SCALE

Move AI from back-office into the heart of the shop floor.

Why this phase exists

The back-office wins of SPARK are real, but they don't touch the floor where most of your margin lives or dies. SCALE goes where the machines are.

Objective

Deploy sensors, cameras, and intelligence on machines and production lines where the biggest cost and quality gains live.

What happens

Activities

Retrofit 5–10 critical machines with IoT sensors for predictive maintenance and OEE
Deploy computer vision quality inspection on one production line (Lincode AI or Jidoka)
Demand forecasting connected to ERP inventory
Hybrid edge-cloud architecture: Jetson Nano for shop-floor inference, cloud for analytics

What you get

Deliverables

Real-time OEE dashboard on mobile
Predictive maintenance alerts via WhatsApp to the maintenance team
AI-powered quality gate on one production line
Demand forecast integration with procurement
Edge computing infrastructure live on the shop floor

How we measure success

KPIs

8–13 percentage-point OEE improvement

30%+ reduction in unplanned downtime

>95% defect detection accuracy

15% reduction in inventory carrying cost

<3% false-positive rate on quality inspection

Investment

Budget

₹10–30 lakhs

Government schemes (CLCSS, IndiaAI, ZED) can reduce effective cost by 20–40%. We navigate those for you as part of the engagement.

Who does what

Roles in this phase

Owner / Promoter

Reviews OEE dashboard weekly; approves capex for sensors and cameras.

Production Manager

Owns OEE and scheduling tools. Works with the FORGE team on sensor placement.

Quality Manager

Owns computer vision. Validates AI decisions against manual for the first 30 days.

Maintenance Supervisor

Acts on predictive alerts. Provides feedback on prediction accuracy.

AI Champions

Bridge between shop-floor workers and technology.

FORGE Tech Team

Sensor installation, model training, edge deployment, integration.

Phase complete when

Checklist

Critical machines identified (Pareto: top 20% causing 80% of downtime)
Sensors installed and transmitting to edge gateway
OEE dashboard live: Availability, Performance, Quality
Predictive maintenance model trained on 3+ months of baseline data
Computer vision installed on selected production line
CV model trained with 5,000+ real production images
Shadow mode: AI decisions running parallel to manual for 2 weeks
Demand forecast generating weekly procurement recommendations
Edge-cloud sync verified during simulated internet outage
Maintenance team trained on responding to predictive alerts

← Previous phase

Phase 3 · SPARK

Quick-win AI pilots that prove value fast.

Next phase →

Phase 5 · SYNC

Break the silos. One screen. Whole factory.

Your move

Tell us what you're trying to do. We'll tell you if we can help.

First conversation is thirty minutes. No pitch deck. You leave with either a clear next step — or an honest "we're not the right team for this". Both are useful.

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