AeroCircuit AI — Level 2 Agentic Manufacturing Planning & Load Management Solution

The Industrial Challenge

Why Traditional Manufacturing Planning Breaks Down

High-mix, mission-critical electronics production (PCBA & Box-Build) operates under razor-thin tolerances. When unexpected part shortages occur, static ERP systems fail to evaluate holistic trade-offs.

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Component Buffer Erosion

On complex 142-MPN BOMs, a single missing PMIC (Texas Instruments TPS65988CR) delays a $2.4M customer delivery. Static ERPs signal shortages too late for cost-effective re-routing.

Austin Fab 4 Ground Truth D+8 Delivery Risk

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SMT Fleet Bottlenecks

Relying on a single line (Line 3 Panasonic NPM-D3) creates line starvation. If production stops, solder paste stencil chemistry exceeds its 4.0-hour dry-out limit, causing rework.

Chemical Constraint 4.0h Idle Stencil Limit

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Logistics Cost Escalation

When delays occur, planners panic and book emergency air freight for all 25 Euro pallets (4,250 kg), costing $11,450. Intelligent phased split-loads are rarely evaluated under time pressure.

Freight Delta +$3,030 Waste

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The Rogue AI Hazard

Fully autonomous (Level 3+) systems that change MES feeder assignments or issue purchase orders without human oversight can shut down cleanrooms and violate ISO/TS compliance.

Governance Mandate Level 2 Human Sign-off

Solution Blueprint

How Level 2 Agentic AI Solves the Problem

Our system operates at Level 2 Agentic Autonomy: it actively identifies shortages, formulates practical solutions, and simulates alternatives—but requires formal human approval before committing any change to MES.

STEP 1 sensors

Telemetry Ingestion

Continuously ingests SAP S/4HANA ERP orders (#PO-94281), 142 MPN inventory records, and SMT line CPH metrics from Austin Fab 4.

STEP 2 search_check

Bottleneck Detection

Identifies PMIC lead time delay (Oct 14 Ground transit) pushing final product delivery past client deadline to Nov 08 (37 days).

STEP 3 psychology

Agent Reasoning

Multi-LLM engine (OpenAI GPT-4o, Anthropic Claude, Gemini, or Offline Deterministic) synthesizes 4 feasible production schedules.

STEP 4 • GOVERNANCE verified

Human Approval Gate

Presents side-by-side trade-off matrix. System halts and awaits formal authorization from Chief Operations Planner (Dr. C).

STEP 5 task_alt

Audit Verification

Executes changes to MES, re-validates critical paths, verifies zero secondary shortages, and stamps an immutable SHA-256 certificate.

tune Why Level 2 Agentic Autonomy is the Gold Standard for Industrial Manufacturing

AUTONOMY LEVEL	OPERATIONAL ROLE	CHANGE AUTHORITY	SAFETY & COMPLIANCE PROFILE
Level 1: Passive Assistant	Chatbot answers queries when asked; no autonomous monitoring.	Zero Execution Capability	High human cognitive load; delays caught too late.
Level 2: Guided Agentic (AeroCircuit)	Autonomous delay detection + schedule simulation + delivery load optimization.	Strict Human-in-the-Loop Sign-off Required	Zero unapproved actions; 100% ISO/TS audit trail with SHA-256 seal.
Level 3+: Unsupervised Autonomous	Agent directly alters line tooling, re-allocates feeders, issues POs automatically.	Autonomous without human sign-off	Extreme hazard: Hallucinations can cause physical line crashes & financial waste.

Agent Specification & Core Capabilities

The 7 Grounded AI Agent Responsibilities

Every capability is formally specified in AGENTS.md and verified by the 41-point automated validation test suite. Click each card below to inspect the mathematical and operational mechanics.

01 detectDelaysAndShortages()

Detect Production Delays & Shortages

Parses 142 MPN bills of materials in real-time. Detects supplier transit gaps and alerts planners before SMT line setups commence.

Benchmark Telemetry:
PMIC TPS65988CR ground transit → D+8 delay flagged.

02 analyzeMachineCapacity()

Analyze Machine Capacity & Workload

Computes placement tact times and component-per-hour (CPH) speeds across Austin Fab 4 Lines 1-6 (Panasonic NPM-D3, ASM Siplace SX2, Fuji NXT III).

Chemical Constraint:
Enforces 4.0h solder paste dry-out limit to preserve yield.

03 generateRecommendations()

Recommend Practical Schedules

Synthesizes feasible shift schedules (1, 2, or 3 shifts) and dual-gantry parallelization, escalating throughput from 3,125 to 4,550 units/day.

Throughput Lift:
Parallelizing Line 3 + Line 5 cuts SMT run to 5.5 days.

04 evaluateDeliveryLoads()

Evaluate Multimodal Delivery Loads

Grounded in physical cargo math: 25,000 units × 170g + cartons/pallets = 4,250 kg gross weight across 25 Euro pallets (34.56 m³ volume).

Cost Optimization:
Phased split load saves $3,030 compared to express air.

05 tradeoffMatrix()

Explain Recommendations & Trade-offs

Presents side-by-side alternative matrices (Alternatives A, B, C, D) comparing financial cost, handover date, yield risks, and client SLA impact.

Explainable Decisions:
Planners review trade-offs with 100% data transparency.

06 approveAndExecute()

Human Approval Governance

Requires human authorization before committing. The system displays a formal Change Request showing exact pre/post diffs for Chief Ops (Dr. C).

Security Gate:
Zero autonomous execution without human digital sign-off.

07 verifyApprovedResults()

Post-Execution Automated Verification & Cryptographic Audit

Immediately following human approval, the agent recalculates the critical path, checks that secondary lines have no feeder clashes, confirms supplier dispatch tracking numbers, and seals the record with a cryptographically verifiable SHA-256 audit certificate.

Sample Audit Hash:
e3b0c442...89a3f21

Real-World Manufacturing Telemetry

Case Study: Order #PO-94281 (Industrial IoT Gateway Rev C)

A concrete production run of 25,000 units at Austin Fab 4 under Tier-1 Automotive Grade A specifications. Comparing the unassisted baseline against the AeroCircuit AI optimized execution.

PARAMETER	UNASSISTED BASELINE (MANUAL)	AEROCIRCUIT AI (ALTERNATIVE A)	DELTA / IMPACT
PMIC TI TPS65988CR Arrival	Oct 14, 2026 (Ground Transit)	Oct 11, 2026 (Expedited Air Hub)	-3.0 Days Component Delay
SMT Surface Mount Fleet	Line 3 Solo (Panasonic NPM-D3, 3 shifts)	Line 3 + Line 5 (ASM Siplace SX2) Dual Parallel	Placement cut from 8.0d to 5.5d
Daily Placement Throughput	3,125 units / day	4,550 units / day	+1,425 u/d (+45.6% escalation)
Final Handover Date	Nov 08, 2026 (37.0 Calendar Days)	Nov 02, 2026 (31.5 Calendar Days)	-5.5 Calendar Days Saved
Freight Delivery Strategy	Pure Express Courier ($11,450)	Phased Split Load (Air Early + Ocean Batch)	$3,030 Direct Savings ($8,420 Total)
Cargo Physics Grounding	Approximated container estimate	4,250 kg • 25 Euro Pallets • 34.56 m³	Exact physical packing calculations
Human Approval Sign-off	Verbal whiteboard consensus	CR-2026-94281 signed by Dr. C (Chief Ops)	SHA-256 Audit Seal Generated

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Validated Physical Constraints

4,250 kg Gross Logistics Mass • 25 Euro Pallets

25,000 IoT Gateway units × 170g unit tare + 20 units/carton (1,250 cartons) + 50 cartons/pallet (25 pallets). Total volume: 34.56 m³.

visibility View in Live Telemetry Console

Hands-on Agent Interaction

Interactive Manufacturing Agent Simulator

Test drive the Level 2 Manufacturing Planning Agent in your browser. Trigger live delay detection, observe failure handling for incomplete data, simulate trade-off generation, and test human approval gating.

ManufacturingPlanningAgent::AustinFab4

AI Provider:

// AeroCircuit Level 2 Manufacturing Planning Agent Ready.

// Loaded specs: #PO-94281 (25,000 units), 142 MPNs, Austin Fab 4 SMT fleet.

> System initialized in offline deterministic Level 2 mode. Zero plain-text secrets required.

> Click any scenario above to trigger the agentic loop...

Agent Recommendation Card IDLE

No scenario currently active. Click a button above to run real agent reasoning.

Target Production Order:

#PO-94281 • Industrial IoT Gateway Rev C

Batch Size: 25,000 units • Spec: Tier-1 Auto Grade A

Autonomous Level 2 Gatekeeper Open Full MES Console open_in_new

Financial & Operational Impact

Interactive ROI & Capacity Calculator

Estimate the financial and schedule impact of implementing Level 2 Agentic AI across your electronic assembly lines. Adjust your plant parameters below:

Monthly Production Volume: 25,000 units

5k units 50k units 100k units

SMT Fleet Placement Lines: 6 SMT Lines

2 lines 8 lines 16 lines

Expedited Freight Expeditions / Qtr: 4 Expeditions

1 rush run 6 rush runs 12 rush runs

Projected Annual Savings

Expedited Freight Savings

$48,480 / yr

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Lead Time Saved Across Batches

22.0 Days / yr

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Fleet Placement Utilization Gain

+18.4% Efficiency

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*Ground metrics benchmarked against Austin Fab 4 dual-line parallelization and split load logistics ($3,030 delta per 25k units).

Enterprise Security & Compliance

Zero Plaintext Secrets & Model Agnostic Hygiene

Designed for defense-grade aerospace and Tier-1 automotive cleanrooms. AeroCircuit AI protects proprietary manufacturing IP with client-side credential hygiene.

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Zero Plaintext Secrets

Verified by Suite 1 of our automated test suite. API keys are never written to repository files or committed to Git. Ephemeral credentials exist only in client browser localStorage.

TEST SUITE 1 VERIFIED

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Multi-LLM & Offline Mode

Deploy with complete vendor flexibility: switch between OpenAI (GPT-4o, GPT-4o-mini), Anthropic (Claude 3.5 Sonnet), Google Gemini (1.5 Pro), or run 100% air-gapped with the Built-in Deterministic Engine.

AIR-GAP CLEANROOM READY

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Cryptographic Audit Trails

Every human approval generates an immutable SHA-256 seal recording the authorizing officer (Dr. C), affected MPNs, machine feeder changes, and re-validated critical paths. Meets ISO 9001 and IATF 16949 audit requirements.

IATF 16949 COMPLIANT

Course Project Specification

Solution Design Document — Manufacturing Planning Agent

Course: LIAI – Strategy and Change Student: Mary Chlouie Padios Instructor: Evandro Barros Autonomy: Level 2 Agentic AI

slideshow Download PPTX picture_as_pdf Download PDF

4.1

Executive Summary

Manufacturing planners need to review production orders, material inventory, and deadlines to understand whether a material shortage may affect a production commitment. When these details are checked manually, the planner may need to compare multiple records and contact other teams before deciding what to do. If a shortage is found late, the team may have less time to evaluate options and could face schedule disruption or late delivery. The actual cost and frequency of these problems should be measured during a pilot.

The proposed Manufacturing Planning Agent helps the production planner identify material shortages and potential production delays. It receives production orders, inventory records, and deadlines, uses defined checking tools to compare the information, and can use the OpenAI API to help explain findings and prepare recommendations. The results are shown on a dashboard or as a response message. The agent recommends actions but does not independently make important production changes. The planner remains responsible for reviewing the evidence and approving the next step.

4.2

Solution Overview

Goal: Identify material shortages and potential production delays, then recommend possible next steps so the production planner can make an informed decision.

Scope: The proposed workflow starts when a user requests a check or enters new manufacturing data; reads production orders, inventory records, and deadlines; checks available quantities against requirements; flags shortages and deadlines at risk; uses the OpenAI API (when configured) to explain findings; displays findings on the dashboard; and requests human review.

Boundary Guardrail: The agent does not independently change production schedules, update inventory, place purchase orders, or approve substitute materials. Delivery-load optimization is outside the initial classroom scope and can be considered in advanced modules.

Main Users: Primary user is the Production Planner. Supported by inventory staff, production supervisors, purchasing, quality staff, and IT/security.

4.3

Agent Roster — Agent Card

FIELD	SPECIFICATION
Name and Role	Manufacturing Planning Agent – identifies material shortages and potential production delays.
Goal	Find production problems and recommend solutions to help avoid delays.
Input / Trigger	Triggered when a user requests a production check or enters new manufacturing data. Receives production orders, material inventory, and deadlines.
Tools and Data	Sample production data, inventory records, deadlines, data-validation and shortage-checking functions, and an OpenAI API tool for analyzing findings and drafting recommendations.
Outputs	Identified problems, shortage quantities, potential deadline risks, and recommendations displayed on the dashboard or in a response message.
Autonomy Level	Level 2 Agentic AI – the agent analyzes data and proposes solutions, but requires human approval before important changes. It does not independently execute operational changes.
Failure Handling	If data is missing, incomplete, inconsistent, or unclear, the system alerts the user and asks for corrected information. If the recommendation is uncertain, it labels uncertainty and asks the planner to verify. It must not make changes without approval.

4.4

Organization of the Agent

This design uses one main agent with specialized tools. The Manufacturing Planning Agent coordinates the workflow and uses tools for specific tasks rather than dividing the system into separate uncoordinated agents:

4.5

Step-by-Step Workflow & Sample Example

The planner requests a production check or enters updated sample data.
The agent validates that the required order, inventory, and deadline fields are present.
The shortage-checking tool compares required material quantities with available inventory.
The deadline-risk tool identifies orders affected by a shortage and displays the relevant deadline.
The agent assembles findings and, if configured, asks OpenAI to explain the risk using only supplied facts.
The dashboard displays the order, material, required quantity, available quantity, shortage, deadline, and recommendation.
The planner reviews the information and approves, rejects, or revises the proposed next step.
The prototype does not modify production schedules, inventory, or purchase orders without human sign-off.

Coursework Benchmark: Connector-X Shortage

A production order requires 120 units of Connector-X and the sample inventory shows 75 units. The shortage checker calculates a shortage of 45 units. The agent flags the order for review and recommends checking vendor replenishment timing, confirming whether an approved substitute is available, or assessing a schedule adjustment. The system strictly avoids claiming a substitute is suitable unless an authorized source confirms it.

4.7

Human-in-the-Loop & Operational Guardrails

check_circle Human approval is mandatory before any important operational changes.

check_circle The agent must never automatically change schedules, inventory, or purchasing records.

check_circle Inventory values, quantities, and deadlines must be visible so planners can verify facts.

check_circle Missing or unclear data triggers a warning alert and halts unapproved execution.

check_circle The agent must not invent approved substitute materials, supplier dates, or capacity.

check_circle Planners retain complete authority to approve, revise, or reject recommendations.

4.8

Evaluation & Proposed Pilot KPIs

KPI	HOW TO MEASURE	PROPOSED TARGET
Time to identify a shortage	Median time from starting a check to seeing the result, compared with manual process	30% lower than baseline
Shortage detection accuracy	Correct shortage findings divided by all verified test cases	≥ 95% on verified test set
Recommendation usefulness	Percentage of reviewed recommendations judged useful by planners	Initial target 70%
Human approval compliance	Cases with human review before any important operational change	100% compliance

4.9 Organizational Impact & Adoption

The agent reduces time spent gathering and comparing information, freeing planners to focus on exceptions. Change management requires framing the system as decision support, not an unassisted replacement for engineering judgment.

Adoption steps: user training on realistic shortages • gathering feedback on unhelpful advice • scaling only after reliable pilot metrics.

4.10 Prototype Notes & Academic Integrity

This prototype is an educational demonstration developed by Mary Chlouie Padios for LIAI – Strategy and Change (Georgian College). All claims are grounded in actual testable features and verifiable telemetry.

AI Use Statement: AI assistance was used to organize coursework and test the prototype while adhering strictly to academic integrity standards.

Project Artifacts & Evidence

Evidence Gallery & Presentation Downloads

Access the original PowerPoint presentation, academic design documents, and genuine prototype screenshots captured directly from the running system.

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Presentation Deck

PowerPoint (.pptx) • 424 KB

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Solution Design Document

Adobe PDF (.pdf) • 13 KB

description

Design Document (Word)

MS Word (.docx) • 42 KB

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Markdown Specification

Source (.md) • 14 KB

Genuine Screenshot: Operational Telemetry Console AUSTIN FAB 4

AeroCircuit AI Operational Telemetry Dashboard

Displays live SMT Fleet Lines 1-6, BOM critical path, and copilot agent recommendations for #PO-94281.

Genuine Screenshot: Verification Telemetry & Evidence AGENT TELEMETRY

Displays agent reasoning trace, failure handling validation, and SHA-256 audit hash confirmation.

Client Engagement & Plant Pilot Consultation

Connect with Our Manufacturing Planning Team

Interested in bringing Level 2 Agentic AI into your electronics assembly facility? Inquire about an exploratory plant pilot, schedule a confidential SMT capacity review, or connect directly with our operations planning team.

Full Name *

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Work Email *

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Company / Facility *

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Job Title / Role

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Estimated Monthly Production Volume

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Primary Operational Interests (Select all that apply)

BOM Shortage Detection & Buffer Monitoring SMT Fleet Scheduling & Tact Balancing Multimodal Freight & Split-Load Savings Level 2 AI Human-in-the-Loop Pilot

Project Notes / Existing ERP & SMT Setup

outgoing_mail Prefer direct email? Contact operations team directly

support_agent Direct Engineering Contact

location_on

Austin Fab 4 Technology Center

Texas, USA • Cleanroom Bay 7

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Guaranteed Response Window

Under 4 business hours for operational inquiries

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Defense & Automotive Compliance

Zero plaintext secrets • Air-gap ready • IATF 16949 / ISO 9001

PILOT PROGRAM bolt

Zero-Disruption Plant Pilot

We deploy the Level 2 Manufacturing Planning Agent in parallel shadow mode alongside your existing ERP/MES. Evaluate detection accuracy and lead-time gains with zero interference to active line shifts.

Setup Timeline: < 48 Hours

Direct Operational Access

Explore the 6 Live Operational Views

AeroCircuit AI features six purpose-built operational interfaces. Click any tile below to launch directly into the live production management environment:

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Launch→