Manufacturing SaaS

Factory performance visibility for operational teams.

Connect production, quality, maintenance, and planning data into a focused operational intelligence layer.

Manufacturing SaaS dashboard

Plant Performance Command Center

Shift ALive telemetryKuala Lumpur plant

Production Output

3,660units

+8.4% vs previous shift

Input Material

4,155kg

+3.1% vs previous shift

Downtime

34min

-12.0% vs previous shift

OEE

89.5%

+4.7% vs previous shift

Machine Utilization

83.2%

+5.3% vs previous shift

Quality Defects

29ppm

-9.6% vs previous shift

Production Output vs Input Material

Shift time

06:0008:0010:0012:0014:0016:00

Downtime & Quality Defects

Shift time

06:0008:0010:0012:0014:0016:00

Neural Production Analysis

Model inference

Production output is likely constrained by servo current drift and tool vibration on CNC-12.Neural network ensemble: temporal convolution + gradient attribution over shift telemetry.

Output prediction

782 units92% confidence

Bottleneck risk

Line 03 feed stage87% confidence

Yield sensitivity

Tool vibration81% confidence

Machine Parameter	Current	Baseline	Model Impact	Shift Trend
Spindle Load	74.8%	68.0%	High	+6.8%
Servo Current	18.6 A	16.9 A	High	+10.1%
Tool Vibration	3.2 mm/s	2.4 mm/s	Medium	+33.3%
Coolant Flow	41 L/min	46 L/min	Medium	-10.9%

Reduce spindle feed rate by 3-5% for the next 45 minutes to stabilize servo current variance.

Increase coolant flow target to 45 L/min before the next production batch to reduce thermal drift.

Schedule tool inspection on CNC-12 after Shift A; vibration pattern matches early tool wear signature.

OEE & Machine Utilization

Shift time

06:0008:0010:0012:0014:0016:00

Energy Usage & Motor Power

Shift time

06:0008:0010:0012:0014:0016:00

Workplace Temperature

Shift time

06:0008:0010:0012:0014:0016:00

Machine Error Code Monitor

Code	Machine	Signal	Events	Severity
E-104	CNC-12	Servo variance	12	High
W-221	Press-04	Thermal drift	8	Medium
I-017	Line-02	Sensor recalibration	5	Low