Automation Workshop

Convert recurring production decisions into validated automation systems.

Engineering teams run the same statistical decisions every week:

Is performance within target?
Did the change improve yield?
Is the defect rate acceptable?
Is the process stable?

This workshop converts those recurring decisions into structured, validated automation inside JMP statistical software.

In three days, your team builds four production-ready engines they can use immediately.

What You Build

Four automation engines focused on high-volume production decision workflows:

Mean-Based Decision Engine
Proportion Decision Engine
Change Detection Engine
Process Monitoring & Capability Engine

Each engine produces:

A standardized decision output
A consistent tabular result
Clear graphical reporting
Structured documentation language

No fragile macros.
No uncontrolled AI.
No data leaving your environment.

How It Works

TriAIQ Automation Architecture

AI is used to draft structured logic.
JMP remains the statistical ground truth.
Automation runs locally under your control.

Your team leaves with working engines — not theory.

What This Changes

Reduces manual report preparation time
Standardizes decision criteria across program
Removes ambiguity in recurring reviews
Creates repeatable, defensible outputs

One correctly automated workflow can recover significant engineering capacity each quarter.

Who Should Attend

Hardware Engineers
Manufacturing & Quality Engineers
Engineering Managers
Program Leaders responsible for metrics

Program 1 training is recommended, not required.

Example Engineering Automation Workflows

Representative workshop outputs built from real engineering decision workflows

Yield Analysis Decision Automation

Before
Engineers manually analyzed yield distributions and SPC charts to determine whether process shifts required intervention.

Automation
TriAIQ builds a statistical decision engine that validates AI analysis against hypothesis testing and capability metrics.

Result
• consistent decision criteria
• reduced manual analysis time
• auditable engineering decisions

Defect Rate Decision Automation

Before

Quality engineers manually reviewed defect trends, sample results, and AI-generated summaries to determine whether escalation was required.

Automation
TriAIQ builds a statistical decision workflow that validates AI-supported conclusions against defect rate analysis and significance testing before actions are triggered.

Result
• faster quality decision cycles
• reduced false escalations
• defensible quality decisions

Manufacturing Process Monitoring

Before
Engineers manually monitored SPC signals and generated reports.

Automation
TriAIQ builds automated statistical monitoring workflows that validate AI alerts against statistical thresholds.

Result
• faster anomaly detection
• fewer false alarms
• scalable monitoring

If your team is still rebuilding reports manually, this workshop pays for itself quickly.

Or reserve a seat in an upcoming engineering training cohort