01 / THE CONNECTED FACTORY
From packed chicken
to a smarter cold chain.
See how robots, machine vision and connected sensors could work together.
Built on your gantry + AGV simulator.
Concept layout · proposed capabilities
Concept layout · proposed capabilities
Preparing your factory cell…
PACKED PRODUCT HANDLINGDrag to orbit · Scroll to zoom
01InspectLabel + appearance
02PalletizeGantry robot
03TransferLift AGV
04Cold infeedUnload + trace
WHAT YOU’RE SEEING
A familiar machine. A poultry-specific job.
The gantry handles closed cases of packed chicken, builds a pallet and hands it to an AGV. A second robot unloads at a proposed cold-room infeed. The layout and case dimensions are illustrative.
All readings are synthetic. Scripted vision decisions illustrate a workflow, not an AI accuracy claim. This geometric model does not validate hygiene, food safety, collision avoidance or production capacity.
02 / EARLIER SIGNALS, BETTER CARE
Intelligence starts
before the factory.
Bring house conditions, water use and flock observations into one view.
House A · demonstration flockBASELINE
HOW THIS COULD WORK
Connect observations to a responsible action.
Sensors + cameras→Local gateway→Rules + ML→Farm team
Track temperature, humidity, water flow and flock distribution. Learn normal patterns for each house and bird age; flag deviations for a stockperson. Equipment controls and veterinary decisions remain with the farm’s approved procedures.
03 / START SMALL. MEASURE THE DIFFERENCE.
One line.
A measurable pilot.
Start with packed-product handling and visibility, then expand on evidence.
Walk the process
Map case sizes, line rates, cold-chain handoffs, sanitation routines and existing PLC interfaces. Record the current loss and downtime baseline.
Output: agreed pilot scopeAdd visibility
Log temperatures and motor signals. Collect real images of readable and defective labels. Run alerts alongside existing checks.
Measure: missed events + false alarmsPilot one cell
Trial the gripper and pallet patterns with closed cases. Verify washdown suitability, segregation, guarding and AGV traffic with plant engineering.
Measure: reliability + cases per hourProve the value
Compare equivalent shifts and product mixes. Include cleaning, changeovers, maintenance and training in the acceptance decision.
Output: scale, revise or stopILLUSTRATIVE BUSINESS CASE
Change the assumptions.
Estimate the contribution recovered from a reduction in avoidable product loss. These defaults are placeholders, not company data or a quotation.
ESTIMATED NET MONTHLY BENEFIT
LKR 250,000
500 kg of product loss avoided per month
SIMPLE PAYBACK20.0 months
Recovered kg = volume × (current loss − target loss) ÷ 100.
Net benefit = recovered kg × contribution/kg − added operating cost.
Payback = investment ÷ net monthly benefit.
04 / GROUNDED IN CONTEXT
A proposal for New Anthoney’s.
Built around its priorities.
Public research informs the direction. A plant visit must establish the requirements.
Quality, chilling and sustainability.
New Anthoney’s describes antibiotic-free farming, chicken chilling as its principal preservation method, and environmental initiatives on its official website. Those themes inform the proposed traceability, cold-chain monitoring and resource visibility.
“Organic” certification was not established by this research. This proposal uses the company’s documented antibiotic-free and sustainability positioning.
Official Green Commandments ↗Think beyond a single machine.
IFC’s published project disclosure describes a value chain spanning feed, breeding, broiler farming, hatchery, processing and retail. This supports a farm-to-factory perspective; it does not establish what automation is already installed.
IFC · NAF Poultry disclosure ↗Vision has a role in poultry.
MARELEC describes AI-based recognition of poultry parts in its I Vision offering. Our case-label exception is a separate proposed use case, requiring plant-specific images, controlled lighting, validation and a manual review path.
MARELEC · I Vision ↗Make conditions visible.
FAO identifies temperature, humidity, air movement, adequate food and water as important parts of poultry management. Sensors can make these conditions visible; alert settings must reflect flock age, housing and farm guidance.
FAO · Management and housing ↗What this demo shows — and what a real deployment needs
| Capability | In this demonstration | For a plant pilot |
|---|---|---|
| Robotics + AGV | Animated case handling on a fixed route | Payload, grip trials, hygienic design, guarding, traffic and layout validation |
| Machine vision | Scripted pass / label exception | Labeled images, OCR / model evaluation, measured recall and false rejects |
| IoT + maintenance | Injected temperature and vibration events | Calibrated sensors, connectivity, age of readings, baseline and alarm procedures |
| Traceability | Downloadable synthetic event history | Lot identification, sensor timestamps, ERP / MES integration and audit records |
| Food quality | No microbiological or residue assessment | Established QA, laboratory and food-safety verification processes |
Concept prepared for discussion, not an official New Anthoney’s system or endorsement. Research checked 22 October 2026.