Your leadership wants AI on the shop floor. Your maintenance team is stretched thin. And every vendor is pitching a solution you cannot evaluate, justify, or implement without a data science team you do not have.

This course closes that gap.

70 lectures covering predictive maintenance AI, computer vision quality inspection and process optimisation AI — taught in plain language by a 20-year plant floor practitioner. No data science degree required. No prior AI knowledge needed.

If you manage a plant, maintain critical assets, or lead manufacturing improvement — and you want Industrial AI working on your floor within 90 days — this is the course that gets you there.

You are not a data scientist.

You are not an IT professional.

You are the person responsible for keeping the plant running — and you need AI to

work for you, not the other way around.

This course is not for people looking for a certificate to hang on a wall.

It is for practitioners who want Industrial AI working on their plant floor within 90 days.

WHAT YOU WILL BE ABLE TO DO

After completing this course you will be able to:

- Implement predictive maintenance on your first critical asset without

disrupting a single shift

- Build a digital twin roadmap your leadership will approve and fund

- Predict asset failures weeks before they happen using data already in your historian

- Evaluate any AI vendor with five questions they cannot bluff through

- Scope and launch a computer vision quality inspection pilot

- Present a board-ready business case with real cost avoidance numbers

- Build a connected worker programme that preserves knowledge before your senior engineers retire

- Protect your OT environment from cyber threats using IEC 62443 in plain language

WHAT MAKES THIS COURSE DIFFERENT

Every concept in this course is applied to one company — Vardan Manufacturing (name changed) —

a real mid-size discrete manufacturer.

850 employees. Eight production lines. Legacy Siemens PLCs. OSIsoft PI historian. SAP S/4HANA.

Exactly the kind of plant most manufacturing engineers work in.

Every example, case study and cost figure is grounded in Indian manufacturing context — automotive, pharma, metals and chemicals. The Vardan Manufacturing case study runs throughout the course.

North American and European students will find the implementation frameworks, vendor evaluation methodology and 90-day pilot structure fully transferable — industrial AI deployment challenges are consistent across geographies.

Case studies draw from automotive, metals, and process manufacturing environments across North America and South Asia.

No vendor bias. No upsell to software.

No theory that cannot be implemented the week after you finish.

WHAT YOU WILL COVER

Section 1 — The Industrial AI Revolution

What Industrial AI actually does in plain language. The four types of AI every vendor uses — and how to identify which one you are being sold. Why PLC logic and AI are fundamentally different. What AI cannot do — and why knowing the limits makes you more effective with these tools.

Section 2 — The Data That's Already There

You do not need new sensors. Your historian already has three years of data. This section shows you the four data types your AI needs, why sampling rate determines what failures you can detect, and the five data problems that kill AI projects before they start.

Section 3 — Predictive Intelligence

From anomaly detection to frequency-based vibration analysis. Thermal, acoustic, oil and motor current technologies.

Remaining Useful Life — the metric every plant head understands. Vardan's first prediction — 19 days warning

before a bearing failure that would have cost Rs 16 lakhs.

Section 4 — Digital Twin Intelligence

The three types of twin and why only one delivers real ROI. What data feeds a twin and the three gaps that kill most programmes.

The four levels of twin intelligence — from descriptive to autonomous.

Section 5 — Computer Vision and Quality

How AI inspection works in manufacturing. Transfer learning — why you do not need 100,000 images to start. Edge deployment

for lines where network reliability cannot be guaranteed.

Section 6 — Process Optimisation AI

Energy optimisation — the fastest ROI in any AI programme. Yield optimisation in batch manufacturing. OEE improvement

through AI scheduling. Real numbers from real plants throughout.

Section 7 — Connected Worker

The knowledge walking out your door when senior engineers retire. RAG systems that make your maintenance

documentation instantly searchable. Voice to work order — eliminating the documentation burden every

technician faces after every job.

Section 8 — Industrial AI Analytics

The 95% accuracy trap — and how vendors use it to mislead buyers. Five questions to ask before approving any AI model.

Model drift — why AI gets less accurate over time and how to prevent it.

Section 9 — OT/IT Architecture and Security

The attack that stopped a factory for 11 days. The modern OT/IT security architecture. IEC 62443 in plain English.

The five team roles no AI programme can succeed without.

Section 10 — Your 90-Day Roadmap

The sequence that every successful Industrial AI implementation follows.

Foundation first. One asset second. Prove the ROI third. Scale fourth.

Vardan's complete 18-month transformation

— every number, every metric, every result.

WHAT YOU WILL RECEIVE

→ 90-Day Pilot Implementation Plan

   Customised framework for your plant and assets — ready to present to leadership the week you finish

→ Vendor Evaluation Scorecard

   Seven criteria. Three vendor comparison. Use it in your next vendor meeting.

→ AI Readiness Checklist

   Five questions that determine whether your plant is ready to start — and exactly what to fix if it is not

→ Business Case Template

   One page. Four numbers.

   CFO-ready on day one.

WHAT STUDENTS ARE SAYING

"Must-watch for Maintenance Managers. Delivers difficult topics in a very simple, practical way."

— Amit Jha, Maintenance Manager

"As a Maintenance Manager, the instructor is very sound and knowledgeable — able to deliver difficult topics in a very

simple, practical way. Great stuff."

— Amit Jha, Maintenance Manager

"Great. Good explanation for new beginners and very helpful to solve the problem."

— Suresh Icecreamwala

"Valuable information. Clear explanations. Engaging delivery."

— Verified Manufacturing Professional

YOUR INSTRUCTOR

Jaimin Banker has spent 20 years on the plant floor and in manufacturing advisory across galvanizing lines, PLCs, SCADA systems, automotive, pharma, metals and chemicals.

He knows what a 2am breakdown costs. He knows what prevents it. And he built this course for the people

he used to work alongside.

No vendor bias. No academic theory. No content that has not been tested against real plant floor reality.

FREQUENTLY ASKED QUESTIONS

Q: Can I implement predictive maintenance without a data science team?

A: Yes. This course is built specifically for maintenance engineers and plant managers with no data science background. Every concept is explained in plain manufacturing language.

Q: How long does a predictive maintenance pilot take to implement?

A: The 90-day pilot plan included in this course walks you through implementation week by week — from data audit on Day 1

to first documented intervention by Day 90.

Q: Is this course relevant for Indian manufacturing plants?

A: Every example, case study and cost figure is based on Indian manufacturing context — automotive, pharma, metals and chemicals. The Vardan Manufacturing case study is set in Pune, Maharashtra.

Q: Do I need to buy any software to follow this course?

A: No. All concepts are demonstrated using platforms your plant likely already has.

No additional software purchase required.

Q: What is the difference between monitoring and predictive maintenance?

A: Monitoring shows you what is happening. Prediction tells you what will happen — weeks before it does. Section 3 covers

this transition in detail.

Q: How does a digital twin work in manufacturing?

A: Section 4 covers the complete digital twin implementation journey — from monitoring twin to autonomous twin — using the Vardan Manufacturing case study throughout.

Q: Is this course suitable for someone new to manufacturing AI?

A: Yes. Section 1 starts from first principles —

what AI actually does, how it differs from your existing PLC and SCADA systems, and what it cannot do. No prior AI knowledge

is required.

Q: I work in a small plant with limited budget. Is this still relevant?

A: Yes. Every framework in this course is designed for mid-size manufacturing plants with legacy OT infrastructure and limited

internal IT capability. You do not need enterprise-scale resources to implement Industrial AI effectively.

If you manage a plant, maintain critical assets, or lead manufacturing improvement programmes — and you want Industrial AI

working on your plant floor within 90 days —

this is the course that gets you there.

Enrol now.

The 90-day pilot plan alone will pay for this course on your first prevented failure.