Manufacturing Decision Intelligence

Contextual insights. Clear decisions.
Every finding quantified.

Daily Briefing
Live

A ranked decision brief, ordered by financial impact, ready at shift start.

Priority Actions

Ranked by financial exposure

CNC-001 bearing wear

$127K

11 days to failure. Inspect before Friday or risk unplanned stop.

Pump B seal degradation

$42K

Pressure cycling indicates cavitation. Schedule PM within 2 weeks.

Line 3 energy anomaly

$18K/wk

8% above baseline for 11 consecutive shifts. Review HVAC setpoints.

Performance Snapshot

Current shift

OEE

76.3%

4pt below plan

Uptime

91.2%

+1.1pt MoM

Energy

89%

vs target

Throughput

340/d

63 units short

Quality

94.7%

on plan

Warn lead

8.1d

avg lead time

MTBF

34d

vs 41d target

Open PMs

7

3 overdue

Action Queue

3 open items
1

Inspect CNC-001 bearing before Friday. 11-day window closing.

2

Release SC-4421 hold. Pump B part confirmed in stock.

3

Review Air Handler 4 overdue service before weekend

Asset Health

Remaining useful life for every asset, updated continuously.

CNC-001 Bearing23d
Act by Friday
Fan Unit B8d
Watch
Pump B47d
Planned
Lathe L-461d
Normal
Production Excellence

Finds your throughput constraint, sizes the loss, and ranks fixes by recovery value.

S1 Prep
95%
S2 Machining
CONSTRAINT
63%
S3 Assembly
91%
S4 QC
89%
Fix S2 = $2.1M / year recovered
Supply Chain Intelligence

Connects equipment health to open orders before risk becomes a customer problem.

OrderAssetDueStatus
SC-4421CNC-001May 12At risk
SC-4455Pump BMay 24Tracking
SC-4462Line 3May 31On track
Sustainability

Energy waste by system, measured in dollars against your own targets.

HVAC systems$9.2K/wk
+9% over target
Compressed air$4.8K/wk
+5% over target
Process heat$3.8K/wk
+2% over target
$925K annual savings opportunity
Contextual Insights

Cross-signal pattern recognition that turns scattered alerts into a single diagnosis.

Vibration +3.2 std dev
PM Overdue 12 days
Temp +12°C above norm
Bearing failure in 11 days
Causal Intelligence

Root cause tracing that stops recurring failures at the source, not the symptom.

Bearing temperature +18°C

Observed symptom

Lubrication film breakdown

Mechanism

Overload during night shift

Root cause

Found$127K
Fix the root cause. Stop the cycle.
Impact Quantification

Every finding converted to a dollar exposure, ranked by cost to ignore.

Total exposure: $1.88M

Unplanned downtime
$1.2M
Line efficiency
$380K
Quality loss
$210K
Maintenance cost
$72K
Energy waste
$18K
Scenario Simulator

Financial modeling of maintenance paths before resources are committed.

Act this week

Optimal

$18K

Planned PM cost

Schedule next month

Moderate risk

$42K

Deferred risk cost

Wait 90 days

High risk

$127K+

Failure + unplanned stop

One continuous decision layer. Not a one-time report.

Manufacturing Decision Intelligence

The decisions your plant needs are already in the data it produces.

Operations, executive, and financial perspectives, all drawn from the same underlying plant data and decision engines.

Digitillis / Operations Hub
✦ARIA Copilot
◈Executive Hub
◉Operations Hub
◆Finance Hub
⚙Equipment
◎Predictions
△Alerts
⚒Tools
◇Value
⬡Agents
·Asset
·Production
·Supply Chain
·Quality
·Sustainability
·Compliance
·Business Value
☀Good morning. Your insights briefing for Saturday, February 14:
✦
Ask
All 8 machines are active with no alarms. Floor is running at full capacity.
Production is running 12% ahead of pace, on track to exceed target by ~94 units.
OEE spread across running machines is 14.2 points (CNC-001 at 96.8% vs Press-02 at 82.6%). Investigate Press-02 for quick-win improvements.
Machines Running
7/8
1 idle
Floor OEE
91.2%
+2.3%
Parts Produced
1,847
Target: 2,400
Active Alarms
2
1 critical
On Shift
12
Day Shift
Machine Status
RunningIdleMaintAlarm
CNC-001running
OEE: 96.8%72%
JOB-4472
CNC-002running
OEE: 94.1%45%
JOB-4468
CNC-003alarm
Requires attention
CNC-004running
OEE: 91.5%88%
JOB-4471
Lathe-01running
OEE: 89.3%62%
JOB-4465
Press-01running
OEE: 87.4%34%
JOB-4470
Press-02idle
Awaiting job
Pump-A1running
OEE: 92.7%51%
JOB-4469
Active Alarms
CNC-003 vibration 4.2mm/s2m
Bearing degradation ~18d5m
Lot M-2847 moisture +8%12m
Shift Progress
Parts Produced1,847 / 2,400
77.0% of target
Quality Rate
99.2%
Scrap Count
14
Hourly Output vs Target
6am7am8am9am10am11am12pm1pm
⚡Energy (kW)
342 kW-4.2% vs avg

Built on industry-standard protocols and technologies

OPC-UAMQTTModbusTime-Series DBKafkaRedisNext.jsFastAPI

Manufacturing intelligence, purpose-built.

Beyond dashboards, to decisions.

A dashboard shows you that Line 3 is at 74 percent. Digitillis tells you what that costs, why, and what to fix first. Decision engines run continuously, within the thresholds you set.

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Trained on real industrial data.

Models trained on industrial benchmark datasets across manufacturing, aerospace, semiconductor, and logistics. Per-dataset accuracy is published in our model registry.

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Built for manufacturing. Not adapted from IT.

Native OPC-UA, MQTT, and Modbus support. ISA-95 equipment hierarchies. Time-series optimized storage. Purpose-built from the factory floor up.

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Continuous analysis

Decision engines. Each an expert in one failure mode.

Each decision engine runs on a fixed analysis cycle, within the thresholds you set, and surfaces ranked findings with the evidence behind each one.

Explore solutions
Agent OrchestratorAll systems active
Cycle: 847ms avg
RUL Prediction
120s loopHigh accuracy
Last: 3s ago
Anomaly Detection
60s loopStrong detection
Last: 12s ago
Equipment Health
120s loopHigh precision
Last: 45s ago
Demand Forecast
300s loopReliable forecasting
Last: 1m ago
Bottleneck Detect
300s loopHigh precision
Last: 22s ago
Carbon ESG
300s loopNear-perfect accuracy
Last: 2m ago
Quality Control
120s loopHigh-confidence detection
Last: 8s ago
Throughput Opt.
300s loopHigh accuracy
Last: 31s ago
Cost Avoidance
300s loopRobust classification
Last: 55s ago
ARIA Copilot

ARIA: ask your plant a question, in plain language.

ARIA is the copilot over the whole platform. Any user can ask a question in plain language and get an answer drawn from the plant's own data, with the calculation and the source readings shown. Role-aware: an operator gets operational guidance, an engineer gets root cause, a manager gets dollar impact. LLM-optional: ARIA falls back to a deterministic classifier when a cloud key is unavailable.

"What is driving the OEE drop on Line 3?"
"Show me equipment at risk of failure in the next 30 days."
"Schedule maintenance and notify the shift supervisor."
See ARIA in action
ARIA
Autonomous Reasoning Industrial Assistant
Online
What is driving the OEE drop on Line 3?
Line 3 OEE dropped from 91.2% to 87.4% over the past 6 hours. Analysis of the three OEE components:Availability: 94.1% (stable)Performance: 96.8% → 92.1% (degraded)Quality: 99.2% (stable)The performance drop correlates with CNC-003 cycle time increase (+14%). Root cause: elevated bearing vibration causing micro-stoppages.
Schedule maintenance for CNC-003 and notify the shift supervisor.
Done. Here's what I've set up:✓ Work order WO-1847 created for Thursday planned window✓ Technician Ramirez assigned (bearing specialist)✓ Spare part BK-440 confirmed in stock✓ Shift supervisor Martinez notified via email
Ask anything about your factory...
CNC-003 / Predictive MaintenanceWarning
Updated 3s ago
Remaining Life
18d
±3 days (95% CI)
Health Score
62%
-5% this week
Confidence
87%
Ensemble model
Degradation Curve (90 Day Forecast)
TodayFailure zone
Root Cause
Bearing wear
Vibration: 4.2mm/s (threshold 3.5). Similar to CNC-001 failure pattern (Q3 2025).
Top Features
vibration_rms92%
temp_bearing78%
current_draw45%
spindle_speed23%
Forward fault outlook

Know the fault is coming. Before the alarm does.

Digitillis produces a forward fault outlook for each asset at six, twenty-four, and seventy-two-hour horizons. Every finding carries its own confidence score and the evidence behind it. Lead times depend on equipment class and signal coverage. Schedule maintenance when the data says to, not on an arbitrary calendar.

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Continuous anomaly detection

Spot problems humans cannot see

Ensemble models across hundreds of sensor channels detect subtle deviations that precede equipment failures. High-frequency detection cycles with automatic alert escalation.

Anomaly Detection / Sensor Correlation
1,247 normal23 anomalies
Temperature (°C)Vibration (mm/s)
Sensor Correlation
TempVibPresRPMCurrFlowTempVibPresRPMCurrFlow
-1.0
+1.0

From sensor data to boardroom decisions.

Every reading, every alert, every intervention: connected, explained, and measured. See it on reference data from your equipment class.

Start with a FirstLookExplore the Platform