deployment architecture. This browser demonstration simulates the devices and decisions; it is not connected to a PLC, sensor or cloud service.

    01 / SENSE

    At the equipment

    Machine cycle / run inputs, energy meters and vibration sensors. Pond DO, temperature, pH and salinity probes.

    →
    02 / CONNECT

    At the edge

    Industrial I/O or supported machine interfaces; pond controllers and gateways. Timestamp data, check freshness and keep local control during uplink loss.

    →
    03 / UNDERSTAND

    In the platform

    Proposed MQTT/TLS ingestion, time-series storage, alerts and audit history. Add anomaly models only after collecting and validating site data.

    →
    04 / ACT

    With the right team

    Controller actions, maintenance work orders, management trends and HR coverage. Real deployments require authenticated permissions and approved controls.

    Sewing factory pilot

    Instrument one line. Validate counts against production records, classify stop reasons and reconcile meter readings. Separate machine events from attendance records. Evaluate cycle-time changes with product mix and setup context.

    JUKI · Networked production visibility ↗

    Shrimp pond pilot

    Start with calibrated probes, motor feedback and a local control panel. Validate sensor placement, oxygen response, electrical protection and alarm escalation with the farm team. The example uses 4.5 / 5.5 mg/L hysteresis; these are demo settings, not husbandry recommendations.

    FAO · Shrimp pond management ↗

    Predictive insights, honestly

    Vibration and oxygen scenarios here are scripted. No live machine-learning model, failure prediction, biological forecast or yield claim is implemented. A real ML pilot needs historical labels, baseline comparisons and measured false alarms.

    Research · Aerator placement and oxygen distribution ↗
    A SEVEN-MINUTE WALKTHROUGH

    Make the value visible.

    1. Start on the sewing floor.

      Use Workstation for the needle, handwheel, fabric and operator motion. IoT screen zooms into the pole-mounted device; Floor shows all eight live displays. Hourly actuals count pieces completed at each operation in the current simulation hour, rather than a projected rate.

    2. Connect the right people.

      Play breakdown & repair for a 32-second sequence at 1×. M03 stops; its operator waits as the mechanic walks over, repairs, completes inspection and returns. The presenter script includes controller restart clearance. Normal shift cancels the sequence. The separate manual fault scenarios still use Maintenance inspection then Controller restart. All four dashboard views remain available.

    3. Move to the shrimp farm.

      Inject an oxygen dip. Paddle wheels start under automatic control. At 5×, watch the oxygen trend recover and the controller stop above its upper demo threshold.

    4. Show resilience.

      Inject a motor trip or disconnect the DO probe. Stale readings are flagged; automatic fallback requests available aerators. Maintenance can restore the simulated device.

    5. Explore the greenhouse.

      Inject heat or humidity and watch the fans. Inject dry soil and watch drip irrigation and the moisture trend. Use Maintenance to clear pump faults or restore a sensor hub.

    6. Close with a pilot.

      Open the blueprint and agree on one sewing line or one pond. Confirm measurements, alarm ownership and acceptance criteria. Export the synthetic session as a takeaway.

    Role tabs are presentation views, not authentication. All actions stay within this simulation. Greenhouse is available with three zones, fan and drip controls, sensor failures and all four role views.